ARENBERG DOCTORAL SCHOOL Faculty of Engineering Science Department of Civil Engineering
Integrated river water quantityquality modelling
Ingrid Keupers
Dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Engineering Science (PhD): Civil Engineering October 2016
Cover image: Adopted from Mathieu Turle, licensed under CC0 1.0 Universal. Original: https:// unsplash.com/ photos/ qo0GIeUrE0s
Integrated river water quantityquality modelling Ingrid Keupers
Promotor: Prof. dr. ir. Patrick Willems Members of the Examination Committee: Em. prof. dr. ir. Carlo Vandecasteele, chair Prof. dr. ir. Jaak Monbaliu Prof. dr. ir. Ilse Smets Prof. dr. ir. Giorgio Mannina (University of Palermo) Prof. dr. ir. Ann van Griensven (VUB - UNESCO-IHE) October 2016
Dissertation presented in partial fulfilment of the requirements for the degree of doctor of Engineering Science (PhD): Civil Engineering
© 2016 KU Leuven - Faculty of Engineering Science Uitgegeven in eigen beheer, Ingrid Keupers, Vijverlaan 35, 3010 Kessel-Lo Alle rechten voorbehouden. Niets uit deze uitgave mag worden vermenigvuldigd en/of openbaar gemaakt worden door middel van druk, fotocopie, microfilm, elektronisch of op welke andere wijze ook zonder voorafgaande schriftelijke toestemming van de uitgever. All rights reserved. No part of the publication may be reproduced in any form by print, photoprint, microfilm or any other means without written permission from the publisher.
We never know the worth of water till the well is dry.
Thomas Fuller, 1732
Voorwoord Na vele jaren gewerkt te hebben aan dit doctoraat heb ik eindelijk de mogelijkheid om de vele mensen die me al deze tijd gesteund hebben te bedanken en deze kans grijp ik dan ook graag aan. In de eerste plaats gaat mijn dankbaarheid natuurlijk uit naar mijn promotor Professor Patrick Willems. Dankzij hem kreeg ik de mogelijkheid om mijn doctoraat te starten bij de afdeling hydraulica van het departement burgerlijke bouwkunde. Dankzij de vrijheid die ik kreeg voor mijn onderzoek kon ik mij volop ontplooien als wetenschappelijk onderzoeker. Onder zijn begeleiding heb ik ook succesvol een doctoraatsbeurs van het Fonds voor Wetenschappelijk Onderzoek (FWO) behaald. Hen wil ik dan ook bedanken voor deze financiële ondersteuning. Voorts wil ik ook heel graag Professor Jaak Monbaliu en Professor Ilse Smets bedanken voor de opvolging van mijn onderzoek gedurende de hele periode van mijn doctoraat. Jullie bedenkingen en suggesties werden ten zeerste geapprecieerd. Ook de andere leden van de jury wil ik bedanken voor het kritisch nalezen van de preliminaire tekst zodat deze sterk kon verbeteren. Doordat het onderzoek van Professor Ann van Griensven zo nauw aansluit bij dit onderzoek kon zij heel gerichte opmerkingen geven waarvoor dank. Ook Professor Giorgio Mannina’s input werd zeer gewaardeerd en zijn kritische bedenkingen hebben geleid tot een verbetering van dit doctoraat. Mijn dank gaat ook naar Professor Emeritus Carlo Vandecasteele voor het voorzitten van mijn jury en aldus alles in goede banen te leiden. Ik zou ook graag enkele leden van het administratieve personeel willen bedanken voor hun hulp. Bedankt Rita De Donder en Anita Vermunicht voor jullie geduld en tijd om samen met mij naar oplossingen te zoeken voor de vele, niet altijd voor de hand liggende, vragen die ik had tijdens mijn doctoraat. Ik wil ook DHI bedanken voor het ter beschikking stellen van de licentie voor de MIKE software, zonder dewelke dit onderzoek niet mogelijk was geweest. De VMM, Aquafin en de provincie Antwerpen mag ik ook zeker niet vergeten te bedanken voor het aanleveren van metingen en modelresultaten die gebruikt zijn voor dit onderzoek. i
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Tijdens de vele jaren die ik aan de afdeling hydraulica heb gewerkt is er ook tijd geweest voor ontspanning met collega’s. Hen wil ik dan ook bedanken voor de broodnodige afleiding op tijd en stond. Vooral de gastronomic journeys zullen me bijblijven. Hieruit bleek dat de Belgische keuken sterk gewaardeerd werd door de buitenlandse medewerkers en er werden ook kooktalenten uit verre uithoeken ontdekt. Het sporten over de middagpauze georganiseerd door UniefActief was naast het sportieve aspect ook zeker een fijne manier om mensen buiten mijn eigen departement te leren kennen en diverse contacten te leggen met medewerkers binnen alle geledingen van de KU Leuven. Ik kan het dan ook al mijn collega’s aanraden. I would also like to thank Erin Shaw for being such a good friend to me. You were always there with a listening ear whenever I needed it. Your help with proofreading some of the articles that I have written was also highly appreciated and improved the English level significantly. Tot slot wil ik mijn vrienden en familie die steeds voor me klaar stonden bedanken. Hierbij denk ik vooral aan mijn ouders voor het vertrouwen dat ze in mij hebben gesteld. Zij hebben mij altijd gesteund in de niet steeds conventionele keuzes die ik heb gemaakt in mijn academische carrière en ik ben hen dan ook zeer dankbaar voor al de kansen die ze mij hebben gegeven. In het bijzonder wil ik ook de belangrijkste man in mijn leven bedanken, Jan, met wie ik getrouwd ben en drie prachtige kinderen, Stef, Tim en Lien, heb gekregen tijdens dit doctoraat. Zonder hem zou dit me nooit gelukt zijn! Samen zijn we er geraakt en ik kijk uit naar het vervolg in het nieuwe hoofdstuk dat nu zal starten. Ingrid Leuven, oktober 2016
Abstract Europe suffers from a historical legacy of pollution of its surface waters leading to a poor water quality status for many of its surface waters. To remediate this, an Integrated Water Resources Management (IWRM) approach has been put forward by the Water Framework Directive (WFD) that was adopted in 2000 by the European Commission. Every member state of the European Union needs to develop 5-yearly River Basin Management Plans (RBMPs) in which the current status is described and actions that will be undertaken to achieve the desired goals are reported. To be able to evaluate the effectiveness of these programmes before they have been implemented, models are essential tools for policy makers. Currently, detailed, physically based river water quality models are being used by Flemish policy-makers for this purpose. A drawback for these types of models is the computational complexity resulting in excessively high calculation times. This impedes proper calibration of the models, statistical analysis of long time series as is required for scenario analysis and impact studies, and integration with other models. To enable a more integrated modelling at catchment scale, computationally efficient models are therefore required. This research concentrates on developing a conceptual river water quality model at river catchment scale with a specific focus on the urban drainage impacts on river water quality. Variables that are included in the analysis are Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), ammonia (NH4), nitrate (NO3), Orthophosphate (OP), Particulate Phosphate(PP) and the water temperature (T). Besides advection and dispersion these variables also undergo biological and chemical transformation processes. In this study, the following processes are being considered: re-aeration, photosynthesis, BOD decay, respiration, sediment oxygen demand, nitrification, de-nitrification, uptake of ammonia by plants during photosynthesis, uptake of ammonia by bacteria during BOD decay, resuspension of BOD, sedimentation of BOD, PP decay, PP formation, uptake of OP by plants, resuspension of PP and sedimentation of PP. Water quality impact analysis for peri-urban catchments require that particular focus is given to the temporal dynamics of the relevant processes in the catchment.
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ABSTRACT
This is so because processes along river catchments and urban drainage systems often have time scales with different orders of magnitude. This requires long-term simulations, and hence quick computational times. For the river system, this is achieved by dividing the river network into conceptual reservoirs based on user information and by applying the same water quality transformation processes that are being used in the detailed, physically based model for each of these reservoirs. The decreased spatial resolution and the modified solution scheme allow for a much larger calculation time step without causing numerical instability errors thus considerably reducing the total time needed to simulate the water quality model. This way, the advantage of the detailed white box models, i.e. accuracy, can be combined with the advantage of a conceptual grey box model, i.e. calculation speed. With applying this method, a speed-up factor in the order of magnitude 104 can be obtained without sacrificing accuracy. For the sewer system, a different approach had to be followed due to the absence of a detailed, physically based sewer water quality model. Based on the scarce data availability, a Multi-Layer Perceptron Neural Network (MLP NN) was selected as approach to provide a fast estimate of the magnitude of pollutant concentrations in the Combined Sewer Overflowing (CSO) water. The developed methodological concepts have been applied to a typical peri-urban catchment in the Flanders area of Belgium, the Grote Nete river catchment. This catchment is located in the north-east of Belgium and has a total area of 386 km2 . The Grote Nete catchment is moderately urbanized with 19 % of the surface area classified as urban and built up land. There are seven Waste Water Treatment Plants (WWTPs) to treat the sewage water produced in the catchment of which two discharge their effluent in rivers situated in the catchment and the other five transport water outside of the catchment. Detailed, physically based sewer models have been developed in InfoWorks CS by Aquafin for the two WWTPs that discharge their effluent in the rivers of the catchment. These model results are used to determine the magnitude of CSOs that occur during rain events. The impact on the river water quality is potentially large as there are 94 CSO structures situated in the catchment. However, an impact analysis with an integrated sewer-river quantity-quality model reveals that the impact of these overflows on the 90th percentile BOD and NH4 values is negligible; this 90th percentile value is currently being used to determine in which quality status the water body can be classified. Nonetheless, the impact of including the CSO pollution on the 99th percentile values is very strong, which means that it is important to take this extra river pollution stress into account for extreme events. The aquatic ecosystem may be strongly affected by such events. From the calibration of the conceptual model, it became clear that the simulation results are very sensitive to small changes in water depth (hydraulic radius) and water velocities. Since it is known that rivers in the studied catchment are subject to vegetation growth in the summer period, this study also investigated the influence
ABSTRACT
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of this macrophyte growth on the roughness and thus on the simulation accuracy for the water levels in the river. The analysis confirms that although the roughness increases during the summer due to plant growth and thus higher water levels are observed under the same discharge conditions, this effect of increasing roughness quickly reduces during periods of peak flows. It is thus important to take into account the season and flow depending roughness coefficient when the hydrodynamic model is coupled to a water quality model. Finally, the developed conceptual model was used to perform a global sensitivity analysis of the model parameters that define the water quality transformation processes. This was impossible with the detailed river water quality model due to high calculation time for the required number of model runs. This global sensitivity analysis enabled identification of the most sensitive model parameters. These 9 parameters (out of a total of 24) should be subject of further calibration.
Samenvatting Europa gaat gebukt onder een historische verontreiniging van haar oppervlaktewateren wat resulteert in een slechte toestand van de waterkwaliteit voor een groot deel van deze oppervlaktewateren. Om dit recht te zetten werd de Kaderrichtlijn Water goedgekeurd door de Europese Commissie in 2000. Deze stelt een geïntegreerde benadering voorop waarbij elke lidstaat een 5-jaarlijks stroomgebiedbeheersplan moet opstellen. Deze plannen moeten een weergave van de huidige status bevatten, samen met de acties die zullen ondernomen worden om de oorspronkelijke status van het oppervlakte water te herstellen. Om de effectiviteit van de vooropgestelde acties te kunnen evalueren alvorens deze uit te voeren zijn mathematische modellen een onontbeerlijk hulpmiddel voor beleidsmakers. In Vlaanderen worden hiervoor momenteel gedetailleerde, fysisch gebaseerde waterkwaliteits modellen voor rivieren gebruikt. Een nadeel van dit soort modellen is echter de rekenkundige complexiteit die leidt tot excessief hoge rekentijden. Dit belemmert de grondige ijking van deze modellen, het uitvoeren van lange-termijn-simulaties om statistische analyse zoals vereist voor scenarioanalyses en impactstudies mogelijk te maken, alsook de integratie met andere modellen. Om tot een geïntegreerde modellering te komen op het niveau van het gehele rivierbekken zijn dus rekenkundig efficiënte modellen vereist. Dit onderzoek richtte zich op het ontwikkelen van een conceptueel rivierwaterkwaliteits model op bekkenniveau met een specifieke focus op de stedelijke drainageeffecten op de kwaliteit van het rivierwater. Variabelen die in de analyse werden opgenomen zijn opgeloste zuurstof (DO), Biologisch Zuurstof Verbruik (BZV), ammonia (NH4), nitraat (NO3), orthofosfaat (OP), Particulair Fosfaat (PP) en de water temperatuur (T). Naast advectie- en diffusieprocessen ondergaan deze variabelen ook biologische en chemische transformatieprocessen. In deze studie werden de volgende processen daarbij beschouwd: beluchting, fotosynthese, BZV afbraak, respiratie van macrophyten, sediment zuurstofverbruik, nitrificatie, denitrificatie, opname van ammonium door planten tijdens de fotosynthese, opname van ammonium door bacteriën tijdens BZV afbraak, resuspensie van BZV, sedimentatie van BZV, PP afbraak, PP formatie, opname van OP door planten, resuspensie van PP en sedimentatie van PP.
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SAMENVATTING
Het uitvoeren van een impactanalyse op de waterkwaliteit in peri-urbane stroomgebieden vereist dat er bijzondere aandacht wordt gegeven aan de tijdsdynamiek die aanwezig is in het stroomgebied. De processen in rivieren rioleringssystemen doen zich immers vaak op tijdschalen met verschillende grootteordes voor. Om dit in rekening te kunnen brengen zijn lange-termijnsimulaties vereist, en daarmee samenhangend dus ook snelle rekentijden. Voor het riviersysteem, wordt dit bereikt door het riviernetwerk in conceptuele reservoirs op te delen op basis van informatie die door de gebruiker wordt aangeleverd en door het toepassen van dezelfde waterkwaliteitstransformatieprocessen die worden gebruikt in het gedetailleerde, fysisch gebaseerde model voor elk van deze reservoirs. De afname in ruimtelijke resolutie en de gewijzigde oplossingsmethode zorgen ervoor dat een veel grotere rekentijdstap mogelijk is zonder dat numerieke instabiliteiten optreden waardoor een aanzienlijke vermindering wordt bereikt van de totale tijd die nodig is om het waterkwaliteitsmodel te simuleren. Op deze manier wordt het voordeel van de gedetailleerde modellen, nauwkeurigheid, gecombineerd met het voordeel van een conceptueel model, namelijk de simulatiesnelheid. Met toepassing van deze werkwijze kon een versnellingsfactor in de grootteorde van 104 worden verkregen zonder in te boeten op nauwkeurigheid. Voor het rioleringsstelsel diende een andere aanpak gevolgd te worden door de afwezigheid van een gedetailleerd, fysisch gebaseerd rioolwaterkwaliteitsmodel. Ook rekening houdens met de schaarse beschikbaarheid van meetgegevens, werd voor een Multi-Layer Perceptron Neural Network (MLP NN) gekozen. Deze liet een snelle simulatie toe van de omvang van de concentratie van verontreinigende stoffen tijdens overstortgebeurtenissen naar de rivier. De ontwikkelde methodologische concepten werden toegepast op een typisch periurbaan stroomgebied in Vlaanderen, namelijk het Grote Nete stroomgebied. Dit stroomgebied bevindt zich in het noordoosten van België en heeft een totale oppervlakte van 386 km2 . Het Grote Nete stroomgebied is matig verstedelijkt met 19 % van het land geclassificeerd als stedelijk. Er zijn zeven rioolwaterzuiveringsinstallaties (RWZI’s) om het rioolwater dat geproduceerd wordt in het stroomgebied te zuiveren. Hiervan lozen er twee hun afvalwater in rivieren gelegen in het stroomgebied en de andere vijf transporteren water buiten het stroomgebied. Gedetailleerde, fysisch gebaseerde hydrodynamische rioleringsmodellen werden in InfoWorks CS geïmplementeerd door Aquafin voor de twee RWZI’s die hun afvalwater lozen in de rivieren van het stroomgebied. Simulatieresultaten met deze modellen werden gebruikt om de omvang van overstorten die optreden tijdens hevige regenbuien te bepalen. De impact op de kwaliteit van het rivierwater is potentieel groot aangezien er 94 overstorten gelegen zijn in het stroomgebied. Echter, uit een impactanalyse met een kwantiteitskwaliteits model van het gekoppeld rivier-rioleringssysteem blijkt dat de impact van deze overstorten op de 90e percentiel BZV- en NH4-waarden verwaarloosbaar is; deze 90e percentielwaarden worden momenteel gebruikt om de waterkwaliteitstatus van het waterlichaam te bepalen. De impact van de vervuiling van overstorten op
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de 99e percentielwaarden is echter wel zeer groot, dus voor extreme gebeurtenissen. Aangezien deze gebeurtenissen waarschijnlijk belangrijker zijn voor het aquatisch ecosysteem, is het wel belangrijk om deze vuilvracht mee in rekening te brengen. Tijdens de ijking van het conceptueel model werd duidelijk dat de simulatieresultaten zeer gevoelig zijn voor kleine afwijkingen in de gebruikte waterdiepte (hydraulische straal) en watersnelheden. Aangezien de rivieren in het bestudeerde stroomgebied last hebben van vegetatiegroei in de zomerperiode heeft deze studie onderzocht wat de invloed is van deze macrofyten op de ruwheid en dus op de simulatienaukeurigheid van het waterpeil in de rivier. De analyse bevestigt dat hoewel de ruwheid toeneemt tijdens de zomer vanwege plantengroei en dus hogere waterstanden bij eenzelfde debiet kunnen worden waargenomen, de ruwheid ook weer vermindert tijdens piekdebieten. Daarom is het niet noodzakelijk om rekening te houden met de seizoenaal veranderlijke ruwheidcoëfficiënt wanneer men geïnteresseerd is in overstromingen, maar is het wel zeer belangrijk om hiermee rekening te houden wanneer het hydrodynamisch model wordt gekoppeld met een waterkwaliteitsmodel dat zeer gevoelig is aan de simulatieresultaten van waterdieptes en snelheden. Tenslotte werd het ontwikkelde conceptueel model gebruikt om een globale gevoeligheidsanalyse uit te voeren van de modelparameters die de waterkwaliteit transformatieprocessen definiëren. Dit was onmogelijk met het gedetailleerde rivierwaterkwaliteitsmodel vanwege de grote rekentijd die vereist is voor het aanbevolen aantal modelsimulaties. Een globale gevoeligheidsanalyse liet toe om 9 parameters (op een totaal aantal van 24) te identificeren waar het model het meest gevoelig voor is. Verdere kalibratie is noodzakelijk en kan deze kennis gebruiken om de multidimensionalieit van het probleem te verkleinen.
List of Abbreviations
AD
Advection Diffusion
AIC
Akaike Information Criterion
ANN
Artificial Neural Network
APHA
American Public Health Association
API
Application Programming Interface
AR
AutoRegressive
Arc-NEMO Nutrient Emission MOdel in ArcGIS ARMA
AutoRegressive-Moving-Average
ARMAX
AutoRegressive-Moving-Average with exogenous inputs model
ASM
Activated Sludge Model
ASM2d
Activated Sludge Model No. 2 + denitrifying activity
BOD
Biological Oxygen Demand
COD
Chemical Oxygen Demand
CODA
‘Centrum voor Onderzoek in Diergeneeskunde en Agrochemie’ (in Dutch)
CORIWAQ COnceptual RIver WAter Quality model CSO
Combined Sewer Overflow
CSTR
Continuously Stirred Tank Reactor
DHI
Danish Hydraulic Institute
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LIST OF ABBREVIATIONS
DO
Dissolved Oxygen
DWF
Dry Weather Flow
ET
EvapoTranspiration
EU
European Union
FIELD_AC Fluxes, Interactions and Environment at the Land-ocean boundary. Downscaling, Assimilation and Coupling FWO
‘Fonds voor Wetenschappelijk Onderzoek’ (in Dutch)
GLUE
Generalized Likelihood Uncertainty Estimation
GUI
Graphical User Interface
HD
HydroDynamic
HSPF
Hydrologic Simulation Program FORTRAN
IDE
Integrated Development Environment
IE
Inhabitant Equivalent
IPCC
Intergovernmental Panel on Climate Change
IQR
InterQuartile Range
IWCS
InfoWorks Collection Systems
IW ICM
InfoWorks Integrated Catchment Modeling
IWRM
Integrated Water Resources Management
IWRS
InfoWorks River Systems
KjN
Kjeldahl Nitrogen
KMI/IRM
Koninklijk Meteorologisch Instituut van België/Institut Royal Météorologique (Royal Meteorological Institute of Belgium)
LHS
Latin Hypercube Sample
MATLAB
MATrix LABoratory
ME
Mean Error
MLP NN
Multi-Layer Perceptron Neural Network
NH4
Ammonia
NO3
Nitrate
LIST OF ABBREVIATIONS
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NSE
Nash-Sutcliffe efficiency
Nt
Total Nitrogen
ODE
Ordinary Differential Equation
OP
Orthophosphate
PCA
Principal Component Analysis
PDF
Probability Density Function
PEGASE
‘Planification Et Gestion de l’ASsainissement des Eaux’ (in French)
PEST
model-independent Parameter ESTimation and Uncertainty Analysis
PFR
Plug Flow reactor
PLS
Partial Least Squares
PP
Particulate Phosphorus
PSE
Population Simplex Evolution
Pt
Total Phosphorus
QUASAR
QUAlity Simulation Along Rivers
RBMP
River Basin Management Plan
RMSE
Root Mean Squared Error
RR
Rainfall Runoff
RS
Random Sampling
RWQM
River Water Quality Model
SC
Storage Cell
SDK
Software Development Kit
SENTWA
System for the Evaluation of Nutrient Transport to Water
SOD
Sediment Oxygen Demand
SRC
Standardized Regression Coefficient
SS
Suspended Solids
T
Temperature
TF
Transfer Function
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LIST OF ABBREVIATIONS
TSS
Total Suspended Solids
TUDP
Technical University Delft bio-P
VHA
‘Vlaamse Hydrografische Atlas’ (in Dutch)
VHM
‘Veralgemeend conceptueel Hydrologisch Model’ (in Dutch)
VLAREM
‘VLAams REglement betreffende de Milieuvergunning’ (in Dutch)
VMM
‘Vlaamse MilieuMaatschappij’ (in Dutch)
WASP
Water Quality Analysis Simulation Program
WFD
Water Framework Directive
WWTP
Waste Water Treatment Plant
List of Symbols symbol α β δ Θ ΘBOD Θdenitr Θnitr ΘOP ΘP P Θreaer Θresp ΘSOD
A Asurf b B C C2 CBOD CDO Cin
description
unit
relative day length momentum correction coefficient advective time delay temperature coefficient temperature coefficient for BOD decay temperature coefficient for de-nitrification temperature coefficient for nitrification temperature coefficient for OP formation temperature coefficient for PP decay temperature coefficient for re-aeration temperature coefficient for respiration temperature coefficient for Sediment Oxygen Demand
s -
cross-sectional flow area surface area coefficient for the exponential term water width at the water surface elevation concentration source/sink concentration concentration of BOD concentration of DO incoming concentration
m2 m2 h−1 m mg l−1 mg l−1 mg l−1 mg l−1 mg l−1
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LIST OF SYMBOLS
symbol CN H4 CN O3 COP Cout Cout,AD CoutAD,N O3 CP P Cr CS D depth Eaf Echange Emax Eref et0 eta f f0 fadvDelay fc fdepth fi fM IKE,N O3 fo fr,CORIW AQ fr,M IKE fu
description
unit
concentration of NH4 concentration of NO3 concentration of OP outgoing concentration after taking into account all processes outgoing concentration after taking into account advection and dispersion processes outgoing nitrate concentration after taking into account advection and dispersion processes concentration of PP Courant number saturation dissolved oxygen concentration dispersion coefficient average hydraulic radius in the reservoir emitted heat radiation percentile river impact concentration after WWTP concentration change maximum absorbed solar radiation percentile river impact concentration for the baseline WWTP concentration reference evapotranspiration actual evapotranspiration infiltration rate initial infiltration rate adjustment factor for the advective delay limiting infiltration rate adjustment factor for the calculated average water depth inter flow fraction of precipitation reduction factor of the MIKE11 model results for nitrate overland flow fraction of precipitation reduction factor of the CORIWAQ model results reduction factor of the MIKE11 model results storage fraction of precipitation
mg l−1 mg l−1 mg l−1 mg l−1 mg l−1 mg l−1 mg l−1 mg l−1 m2 s−1 m W h/(m2 d) mg l−1 W h/(m2 d) mg l−1 mm mm mm h−1 mm h−1 mm h−1 -
LIST OF SYMBOLS
symbol fvelExceed g h H HSBOD HSN H4 HSnitr HSresp HSSOD I K k k20 kbact kBF kBOD kdenitr kIF knitr kOF kOP kOP plant kplant kP P kreaer kresp kresuspBOD kresuspP P
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description
unit
adjustment factor for the calculated velocity exceedance gravitational acceleration water surface elevation above a datum or reference level water depth normal to the bed half-saturation oxygen concentration for BOD decay half-saturation ammonia concentration for ammonia plant uptake half-saturation oxygen concentration for nitrification half-saturation oxygen concentration for respiration half-saturation oxygen concentration for SOD rainfall intensity conveyance capacity recession constant reaction rate at reference temperature = 20 ◦C rate of ammonia taken up by bacteria during BOD decay reservoir constant for the linear reservoir that routes the base flow BOD decay rate at 20 ◦C de-nitrification rate at 20 ◦C reservoir constant for the linear reservoir that routes the inter flow nitrification rate at 20 ◦C reservoir constant for the linear reservoir that routes the overland flow OP formation rate at 20 ◦C rate of OP uptake in plants rate of ammonia taken up by plants during photosynthesis PP decay rate at 20 ◦C re-aeration rate at 20 ◦C respiration rate at 20 ◦C resuspension rate for BOD resuspension rate for PP
m s−2 m m mg l−1 mg l−1 mg l−1 mg l−1 mg l−1 mm h−1 m3 s−1 s s−1 gNH4 /gO2 h d−1 d−1 h d−1 h d−1 gP /gO2 gNH4 /gO2 d−1 d−1 d−1 g m−2 d−1 g m−2 d−1
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LIST OF SYMBOLS
symbol
description
unit
ksedBOD ksedP P kSOD kT L lat long n N nr
sedimentation rate for BOD sedimentation rate for PP Sediment Oxygen Demand rate at 20 ◦C reaction rate at temperature = T◦ C monthly nutrient load latitude longitude Manning coefficient number of subsections number of days taken into account to calculate the antecedent rainfall volume wetted perimeter for subsection i maximum photosynthesis production at noon discharge lateral discharge antecedent rainfall volume rainfall run-off resistance radius correlation coefficient resistance factor hydraulic radius rainfall run-off coefficient relative resistance for subsection i bed slope friction slope water level slope Sediment Oxygen Demand relative sensitivity index time temperature time residence time soil moisture storage
m d−1 m d−1 gDO m−2 d−1 s−1 kg/month ° ° s m−1/3 d
Pi pmax Q qlat r R R∗ R2 rf Rh RRc rr,i S0 Sf Sh SOD Sr t T th Tr u
m gO2 d−1 m3 s−1 m2 s−1 mm m3 h−1 m m g m−3 d−1 s ◦ C h s mm
LIST OF SYMBOLS
symbol uevap
uini umax v vcritBOD vcritP P w x y YN H4 Ynitr YP
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description
unit
threshold value for u above which no water availability restriction is present to limit the evapotranspiration initial soil moisture storage maximum soil moisture storage flow velocity critical velocity for resuspension of BOD critical velocity for resuspension of PP weight fraction used to calculate the weighted average distance along the longitudinal axis of the waterway local water depth yield factor describing the amount of ammonia released during BOD decay yield factor describing the amount of oxygen used during nitrification release rate of phosphorus from BOD during decay
mm mm mm m s−1 m s−1 m s−1 m m gNH4 /gBOD gO2 /gNH4 gOP /gBOD
Contents Voorwoord
i
Abstract
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Samenvatting
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List of Abbreviations
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List of Symbols
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Contents
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List of Figures
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List of Tables
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1 Problem statement and research objectives 1.1
1.2
1
Scientific rationale . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.1.1
River water quality status and integrated river water management . . . . . . . . . . . . . . . . . . . . . . . . . .
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1.1.2
Urbanization as a pressure on river water quality . . . . . .
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1.1.3
Climate change as a pressure on river water quality
. . . .
5
Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
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CONTENTS
1.3
1.2.1
Develop a conceptual river water quality model . . . . . . .
7
1.2.2
Develop a parsimonious sewer water quality model . . . . .
9
1.2.3
Analyse the impact of sewer overflows on the river water body by means of an integrated model . . . . . . . . . . . .
10
Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . .
11
2 Case study and detailed model description 2.1
. . . . . . . . . . . . . . . . . . . . . .
13
2.1.1
Geography, climate and land use . . . . . . . . . . . . . . .
13
2.1.2
Available measurements . . . . . . . . . . . . . . . . . . . .
15
2.2
Hydrological model . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
2.3
Hydraulic models . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.3.1
InfoWorks CS sewer model . . . . . . . . . . . . . . . . . .
21
2.3.2
MIKE11 river model . . . . . . . . . . . . . . . . . . . . . .
24
Water quality models . . . . . . . . . . . . . . . . . . . . . . . . . .
34
2.4.1
AD-ECOLab module MIKE11 . . . . . . . . . . . . . . . .
34
2.4.2
Industrial pollution . . . . . . . . . . . . . . . . . . . . . . .
41
2.4.3
Domestic pollution . . . . . . . . . . . . . . . . . . . . . . .
42
2.4.4
Pollutants entering via rainfall run-off . . . . . . . . . . . .
49
2.4
The Grote Nete catchment
13
3 Methodology 3.1
3.2
55
Conceptual River Water Quality Model . . . . . . . . . . . . . . .
55
3.1.1
Applied concepts . . . . . . . . . . . . . . . . . . . . . . . .
55
3.1.2
Semi-automatic model set-up . . . . . . . . . . . . . . . . .
58
3.1.3
Division into conceptual reservoirs . . . . . . . . . . . . . .
59
3.1.4
Determining the boundary information . . . . . . . . . . . .
60
3.1.5
Calibrating the conceptual model to detailed model results
61
CSO water quality model . . . . . . . . . . . . . . . . . . . . . . .
64
3.2.1
64
Available data . . . . . . . . . . . . . . . . . . . . . . . . . .
CONTENTS
3.2.2 3.3
xxiii
Architecture of the Artificial Neural Network . . . . . . . .
66
Impact analysis of CSOs on river water quality by means of an integrated model . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
3.3.1
Molse Nete catchment . . . . . . . . . . . . . . . . . . . . .
68
3.3.2
Conceptual models used for the different subsystems . . . .
70
3.3.3
Integration of the conceptual models . . . . . . . . . . . . .
71
3.3.4
Quantification of the impact of CSOs . . . . . . . . . . . .
71
4 Results and discussion 4.1
73
Conceptual River Water Quality Model . . . . . . . . . . . . . . .
73
4.1.1
Calibration to the detailed model results . . . . . . . . . . .
73
4.1.2
Accuracy compared to detailed model results . . . . . . . .
76
4.1.3
Robustness of the CORIWAQ model . . . . . . . . . . . . .
79
4.1.4
Global sensitivity analysis of the water quality parameters .
81
4.2
CSO water quality model . . . . . . . . . . . . . . . . . . . . . . .
89
4.3
Impact analysis of CSOs on river water quality by means of an integrated model . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
4.3.1
Conceptual water quantity models sewer systems Mol and Geel 92
4.3.2
Conceptual water quantity model Molse Nete River
. . . .
96
4.3.3
ANN water quality model CSOs . . . . . . . . . . . . . . .
97
4.3.4
Conceptual water quality model Molse Nete River . . . . .
98
4.3.5
Impact analysis on simulated percentile values . . . . . . .
98
5 General conclusions and recommendations 5.1
103
Contributions of this research . . . . . . . . . . . . . . . . . . . . . 103 5.1.1
Impact of vegetation on roughness and simulation of the water levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.1.2
Sensitivity of WWTP model results on percentile analysis river water quality . . . . . . . . . . . . . . . . . . . . . . . 103
5.1.3
Conceptual river water quality model . . . . . . . . . . . . 104
xxiv
CONTENTS
5.2
5.1.4
Global sensitivity analysis of the model parameters of the river water quality model . . . . . . . . . . . . . . . . . . . 105
5.1.5
CSO water quality model . . . . . . . . . . . . . . . . . . . 106
5.1.6
Impact analysis of CSOs on river water quality . . . . . . . 107
Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.2.1
Extension of the river water quality model . . . . . . . . . . 107
5.2.2
Calibration of the water quality parameters . . . . . . . . . 108
5.2.3
Pollutant loading from the sewer system . . . . . . . . . . . 109
5.2.4
Integration with other models . . . . . . . . . . . . . . . . . 110
Appendix A Differential equations of the modelled transformation processes 113 A.1 Dissolved Oxygen - DO . . . . . . . . . . . . . . . . . . . . . . . . 114 A.2 Ammonia - NH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 A.3 Nitrate - NO3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 A.4 Biological Oxygen Demand - BOD . . . . . . . . . . . . . . . . . . 118 A.5 Orthophosphate - OP . . . . . . . . . . . . . . . . . . . . . . . . . 119 A.6 Particulate Phosphorus - PP . . . . . . . . . . . . . . . . . . . . . 120 A.7 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Appendix B Comparison of the river water quality model with the observations 123 Appendix C Manual for the GUI to set up the CORIWAQ model
149
C.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 C.2 Software Requirements . . . . . . . . . . . . . . . . . . . . . . . . . 149 C.2.1 MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 C.2.2 MIKE11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 C.2.3 DHI MATLAB toolbox . . . . . . . . . . . . . . . . . . . . 151 C.2.4 Windows SDK 7.1 . . . . . . . . . . . . . . . . . . . . . . . 152
CONTENTS
xxv
C.2.5 Ghostscript . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 C.2.6 [Optional] Microsoft Visual C++ 2010 Express . . . . . . . 152 C.3 Opening the CORIWAQ model . . . . . . . . . . . . . . . . . . . . 153 C.4 Creating a conceptual model semi-automatically . . . . . . . . . . 154 C.4.1 Division of the river network in reservoir blocks . . . . . . . 154 C.4.2 Determining boundary data of the model . . . . . . . . . . 156 C.5 Calibrating the conceptual model to the detailed model results . . 160 C.6 Simulating the conceptual model . . . . . . . . . . . . . . . . . . . 166 C.6.1 Setting the time information . . . . . . . . . . . . . . . . . 166 C.6.2 Selecting the water quality parameters . . . . . . . . . . . . 167 C.6.3 Running the model: possible outputs . . . . . . . . . . . . . 169 Appendix D Accuracy of the CORIWAQ model compared to the MIKE11 model 173 Appendix E Monte Carlo analysis: sampling procedure
181
Appendix F PDF parametrization of the river water quality model parameters 185 Bibliography
189
Curriculum vitae
205
Publications by the author
207
List of Figures 1.1
Major ecosystem services provided by rivers, riparian areas and floodplains/wetlands in Europe [61] . . . . . . . . . . . . . . . . . .
2
1.2
Built-up area (% of total area) in the year 2030 according to an economic globalization scenario [155] . . . . . . . . . . . . . . . . .
4
1.3
Projected differences in annual mean temperature (left) and annual precipitation (right) due to climate change [46] . . . . . . . . . . .
5
1.4
Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . .
12
2.1
Location of the Grote Nete catchment in Belgium . . . . . . . . . .
14
2.2
Land use in the Grote Nete catchment in Belgium and treatment area of the seven WWTPs that influence the water transport over the catchment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
2.3
Rainfall stations with Thiessen area and location of the discharge and water quality monitoring stations in the Grote Nete catchment
16
2.4
Model structure of the VHM rainfall run-off model . . . . . . . . .
18
2.5
Sub-catchments of the two considered InfoWorks Collection Systems (IWCS) models and location of the Combined Sewer Overflows (CSOs) and Waste Water Treatment Plants (WWTPs) . . . . . . .
23
2.6
Channel section with computational grid . . . . . . . . . . . . . . .
25
2.7
Existing MIKE11 river model and extensions made during this research 26
2.8
Observed versus MIKE11 simulated water depths at Meerhout, after use of a constant Manning coefficient of 0.35 s m−1/3 . . . . . . . .
xxvii
30
xxviii
2.9
LIST OF FIGURES
Calibrated Manning’s n values per five-day period with RMSE for calibrated value (a) and piecewise linear relation of the Manning’s n versus day of the year (b) . . . . . . . . . . . . . . . . . . . . . . .
31
2.10 Observed versus MIKE11 simulated water depths at Meerhout, after use of a time-varying Manning coefficient . . . . . . . . . . . . . .
32
2.11 Cumulative probability plot for observed and simulated water depths for summer 2005, calibration (a) and summer 2007, validation (b) .
32
2.12 Observed versus MIKE11 simulated water depths, after use of a time-varying and flow-dependent Manning coefficient for summer 2007 33 2.13 Arrhenius temperature dependency of the reaction rate
. . . . . .
37
2.14 Histogram of the calculated kreaer in the Grote Nete catchment in the year 2000 with the four different expressions . . . . . . . . . .
39
2.15 Daily average discharge and pollutant loading for Tessenderlo Chemie compared to the measured values . . . . . . . . . . . . . . . . . . .
42
2.16 Sensitivity of the 90th , 95th and 99th percentile river concentrations downstream of the WWTP inflow versus the time aggregation level of the modelled WWTP effluent time series for ammonia (a), nitrate (b) and BOD (c) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45
2.17 Relative sensitivity indices: 1 km (a) and 5 km (b) downstream of the WWTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
2.18 The VHA zones that form the Grote Nete catchment . . . . . . . .
48
3.1
GUI of the COnceptual RIver WAter Quality (CORIWAQ) model
58
3.2
River branch represented by linear reservoirs in series with divisions based on key locations of interest . . . . . . . . . . . . . . . . . . .
59
3.3
Highly varying velocity profile resulting in both sedimentation and resuspension to occur simultaneously in one reservoir . . . . . . . .
60
3.4
Optimal adjustment factor water depth in function of the number of samples drawn from the parameters space for a typical reservoir
64
3.5
Locations of the 15 selected WWTPs in Flanders for which the Artificial Neural Network (ANN) model was calibrated . . . . . . .
65
3.6
Rainfall measurement stations operated by the VMM in Flanders have a high density of one per 90 km2 . . . . . . . . . . . . . . . .
65
LIST OF FIGURES
3.7
xxix
Typical time series of daily discharge measurements (left) and biweekly water quality measurements (right) available at the WWTP influent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
3.8
Multi-Layer Perceptron neural network architecture . . . . . . . .
67
3.9
Molse Nete catchment(VHA zone 501) and river network with indication of the locations of the CSOs, WWTP and water quality monitoring sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69
Scatter plot of theoretical reduction factor versus conceptual reduction factor for PP for reservoir GroteNete1_ch6562_7191 - a) original velocities - b) updated velocity chainage 6752 . . . . . . .
75
NSE values between conceptual and detailed modelled result for DO (a) and BOD (b) for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . .
78
4.3
Time series comparison for the conceptual model output for reservoir number 29 with the MIKE11 results at chainage 5.29 km . . . . . .
80
4.4
Scatter plot for the conceptual model output for reservoir number 29 with the MIKE11 results at chainage 5.29 km . . . . . . . . . .
81
4.5
Monotonic relation between the BOD decay rate and the 90th percentile DO concentration . . . . . . . . . . . . . . . . . . . . . .
83
4.6
SRC based sensitivity ranks of the WQ model parameters along the river reach of the 90th percentile model output values . . . . . . .
85
4.7
SRC based sensitivity ranks of the WQ model parameters along the river reach of the 50th percentile model output values . . . . . . .
86
4.1
4.2
th
4.8
Absolute SRC values of the WQ model parameters for the 90 percentile model output values at the penultimate reservoir . . . .
87
4.9
Absolute SRC values of the WQ model parameters for the 50th percentile model output values at the penultimate reservoir . . . .
88
4.10 Time series (a) and scatter plot (b) comparing the model results with measurements of ammonia at the influent of the WWTP of Aalst 90 4.11 Relation between the number of samples available for calibration and the R2 goodness-of-fit statistic of the resulting model for the testing period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
xxx
LIST OF FIGURES
4.12 Scatter plot of R2 for rain events simulated with NN trained for all measurements and NN trained only on measurements during rain events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
4.13 Division in storage cells (SC) of the conceptual water quantity models of the sewer systems of Mol and Geel . . . . . . . . . . . . . . . . .
94
4.14 Conceptual model versus detailed model simulation results for the conceptual model output reservoir number 24 and MIKE11 results at chainage 9477, excluding the input of the CSOs . . . . . . . . .
99
4.15 90th percentile (a) and the 99th percentile (b) BOD concentration including and excluding the input of the CSOs (% at the CSO shows the percentage of the time the CSO is active) . . . . . . . . . . . . 100 4.16 90th percentile (a) and the 99th percentile (b) NH4 concentration including and excluding the input of the CSOs (% at the CSO shows the percentage of the time the CSO is active) . . . . . . . . . . . . 101 B.1 River water quality model result versus observations at Grote Nete 4 chainage 10033 (identifier 257500) . . . . . . . . . . . . . . . . . 124 B.2 River water quality model result versus observations at Grote Nete 4 chainage 8720 (identifier 258000) . . . . . . . . . . . . . . . . . . 125 B.3 River water quality model result versus observations at Grote Nete 4 chainage 7223 (identifier 258500) . . . . . . . . . . . . . . . . . . 126 B.4 River water quality model result versus observations at Grote Nete 4 chainage 3034.21 (identifier 260000) . . . . . . . . . . . . . . . . 127 B.5 River water quality model result versus observations at Grote Nete 3 chainage 9763.01 (identifier 260500) . . . . . . . . . . . . . . . . 128 B.6 River water quality model result versus observations at Grote Nete 3 chainage 3207.14 (identifier 262000) . . . . . . . . . . . . . . . . 129 B.7 River water quality model result versus observations at Grote Nete 1 chainage 12358.14 (identifier 262200) . . . . . . . . . . . . . . . . 130 B.8 River water quality model result versus observations at Grote Nete 1 chainage 781.11 (identifier 2627000) . . . . . . . . . . . . . . . . 131 B.9 River water quality model result versus observations at Grote Laak chainage 14030 (identifier 325000) . . . . . . . . . . . . . . . . . . 132 B.10 River water quality model result versus observations at Grote Laak chainage 5510 (identifier 326100) . . . . . . . . . . . . . . . . . . . 133
LIST OF FIGURES
xxxi
B.11 River water quality model result versus observations at Grote Laak chainage 2910 (identifier 326500) . . . . . . . . . . . . . . . . . . . 134 B.12 River water quality model result versus observations at Grote Laak chainage 604.44 (identifier 326900) . . . . . . . . . . . . . . . . . . 135 B.13 River water quality model result versus observations at Kleine Laak chainage 0 (identifier 328000) . . . . . . . . . . . . . . . . . . . . . 136 B.14 River water quality model result versus observations at Molse Nete chainage 13106.94 (identifier 329000) . . . . . . . . . . . . . . . . . 137 B.15 River water quality model result versus observations at Molse Nete chainage 7418.06 (identifier 329800) . . . . . . . . . . . . . . . . . 138 B.16 River water quality model result versus observations at Molse Nete chainage 5064.33 (identifier 330200) . . . . . . . . . . . . . . . . . 139 B.17 River water quality model result versus observations at Molse Nete chainage 2054.04 (identifier 331000) . . . . . . . . . . . . . . . . . 140 B.18 River water quality model result versus observations at Molse Neet chainage 5884.81 (identifier 333000) . . . . . . . . . . . . . . . . . 141 B.19 River water quality model result versus observations at Molse Neet chainage 419.66 (identifier 333100) . . . . . . . . . . . . . . . . . . 142 B.20 River water quality model result versus observations at Oude Nete chainage 483.45 (identifier 333400) . . . . . . . . . . . . . . . . . . 143 B.21 River water quality model result versus observations at Scheppelijke Neet chainage 7797.30 (identifier 333500) . . . . . . . . . . . . . . 144 B.22 River water quality model result versus observations at Geeploop chainage 1670.64 (identifier 335000) . . . . . . . . . . . . . . . . . 145 B.23 River water quality model result versus observations at Hanskelselsloop chainage 1638.91 (identifier 335880) . . . . . . . . . . . . . . 146 B.24 River water quality model result versus observations at Ongelbergloop chainage 1963.97 (identifier 336000) . . . . . . . . . . . . . . 147 C.1 Required files for a semi-automatic conceptual river water quality model set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 C.2 River branch represented by linear reservoirs in series with divisions based on key locations of interest . . . . . . . . . . . . . . . . . . . 150 C.3 Setting the search path of MATLAB to the CORIWAQ model folder 151
xxxii
LIST OF FIGURES
C.4 Attaching the MATLAB.exe process to Microsoft Visual C++ 2010 Express . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 C.5 GUI of the COnceptual RIver WAter Quality (CORIWAQ) model
154
C.6 Exporting the processed cross-section information from MIKE 11 . 158 C.7 Progress of reading in the boundary and hydrodynamic information 160 C.8 Process of reading in boundary data finished successfully . . . . . . 160 C.9 GUI calibration of the conceptual river water quality model . . . . 161 C.10 Optimal adjustment factor water depth in function of the number of samples drawn from the parameters space for a typical reservoir
164
C.11 Scatter plot of theoretical reduction factor versus the conceptual reduction factor for PP for reservoir GroteNete1_ch6562_7191 - a) original velocities - b) updated velocity chainage 6752 . . . . . . . 165 C.12 Example of the extra figure showing the minimum, maximum and average velocity at each calculation node of the reservoir . . . . . . 166 C.13 Generating the *.WQAdd file containing the processes information
172
D.1 NSE values between conceptual and detailed modelled result for DO for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 D.2 NSE values between conceptual and detailed modelled result for NH4 for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 D.3 NSE values between conceptual and detailed modelled result for NO3 for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 D.4 NSE values between conceptual and detailed modelled result for BOD for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 D.5 NSE values between conceptual and detailed modelled result for OP for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 D.6 NSE values between conceptual and detailed modelled result for PP for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
LIST OF FIGURES
xxxiii
D.7 NSE values between conceptual and detailed modelled result for T for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 E.1 Example of LHS: Random stratified sampling of variables x1 and x2 at 5 intervals (left) and random pairing of sampled x1 and x2 forming a Latin hypercube (right) . . . . . . . . . . . . . . . . . . 183
List of Tables 2.1
Inhabitants at 1 January 2015 per municipality situated in the Grote Nete catchment [49] . . . . . . . . . . . . . . . . . . . . . . . . . .
15
2.2
Location of the available water quality monitoring stations . . . . .
17
2.3
VHM calibrated parameters . . . . . . . . . . . . . . . . . . . . . .
21
2.4
Characteristics of the two IWCS models available . . . . . . . . . .
24
2.5
Modelled water quality transformation processes . . . . . . . . . .
36
2.6
Range of applicability of empirical formulas compared to the observed values in the Grote Nete catchment . . . . . . . . . . . . . . . . . .
38
2.7
Water quality standards for rivers in the Grote Nete catchment . .
40
2.8
Company and industry branch of the 9 monitored industrial effluents in the Grote Nete catchment . . . . . . . . . . . . . . . . . . . . .
41
2.9
Definition of one Inhabitant Equivalent
47
. . . . . . . . . . . . . . .
2.10 IE that discharge directly into the environment per VHA zone
. .
47
2.11 Description of the VHA zones situated in the Grote Nete catchment 48 2.12 Proportion of nitrate and ammonia relative to the total Nitrogen load 52 2.13 Modelled BOD load draining to the surface waters in Flanders on a yearly basis [147] . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
Different max ∆x that are tested for the case study indicating the speed-up that can be obtained compared to the detailed MIKE11 model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
4.1
xxxv
xxxvi
LIST OF TABLES
4.2
Mean, median and standard deviation for the calibration factors of the three different conceptual model set ups . . . . . . . . . . . .
74
4.3
Reservoir that required an adjustment of the chainages used to calculate the percentage velocity exceedance . . . . . . . . . . . . .
76
4.4
Median (IQR) of the NSE values between conceptual and detailed modelled result for all reservoirs in the catchment during the validation period 2001-2002 . . . . . . . . . . . . . . . . . . . . . .
77
4.5
Parameters of the water quality transformation processes that should not be subject to calibration . . . . . . . . . . . . . . . . . . . . . .
82
4.6
R2 values of the multiple linear regression performed on the standardized inputs and outputs of the model . . . . . . . . . . . .
84
4.7
Parameters with an absolute SRC value larger than 0.2 . . . . . .
88
4.8
Summary of the ANN model results (average ± standard deviation) 89 R2 test results when considering different ANN inputs . . . . . . .
90
4.10 Relative volume errors of spilled volumes of the 3 largest overflows compared to the results of the IWCS model results. The spilled volume in the IWCS simulation results is shown between brackets.
95
4.11 Comparison of the spilled volumes by the conceptual model of all overflows for which simulation results of the detailed sewer model of Mol were available for the long term simulation . . . . . . . . . . .
95
4.12 Comparison of the spilled volumes by the conceptual model of all overflows for which simulation results of the detailed sewer model of Geel were available for the long term simulation . . . . . . . . . . .
95
4.13 Results water quality model sewer system Mol
. . . . . . . . . . .
97
4.14 Results water quality model sewer system Geel . . . . . . . . . . .
97
4.9
C.1 Necessary order of the parametersWQ variable . . . . . . . . . . . 167 C.2 Order of the processes information . . . . . . . . . . . . . . . . . . 171 F.1 Model input parameters included in the sensitivity analysis and parametrization of probability density functions . . . . . . . . . . . 186
Chapter 1
Problem statement and research objectives 1.1 1.1.1
Scientific rationale River water quality status and integrated river water management
River ecosystems encompass river channels and its floodplains and form a diverse mosaic of habitats with the riparian area at the transition zone between the land and water. Looking more precisely at the specific services provided by river ecosystems, their important role for human well-being becomes obvious (Figure 1.1). Besides the fresh water supply for drinking water and sanitation purpose, the ecosystem also provides regulatory, cultural and supporting services [61]. For the water body to be able to provide all of these ecological services, a good surface water status is required. The surface water status is determined by the lowest of the ecological status and chemical status of the water body. Unfortunately, Europe suffers from a historical legacy of pollution of the surface waters leading to a poor status for most surface waters. To rectify this the Water Framework Directive (WFD) was adopted by the European parliament and the Council on 23 October 2000. It imposes every member state of the European Union (EU) to achieve a good status of all their river water bodies by 2027 as ultimate deadline [45]. The Integrated Water Resources Management (IWRM) approach that was advocated by the United Nations at the 1992 Conference on Environment and Development in Rio de Janeiro forms the corner stone of the WFD. The
1
2
PROBLEM STATEMENT AND RESEARCH OBJECTIVES
Figure 1.1: Major ecosystem services provided by rivers, riparian areas and floodplains/wetlands in Europe [61] IWRM concept prioritises an inter-disciplinary approach involving economic, social and environmental impact assessments. There are two instruments that play an important role in integrated impact assessment: monitoring and modelling [142]. The monitoring allows for identification and assessment of status and risk. Based on this monitoring, a programme of measurements for re-mediation can be developed as part of River Basin Management Plans (RBMPs). To be able to evaluate the effectiveness of these programmes before they have been implemented, models are essential tools. European waters have improved a great deal over the last two decade as a results of the WFD stimulus, as national legislation has successfully reduced many types of pollution and improved waste water treatment. However, still half of the river bodies is not meeting the demand of a good status as reported by the member states. Many European water bodies remain polluted by excess nutrients, mainly from fertilizer application. When fertilizers run off from crop-lands into a water body, it can create eutrophication, a process characterized by increased plant growth and harmful algal blooms, depletion of oxygen and subsequent loss of life in bottom water. Diffuse pollution from agriculture is a significant pressure for more than 40 % of Europe’s water bodies in rivers and coastal waters [78]. Besides
SCIENTIFIC RATIONALE
3
diffuse pollution, point source pollution also still poses significant risks to the river water quality such as for example the discharge of untreated sewage through CSOs during heavy rainfall events where the urban waste water is collected by means of a combined sewer systems. To put it boldly, to maintain and improve the essential functions of our water ecosystems, they need to be managed better. This can only succeed if an integrated approach is adopted, as introduced in the IWRM concept. All sectors in a river basin need to fully implement the WFD to reduce pressures on water bodies, ensuring all users are committed to healthy water bodies achieving good status [78]. Two important driving forces that put a strong pressure on the water bodies in Europe in the future, increased urbanization and climate change are discussed shortly below.
1.1.2
Urbanization as a pressure on river water quality
During the last decades, the transformation of western European landscapes has mainly been characterized by an expansion of the built-up area at the expense of arable land and natural areas. This urbanization trend is expected to continue in the near future. Figure 1.2 shows the proportion of built-up land in different regions in Europe according to an economic globalization scenario for 2030 [120]. The impacts of this vast urban expansion can be divided into two categories: environmental impacts and social-economic impacts [137]. Possible environmental problems include air pollution, increased energy consumption, loss of open space and farmland, reduced biodiversity, increased run-off and risk of flooding [68]. Socio-economic impacts are mostly a direct consequence of these environmental impacts such as the cost occurred after a flood event. Regarding the river ecosystem, the impact of increased built-up land is very strong. The loss of vegetation and replacement of soil with impervious surfaces leads to an increase in direct routing of the surface run-off to river channels and has thus a significant effect on the hydrology of the river catchment. The decreased infiltration in the built-up area results in an increased surface run-off and increased peak flow magnitudes and flood risk, but also in decreased low flows caused by a drop in the groundwater recharge rates. The water quality is also degraded due to an increased supply of and a decreased filtering capacity for pollutants [108].
4
PROBLEM STATEMENT AND RESEARCH OBJECTIVES
Figure 1.2: Built-up area (% of total area) in the year 2030 according to an economic globalization scenario [155]
SCIENTIFIC RATIONALE
1.1.3
5
Climate change as a pressure on river water quality
Besides the advancement of the urbanization, an important driving force that is influencing the river ecosystem is climate change (Figure 1.3). The Intergovernmental Panel on Climate Change (IPCC) has reported an increase in the frequency of extreme rain storm events for the late 20th century and expects this trend to continue further [100]. Specifically for Flanders, studies have indicated that more precipitation will fall in winter but that summers will become drier with more extreme rain storms [170, 173, 168, 132, 149, 133, 26]. Increased floods and droughts directly impact water quality by dilution and concentration of dissolved substances, but also indirectly by modified drought/rewetting cycles as it enhances decomposition and flushing of organic matter into streams and can lead to an increased leaching of nutrients from arable pastures. Especially periods of drought might become problematic for the ecological and chemical status of rivers as the loss of stream dilution capacity and the low water velocities derived from the reduced stream flows might increase the concentration of pollutants and decrease the dissolved oxygen concentrations. On top of these hydrological changes, surface temperature is projected to rise over the 21st century under all assessed emission scenarios [100], possibly deteriorating the water quality status of water bodies even further. Temperature is the main factor affecting almost all physico-chemical equilibrium and biological reactions. With an increased
Figure 1.3: Projected differences in annual mean temperature (left) and annual precipitation (right) due to climate change [46]
6
PROBLEM STATEMENT AND RESEARCH OBJECTIVES
temperature, reaction rates of water quality transformation processes escalate and an increase can be observed in the concentration of dissolved substances in water but also a decrease of dissolved gazes is expected. This last point is very important with respect to dissolved oxygen in water [39].
1.2
Objectives
The general objective of this doctoral research is to improve the scientific knowledge and understanding of the processes that affect the river water quantity and quality through considering the natural system, the human environment and the complex interactions between both in an integrated way. This research focusses more in particular on developing a mathematical model at river catchment scale with a specific focus on the urban drainage impacts on river water quality. Therefore a typical peri-urban catchment in the Flanders area of Belgium, the Grote Nete river catchment, has been chosen as a case study to demonstrate the developed concepts. Water quality impact analysis for peri-urban catchments require that particular focus is given to the temporal dynamics that are present in the catchment. This is so because processes along river catchments and urban drainage systems often have time scales with different orders of magnitude with very quick response times of the sewer system and a longer response time of the river catchment system. This requires long-term simulations, and hence quick computational times. These fast computational times are also required for reasons of water quality model calibration (since many parameters are involved in describing the water quality transformation processes), lack of knowledge (uncertainty quantification by means of statistical analysis is important), and scenario studies including optimization requiring many runs. The research hypothesis is that all these needs can be met through the use of a modelling approach that differs from the traditional detailed, physically based approach, and makes use of simplified conceptual models. This will be done in a step-wise way, considering the following three specific objectives which are discussed in more detail below. • Develop a conceptual river water quality model • Develop a parsimonious sewer water quality model • Analyse the impact of sewers overflows on the river water body by means of an integrated river-sewer water quantity and quality model The benefits of a multidisciplinary integrated water management approach are more and more recognized by practitioners. This evolution is also reflected by the paradigm shift in legislation from isolated end-of-pipe solutions to a
OBJECTIVES
7
more holistic approach as propagated in the WFD. However, such integrated modelling induces additional challenges to the way systems are being modelled, since the inherent scope and scale enlargement increases the complexity significantly. Most modelling tools employed nowadays try to incorporate as much detail as possible. Linking such models, especially if they were implemented in different software programs, is time-consuming and often technically very difficult or even impossible. In addition, interfaced detailed models have an even increased (and likely excessive) model complexity and calculation time, impeding their use in numerous applications such as optimization problems, uncertainty and risk analysis and scenario investigations. Therefore, the use of simplified models has been advocated often when dealing with integrated systems [84, 166, 178]. Such models mimic more detailed models, but focus on the dominating processes. Since these models rely on fewer relationships and employ a different numerical scheme to solve the Ordinary Differential Equations (ODEs), the overall complexity and calculation time are reduced significantly. This makes simplified models ideally suited for integrated modelling on larger scales, which can in turn be employed in decision support systems, optimization questions and risk and uncertainty analysis. In the field of urban drainage modelling, much research has already been devoted to integrating of the different models of the urban drainage system (sewer system and WWTP) with a model of the receiving river water body [51, 29]. However, most studies still report that expensive data requirements and limitations in computational hardware have greatly challenged the integrated modelling of such urban water systems. The three major reasons why the choice of suitable sub-models for integration remains limited are [10]: • Their excessive complexity • Different aims at their time of development • Incompatible parameters and variables The current research tries to tackle all three of these problems with the proposed model structure for the different sub-models, starting from the point of view of the receiving river system instead of taking the urban drainage system as a starting point. Especially the parameters modelled differs in this manner compared to other studies in the integrated urban drainage field as well as the availability of monitoring data of this drainage system (which are limited both in time and space in this case).
1.2.1
Develop a conceptual river water quality model
To asses the surface water status in a catchment and to be able to investigate the impact of mediation actions, river water quality models are needed that can simulate
8
PROBLEM STATEMENT AND RESEARCH OBJECTIVES
the temporal evolution of the concentration of pollutants at different locations in the water body under different scenarios. To simulate the fate and transport processes of pollutants released into river water bodies, mathematical equations are exploited that describe the advection-dispersion of conservative pollutants and the biological and chemical transformation processes of non-conservative pollutants. Currently, there are several models developed for river water quality simulations of which only a few examples are Hydrologic Simulation Program FORTRAN (HSPF) [50], Water Quality Analysis Simulation Program (WASP) [181], QUAL2E [103], QUAL2K [183], River Water Quality Model (RWQM) [116, 25], MIKE11 [43], InfoWorks River Systems (IWRS) [98], SIMCAT [156] and QUAlity Simulation Along Rivers (QUASAR) [70]. A more detailed overview of the different types of models can be found in Benedini and Tsakiris [16]. These models are usually applied for water resources planning or industrial and urban waste water control but mostly remain limited to relatively small catchments because of the extensive calculation time required. Even for a moderately sized Belgian catchment of less than 400 km2 , it takes approximately four days on a desktop PC to simulate only one year of historical data with a MIKE11-ECOLab model clearly indicating that long term simulations, parameter calibration, scenario analysis or integrating different sub-systems into one holistic model are not feasible when using this modelling approach. The main reason for the long computational time required for the full detailed hydrodynamic models is the high spatial resolution used in the model set-up resulting in a high number of calculation nodes that require a small time resolution to solve the differential equations in order to avoid numerical instability. Therefore it might prove a useful approach to lump different adjacent calculation nodes into one conceptual reservoir and employ a more simplified calculation scheme to solve the differential equations. In this manner, the computation time is decreased while maintaining the representation of the physical processes. This transition to a grey box model instead of resorting to a purely empirical black box model allows for confidence in extrapolation results and thus endorses the models to be used for scenario analysis. Conceptual reservoir-type models have been widely used to model the conversion of precipitation volumes to rainfall run-off discharges [104, 19], the hydrodynamic behaviour of rivers [28, 27, 20] and sewer systems [140, 69, 178] and even to simulate the water quality in sewer systems [2, 166]. The use of conceptual reservoirs to calculate the water quality in rivers is less well established; only a few applications exist [110, 85]. The grey box modelling approach may, however, be very valuable to model water quality concentrations along rivers as it can provide both good accuracy and reasonable computational cost. Parsimonious models that represent the simplest approach that fits the application has also been advocated as a good middle ground between the available continuum of detailed physical models and simplified black box models in water quality modelling [83]. The appropriate
OBJECTIVES
9
modelling approach thus depends on the research goal as well as on data available for correct model application. In this research, a modelling approach has been developed that can semiautomatically build a conceptual model of reservoirs in series based on an existing detailed, physically based MIKE11-ECOLab model. This is a QUAL-2E type model which has been shown to be able to provide good model results for dissolved oxygen, ammonia, organic loading and phosphorus [141]. The same physical relationships that govern the system in the detailed model are being used in the conceptual model. The variables considered for this study are Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Ammonia (NH4), Nitrate (NO3), Orthophosphate (OP), Particulate Phosphorus (PP) and Temperature (T) as nutrients are the most important factor impeding the achievement of a good water quality status of river bodies in Europe. The developed model is called COnceptual RIver WAter Quality model (CORIWAQ) and combines the strength of detailed models, i.e. accuracy, with the strength of black box models, i.e. speed, without loosing the capability to extrapolate.
1.2.2
Develop a parsimonious sewer water quality model
To assess the river water quality status of a river water body under current and future conditions, mathematical models are set up that take into account all pollutants entering the system as well as the transport and transformation of these pollutants in the river system. As noted by Even et al. [47] these water quality models should take into account the pollutants entering the river system through CSOs in order to be reliable given the spatial and temporal extent of these spills. However, modelling water quality concentrations at CSOs is very challenging when no CSO water quality measurements are available. On the contrary, WWTP influent measurements are often readily available, at least with low temporal frequency. Therefore we hypothesize that the measurements at the influent of the WWTP can be considered as a proxy for the assessment of the CSO loads. Many models are available that can simulate the dynamics of the WWTP influent [53, 15, 79, 87, 48]. Broadly, they can be classified in grey box models, which are semi-physical models, and black box models that derive an empirical relation between the input and output of the observed systems. In a grey box model approach the influent time series dynamics can be described through a conceptual reservoir [14, 166]. The main processes such as Dry Weather Flow (DWF) loads and diurnal variations, wash-off, sedimentation and resuspension can be taken into account. This approach, however, requires measurements of both discharges and water quality concentrations at the time step required for the model (i.e. at least hourly and preferably in the order of magnitude of minutes). If discharge and
10
PROBLEM STATEMENT AND RESEARCH OBJECTIVES
WWTP influent concentration measurements are available at a lower temporal resolution, calibration of a grey box model becomes a very difficult and time consuming task. In a black box or empirical model, the relation between model input and output solely relies on the measurements at hand without any prior model or physical knowledge available. Well known examples are ANNs and transfer functions. ANN based models have the advantage that they can be trained to temporary sparse data, as is often the case for water quality data at CSOs. A disadvantage of this type of models is the risk of over fitting to the data, loosing the capability to perform well under extrapolation. To avoid this, special care is given to a division of the data set in a training set on which the calibration is performed, a validation set which provides an independent stopping criterion and another independent testing set to show that the model also performs well under different circumstances than those used during training. The model developed in this study was built up to simulate the pollutant concentration of CSO such that it can be included in the conceptual river water quality model for impact analysis. This modelling strategy takes into account the readily available discharge and rainfall measurements and the WWTP influent concentration measurements with a much lower temporal resolution.
1.2.3
Analyse the impact of sewers overflows on the river water body by means of an integrated river-sewer water quantity and quality model
To demonstrate the capability of the developed methodologies for river and sewer water quality modelling, the impact of sewer overflows on river water quality is analysed. The hydraulic models of the sewer and river system have also been conceptualized to obtain one integrated model in the same software. The conceptual models to simulate the hydraulic behaviour of the two systems have been set up by Wolfs [176]. The used methodology is founded on the same storage reservoir concept as has been applied for the conceptual river water quality model. The calibration is performed based on the model results of the detailed IWCS model of the sewer systems in the catchment and on the model results of the detailed MIKE11 hydraulic river model. Each sub-model is individually calibrated and verified before being used in the integrated model as this is crucial to ensure each model has a physically correct representation of the considered subsystem [114]. The calculation schemes of the different models are written in C code, which is not only computationally very efficient, but also ensures straightforward interfacing of the different models. The interface is implemented in MATLAB to ensure a user-friendly experience in creating and running the models. The four simplified models are integrated and employed to simulate a long term, hourly rainfall series
OUTLINE OF THE THESIS
11
for the period 2001-2008. Simulation of a historical time series instead of composite storms as is usually applied for sewer impact analysis is important to be able to study the impact correctly since the sewer system and the river system have different time scale at which they operate with much shorter reaction times in the sewer system and longer reaction times in the river system. A statistical analysis of a long time series can account for the effect of this different time scales and also allows to calculate the frequency by which concentrations are exceeded or reversely the concentration that is not being exceeded during a given time, i.e. the percentile values. The current water quality legislation is based on these latter percentile values so it is important that the river water quality model can accurately predict these values. The impact on the river water quality is quantified by comparing the 90th and 99th percentiles with and without taking into account the extra discharges that enter the river through the CSOs for BOD and NH4.
1.3
Outline of the thesis
A schematic overview of the outline of this thesis is provided in Figure 1.4. In this first chapter, the general background and specific objectives of the research were described. The following chapter describes the case study that was used to demonstrate the developed methodology, namely the Grote Nete catchment. This second chapter also describes in high detail the different existing models for the hydrological, hydraulic and water quality subsystems as well as the adjustments made to these models as part of this Ph.D. research. Additionally, the accuracy and sensitivity of these models have been investigated in more detail. More specifically, the accuracy of the hydraulic river model under the influence of vegetation growth is discussed. The sensitivity of the river water quality model output to WWTP pollutant loading is investigated thoroughly as well. In the third chapter, the developed methodology for the sewer water quality model and the river water quality model is explained comprehensively. The results of applying both approaches, as well as of the integration of both, are shown in the fourth chapter together with an interpretation. Finally, general conclusions and recommendations for further research are given in chapter 5.
12
PROBLEM STATEMENT AND RESEARCH OBJECTIVES
Chapter 1
Chapter 2
Why? Existing models How? Integrated river water quantityquality modelling
New models Chapter 3
Results Chapter 4
Conclusions
Chapter 5
Figure 1.4: Outline of the thesis
Chapter 2
Case study and detailed model description 2.1
The Grote Nete catchment
2.1.1
Geography, climate and land use
The Grote Nete catchment was used as case study area to test the methodologies developed for this research. This catchment is located in the north-East of Belgium (Figure 2.1). It has a total area of 386 km2 and is flat with an average slope of 3%. The total length of the waterways is 539 km, equivalent to a drainage density of 1.4 km/km2 . The Grote Nete catchment experiences moderate average winter (October until March) and summer (April to September) temperatures of 5 and 14 ◦C respectively. The average winter and summer precipitation are respectively 372 and 392 mm giving a long-term mean annual precipitation equal to 764 mm. The average annual potential EvapoTranspiration (ET) is 670 mm [175]. Because the soil type predominantly consists of sandy and loamy sand, rainfall easily percolates to the groundwater resulting in a shallow water table. The groundwater therefore has a large influence on the flow characteristics. Seasonal groundwater fluctuations determine the flow profile with a daily mean discharge of 3.6 m3 s−1 during summer (mean water level of 1.0 m) and a higher mean discharge of 5.7 m3 s−1 during winter (mean water level of 1.3 m) at the basin outlet. Peak discharges can reach up to 15 m3 s−1 during winter, which corresponds to peak water levels of 2.1 m [161]. The Grote Nete catchment is moderately urbanized with 19 % of the surface area classified as urban and built up land. Agriculture plays a main role as 52 % of the 13
14
CASE STUDY AND DETAILED MODEL DESCRIPTION
Figure 2.1: Location of the Grote Nete catchment in Belgium land is used as grassland or crop-land. The remaining 29 % are natural forests, rivers and permanent wetlands. There are seven WWTPs to treat the sewage water produced in the catchment of which two discharge their effluent in rivers situated in the catchment and the other five transport water outside of the catchment (Figure 2.2).
Figure 2.2: Land use in the Grote Nete catchment in Belgium and treatment area of the seven WWTPs that influence the water transport over the catchment The Grote Nete catchment is a sub-catchment of the Scheldt basin. Administratively, the Grote Nete catchment is almost entirely under the authority of the province of Antwerp. The Grote Laak catchment, however, is located in and coordinated
THE GROTE NETE CATCHMENT
15
by the province of Limburg. Eleven municipalities form part of the catchment with an approximate population of 125 000 inhabitants residing in the Grote Nete catchment when assuming a constant population density within one municipality (Table 2.1). Table 2.1: Inhabitants at 1 January 2015 per municipality situated in the Grote Nete catchment [49] Province
Antwerp
Limburg
2.1.2
Municipality
Inhabitants
Mol Laakdal Geel Meerhout Balen Beringen Overpelt Lommel Hechtel-Eksel Ham Leopoldsburg Tessenderlo
35685 15810 38837 10190 21969 44885 14848 33852 12266 10617 15213 18334
% of surface in catchment 32 70 36 100 100 9 5 36 67 96 100 35 TOTAL
Inhabitants in catchment 11556 11131 13944 10190 21969 3987 700 12095 8197 10140 15213 6414 125536
Available measurements
There are six rainfall measurement stations available in and around the Grote Nete catchment. These rain gauges are maintained by the ‘Vlaamse MilieuMaatschappij’ (in Dutch) (VMM) and are available online1 . Hourly measurements were accessible for the period 14 September 2001 to 31 December 2008. Missing rainfall periods were filled in by using the nearest neighbour method. The average areal rainfall over the catchment is calculated with the Thiessen polygon method [134]. These areas are indicated in Figure 2.3. For the same period daily measurements performed by the Koninklijk Meteorologisch Instituut van België/Institut Royal Météorologique (Royal Meteorological Institute of Belgium) (KMI/IRM) at Uccle were available for the ET time series. The water level is gauged at 4 locations from which the discharge is derived through a rating curve. These measurements are available online with an hourly time resolution1 . There are also 24 water quality monitoring points spread out 1 At
the moment of download on http://www.hydronet.be; now on http://www.waterinfo.be
16
CASE STUDY AND DETAILED MODEL DESCRIPTION
over the catchment (Figure 2.3, Table 2.2). These measurement stations are also maintained by the VMM and the measurements are made publicly available online2 . The parameters that are sampled at most locations include temperature, pH, DO, percent of saturated oxygen, chloride, electrical conductivity, BOD, Chemical Oxygen Demand (COD), Kjeldahl Nitrogen (KjN), NH4, NO3, Total Phosphorus (Pt), OP, sulphates, Suspended Solids (SS), nickel, copper, cadmium, chromium, lead and zinc. The number of parameters sampled and the spatial resolution is sufficient but sampling only occurs approximately once a month, hence only providing twelve measurements per year. This information can be used for calibration of a trend but of course a higher time resolution would be beneficial during the calibration process.
Figure 2.3: Rainfall stations with Thiessen area and location of the discharge and water quality monitoring stations in the Grote Nete catchment
2 http://geoloket.vmm.be
HYDROLOGICAL MODEL
17
Table 2.2: Location of the available water quality monitoring stations Identifier 257500 258000 258500 260000 260500 262000 262200 262700 325000 326100 326500 326900 328000 329000 329800 330200 331000 333000 333100 333400 333500 335000 335880 336000
2.2
Lambert72 coordinates X Y 193185 193171 193340 195991 199925 204776 210560 220055 193292 200836 202961 205021 193292 194433 198042 199632 201657 206536 211498 202673 203131 198585 206487 207151
199665 200900 202124 204584 203207 204765 206194 203530 196578 197115 196490 196851 197114 204368 206964 207175 207709 208313 209067 208394 209305 204048 204467 205751
MIKE11 location GROTENETE4 10033 GROTENETE4 8720 GROTENETE4 7223 GROTENETE4 3034.21 GROTENETE3 9763.01 GROTENETE3 3207.14 GROTENETE1 12358.14 GROTENETE1 781.11 GROTELAAK 14030 GROTELAAK 5510 GROTELAAK 2910 GROTELAAK 604.44 KLEINELAAK 0 MOLSENETE 13106.94 MOLSENETE 7418.06 MOLSENETE 5064.33 MOLSENETE 2054.04 MOLSENEET 5884.81 MOLSENEET 419.66 OUDENETE 483.45 SCHEPPELIJKENEET 7797.30 GEEPLOOP 1670.64 HANSKENSELSLOOP 1638.91 ONGELBERGLOOP 1963.97
Hydrological model
To convert the available rainfall time series to a rainfall run-off time series that enters the network of rivers, a catchment rainfall run-off model is needed. For this case study, the ‘Veralgemeend conceptueel Hydrologisch Model’ (in Dutch) (VHM) approach developed by Willems [169] was used. VHM is a Dutch abbreviation for ‘generalized lumped conceptual modelling approach’. The approach is based on a step-wise procedure to determine (identify and calibrate) the equations that control the split of the rainfall for input in the different conceptual reservoirs, namely soil moisture (which is emptied by evapotranspiration), overland flow, inter flow and base flow. Calibrated recession constants of the conceptual reservoirs of the three flow components determine the magnitude of the sub-flows, which are added together to determine the total rainfall run-off (Figure 2.4).
18
CASE STUDY AND DETAILED MODEL DESCRIPTION
Rainfall input x Xo = (1 − fu − fo − fi ) · x
X
X
dependent on u
X
Xo = fo · x
EvapoXu = fu · x transpiration Xi = fi · x eta Soil moisture storage u fu
fo
fi
Time variable distributing valve
Routing overland flow
Overland flow
Routing interland flow
Interland flow
Routing baseflow
Baseflow
Total discharge Figure 2.4: Model structure of the VHM rainfall run-off model The first and most important sub model, that needs calibration, is the soil water storage model. The time-variability of the different fractions is related to the time-variability of the soil moisture storage u or the relative soil moisture u/umax . The appropriate storage model is evaluated by plotting the storage fraction of precipitation versus the soil water state. Therefore, a simple linear function (Equation 2.1) as well as a more complex exponential relation (Equation 2.2) of the relative soil moisture can be applied. The more complex function is used in case the linear modelling approach does not provide accurate enough results.
fu = c1,SM − c2,SM
u
(2.1)
umax
fu = c1,SM − exp c2,SM
u umax
c3,SM !
(2.2)
HYDROLOGICAL MODEL
where:
19
fu is the storage fraction of precipitation [-] u is the soil moisture storage [mm] umax is the maximum soil moisture storage [mm]
c1,SM ,c2,SM , are adjustment parameters that require calibration [-] c3,SM The storage fraction of precipitation fu does not lead to rainfall run-off but increases the moisture which is stored in the soil, thus giving rise to evapotranspiration. The actual evapotranspiration eta is calculated as a fraction of the reference evapotranspiration et0 (Equation 2.3) and will empty the soil moisture storage volume. The reference evapotranspiration is a representation of the environmental demand for evapotranspiration and represents the evapotranspiration rate of a short green crop (grass), completely shading the ground, of uniform height and with adequate water status in the soil profile. It is a reflection of the energy available to evaporate water, and of the wind available to transport the water vapour from the ground up into the lower atmosphere.
eta =
( et0 u uevap
where:
· et0
if u > uevap otherwise
(2.3)
eta is the actual evapotranspiration [mm] et0 is the reference evapotranspiration [mm] uevap is the threshold value for u above which no water availability restriction is present to limit the evapotranspiration [mm]
Depending on the state of the system, i.e. the current soil moisture content, the division of the rainfall in the different fractions fu , fi and fo is altered. The precipitation fractions that contribute to the overland flow run-off are calculated by means of Equation 2.4.
fo = exp c1,OF + c2,OF ·
u umax
+ c3,OF + c4,OF ln r
!
(2.4)
20
CASE STUDY AND DETAILED MODEL DESCRIPTION
where:
fo is the overland flow fraction of precipitation [-] r is the antecedent rainfall volume [mm]
c1,OF ,c2,OF , are adjustment parameters that require calibration [-] c3,OF ,c4,OF The dependency of fo on the soil saturation level is known as the saturation excess (first term of Equation 2.4). It is also possible to extend the model to take into account precipitation intensities for modelling the infiltration excess process (second term of Equation 2.4). Therefore an antecedent rainfall volume r, representing the soil surface wetness, needs to be included. The number of days nr used to calculate this volume is subject to calibration. Based on the same procedure for the overland run-off flow, an identical sub model can be derived for the fi (Equation 2.5).
fi = exp where:
c1,IF + c2,IF ·
u umax
+ c3,IF + c4,IF ln r
!
(2.5)
fi is the inter flow fraction of precipitation [-] r is the antecedent rainfall volume [mm]
c1,IF ,c2,IF , are adjustment parameters that require calibration [-] c3,IF ,c4,IF The rest term after having derived the quick flow fractions, is considered to go to the base flow. After this separation of the precipitation volumes in the fractions, these different storages are combined with reservoir models to describe the routing of the sub flows. Linear reservoirs are preferably applied for this routing. However, they can be detailed to more complex routing models (cascade of linear reservoirs) if necessary. The rainfall run-off model was calibrated against the measured hourly discharge at the basin outlet, after subtraction of the discharges from industries and WWTPs located in the catchment. The latter discharges artificially increase the natural rainfall run-off and are treated separately in the river model as point source contributions and double counting was thus avoided. This calibration was performed by Vansteenkiste [148] and the obtained parameters are given in Table 2.3. The calculated rainfall run-off is distributed over the catchment according to the relative areal contribution of the sub-catchment draining to that specific river reach. In this distribution, it is taken into account that there is a small area in the catchment,
HYDRAULIC MODELS
21
24.4 km2 , that does not drain to the rivers but directly drains into the canals that cross the catchment i.e. the Albert canal, the canal Kwaadmechelen-Dessel and the canal Beverlo. Table 2.3: VHM calibrated parameters Storage model umax [mm] uevap [mm] uini [mm] C1,SM [-] C2,SM [-] C3,SM [-]
2.3
220 90 120 1.97 0.99 1.7
Overland flow model
Inter flow model
C1,OF [-] C2,OF [-] C3,OF [-] C4,OF [-] nr [days]
C1,IF [-] C2,IF [-] C3,IF [-] C4,IF [-] nr [days]
-4.2 2.5 -
-4.1 2.8 -
Routing model kBF [hrs] kIF [hrs] kOF [hrs]
2100 120 17
Hydraulic models
2.3.1
InfoWorks CS sewer model
To model the effect of urbanization on the river hydrology, both the WWTP and the CSO discharges that are emitted into the river need to be known. Two detailed, hydrodynamic sewer models developed in IWCS by the Flemish water company Aquafin NV, namely for the cities of Geel and Mol are employed for this purpose. The IWCS software solves the Saint-Venant equations to calculate the water levels and discharges in the sewer system nodes and links respectively. The Saint-Venant equations are a system of two coupled equations, the continuity equation (Equation 2.6) and momentum equation (Equation 2.7) which are valid under several assumptions [36, 130, 31]. Since these equations cannot be solved analytically with the exception of some simple cases, a numerical scheme is used to approximate the Saint-Venant equations. IWCS makes use of the Preissmann 4-point scheme [109] in which functions and derivatives are replaced by weighted averages over the four corners of a box in (x,t) space [65].
δQ δA + = qlat δx δt δ δQ + δt δx
βQ2 A
!
+ gA
δh δx
+ gA Sf − S0 = 0
(2.6) (2.7)
22
CASE STUDY AND DETAILED MODEL DESCRIPTION
where:
Q is discharge [m3 s−1 ] A is cross-sectional flow area [m2 ] x is distance along the longitudinal axis of the water way [m] t is time [s] qlat is lateral inflow [m2 s−1 ] β is the momentum correction coefficient [-] g is the gravitational acceleration [m s−2 ] h is the water surface elevation above a datum or reference level [m] S0 is the bed slope [-] Sf is the friction slope [-]
The value of the friction slope Sf is dependent on the empirical formula that is used to calculate the roughness. Commonly used formulations are the White-Colebrook, Chézy, Manning or Darcy-Weisbach formulation [32, 30, 31]. To model the incoming rainfall run-off, two different models are being used in IWCS. For the permeable areas this is a modified Horton Infiltration model [151] (Equation 2.8) and for the impervious areas this is the fixed infiltration model (Equation 2.9).
f = fc + (f0 − fc ) e−bth where:
f is the infiltration rate [mm h−1 ] fc is the limiting infiltration rate [mm h−1 ] f0 is the initial infiltration rate [mm h−1 ] b is a coefficient for the exponential term [h−1 ] th is time [h]
(2.8)
HYDRAULIC MODELS
R= where:
RRc · A · I 1000
23
(2.9)
R is the rainfall run-off [m3 h−1 ] RRc is the rainfall run-off coefficient [-] I is the precipitation intensity [mm h−1 ] Asurf is the surface area [m2 ]
The IWCS models of the cities of Geel and Mol are highly detailed and include expected changes for the coming five years. They include in total almost 8000 nodes, and have a contributing area of 5311 ha and a population of 68 749 people equivalents. The flow in the sewer systems is highly regulated: the models comprise 120 pumps and over 350 other hydraulic structures, such as weirs, orifices and flap valves. In total there are 124 outfall nodes of which 94 are located within the catchment (Figure 2.5). The Rainfall Runoff (RR) coefficient used in Equation 2.9 is set to 0.8 for impervious areas, and ranges from 0 to 0.25 for permeable areas depending on the specific land use definition. The impervious area and average RR coefficients thus obtained for the two models are given in Table 2.4.
Figure 2.5: Sub-catchments of the two considered IWCS models and location of the CSOs and WWTPs Because of the small response time of sewer systems, especially when compared to catchment rainfall run-off, a higher resolution rainfall time series (∆t 15 min) measured at the basin outlet is being used as input for the sewer model. The same daily potential evapotranspiration time series based on hydro-meteorological observations at Uccle is utilized as evapotranspiration input. The modelled time
24
CASE STUDY AND DETAILED MODEL DESCRIPTION
Table 2.4: Characteristics of the two IWCS models available
Geel Mol
total impervious area [km2 ]
total area [km2 ]
impervious area [%]
Average RR coefficient [-]
4.2 6.6
15.0 38.1
28.15 % 17.23 %
0.23 0.14
series (∆t 10 min) of urban fluxes that are situated within the catchment are input as point sources in the river model.
2.3.2
MIKE11 river model3
An adequate characterization of the hydraulic state of the river is fundamental to the success of any water quality model since the hydraulic state of the river strongly affects various kinetic processes [89]. To model the hydrodynamic behaviour of the river system, a full hydrodynamic model is set up, implemented in the MIKE11 software. The HydroDynamic (HD) module of MIKE11 is a modelling system for one dimensional HD computation of unsteady flows along rivers. As IWCS, it solves the ‘Saint Venant’ equations (Equation 2.6 and Equation 2.7) but in contrast, a different solution method is used, namely the six-point implicit difference scheme developed by Abbott and Ionescu [1]. A computational grid of alternating discharge (Q) and water level (h) points is used as illustrated in Figure 2.6. The computational grid is automatically generated on the basis of the user requirements. Q-points are placed midway between neighbouring h-points and at structures, while h-points are located at cross-sections, or at equidistant intervals in between if the distance between cross-sections is greater than the maximum space step (∆x) between two Q or h points, as specified by the user [44]. The Courant criterion (Equation 2.10) is often applied in connection with rivers and channels. The Courant number expresses the number of grid points a wave, generated from a minor disturbance, will move during one time step. The finite difference scheme used in MIKE11 (6-point Abbott scheme), allows Courant numbers up to 10-20 if the flow is clearly sub-critical (Froude number less than 1), but much higher values have been used (up to 100) with small errors of less than 2 % [44]. However, the 6-point Abbott scheme does impose a limitation on the maximum simulation time step that can be used, especially in the case of rapidly varying cross-sections since the velocity condition, given in Equation 2.11 needs to be satisfied [44]. The velocity criterion requires that ∆t and ∆x are selected in the 3 The results of the influence of vegetation on the roughness is largely based on the article: Keupers, I., Thanh, T. N., and Willems, P. Modelling the time variance of the river bed roughness coefficient for improved simulation of water levels. International Journal of River Basin Management 13, 2 (2015), 167-178.
HYDRAULIC MODELS
25
Figure 2.6: Channel section with computational grid order that a water body will not be transported more than one grid point per time step to avoid instabilities. √ ∆t v + gy Cr = ∆x
v
∆t ≤1 ∆x
(2.10)
(2.11)
where: Cr is the Courant number [-] v is the flow velocity [m s−1 ] g is the gravitational acceleration [m s−2 ] y is the local water depth [m] t is the time coordinate [s] x is the space coordinate [m] The MIKE11 model of the Grote Nete catchment has been set up and used at our research group of the KU Leuven Hydraulics section by several researchers [52, 122, 38, 107, 4, 101, 102, 123, 145]. The available model included only two out of three main river branches of the catchment namely the Grote Nete and the Grote Laak. As part of this research, the remaining main river branch and its tributaries were added based on data from measurement campaigns conducted
26
CASE STUDY AND DETAILED MODEL DESCRIPTION
by the VMM (non-navigable waterways of first category4 ) and the province of Antwerp (non-navigable waterways of second category4 ). The final model covers all main branches and their tributaries for a total river length of 139 km (Figure 2.7). Cross-section information is included approximately every 50 m, except where there are hydraulic structures that require a smaller spatial resolution. The structures that were included encompassed 7 weirs, 233 culverts, 50 bridges and 6 control structures. The high spatial resolution of the computational model requires a small time resolution for the simulation (∆t 30 s) to avoid numerical instability, hence long calculation times are obtained, approximately five hours to simulate one year on a computer with i7 processor clocked at 3.40 GHz and 16 GB RAM memory using a single core.
Figure 2.7: Existing MIKE11 river model and extensions made during this research As noted by McIntyre [89], it is very important to simulate the hydrodynamic state of the river system in order to be able to simulate the water quality variables in an accurate way. However, the Grote Nete catchment is known to be subject to considerable aquatic macrophyte growth during summer period [21] which can create seasonally variable changes in river bed roughness [158, 11, 58, 13, 77]. The effect of aquatic macrophytes on discharge-water level relationships is potentially large in small rivers and streams, creating significant errors in flow estimates and water level simulations. Hydrodynamic river studies nevertheless often assume the roughness coefficient to be constant, hence disregarding the effect of vegetation growth. For water quality studies, this may lead to biased estimates of river flow velocities, dilution and other water quality processes. Where high nutrient loadings are present in rivers with low velocities, aquatic macrophytes can grow abundantly and possibly mitigate the detrimental effects of this pollution stress. Indeed, a 4 non-navigable waterways are divided in three categories, the third category comprises waterways with an upstream watershed area of 100 ha, the second category are those waterways from the third category that cross a municipality boundary and the first category are those waterways for which the upstream watershed area is at least 5000 ha
HYDRAULIC MODELS
27
lower velocity means longer residence time and higher sedimentation rates and thus a higher self purifying capacity [126]. Despite the possible significant effect of seasonal river bed vegetation change on discharge estimation, it is not commonly studied in contrast to the effect of vegetation on the flow and water levels in floodplains, which has received ample attention [5, 129]. Studies that did focus on the effect of vegetation on in-channel flow mainly focused on intensive measurements campaigns to determine the type and extent of the vegetation cover [11, 56, 37, 106]. However, such detailed measurement campaigns cannot be always conducted, thus giving rise to the need for a modelling framework that uses the widely available water level measurements to account for the seasonal variation of the river bed roughness. This need has also been addressed by Aricò et al. [8, 7], where discharge and channel roughness are estimated simultaneously based on water level measurements at different sections along the river. In many cases, however, the density of water level gauging stations is rather low such that seldom more than one station is available along a river reach. Due to this importance, it was investigated as part of this research how the MIKE11 river model can be adopted to take into account the time varying river bed roughness due to plant growth. It is also studied if the changed roughness due to vegetation is dependent on the magnitude of the discharge. It indeed has been shown previously that river vegetation responds dynamically to increased flow velocity, with the streamlining of leaves reducing the effective drag coefficient of the water plants [6, 55, 66, 174, 37]. If it is found that the roughness due to vegetation is diminished during high flow conditions, this dependence of course needs to be taken into account for river flood modelling as to not overestimate the occurrence and extent of flooding and shows that it is mostly important to take into account the changing roughness coefficient due to vegetation when interested in low flow conditions. In this study, it was chosen to model the roughness by using the Manning coefficient, n. The value of n is typically in the range from 0.01 s m−1/3 (smooth channel) to 0.10 s m−1/3 (thickly vegetated channel) [44]. A global value can be imposed, or different values can be set for particular rivers or river reaches. It is not possible to define a time-varying roughness coefficient. However, a global or distributed boundary can be imposed that introduces a resistance factor (rf ) time series to specified river reaches. At each time step, rf is multiplied by the resistance number, thus effectively making the roughness coefficient a function of time. It is not possible to directly take into account the effect that time-varying flow magnitude may have on the vegetation and resistance. This effect can be incorporated, but only indirectly, if the RR model and the HD model are run offline. The temporal flow magnitude variations can be determined from the result of the RR model, from which the relative resistance factors can be calculated. These factors can then be used in the hydrodynamic simulation to obtain correct water level estimates.
28
CASE STUDY AND DETAILED MODEL DESCRIPTION
The momentum equation (Equation 2.7) can be rewritten so that the last term is expressed in function of the conveyance capacity, K (Equation 2.12). In this equation the first term is called the local acceleration term, the second term, the convective term, the third term, the pressure term and the last term, the source/gravity term. It is this last term that causes the water to flow. How much water can flow through the river is quantified by the conveyance capacity of the river. The ability of a channel to convey water directly influences the water levels as resistance to flow results in smaller velocities and greater depths. The conveyance capacity of the river depends on channel morphology, that is, cross-section shape and plan-form sinuosity, and the roughness characteristics of the river bed.
δQ δ + δt δx
K=
αQ2 A
!
+ gA
δh − S0 δx
2/3 A · Rh n
with Rh =
2/3 A · R∗ n
with
√
+g
AQ|Q| =0 K2
(2.12)
!3/2 A 2/3
5/3
N P
Ai
(2.13)
i=1 rr,i ·Pi
N P R∗ = 1
i=1
Ai rr,i
·
RB 0
H 3/2 rr
db
where: K is the conveyance [m3 s−1 ] A is the cross-sectional flow area [m2 ] Ai is the cross-sectional flow area resistance for subsection i [m2 ] Pi is the wetted perimeter for subsection i [m] Rh is the hydraulic radius [m] R∗ is the resistance radius [m] N is the number of subsections [-] rr,i is the relative resistance for subsection i [-] H is the local water depth normal to the bed [m] B is the water width at H [m] n is the Manning coefficient [s m−1/3 ]
(2.14)
HYDRAULIC MODELS
29
The conveyance capacity, K, can be calculated based on the roughness coefficient n by using either the hydraulic radius (Equation 2.13) or the resistance radius formulation (Equation 2.14). If the relative resistance is constant across the whole cross-section, Equation 2.13 simplifies to dividing the wet cross-section area by the wet perimeter. If the resistance radius is applied, the conveyance may be overestimated, especially for narrow channels as its formulation does not fully take into account the friction from the cross-section sides as does the formulation for the hydraulic radius. For both radius types, verification is required for each crosssection on the conveyance relationship which has to be monotonously increasing with increasing water level, as this is one of the key assumptions for open water hydraulics. A non-monotonous relationship could be obtained when there is a sudden increase in width in the section geometry [44]. There are four limnigraphic stations situated in the Grote Nete catchment (Figure 2.3). Of these, the Meerhout measuring station on the Grote Nete river shows the largest influence of vegetation growth on the water level during summer months. These measurements are therefore used for calibration of the Manning’s n. This calibration was done by minimizing the Root Mean Squared Error (RMSE) or difference between simulated and observed water depths. The Population Simplex Evolution (PSE) method [42] was applied with the goal to reach global optimum values for the Manning coefficient during the specified period. The initial values for n were set by Monte Carlo sampling and n was allowed to vary from 0.01 to 0.2 s m−1/3 . The calibration procedure was repeated each five days during the calibration period 2004-2006, each time with a two-day warm-up period. The warm-up periods were discarded from the error calculation. The year 2007 was used for validation. In this way, daily varying roughness coefficients were obtained, to be applied to all river branches in the model. First, the hydrodynamic simulation is performed with a constant value of the Manning coefficient equal to 0.035 s m−1/3 as is common practice for this study area (Figure 2.8). It is clear that a constant roughness coefficient is insufficient for this case study as overestimation of the water depths occur during the winter months and underestimations during the summer periods. This is also shown by the very low Nash-Sutcliffe efficiency (NSE) [95] of 0.31 when simulated and observed water levels are compared for the whole period. After calibration based on the water level observations for 2004-2006, Manning’s n values range from 0.014 to 0.156 s m−1/3 . This range is consistent with the range reported by Danish Hydraulic Institute (DHI) [44], the measured Manning coefficients recorded by Bakry et al. [11], the calibrated Manning coefficients reported by Aronica et al. [9] and the theoretical Manning coefficients noted in Green [55]. From October till May, the values fluctuate around 0.03 s m−1/3 . When in May the aquatic macrophytes start to grow, the roughness strongly increases to reach a peak in August after which a quick decrease is observed to the value for a river bed with little vegetation. The top panel of Figure 2.9 shows the calibrated
30
CASE STUDY AND DETAILED MODEL DESCRIPTION
1.2
Water depth [m]
1
observed simulated summer period
Correlation = 0.58 NSE = 0.33
0.8 0.6 0.4 0.2 01-2004
01-2005
01-2006 Time
01-2007
01-2008
Figure 2.8: Observed versus MIKE11 simulated water depths at Meerhout, after use of a constant Manning coefficient of 0.35 s m−1/3 n values together with the RMSE after calibration. When this value was larger than 0.005 m for some periods, it was considered non-reliable, hence disregarded when analysing the temporal variations. Finally, a piecewise linear relationship of Manning coefficient versus day of the year could be distilled as shown in the bottom panel of Figure 2.9. After simulation of the daily changing Manning coefficient in MIKE11 (Figure 2.10), the NSE increases from 0.31 for the whole period to 0.68 in the calibration period (2004-2006) and 0.65 in the validation period (2007), indicating a much higher accuracy in simulated water levels. This is also shown in Figure 2.11 as water depths simulated with a time-varying roughness coefficient approach the cumulative probability function of the observed water depths much closer than water depths that are simulated with a constant roughness coefficient. Especially the lower water depths are simulated well, whereas the probability of higher water depths is slightly overestimated for the calibration period. For the validation period, this overestimation is much larger. When investigating the results for the summer of 2007, it becomes clear that there are many high peak flow events during this summer and that water levels are overestimated (Figure 2.12). This indicates diminished roughness during such high flow periods as compared to the base roughness. Hence, it can be concluded that the roughness coefficient depends on the river discharge next to the season or vegetation state. The increased roughness due to vegetation thus decreases
HYDRAULIC MODELS
31
(a) 0.1
calibrated n RMSE threshold RMSE
0.16 0.12
0.08 0.06
0.08
0.04
0.04
0.02
0 Jan
Mar
May
Aug
Oct
RMSE [m]
Manning n [s m−1/3 ]
0.2
0 Dec
Month (b) Manning n [s m−1/3 ]
0.2
calibrated n piecewise linear fit
0.16 0.12 0.08 0.04 0
0
50
100
150 200 Day number
250
300
350
Figure 2.9: Calibrated Manning’s n values per five-day period with RMSE for calibrated value (a) and piecewise linear relation of the Manning’s n versus day of the year (b)
32
CASE STUDY AND DETAILED MODEL DESCRIPTION
Water depth [m]
1.5
Correlation = 0.83 NSE = 0.67
observed simulated summer period
1
0.5
0 01-2004
01-2005
01-2006 Time
01-2007
01-2008
Figure 2.10: Observed versus MIKE11 simulated water depths at Meerhout, after use of a time-varying Manning coefficient
Cumulative probability [-]
(a)
(b)
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0.5 1 Water depth [m]
0
observed constant Manning variable Manning
0
0.5 1 1.5 Water depth [m]
Figure 2.11: Cumulative probability plot for observed and simulated water depths for summer 2005, calibration (a) and summer 2007, validation (b)
HYDRAULIC MODELS
33
during high flow events due to the water flow pressing down the water plants. As indicated previously it is not possible to account for this effect directly in the MIKE11 software. Indirectly, however, this effect can be taken into account by relating the time-varying Manning’s n to the magnitude of the rainfall run-off flow which then serves as an indicator of the river flow magnitude. The time-varying Manning’s n then considers both the seasonal and flow-dependent changes. It was chosen to lower the Manning coefficient to 0.04 s m−1/3 when the modelled rainfall run-off was larger than 3.5 m3 s−1 because previous studies suggest that during high flow conditions, the Manning coefficients lower to almost the base Manning coefficient, that is, when there is no vegetation present [159, 37]. This intervention increases the NSE for the year 2007 from 0.65 to 0.71 (Figure 2.12, showing the importance to take into account this dependency.
1.5
modelled catchment rainfall runoff measured water depth simulated water depth with seasonal Manning: NSE = 0.65 simulated water depth with seasonal Manning 0.04 s m−1/3 when flow >3.5 m3 s−1 : NSE = 0.71
1 5.5 4.5 3.5 2.5 1.5
0.5 0 01-06-07
19-06-07
07-07-07 25-07-07 Time
12-08-07
31-08-07
Rainfall Runoff [m3 s−1 ]
Water depth [m]
2
Figure 2.12: Observed versus MIKE11 simulated water depths, after use of a time-varying and flow-dependent Manning coefficient for summer 2007 The calibration procedure applied to determine the seasonally varying Manning coefficient obviously has been influenced by these peak flows. However, only 12 out of the 193 n values after calibration, which were used to fit the linear piecewise relationship, are affected by summer peak flows as defined by a rainfall run-off larger than 3.5 m3 s−1 . Since these values do not show systematic deviations from the linear piecewise relation, there was no need to repeat the calibration taking into account deflection by peak flows.
34
CASE STUDY AND DETAILED MODEL DESCRIPTION
2.4
Water quality models
2.4.1
AD-ECOLab module MIKE11
To model the concentration of water quality state variables in the river, the Advection Diffusion (AD) and ECOLab modules in MIKE11 were employed. The AD module models the transport of (conservative) substances through advection and diffusion whereas the ECOLab module incorporates the effect of (user defined) chemical and biological water quality transformation processes during this transport through the river. The AD module is based on the one-dimensional equation of conservation of mass of dissolved or suspended material, i.e. the advection-dispersion equation (Equation 2.15). The module requires output from the hydrodynamic module, in time and space, in terms of discharge and water level, cross-sectional area and hydraulic radius. The time step for the AD module should be selected in such a way that the velocity condition (Equation 2.11) is met. However, since the AD solver includes both h and Q points as calculation nodes the ∆x in the AD module is half the ∆x in the HD module (Figure 2.6). Therefore, if the time step used in the HD computation criteria has been limited by the velocity criteria, this should be reduced by a factor of two in the succeeding AD computation [44]. dAC dQC d + − dt dx dx
AD
dC dx
= C2 · qlat
(2.15)
where: A is the cross-sectional area [m2 ] C is the concentration [mg l−1 ] Q is the discharge [m3 s−1 ] D is the dispersion coefficient [m2 s−1 ] C2 is the source/sink concentration [mg l−1 ] qlat is the lateral inflow [m2 s−1 ] x is the space coordinate [m] t is the time coordinate [s] ECOLab is a numerical simulation software for Ecological Modelling developed to describe processes and interactions between chemical and ecosystem state variables.
WATER QUALITY MODELS
35
Also the physical process of sedimentation of state variables can be described. The description of the ecosystem state variables in ECOLab is formulated as a set of ordinary coupled differential equations describing the rate of change for each state variable based on processes taking place in the ecosystem. The following integration methods are available in ECOLab (in increasing order of accuracy and required calculation time): Euler, Runge Kutta 4th order and Runge Kutta 5th order with quality check. In this research, the Runge Kutta 4th order integration method was selected as a compromise between speed and accuracy. All information about ECOLab state variables, processes and their interaction are stored in a generic ECOLab template [43]. When the ECOLab module is selected, the mass balance of the state variables are calculated for all gridpoints at all time steps using a rational extrapolation method in an integrated four-step procedure with the AD module. The final result returned from the integration routine, is thereby calculated as a numerical integration of the time step gradients from both the AD differential equation and from the coupled water quality differential equations. 1. Calculation of Advection/Diffusion at time n+1 with AD module 2. Calculation of Advection/Diffusion gradient 3. Calculation of water quality at time step n+1 with ECOLab module 4. Integration of the water quality gradient with the AD gradient For this study, it was chosen to use the level 4 of the available water quality templates as a starting point. This template includes the BOD-DO relationships including exchange with the riverbed and nitrification and de-nitrification. The water quality state variables being considered are DO, BOD, NH4, NO3, OP, PP and temperature. The water quality transformation processes that are included in the model and which state variables they influence are given in Table 2.5. The exact equations for each process as defined in the template used can be found in Appendix A. A comparison between the model equations used in the ECOLab template with those used in the IWRS and InfoWorks Integrated Catchment Modeling (IW ICM) models can be found in the study of Nguyen and Willems [98]. All the biological transformation processes are temperature dependent and are modelled as an Arrhenius process (Equation 2.16). The temperature coefficient Θ gives an indication of the rate at which the reaction rate kinetics increase when higher temperatures are present and is equal to one (no influence of the temperature on the kinetics) or higher than one (Figure 2.13).
36
CASE STUDY AND DETAILED MODEL DESCRIPTION
Table 2.5: Modelled water quality transformation processes DO Re-aeration Photosynthesis BOD decay Respiration Sediment Oxygen Demand Nitrification De-nitrification Uptake NH4 by Plants Uptake NH4 by Bacteria Resuspension BOD Sedimentation BOD PP decay PP formation Uptake OP by Plants Resuspension PP Sedimentation PP Radiation into the water Radiation out of the water
+ + -
NH4
NO3
+ -
BOD
OP
-
+
PP
T
+ + -
+ -
+ + -
+ -
+: positive contribution to the amount of state variable - : negative contribution to the amount of state variable
kT = k20 · Θ(T −20) where:
(2.16)
kT is the reaction rate at temperature = T ◦C [s−1 ] k20 is the reaction rate at reference temperature = 20 ◦C [s−1 ] Θ is the temperature coefficient for the reaction process [-]
In the level 4 template, the re-aeration rate is calculated based on a temperature dependent re-aeration coefficient (Equation 2.17). Three possible methods for the estimation of the re-aeration coefficient for oxygenation at the water-air boundary are given namely the Thyssen expression (Equation 2.18) [135], the O’Connor/Dobbins expression (Equation 2.19) [99] and the Churchill expression (Equation 2.20) [33]. In the MIKE11 manual the Thyssen expression is recommended for application to small streams, the O’Connor/Dobbins equation for ‘ordinary rivers’ and the Churchill equation for rivers with high flow velocities [43]. However, when we look at the ranges of flow velocity and water depths for
37
Reaction rate
WATER QUALITY MODELS
5 4 3 2 1 0
0
5
10 15 20 Temperature
25
Figure 2.13: Arrhenius temperature dependency of the reaction rate which these expression are valid and compare this to the values typical in our catchment (Table 2.6), it becomes obvious that none of the proposed expression can be applied with confidence to the whole catchment. This also becomes apparent when we do apply these equations for the year 2000 in the Grote Nete catchment giving rise to unrealistically high re-aeration coefficient, even up to 100 when the realistic range of values is between 0 (no re-aeration) and 15 (fully mixed, maximum re-aeration) (Figure 2.14). Using these values unchanged in the simulation leads to an overestimation of the oxygen concentration in the river and the modelled dissolved oxygen concentration in the stream will always reach approximately saturated oxygen concentration. It is therefore important to select a different empirical expression that is valid in the range of values observed in the Grote Nete catchment. For this purpose, the empirical formula derived by Tsivoglou and Neal (Equation 2.21) [136] is used. They derive that the channel slope is the primary hydraulic property that causes re-aeration and that the steeper the slope, the more violent the tumbling and mixing action is that creates the water surface replacement. Since the empirical relation is not dependent on the water depth nor velocity it can also be applied to our case study where other formulations are limited to be applied when larger water depths and velocities are observed.
(T (t)−20) re-aeration rate = kreaer · Θreaer · CS − concDO (t)
kreaer =
(2.17)
27185 · v 0.931 · H −0.692 · Sh1.09 3.9 · v 0.5 · H −1.5
(2.18)
5.233 · v · H −1.67 3168 · Sh
(2.20)
(2.19)
(2.21)
38
CASE STUDY AND DETAILED MODEL DESCRIPTION
where:
kreaer is the re-aeration rate at 20 ◦C [d−1 ]
Θreaer is the temperature coefficient for re-aeration [-] T is the temperature [◦C] CS is the saturation dissolved oxygen concentration [mg l−1 ] v is the flow velocity [m s−1 ] H is the water depth normal to the bed [m] Sh is the water level slope [-] Table 2.6: Range of applicability of empirical formulas compared to the observed values in the Grote Nete catchment
Thyssen O’Connor/Dobbins Churchill Grote Nete catchment
minimum average maximum
H [m]
v [m s−1 ]
0.1 < H < 1.4 0.3 < H < 9 0.6 < H < 3
0.06 < v < 0.5 0.15 < v < 0.5 0.55 < v < 1.5
0.033 0.372 1.688
0 0.30 3.83
Simulating both the AD and the 21 defined water quality transformation processes in the ECOLab module at all calculation nodes increases the required simulation time from approximately five hours when only the HD is simulated to almost four days to simulate one year on a computer with i7 processor clocked at 3.40 GHz and 16 GB RAM memory using a single core. The corresponding output res11 file has a size of 1.5 GB. This computational requirement makes it practically impossible to calibrate the water quality model sufficiently or to perform long term simulations required for statistical analysis of the results. The obtained results are compared to the observed values at the 24 monitoring stations in the catchment during calibration and validation. The validated results can then be used to draft magnitude-frequency curves for each studied water quality variable at locations of interest. In these curves, the percentage of exceedance of concentration levels is plotted against the concentration. From these curves compliance with water quality standards can be easily checked as these are most often provided in the form of percentile values. For the rivers in the Grote Nete catchment, these standards are set in the ‘VLAams REglement betreffende de Milieuvergunning’ (in Dutch) (VLAREM) decree of the Flemish Government [154] and are summarized for oxygen, nitrate, phosphorus and BOD in Table 2.7. For
frequency
WATER QUALITY MODELS
39
·107
1
Thyssen
2
0.5
frequency
0
1 0
50 ·10
4
100
0
0
50
100
6
6
Churchill
·10
6
Tsivoglou & Neal
4
2 0
·106 O’Connor/Dobbins 3
2 0
50 kreaer
100
0
0
10 20 kreaer
30
Figure 2.14: Histogram of the calculated kreaer in the Grote Nete catchment in the year 2000 with the four different expressions ammonium no standards are imposed directly. Only the 90th percentile of Kjeldahlnitrogen, which consists of the organic bound nitrogen and ammonium combined, and the summer average of total nitrogen are considered. Comparing the model results to the observations is done visually since there are not enough measurements available to calculate statistical goodness-of-fit evaluators. As can be seen from the time series comparison plots (Appendix B) temperature is predicted the best at all locations while the concentration of Pt and NO3 is not always predicted accurately, with sometimes large underestimations of especially peak Pt concentrations. For BOD most of the available measurements are below the detection limit, making it difficult to determine if the time variation is predicted well. The magnitude and time variation of DO is reproduced well for most reservoirs. A general conclusion based on the comparison with observations for all 24 reservoirs is that the current version of the MIKE11-ECOLab model could be improved further with calibrating the input and model parameters of the model, however, this is currently not feasibly due to the long calculation time of the detailed model set-up. The detailed MIKE11-ECOLab model is thus further used in this study as reference model to which the conceptual model is calibrated. Due to the robustness of the conceptual model (see Section 4.1.3) this approach does not pose a problem.
40
CASE STUDY AND DETAILED MODEL DESCRIPTION
Table 2.7: Water quality standards for rivers in the Grote Nete catchment Variable
Status
Standard [mg l−1 ]
Oxygen
Very good Good Moderate Inadequate Bad
>8 6 4 3 366 > 6 6 10 > 10 6 25 > 25
90 % of the values should comply with the given values
Nitrate
Very good Good Moderate Inadequate Bad
62 > 2 6 10 > 10 6 11.3 > 11.3 6 17 > 17
90 % of the values should comply with the given values
Kjelhdal-nitrogen
Very good Good Moderate Inadequate Bad
6 1.5 > 1.5 6 6 > 6 6 12 > 12 6 18 > 18
90 % of the values should comply with the given values
Total Nitrogen
Very good Good Moderate Inadequate Bad
63 >364 >468 > 8 6 12 > 12
Arithmetic average from beginning of April to end of September should comply with the given values
Orthophosphate
Very good Good Moderate Inadequate Bad
6 0.04 > 0,04 6 0.14 > 0,14 6 0.35 > 0,35 6 0.7 > 0.7
Arithmetic average should comply with the given values
Total Phosphorus
Very good Good Moderate Inadequate Bad
6 0.04 > 0.04 6 0.07 > 0.07 6 0.14 > 0.14 6 0.28 > 0.28
Arithmetic average from beginning of April to end of September should comply with the given values
WATER QUALITY MODELS
2.4.2
41
Industrial pollution
There are 9 industries that directly discharge their untreated water in the river that are being monitored by the VMM (Table 2.8). Together they emit on average 0.381 m3 s−1 . For these industries, a daily average effluent discharge and pollution load is available for the reference year 2006 as used in the river basin management model developed by the VMM in ‘Planification Et Gestion de l’ASsainissement des Eaux’ (in French) (PEGASE). However, for most industries there are also measurements available from 2001 onwards5 . When we compare these data to the daily average load, it becomes obvious that there is a significant temporal variation in the pollution load that is emitted to the river which cannot be ignored when modelling a time varying pollutant concentration in the river (Figure 2.15). However, the temporal resolution of the measurements of the pollutant loading is insufficient to be used in the model as large errors will be introduced during interpolation. Therefore, the measured, time varying discharge is used in combination with a fixed pollutant concentration. In addition to the difficulty of data availability, not all industries discharge directly in a modelled branch. For those industries not directly discharging, a reduction coefficient factor needs to be applied to the pollutant loading which is inversely proportional to the distance to the modelled river branch to take into account the self-purification capacity of the river. Table 2.8: Company and industry branch of the 9 monitored industrial effluents in the Grote Nete catchment Identifier 2440002 2440003 2440034 2450001 2480001 2490001 3920004 3945003 3980014
company
type
BP Chembel Van Dalen KHK Exxonmobil Belgoprocess Ajinomoto Omnichem EMGO Tessenderlo Chemie Ham Tessenderlo Chemie Tessenderlo
chemical industry recycling of waste eduction chemical industry production of nuclear fuel chemical industry production of machinery and equipment chemical industry chemical industry
Besides the monitored industrial effluents, there are also 15 unmonitored industrial effluents present in the Grote Nete catchment. The pollutant load that is emitted from these industries is estimated based on tax and municipality files. These unmonitored effluents are not expected to send a highly polluted effluent to the 5 http://geoloket.vmm.be
42
CASE STUDY AND DETAILED MODEL DESCRIPTION
6 5 4 3 2 1 0 Jan
BOD [mg l−1 ] May
Aug
Dec
30 25 20 15 10 5 0 Jan
May
Aug
Dec
May
Aug
Dec
0.7 P t [mg l−1 ]
14 12 10 8 6 4 2 Jan
NH4 [mg l−1 ]
Discharge [m3 d−1 ]
Measured Daily average value
May
Aug
Time
Dec
0.6 0.5 0.4 0.3 Jan
Time
Figure 2.15: Daily average discharge and pollutant loading for Tessenderlo Chemie compared to the measured values river; explaining why these effluents are not monitored on a regular basis. Again, not all discharge locations are incorporated in the MIKE11 model and for those that are situated further away a reduction concentration factor needs to be applied to the loads, inversely proportional to the distance of the effluent point to the model.
2.4.3
Domestic pollution
WWTPs and CSOs6 As discussed in Section 2.3.1 where the IWCS model for the sewer system was introduced, there are two WWTPs that discharge their effluent in the Grote Nete 6 The results shown in this section have been presented at the 13th ICUD in Malaysia: Keupers, I., Wolfs, V., Pham, H. H., Smets, I., and Willems, P. On the role of the WWTP in integrated sewer-WWTP-river impact modelling. In 13th International Conference on Urban Drainage, Sarawak, Malaysia, 7-12 September 2014 (2014).
WATER QUALITY MODELS
43
catchment (Figure 2.5). These two sewer systems also have 94 CSOs that overflow in rivers in the Grote Nete catchment. The discharge coming from these effluents can be estimated with the IWCS model. However, to determine the pollution load of this effluent a sewer water quality model needs to be set up. This can be done in IWCS but requires a large data set for calibration and validation which is not available for this case study catchment. Therefore, a methodology to estimate the concentration of the pollutants of interest is developed as part of this research and is discussed in detail in Section 3.2. The result of this sewer water quality model can be applied directly to the discharges of the CSOs to estimate the load send to the river. For the WWTP, another model needs to be applied to this incoming pollutant concentrations to take into account the purification of the raw sewage before it is being sent to the river. To this end, three different approaches can be used: • a white box model constructed based on the underlying physical processes (Activated Sludge Model (ASM) family [64], Technical University Delft bio-P (TUDP) [91]) • a grey box model that represents a simplified representation of the processes ([127, 105]) • a black box model that is entirely identified based on input-output data without reflecting the processes explicitly (AutoRegressive (AR), AutoRegressive-Moving-Average (ARMA), AutoRegressive-Moving-Average with exogenous inputs model (ARMAX), Principal Component Analysis (PCA), Partial Least Squares (PLS) [54], ANN [93]) With the goal of an integrated model in mind, a white box model is not suited for this purpose due to the large calculation time. Therefore, in this case study, a simplified black box model approach is used that makes use of constant purification efficiencies that are derived based on the available data set. This simplified representation can be easily replaced by a more physically based grey box model such as that based on the modified activated sludge model Activated Sludge Model No. 2 + denitrifying activity (ASM2d) developed in MATrix LABoratory (MATLAB) by Pham [105] as soon as it becomes available. This model is developed with as main focus to maximize the compatibility with the sewage and river water quality models while still assuring a sufficient prediction capacity of the activated sludge system. The original structure of the ASM2d model is adjusted step-by-step to enable a clear understanding of the subsequent adaptations. To this end, and to avoid the commonly required fractionation into the classic ASM state variables, the latter are directly derived from the outputs of the sewage model (e.g. BOD, COD, Total Nitrogen (Nt), Total Phosphorus (Pt)) for the modified model. Furthermore, the modified ASM2d model is made more realistic and can be more broadly applied due to the re-introduction of organic nitrogen and phosphorus component state variables.
44
CASE STUDY AND DETAILED MODEL DESCRIPTION
To assess if the WWTP model can be used in an integrated analysis, the following questions need to be answered: • What accuracy do we need for the WWTP model results? • At which time scales do we need these results and for which variables (for a standard application, as the one of the selected case study)? The different models that are connected in an integrated modelling approach, in this case the hydrological rainfall run-off model, the sewer model, the WWTP model, the hydraulic river model and the river water quality model, all have a different simulation time step since time is relative and the models were chosen to be consistent with the time scales of the processes they represent. In the coupling of the different models this discrepancy of time scales needs to be explicitly addressed to enable a correct linking without losing computational efficiency. The grey box WWTP model developed by Pham [105] uses a calculation time step of 30 seconds to enable good representation of all the transformation processes that take place in the plant. The river water quality model uses a calculation time step of 1 minute so in theory this is the smallest time scale of the modelled WWTP effluent that can be used. However, this time scale is not practical; it would cause a huge overload in terms of computational burden and data storage. An analysis of the river water quality time series is therefore performed to see which level of aggregation can be applied to the WWTP effluent time series to reduce the overload. The MIKE11 software uses a linear interpolation if the input values are at a larger time scale than the calculation time step. This time scale analysis was conducted based on the 90th , 95th and 99th percentile concentrations for different aggregation levels ranging from 1 minute to 2 days. From Figure 2.16 it is clear that the maximal time aggregation of the WWTP model effluent time series is dependent on the events of interest. To estimate the impact on the 90th percentile, a daily aggregation would be sufficient whereas when more extreme events are being studied, i.e. the 99th percentile, a smaller time scale is required. From this we can conclude that the modelled effluent time series should be preferably at a three-hourly time scale such that the results can be used for all types of analysis. Larger aggregation levels lead to a loss of information and different, unstable, river impact simulation results. To estimate the sensitivity of the river model to the WWTP effluent input, a base line scenario has to be selected. The average yearly WWTP effluent load (discharge and concentrations) is taken as base line. To these input concentrations a percentage change is applied and the relative sensitivity index, Sr , is calculated with Equation 2.22 [125]. This is done by changing one input at the time (±20 % and ±40 %, first order sensitivity analysis), but also by changing all inputs at the same time from −60 % to 60 % with an interval of 10 %. The latter is done to
WATER QUALITY MODELS
45
99th percentile
95th percentile (b)
Concentration [mg l−1 ]
(a) 1.4
8
1.3
7.5
1.2 1.1 1 0.9
8
6.5
7.5
6
7
5.5
0.8
6.5
5
0.7 0.5 1 1.5 2 Time scale [days]
(c) 8.5
7
0
90th percentile
4.5
0
0.5 1 1.5 2 Time scale [days]
6
0
0.5 1 1.5 2 Time scale [days]
Figure 2.16: Sensitivity of the 90th , 95th and 99th percentile river concentrations downstream of the WWTP inflow versus the time aggregation level of the modelled WWTP effluent time series for ammonia (a), nitrate (b) and BOD (c) investigate the influence of the interactions between the different concentration changes; hence the importance of the second order sensitivity terms on the impact results. Sr = where:
Echange − Eref
/Eref
%inputChange
(2.22)
Sr is the relative sensitivity index [-]
Echange is the percentile river impact concentration after WWTP concentration change [mg l−1 ] Eref is the percentile river impact concentration for the baseline WWTP concentration [mg l−1 ] The sensitivity analysis indicates (results not shown) that the percentile concentrations for the river impact are only influenced by the change in the WWTP effluent concentration for the same water quality variable, i.e. no strong interactions occur between the concentrations of the different WWTP effluent
46
CASE STUDY AND DETAILED MODEL DESCRIPTION
variables. When all the inputs are changed by the same amount at the same time, this leads to approximately the same relative sensitivity indices as the separate simulations. This means that the sensitivities can be derived from first order sensitivity analysis; second order sensitivity terms can be neglected. From Figure 2.17 it is clear that the percentile concentrations of the river impact are very sensitive to changes in the accuracy of the WWTP model. An error of 10 % on the ammonia WWTP effluent concentration leads to an error of 8 % in the ammonia river percentile concentration 1 km downstream of the WWTP location. This error still remains 7 % 5 km downstream of the WWTP, so the error is propagated along the river for a long distance. Also errors in the nitrate WWTP concentrations lead to large errors in the nitrate river percentile concentrations with a relative sensitivity index of 0.65 1 km downstream of the WWTP, with nearly no reduction in sensitivity more downstream. For ammonium and nitrate, the relative sensitivity indices do not depend on the percentage change applied (Figure 2.17). However, for BOD, the percentage change applied does have a significant influence on the relative sensitivity, thus indicating that non-linear dynamics are dominating. Especially for the highest percentile values, lowering the BOD WWTP effluent concentrations has a smaller impact on the peak concentrations than increasing the BOD effluent concentrations indicating that for these low effluent concentrations the peak river BOD concentration 1km downstream the WWTP are not determined by the WWTP effluent but by external factors. Unconnected households Next to the household effluents coming from the sewer system, there are also some households in the catchment that are not connected to the sewer system or that are connected to the sewer system, but not to the WWTP. Both discharge their waste water untreated into the receiving rivers. To model the magnitude of this discharge and the associated loads, an estimation is based on an average emission per Inhabitant Equivalent (IE) (Table 2.9). Those households not connected to the sewer system are aggregated per ‘Vlaamse Hydrografische Atlas’ (in Dutch) (VHA) zone (Figure 2.18, Table 2.11) and the pollution is distributed proportionally over the river reaches in the VHA zone (Table 2.10). Households connected to the sewer system but not to the WWTP do not need to be distributed proportionally as the exact location of the effluents of the sewer are known. The distance of the discharge point to the modelled MIKE11 river branch does however need to be taken into account with a concentration reduction factor as previously discussed for industrial effluents.
WATER QUALITY MODELS
47
Sr
(a) 1 0.8 0.6 0.4 0.2 0 -60
-40
-20 0 20 40 Percent input changed
60
90th %ile nitrate 95th %ile nitrate 99th %ile nitrate
Sr
(b) 1 0.8 0.6 0.4 0.2 0 -60
90th %ile ammonia 95th %ile ammonia 99th %ile ammonia
90th %ile BOD 95th %ile BOD 99th %ile BOD -40
-20 0 20 40 Percent input changed
60
Figure 2.17: Relative sensitivity indices: 1 km (a) and 5 km (b) downstream of the WWTP
Table 2.9: Definition of one Inhabitant Equivalent Parameter
Value
Unit
Discharge DO NH4 NO3 BOD Pt
136 0 71.3 0 279.4 10.3
ld mg d−1 mg d−1 mg d−1 mg d−1 mg d−1 −1
Table 2.10: IE that discharge directly into the environment per VHA zone VHA zone
IE
500 501 502 510 511 512
4424 5984 5689 2630 2162 5542
48
CASE STUDY AND DETAILED MODEL DESCRIPTION
Figure 2.18: The VHA zones that form the Grote Nete catchment
Table 2.11: Description of the VHA zones situated in the Grote Nete catchment VHA number 102 500 501 502 510 511 512
VHA name Albert Canal from Canal Dessel-Kwaadmechelen (exclusive) to Canal Bocholt-Herentals Grote Nete to confluence with Asbeek (inclusive) Mol Neet Grote Nete from confluence with Asbeek (exclusive) to confluence with Mol Neet (exclusive) Grote Nete from confluence Mol Neet (exclusive)to confluence with Grote Laak (exclusive) Grote Laak from confluence with Dode Beek/Luikse Beek (exclusive) Grote Laak from confluence with Dode Beek/Luikse Beek (inclusive) to confluence in Grote Nete
Area [km2 ] 27.06 99.15 86.64 41.24 34.30 41.88 55.62
WATER QUALITY MODELS
2.4.4
49
Pollutants entering via rainfall run-off
The run-off generated by the rainfall that falls over the catchment is simulated with the rainfall run-off model discussed in Section 2.2. For each state variable modelled in the water quality model, an estimate needs to be available for its concentration in this run-off. Which models were used for this is discussed in the three sections below, one for the ‘natural’ background temperature and oxygen levels in the rainfall run-off, one for the BOD present in this run-off and one for the estimation of the nitrogen and phosphorus concentrations leached to the surface waters from agricultural practices. In the implementation in the MIKE11 model, it is important to not use the rainfall run-off links that can be defined in the network file, but include the proportionally distributed rainfall run-off in the boundary data file where the concentration of the different state variables can be provided. Temperature and Oxygen in the rainfall run-off To estimate the temperature of the rainfall run-off, use is being made of the measured temperature time series from Uccle (operated by the KMI/IRM), adjusted with a calibration relation based on the measured soil temperature (5 cm below the ground surface) at Herentals, a measurement station that is operated by the VMM. The latter is not used directly since it is only available for a shorter time period than needed namely from May 2005 to December 20117 . Another temperature time series that is available in the catchment is located at Zonhoven. However, this time series is measured at 5 cm above the ground surface. To take into account the inertia that is inherent ot the warming and cooling of the surface water, it was chosen to use the soil temperature as a better approximation than the ground temperature. The run-off water is assumed to have a saturated DO concentration since it is mainly influenced by natural re-aeration processes [163]. The DO concentration can be calculated with the experimental relation derived by the American Public Health Association (APHA) [57] (Equation 2.23).
Cs = 14.652 + T · −0.41022 + T (0.007991 − 0.000077774 · T ) where:
CS is the saturation dissolved oxygen concentration [mg l−1 ] T is the water temperature [◦C]
7 Downloaded
from http://www.hydronet.be; now on http://www.waterinfo.be
(2.23)
50
CASE STUDY AND DETAILED MODEL DESCRIPTION
Agricultural leaching in the run-off The run-off that enters the river system also contains high amounts of nitrogen and phosphorus, leached from agricultural fields after manuring. These pollution loads are budgeted by means of the System for the Evaluation of Nutrient Transport to Water (SENTWA) model [144, 162] and are treated as distributed sources. The SENTWA model has been developed by the ‘Centrum voor Onderzoek in Diergeneeskunde en Agrochemie’ (in Dutch) (CODA) and the former Federal Ministry of Agriculture [34] and has been maintained by the VMM since 1997. It is an empirical model that makes an estimate of the leaching of nutrients based on simple statistics on livestock, fertilizer use, agricultural areas and precipitation data. Depending on the path and source that the nutrient flow follows, a distinction is made between • direct losses to the surface water • drainage losses (drained and undrained agricultural parcels with normal fertilization) • groundwater losses (overflow of groundwater to surface water under normal fertilization) • excess losses (drainage and groundwater losses when administered more/less fertilization than normal) • erosion losses • run-off losses The direct losses are divided in losses arising from • the use of artificial fertilizers • animal manure production in pastures • stalling of animals • manure storage • storing of animal feed in silo’s These SENTWA results are available per VHA zone (Figure 2.18, Table 2.11). The model uses a number of assumptions, among other things, that the current legislation is applied by farmers strictly. The error margins on the estimation are approximately 33 % and 60 % for the nitrogen losses and phosphorus losses respectively [121].
WATER QUALITY MODELS
51
There is also a new, raster based emission model available, the Nutrient Emission MOdel in ArcGIS (Arc-NEMO) developed at the Department of Earth and Environmental Sciences from the KU Leuven and the Soil Service of Belgium, commissioned by the VMM to simulate the contribution of agriculture to the loss of nutrients to surface waters and to analyse the effects of different policy options on short and medium term [153]. Processes that are taken into account are the mineralization and immobilization of nutrients, uptake of nitrogen and phosphorus by plants, the water balance in the unsaturated zone, groundwater flow and soil erosion. The model has been largely developed in Python and integrated into ArcGIS (version 10). The model is set up area-wide. The model results consist of the monthly losses of nitrogen and phosphorous from the grid cells to the surface water and can be aggregated to various geographical levels (Flemish water bodies, VHA zones, catchments, ...). Tools available within ArcGIS enable the preprocessing of input files to raster format possible. The used time step is primarily monthly. For the faster transport processes, such as the nitrate transport in the unsaturated zone, a daily time step is used. The spatial resolution of the Arc-NEMO model is 50x50 m which allows for a much finer attribution to the river network as is currently possible with the SENTWA model for which the results are only available at VHA zone scale. The required input data include vector and raster data as well as time series. A part of the data for the bottom balance model is static, part is dynamic and time-bound. The entire model is calibrated and validated by using monthly concentration measurements. When this data becomes available, it can be incorporated in the river water quality model instead of the SENTWA model without problem. To transform the monthly loading of nitrogen and phosphorus components provided by the SENTWA or Arc-NEMO model into hourly concentration values, use is being made of the hourly rainfall run-off results for the different sub-flows by applying the method proposed by Willems et al. [162]. The loads are attributed to their respective sub-flow as given in Equation 2.24. The concentrations are then calculated by dividing by the discharge. Lbase = Lgroundwater Linter = Ldrainage + Lexcessive + Lerosion Loverland = Lrunof f Ltotal = Lbase + Linter + Loverland + Ldirect where: L is the monthly nutrient load [kg/month]
(2.24)
52
CASE STUDY AND DETAILED MODEL DESCRIPTION
After determining the concentration of nitrogen and phosphorus losses in the total flow, a division still needs to be made to ammonia and nitrate on the one side and orthophosphate and particulate phosphorus on the other side. The first is done by applying monthly changing proportion factors given in Table 2.12 [97]. The latter is calculated by applying a constant proportion fraction of 55 % for orthophosphate. It is assumed that the contribution of dissolved organic materials to total phosphorus is very small. Therefore, the proportion of orthophosphate in total phosphate is also the proportion of dissolved phosphorus in the total phosphorus and the remainder is considered to be particulate phosphorus. Table 2.12: Proportion of nitrate and ammonia relative to the total Nitrogen load N O3/Ntotal January February March April May June July August September October November December
N H4/Ntotal
84.6 83.6 71.0 57.7 29.9 21.6 22.5 19.5 40.7 50.8 69.3 76.7
8.5 7.1 11.0 14.9 30.9 39.1 39.0 39.4 28.4 17.4 10.5 9.9
BOD in the rainfall run-off The organic pollution introduced by the rainfall run-off in the waterway is expressed as BOD concentration. The organic matter can be attached to the transported sediment or can come from falling leaves that follow a seasonal cycle. At the beginning of winter, BOD increases until the summer season when it reduces again. This seasonal cycle can be described with a sinusoidal wave equation and has been empirically calibrated from river measurements for the Dender basin by Radwan [110] (Equation 2.25). The same equation is used for the Grote Nete catchment.
cBOD
4 + 3 · cos 2π hour in non-leap year 8760 = 4 + 3 · cos 2π hour in leap year 8784
(2.25)
WATER QUALITY MODELS
53
where: CBOD is the concentration of BOD [mg l−1 ] hour is the hour in any given year starting from 1st January Alternatively, a semi-empirical model similar to the SENTWA model can be developed to estimate the BOD load that is eroded by the rainfall runoff. Such a model is based on emission factors derived from statistics such as number of cattle in the catchment. It has been developed in a study executed by the consultancy agency Ecolas NV that was commissioned by the VMM. The results of this study for the total Flanders region are summarized in Table 2.13. If one looks at the total contribution of processes related to agriculture (18 242 t), it can be considered to be very important in relation to the BOD originating from industry and households, which are classically considered in a BOD balance. The industrial supply of BOD was only approximately 4432 t in 2004 and from households 26 785 t in 2005 resulting in a total BOD load of 31 014 t per year for the whole of Flanders for these two sources. The contribution from natural processes (1597 t) is relatively small compared to the other sources so focus should lie on identifying the agricultural, industrial and household sources. The application of this approach for modelling the pollution inputs to the sub-catchment of the Grote Laak by the Master thesis research by Van Schepdael [146]8 , showed promising results in improving the simulation of the BOD concentration in the river reach and it is therefore recommended to extend this approach to the whole Grote Nete catchment in further studies. Table 2.13: Modelled BOD load draining to the surface waters in Flanders on a yearly basis [147] Total BOD load [t] Natural processes Drainage & groundwater Rainwater Falling leaves
1308 260 28
Agricultural origin Soil erosion Farm run-off from livestock Waste water from dairy cattle
750 16440 1052
1596
18242
8 This thesis applied a preliminary version of the river water quality model developed in this Ph.D. research
Chapter 3
Methodology 3.1 3.1.1
Conceptual River Water Quality Model1 Applied concepts
Instead of calculating the water quality concentrations at each water level and discharge node, as is done in the detailed river water quality model discussed in Section 2.4.1, the river branch is divided into conceptual reservoirs based on user information. For each of these reservoirs, the same water quality transformation processes are being modelled to maintain the physically based link. The decreased spatial resolution and the modified solution scheme allow for a much larger calculation time step without causing numerical instability errors thus effectively reducing the total time needed to simulate the water quality model. To describe the advection and diffusion processes the reservoirs are conceptualized as behaving both as a Plug Flow reactor (PFR) and as a Continuously Stirred Tank Reactor (CSTR) such that the concentration can be calculated with Equation 3.1. Such a conceptual river water quality modelling approach has shown to be promising for Belgian rivers by Willems [163] and Radwan [110].
1 This section is largely based on Keupers, I., and Willems, P. (revision submitted) Development of a conceptual river water quality model for scenario analysis and decision support systems. Water Research.
55
56
METHODOLOGY
Cout,AD (t) = exp
−1 k
"
Cout (t − 1) + 1 − exp
−1 k
#
Cin (t − δ)
(3.1)
where: Cout,AD is the outgoing concentration after taking into account advection and dispersion processes [mg l−1 ] Cout is the outgoing concentration after taking into account all processes [mg l−1 ] Cin is the incoming concentration [mg l−1 ] k is the recession constant [s] δ is the advective time delay [s] The recession constant is a measure of the dispersion processes that occur in the reservoir and is related to the velocity by calibration. The advective time delay takes into account the advection process and is calculated by dividing the length of the considered reach by the average flow velocity. Together, the recession constant and the advective time delay constitute the total reservoir residence time i.e. the maximum time the water quality transformation processes can affect the water quality concentration (Equation 3.2). Cout (t) = Cout,AD (t) + where:
dC Tr dt
(3.2)
Tr is the residence time [s]
The magnitude of the water quality transformation processes dC/dt is calculated with the same differential equations and parameters as in the detailed model. An example of such a differential equation is given in Equation 3.3 for nitrate. The equations for the other variables can be found in Appendix A. No changes were applied to the equations compared to the MIKE11 model to enable the comparison between both models but of course in a later stage, modifications can be made to study the influence of model structure uncertainty. 2 dCN O3 CDO (T −20) =knitr · CN H4 · Θnitr · 2 dt CDO + HSDO (T −20)
− kdenitr · Θdenitr · CN O3
nitrif ication de-nitrif ication
(3.3)
CONCEPTUAL RIVER WATER QUALITY MODEL
where:
57
CN O3 is the nitrate concentration [mg l−1 ] CDO is the dissolved oxygen concentration [mg l−1 ] knitr is the nitrification rate at 20 ◦C [d−1 ] Θnitr is the temperature coefficient for nitrification [-] T is the temperature [◦C]
kdenitr is the de-nitrification rate at 20 ◦C [d−1 ] Θdenitr is the temperature coefficient for de-nitrification [-] A first assessment of dC/dt is made based on the incoming concentrations and this is used to estimate the outgoing concentration by applying Equation 3.2. Since this results in an overestimation of the outgoing concentration in case of an increasing dC/ dt and underestimation in case of a decreasing dC/ dt, the magnitude of dC/ dt is also calculated for this first estimate of the outgoing concentration and a weighted average is taken of both values to determine the final magnitude of the water quality transformation process at that time step (Equation 3.4). This explicit scheme only requires two evaluations of the functions at each time step and allows for a reasonable approximation of the solution obtained with the fourth order Runge-Kutta method as shown in the results section. dC dCin dCout,1 =w + (1 − w) dt dt dt
0.6 Cout,1 0.6 + 0.25 ∗ with w = Cout,AD Cout,1 0.433 + 0.083 ∗ C out,AD
(3.4)
if Cout,1 > 0 Cout,1 elseif > −1 Cout,AD otherwise
where: w is the weight fraction used to calculate the weighted average [-]
58
METHODOLOGY
3.1.2
Semi-automatic model set-up
The procedure of creating the conceptual reservoirs and associated input series and transformation processes into a conceptual model as described above has been automated through the creation of a Graphical User Interface (GUI) in MATLAB (Figure 3.1). This GUI helps the users through the different steps that are required to set up a conceptual model when a detailed model is available, maximally making use of the information on the catchment already available. A manual has been written to help users to use this GUI correctly (Appendix C). Before simulations with the conceptual model can be carried out, the following steps have to be followed: • Division of the river network into conceptual reservoirs • Determining the hydrodynamic and water quality boundary information for each reservoir • Calibrating the conceptual model to detailed model results
Figure 3.1: GUI of the COnceptual RIver WAter Quality (CORIWAQ) model
CONCEPTUAL RIVER WATER QUALITY MODEL
3.1.3
59
Division into conceptual reservoirs
First, it is determined how the different river branches of the network are connected to each other. For this purpose, the definitions of the upstream and downstream chainage connections as defined in the network file are being used. For each river reach, it is determined in how many reservoirs it needs to be subdivided based on the locations of interest (for which output concentrations need to be known), boundary locations and network set-up (Figure 3.2). Boundary input: WWTP
Measurement location: output required
2
3
4
1 Rainfall-runoff link 1
Rainfall-runoff link 2
Figure 3.2: River branch represented by linear reservoirs in series with divisions based on key locations of interest Based on the parameters specified for the minimum length of a river reach reservoir (minimum ∆x) and the maximum ∆x the conceptual model configuration (series of conceptual reservoirs) is checked. If the first is not the case, the model set-up has to be adjusted and checked if the small spatial resolution is required or problematic. The latter condition is automatically respected during the creation of the model. If the length of the river reach reservoir, based on the requirements as defined by the locations of interests, is larger than the specified maximum, then the river reach is divided by two until this condition is met. A trade-off exists between model accuracy and simulation time resulting in an optimal maximum ∆x depending on the specific case study characteristics. This value can therefore be specified to meet the specific case study demands, the value of which is subject to expert calibration. After determining the division of the river reaches in conceptual reservoirs of appropriate size for the study, the reservoirs are sorted based on their needed input so that no output from a reservoir is required as input for another reservoir before it has been calculated.
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3.1.4
Determining the hydrodynamic and water quality boundary information for each reservoir
The pollutant loading (discharge times concentration) to the reservoirs is calculated based on the information provided in the boundary data file. Other boundary information required is the average velocity over the reservoir, the average water depth and the slope of the water profile. This information is calculated based on the hydrodynamic simulation results (discharges, water levels and velocities) together with the processed cross-section information. For the determination of the residence time in the reservoir, the average velocity over the considered river reach suffices. However, when looking at the sedimentation and resuspension of BOD and PP, the strong dynamics of the velocity profile over the reservoir can no longer be ignored. Indeed, in one conceptual reservoir both sedimentation and resuspension can occur at the same time, especially in the presence of structures that generate velocity accelerations. Using an average velocity would ignore these dynamics and lead to false results, mostly of a gross underestimation of resuspension (Figure 3.3). Therefore, for each calculation node of the detailed model, the velocity is calculated and the percentage of exceedance of the threshold velocity for the reservoir is calculated. This percentage is then used during simulation to calculate the contribution of resuspension and sedimentation to the overall concentration. 1.4
Velocity [m s−1 ]
1.2
Velocity Critical velocity Average velocity
1 0.8 0.6 100 % resuspension 58.8 % resuspension 41.2 % sedimentation
0.4 0.2
500 600 700 Chainages Molse Neet
800
Figure 3.3: Highly varying velocity profile resulting in both sedimentation and resuspension to occur simultaneously in one reservoir
CONCEPTUAL RIVER WATER QUALITY MODEL
61
The average water depth is calculated based on the hydraulic radius since in MIKE11 the depth transferred from the HD engine into ECOLab is actually not the exact depth, but the hydraulic radius. The approximation that the hydraulic radius is equal to the depth is valid for wider cross sections but with more narrow sections, there is a notable difference in the two values so it is important to make this distinction [41].
3.1.5
Calibrating the conceptual model to detailed model results
To accommodate the transition from a calculation of the water quality at each calculation node to only one calculation per reservoir, adjustment factors to the estimated hydraulic characteristics, i.e. residence time, average water depth and percentage velocity exceedance, is required. To facilitate the calibration of these adjustment factors, the concept of a reduction factor, fr,M IKE , is introduced. This approach has previously been suggested by Radwan [110, 113]. The reduction factor is defined as shown in Equation 3.5 and can be easily calculated for the detailed model results. Since the concentration of the water quality variables after transformation processes is equal to the concentration after advection-dispersion plus the change of concentration times the residence time (Equation 3.2), this concentration reduction factor can also be calculated for the conceptual model results with Equation 3.6. During calibration, the error between the MIKE11 reduction factor and the CORIWAQ reduction factor is minimized in a leastsquares optimization.
fr,M IKE = fr,CORIW AQ = 1 +
Cout Cout,AD
dC/ dt
· Tr
Cout,AD
(3.5) (3.6)
where: fr,M IKE is the theoretical reduction factor [-] Cout is the outgoing concentration after taking into account all processes [mg l−1 ] Cout,AD is the outgoing concentration after taking into account advection and dispersion processes [mg l−1 ] fr,CORIW AQ is the reduction factor calculated based on the CORIWAQ model results [-] C is the concentration [mg l−1 ]
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METHODOLOGY
t is the time [s] Tr is the residence time [s] The first value that requires calibration is the residence time of the pollutants in the reservoir. A first estimation is made on the basis of the average velocity and the length of the reservoir reach. The time variation of the residence time is captured well with this estimation, but the absolute magnitude sometimes needs adjustment, depending on the characteristics of the reservoir. Since nitrate is the only variable where the change in concentration is not dependent on the water depth, calibration is performed by a least square linear regression on the time series of the reduction factor of the detailed and conceptual model results for nitrate for each reservoir separately. These factors can be calculated independent from each other since in calibration mode the input for each reservoir is taken from the results of the detailed model instead of the result of the previous reservoir as is done in simulation mode. This is done to avoid an accumulation of error when going from upstream to downstream during the calibration. Equation 3.7 shows the system of linear equations that needs to be solved to estimate the adjustment factor for the advective delay. The MATLAB’s backslash operator (x = A\B) is used to solve it which returns a least-squares solution.
A ∗ x = B with
A= x= B=
Tr,t1 · dCN O3,t / dt CoutAD,N O3,t1 1
Tr,t2 · dC / dt CoutAD,N O3,t 2 .. . T · dC / dt N O3,t2
r,tend
N O3,tend
CoutAD,N O3,tend
fadvDelay fM IKE,N O3,t1 − 1 fM IKE,N O3,t2 − 1 .. . fM IKE,N O3,tend − 1
(3.7)
CONCEPTUAL RIVER WATER QUALITY MODEL
63
where: fadvDelay is the adjustment factor for the advective delay [-] Tr is residence time [s] CN O3 is the concentration of nitrate [mg l−1 ] t is the time [s] CoutAD,N O3 is the outgoing nitrate concentration after taking into account advection and dispersion processes [mg l−1 ] fM IKE,N O3 is the reduction factor of the MIKE11 model results for nitrate [-] After calibration of the advective delay, the representative water depth and the percentage exceedance of the critical velocity requires calibration for some reservoirs. This is done by defining the adjustment factors fdepth and fvelExceed , used to multiply the time series of calculated representative water depth and percentage exceedance of the critical velocity with. A value of one means no adjustment was required and the first estimate provided a good estimate. A value lower than one means that an overestimation occurred and the time series values are shifted downwards. Likewise, a value higher than one means that an underestimation is present in the time series and the values are increased proportionally. Especially BOD and PP are very sensitive to small errors on the estimation of the representative water depth and the percentage exceedance of the critical velocity as both the sedimentation and resuspension processes are strongly dependent on these variables. Since changing these variables does not result in a linear problem and requires the simulation to be re-run for each fdepth and fvelExceed , the backslash operator can no longer be used and a different problem solving technique was applied. For this purpose, a Latin Hypercube Sample (LHS) was drawn for both adjustment factors to minimize the number of samples that need to be drawn to achieve a high accuracy in the estimation of the factors (Appendix E). If the calculated estimations of the water depth and the percentage exceedance of the critical velocity are unbiased, the mean of the adjustment factors should be equal to 1. Therefore a truncated normal distribution (no negative values possible) with a mean of 1 and a standard deviation of 1.1 was assumed to sample possible parameters. The simulation was run for all the parameter sets and the RMSE between the MIKE11 reduction factor and the conceptual reduction factor for BOD is calculated to determine the most optimal parameter set for fdepth and fvelExceed . To resolve how many samples need to be drawn from the parameter space to find a good optimal solution, the optimal parameter value is plotted against the number of samples used (Figure 3.4). From this analysis, it was decided that a sample of 250 parameter sets provides a good trade off between a high accuracy in the estimation of the calibration factors and the required calculation time.
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fdepth
1.15 1.1 1.05 1
0
200 400 600 Number of runs
800
Figure 3.4: Optimal adjustment factor water depth in function of the number of samples drawn from the parameters space for a typical reservoir
3.2 3.2.1
CSO water quality model2 Available data
Since there are only two WWTPs present in the Grote Nete catchment, the amount of available data is too limited to identify an optimal model structure for the sewer water quality model. Hence, other WWTPs were included in this study, 15 in total, spatially randomly spread over Flanders (Figure 3.5). The Flanders region has an area of 13 754 km2 and has a population of around 6 million giving a population density of more than 400 people per square kilometre. It is a highly urbanized region with 98 % of the population living in urban areas [180]. Despite this high urbanization, the percent of households of which the waste water is treated by a WWTP before discharging into the river system deals with a historical lag although strong efforts have been made to increase this as demanded by the European Urban Waste Water directive. To this end, a public company, Aquafin NV, has been created and given the mandate to construct and operate WWTP facilities. Under their direction, the percentage of households for which the waste water is treated by a WWTP before discharging into the environment rose from 26 % in 1991 to 48 % in 2000 to 81 % by the end of 2014 [26]. Flanders has a dense rainfall measurement network of which the hourly measurements are available online3 . In total, 70 rainfall stations are being kept operational by the VMM (Figure 3.6), most of them are measuring the rain intensity by means of a tipping rain gauge bucket. 2 This methodology has been presented at the 10th UDM conference in Canada: Keupers, I., and Willems, P. CSO water quality generator based on calibration to WWTP influent data. In UDM2015, 10th international Urban Drainage Modelling Conference, September 20-23, Mont-Saint-Anne, Québec, Canada (2015). 3 http://www.waterinfo.be
CSO WATER QUALITY MODEL
65
Figure 3.5: Locations of the 15 selected WWTPs in Flanders for which the ANN model was calibrated
Figure 3.6: Rainfall measurement stations operated by the VMM in Flanders have a high density of one per 90 km2
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METHODOLOGY
40
0.8 ConcNH4 [mg l−1 ]
Discharge [m3 s−1 ]
For the selected WWTPs, daily flow measurements are available for at least 5 years. Concentration measurements of NH4, NO3, OP, and Pt are also available, but with a much lower temporal resolution of once per week or every two weeks (Figure 3.7). These data can be downloaded online from the geoloket water4 .
0.6 0.4 0.2 0 Jan
Apr
Aug Time
Dec
30 20 10 Jan
Apr
Aug
Dec
Time
Figure 3.7: Typical time series of daily discharge measurements (left) and biweekly water quality measurements (right) available at the WWTP influent
3.2.2
Architecture of the Artificial Neural Network
A static Multi-Layer Perceptron Neural Network (MLP NN) (Figure 3.8) is applied since this model can handle non linearity and has been widely applied in a variety of scientific and engineering fields. For example, Verma et al. [150] showed that MLP NN outperformed other algorithms when predicting Total Suspended Solids (TSS) in waste water. Most applications of ANN models for concentration simulation and prediction make use of past recorded values of the output variable(s), hence are applied in real-time prediction mode. Such past values of the output variables are known as the memory values of the system and may greatly increase the prediction power of the model [82]. However, because reliable recorded past values are not always available, such approach is prone to drift and instability, which makes it less useful for long term model simulations and related scenario analysis. For this reason, a strictly feed forward neural network is applied in this study. As noted by Maier and Dandy [82] data preprocessing can have a significant effect on model performance. Rainfall model input data is highly skewed with many days without rain and only a few days with high intensities. To reduce this skewness, the rainfall model input data, i.e. maximum rain intensity during the day, average 4 http://geoloket.vmm.be/Geoviews/
CSO WATER QUALITY MODEL
67
Input layer
Hidden layer
Output layer
Month (logical array) Week/weekend (logical array)
Conc NH4
Discharge (t, t-1, t-2)
Conc NO3
maxRainDaily (t, t-1, t-2)
Conc OP
meanRainDaily (t, t-1, t-2)
Conc TP
numberPreviousDryDays Figure 3.8: Multi-Layer Perceptron neural network architecture rain intensity during the day and number of previous dry days, was transformed by using Equation 3.8. y = log(x + 0.001) where:
(3.8)
x is the model rainfall input data y is the transformed rainfall data
After this transformation, all input variables are scaled to the same range from −0.8 to 0.8 to ensure the same importance is given to all input variables. Also the target values can be highly skewed and are therefore first transformed by means of a Box Cox transformation as shown in Equation 3.9 [24]. The single parameter of the transformation, λ, was optimized between 0 and 1, by the maximum likelihood approach (maximizing the likelihood that the sample originates from the normal distribution). This probability was calculated with the one sample KolmogorovSmirnov test [88]. Prior to this transformation, the measurement outlier data points were removed from the data set. A normal, two-sided, outlier-detection approach was used for this purpose, which considers outliers to be values outside the range [−3σ, +3σ]. Finally, the transformed target data are scaled between −1 and 1 before being used for training the network. y=
xλ − 1 λ
(3.9)
68
where:
METHODOLOGY
x is the model target data y is the transformed target data λ is the Box-Cox transformation parameter (λ > 0)
The optimal number of nodes in the hidden layer was obtained by varying the number of nodes between 1 and 2 · input layers + 1 [131] and selecting the number that provided the best fit for the validation data to avoid over fitting of the network. The network training function updates weight and bias values according to the Levenberg-Marquardt optimization. This algorithm is designed to approach second-order training speeds without having to compute the Hessian matrix by using a Newton like approximation [86, 59]. During the training a combination of squared errors and weights is minimized after which the correct combination is determined to produce a network that generalizes well. This process is also called Bayesian regularization. The available data set was divided in three blocks of training, validation and testing data sets with a ratio of 55/20/25 respectively. The validation set was used to provide a stopping criterion to the training to avoid over fitting of the network. The testing data set allows for an independent evaluation to assess the capability of the model to extrapolate. As activation function for the hidden layer nodes, the tansig function was implemented to account for the non-linear behaviour of the system. The output layer has a purelin activation function.
3.3
3.3.1
Impact analysis of CSOs on river water quality by means of an integrated model5 Molse Nete catchment
As a first show case to demonstrate the capability of an integrated sewer-river quantity-quality model, it was chosen to focus on a sub catchment of the Grote Nete catchment namely on VHA zone 501 (Figure 2.18), also known as the Molse Nete catchment (Figure 3.9). The catchment has an area of 83 km2 and is flat, with an average slope of 4 %. The total length of the waterways is 87 km, equivalent to a drainage density of 1 km/km2 . The Molse Nete catchment is highly urbanized with 29 % of the land classified as urban and built up. Agriculture plays a main 5 This methodology has been developed in cooperation with Vincent Wolfs and presented at the 10th UDM conference in Canada: Keupers, I., Wolfs, V., Kroll, S., and Willems, P. Impact analysis of CSOs on the receiving river water quality using an integrated conceptual model. In UDM2015, 10th international Urban Drainage Modelling Conference, September 20-23, Mont-Saint-Anne, Québec, Canada (2015).
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL
69
role as 48 % of the land is used as grassland or crop land. The remaining 23 % are natural forests, rivers and permanent wetlands. There are two WWTPs to treat the sewage water produced in the catchment of which one discharges its effluent into the Molse Nete River and the other transports water outside of the catchment. There are 4 industrial sites that directly discharge their untreated water in the river but together they only emit on average 0.03 m3 s−1 compared to an average flow of 0.6 m3 s−1 at the basin outlet. Besides these point sources, the agricultural activities in this catchment also contribute pollutants to the river reach. This diffuse pollution is accounted for in the same manner as described in Section 2.4.4.
Figuur 3.9: Molse Nete catchment(VHA zone 501) and river network with indication of the locations of the CSOs, WWTP and water quality monitoring sites Rainfall is the driving force for both the sewer and the river system. Since the sewer system has a short response time to rainfall, it was decided to use a historical 15-minutes measured rainfall time series as input for the sewer models. It was based on the nearest rain gauge at Varendonk. Missing periods were filled based on the data from neighbouring stations. For the river model, hourly rainfall records at stations of the VMM were applied as explained in Section 2.1.2. There is no river flow gauging station in the catchment of the Molse Nete River but the river model could be calibrated as part of the larger Grote Nete catchment discussed in Section 2.3.2. There are 7 water quality monitoring points spread out over the catchment (Figure 3.9). The temporal resolution of the water quality observations is, however, limited: one sample approximately once a month, hence providing about twelve measurements per year. The water quality data hence can be applied for evaluating the overall water quality concentration variations in the model, but their temporal resolution is too limited to allow evaluation of the temporal dynamics at higher resolutions.
70
3.3.2
METHODOLOGY
Conceptual models used for the different subsystems
The conceptual models used in this study are surrogates of more detailed models. The primary aim of these simplified models is to emulate the behaviour of such detailed models while minimizing the calculation time. Separate conceptual water quantity and quality models are created for the Molse Nete River and sewer systems of the cities Mol and Geel based on simulation results of detailed models. Individual calibration and verification is crucial to ensure each model has a physically correct representation of the considered subsystem [114]. An exception to this approach is the water quality model of the CSOs for which no detailed model is available. Due to the limited knowledge on the physical-chemical, biological and transport processes occurring in sewer systems [18] and limitations in sewer water quality data, such detailed models are difficult to construct, calibrate and validate. For the same reason, also the water quality model could not be validated. The modelling framework that was developed for the water quality modelling has been explained in detail in the previous two sections. The methodology that has been applied to set up the conceptual water quantity models has been developed by Vincent Wolfs and is shortly described below. A modular framework was developed for conceptual water quantity modelling. Although the dynamics of rivers and sewers differ significantly, the approach can be used for both system types. In addition to a common set of modelling tools and principles, the framework features some model structures and algorithms tailored for either rivers or urban drainage systems. The reservoir approach to model sewer overflows has been first suggested by Vaes [138] and has been successfully applied for uncertainty quantification [171] and has been further extended in this research. Only the basic concepts and features are discussed below. The reader is referred to Wolfs et al. [177] and Wolfs and Willems [179] for a more comprehensive elaboration of the conceptual quantity modelling approach for river and sewer systems respectively. The modelling approach is based on the storage cell concept, which means that the river or sewer system is divided into multiple interconnected cells or reservoirs. Processes in and between these cells are lumped in space and time. Model detail can be tailored to the desired accuracy and the intended use. In this study, it is for instance important to ensure that the sewer overflow discharges are modelled precisely. By focusing solely on the dominant processes, overly complicated models can be avoided, as recommended by different authors in the past [114]. The methodology has a data-based mechanistic nature [182, 81], which means that most of the incorporated model structures are objectively derived from the data, but are still interpretable in physically meaningful terms. One example of a mechanistic feature of the developed approach is the explicit closing of the water balance in each storage cell, which ensures that no mass is lost or generated during simulations. The modeller can choose from a wide gamut of non-linear model structures to
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL
71
emulate the flow between different cells, ranging from process based equations (e.g. relationships that describe the flow over hydraulic structures) to more advanced techniques such as neural networks. Such data-driven structures are flexible since they generally do not rely on stringent assumptions, and are therefore well suited for lumping on larger scales. This results in fewer equations and model parsimony, which in turn leads to a reduced calculation time and ditto calibration effort. In addition, most incorporated structures can adapt themselves to the data provided during training, which enables them to simulate complex behaviour, including backwater effects and pressurized flow. The conceptual modelling approach also allows to model water levels in rivers. In contrary, discharges in conceptual sewer models are always driven by volumes, other flows and/or water levels from receiving rivers in the used modelling framework. Finally, rainfall run-off can also be modelled conceptually using different hydrological reservoir models or transfer functions [169]. The dynamics of the WWTP were not modelled explicitly. Instead, the flow rates at the influent of the WWTP (modelled via 9 pumps in the conceptual sewer model of Mol) are summed to obtain the flow rate that is discharged into the river.
3.3.3
Integration of the conceptual models
After individual configuration of the water quantity and quality conceptual models, the different models are coupled so that the interactions between the sewer and river systems can be simulated. The calculation schemes of the models are written in C code, which is not only computationally very efficient, but also ensures straightforward interfacing of the different models. The interface is implemented in MATLAB to ensure a user-friendly experience in creating and running the models. The linking is obtained by considering the simulated overflow discharges of the urban drainage systems of the cities Mol and Geel and the estimated effluent discharges of the WWTP of Mol as inputs for the water quantity and quality model of the Molse Nete River. Since many of these overflows spill into tributaries of the investigated Molse Nete River instead of directly into the modelled part of the river, an additional routing is performed on these overflow discharges. This routing is implemented by a cascade of linear reservoirs [152]. The mean travel time is estimated based on the velocity of the water flow and the distance from the overflow to the mouth of the tributary into the Molse Nete River. The simulated flows and water levels in the river are taken as basis for the conceptual water quality model.
3.3.4
Quantification of the impact of CSOs
As discussed in Section 2.4.1, most water quality standards for the surface waters in Flanders are expressed as percentile values that should not be exceeded. For BOD and KjN the 90th percentile is used to demarcate the different classes (Table 2.7). Given the importance of this value in the current legislation, it is decided to perform
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METHODOLOGY
a statistical analysis on the historical time series from 2001 to 2008 to calculate the 90th percentile value of exceedance for BOD and NH4 for the scenario where no CSOs are included and compare this to the values obtained with the model that does include CSOs as point sources into the river network as it is expected that raw sewage will have the largest impact on these two variables. Besides this, the 99th percentile is also determined to see how the CSO discharges influence the high extremes as these are of course also important in determining the ecological impact.
Chapter 4
Results and discussion 4.1 4.1.1
Conceptual River Water Quality Model Calibration to the detailed model results
First the network of the river model is divided into reservoirs. Based on the maximum length of each reservoir, a total number of reservoir is obtained. The higher the value of the maximum length, the less reservoirs that need to be created and hence the smaller the total calculation time will be. However, a trade off exists between simplicity and accuracy so different values are tested for the reservoir length to determine the most optimal one. Therefore three different maximum ∆x values of 500, 750 and 1000 m are tested in this study. The resulting calculation time is given in Table 4.1 from which this increase in speed can be seen when allowing for larger maximal ∆x values during set-up of the conceptual model. The calibration was performed for the year 2000, the validation for the years 2001 and 2002. The automatic calibration of the advective delay, the water depth and the velocity exceedance performed very well for most reservoirs. The median and average value for the three calibration factors lies around 1 (Table 4.2), as is expected when the method for calculating the average advective delay, water depth and percentage velocity exceedance provides a good, unbiased estimate. However, for some reservoirs, significant deviations from 1 can be observed indicating the necessity of a careful manual check of the automatic calibration given the sensitivity of the model to these three variables. From Table 4.2 it can also be seen that the size of the reservoirs does not have a significant influence on the accuracy of the first estimate. On average, the advective delay and the water depth are underestimated
73
74
RESULTS AND DISCUSSION
Table 4.1: Different max ∆x that are tested for the case study indicating the speed-up that can be obtained compared to the detailed MIKE11 model max ∆ x [m]
Number of Reservoirs
∆ t [h]
Simulation time one year [s]
Speed-up factor
398
0.25 0.5 1
78 48 36
276
0.25 0.5 1
56 38 28
4.5 × 103 7.3 × 103 9.7 × 103
210
0.25 0.5 1
44 29 22
500
750
1000
6.2 × 103 9.2 × 103 1.2 × 104
7.9 × 103 1.2 × 104 1.6 × 104
indicated by a mean value of the adjustment factors that is larger than 1. On the contrary, the percentage velocity exceedance is slightly overestimated. Table 4.2: Mean, median and standard deviation for the calibration factors of the three different conceptual model set ups
fadvDelay
fdepth
fvelExceed
mean median standard deviation mean median standard deviation mean median standard deviation
∆x 500
∆x 750
∆x 1000
1.18 1.06 0.34 1.06 1.02 0.21 0.94 1.00 0.20
1.21 1.08 0.36 1.07 1.03 0.25 0.94 1.00 0.24
1.21 1.09 0.35 1.05 1.01 0.23 0.98 1.00 0.24
During calibration it became clear that for some reservoirs the percentage fraction of resuspension was not determined correctly. This problem is visible in the scatter plot of the theoretical reduction factors versus the reduction factors calculated for the conceptual model (example see Figure 4.1). If two distinct clouds with different slopes are visible instead of one, this means that the conceptual model identifies two distinct regimes whereas in the detailed model there is only one regime. This requires an extra calibration step, i.e. an update of the velocities that were used to calculate the percentage exceedance. This can be achieved via two different approaches namely by removing a chainage from the list of calculation nodes that
CONCEPTUAL RIVER WATER QUALITY MODEL
75
were used to determine the percentage velocity exceedance or by adding/removing a constant factor to the simulated velocity at a specific calculation node (see also Figure C.12 . This calibration cannot be automated as it requires expert judgement. However, it was only required in approximately 1 % of the reservoirs so the effort remains limited. From Table 4.3 it becomes clear that there are only four different locations in the model that required this adjustment. At chainage 2829 and 7191 of the Grote Nete 1, chainage 2269 of the Ongelbergloop and chainage 3915 of the Heiloop river branch a structure is situated, creating a velocity peak which makes the calibration more difficult. However, keeping in mind that there are 296 structures implemented in the hydraulic model, it by no means indicates that an auto-calibration is impossible at locations were structures are situated. It can be concluded again that a larger maximum ∆x is favourable from a calibration point of view as less adjustments are required when larger maximum ∆x values are allowed. (b)
(a) 1.4
1.4
fCORIW AQ
1.3
1.3
1.2 1.2
1.1
1.1
1 1
1.2 fM IKE11
1.4
1.1
1.2 1.3 fM IKE11
1.4
Figure 4.1: Scatter plot of theoretical reduction factor versus conceptual reduction factor for PP for reservoir GroteNete1_ch6562_7191 - a) original velocities - b) updated velocity chainage 6752
76
RESULTS AND DISCUSSION
Table 4.3: Reservoir that required an adjustment of the chainages used to calculate the percentage velocity exceedance
4.1.2
max ∆ x [m]
% of total number of reservoirs
500
0.75
grotenete1_ch6893_7191 ongelbergloop_ch1964_2269 heiloop_ch3935_4335
750
1.09
grotenete1_ch2178_2829 grotente1_ch2829_3610 grotenete1_ch6562_7191
1000
0.48
grotenet1_ch6562_7191
Reservoir name
Accuracy compared to detailed model results
The accuracy of the conceptual model in simulating the concentrations as obtained with the detailed model is evaluated by calculating the correlation coefficient, R2 , and the Nash-Sutcliffe efficiency, NSE, for each reservoir. The (spatial) variation of these statistical indicators are also analysed. For all model set-ups, the lowest median correlation coefficient was 0.99 and the highest InterQuartile Range (IQR) was 0.02. Therefore, these values are not analysed or shown further; focus is put on the NSE values, which are sensitive to extreme values and can indicate if peak concentrations are accurately simulated and at the correct time steps. Table 4.4 shows very high median NSE values between 0.97 and 1 indicating that the conceptual model can emulate results closely to the detailed, physically based models. The nitrate concentration results have the highest accuracy. This is not surprising considering that there are only two processes that determine the nitrate concentration, nitrification and de-nitrification, which are both independent of the water depth or percentage velocity exceedance. The dissolved oxygen concentration is predicted worst; also this was expected since there are six processes that modify the dissolved oxygen concentration, one of which is the photosynthesis production for which the exact calculation of the relative day length could not be identified from the MIKE11 manual. Small errors in the estimation of this value can accumulate to larger errors and to the simulation of peaks for which the timing is not perfect but with time shifts of about one hour, leading to lower NSE values than the other parameters although still high. Increasing the spatial resolution of the model set-up did not result in better agreement of the conceptual model and MIKE11 results. Considering the gain in speed that is obtained when allowing for larger maximum ∆x values, a high value of 1 km was selected for the maximum ∆x in this case study. Moreover,
1000
750
500
max ∆x [m]
0.97 (0.04) 0.97 (0.04)
1
0.97 (0.04)
0.25 0.5
0.97 (0.04)
1
0.97 (0.04)
0.25 0.97 (0.04)
0.97 (0.04)
1 0.5
0.97 (0.04)
0.97 (0.04)
0.25 0.5
DO
∆t [h]
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
1.00 (0.02)
1.00 (0.02)
1.00 (0.02)
NH4
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
1.00 (0.002)
NO3
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.03)
0.98 (0.03)
0.98 (0.03)
0.98 (0.02)
0.98 (0.02)
0.98 (0.02)
BOD
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
1.00 (0.01)
OP
0.99 (0.01)
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.02)
0.99 (0.01)
0.99 (0.01)
0.99 (0.01)
PP
0.98 (0.04)
0.98 (0.04)
0.98 (0.04)
0.99 (0.03)
0.99 (0.03)
0.99 (0.03)
0.98 (0.04)
0.98 (0.04)
0.98 (0.04)
T
Table 4.4: Median (IQR) of the NSE values between conceptual and detailed modelled result for all reservoirs in the catchment during the validation period 2001-2002
CONCEPTUAL RIVER WATER QUALITY MODEL 77
78
RESULTS AND DISCUSSION
decreasing the time resolution did not provide more accurate conceptual model results. A simulation time step of 1 hour was selected leading to a speed-up factor of 1.6 × 104 when using the conceptual model in comparison with the detailed, physically based model (Table 4.1). The latter has a simulation time step of 30 s to avoid instabilities in the finite difference solution of the process equations.
(a)
NSE 1 0.9 0.8 0.7 0.6
5 km
(b)
NSE 1 0.95 0.9 0.85 0.8 5 km
0.75
Figure 4.2: NSE values between conceptual and detailed modelled result for DO (a) and BOD (b) for all reservoirs in the catchment during the validation period 2001-2002 For the model set-up that uses ∆x and ∆t values of 1 km and 1 h, the geographical
CONCEPTUAL RIVER WATER QUALITY MODEL
79
distribution of the NSE values for DO and BOD is shown in Figure 4.2. For the other water quality variables, these figures can be found in Appendix D. It is clear that there is a propagation of the deviations in DO from one reservoir to the other with only three locations were a larger deviation between the MIKE11 and conceptual model results can be observed which diffuses downstream the river branch. The same pattern can be observed for BOD although only two locations show strong deviations that propagate less far into the model. The locations were this occurs is different from the locations where the largest errors could be observed for DO indicating that the cause of the deviation is different for both parameters.
4.1.3
Robustness of the CORIWAQ model
To test the robustness of the conceptualization approach, several runs were performed with the detailed MIKE11-ECOLab model considering various sets of water quality parameter values. This way it could be determined whether the conceptual model still provides high accuracy as for the parameter set that was used for calibration. To limit the required calculation time, the model is reduced to only represent the first 6 km of the Grote Laak river (Figure 2.7). This part of the river network was chosen because of the location of an agricultural area upstream of the modelled reach in combination with two large industries discharging more downstream, followed by two water quality monitoring points. The hydrodynamic simulation results can also be validated for this reach due to the presence of the hydrodynamic monitoring station Tessenderlo (Figure 2.3). This reduces the number of computational nodes from 6040 for the whole catchment to 257 for the Grote Laak. The reduced model only requires 2.1 h to simulate one year on a desktop with an Intel Core i7 3.4 GHz processor as compared to approximately 4 days for the whole catchment. This reduction was deemed satisfactory for the purpose of testing the robustness of the proposed approach (40 days required for the simulation) but not sufficient to perform the sensitivity analysis directly on the detailed water quality model (would take 55 years to run all the required runs). For all ten parameter sets that were sampled with the LHS method (Appendix E), the goodness-of-fit of the conceptual water quality model with reference to the detailed MIKE11-ECOLab model is assessed both graphically and with statistical indicators for the year 2000. Since errors in the conceptual model accumulate from the most upstream reservoir to the most downstream one, the results are shown here for the penultimate reservoir modelled. The results of the last reservoir were discarded for this analysis due to the influence of the downstream boundary condition imposed in the MIKE11 model. The output of the penultimate reservoir (the 29th reservoir) is situated at 5.29 km from the most upstream location of the modeled Grote Laak branch. The smallest R2 and NSE at this location are 0.98 and 0.96 respectively, which both occur for DO.
80
RESULTS AND DISCUSSION
For all the other variables these values are 1.00 and 0.99. The conceptual model thus provides results very close to the MIKE11-ECOLab model over the parameter range considered. The larger deviation for DO compared to the other state variables studied stems from the uncertainty in the calculation of the relative day length for which the exact calculation cannot be deduced from the MIKE11 manual. The estimation that was used as a replacement in the conceptual model does not correspond exactly to the relative day length used in the detailed simulation, which influences the timing of the photosynthesis process with small errors accumulating in time and space. Figure 4.3 and Figure 4.4 show the model results for the parameter set that gave the most unfavourable conceptual model result. These figures show the larger uncertainty in the DO results but without systematic model deviations. Based on this goodness-of-fit evaluation, the conceptual model was considered useful to be applied for the global sensitivity analysis described in the next section. DO (mg l−1 )
Result
12
2
R =0.98 NSE=0.96
10 8 6 4 Jan May Sep
1.2 1
2
R =1
Result
8
R =1
NSE=1
2
0.8
1.5
0.6
1
0.4
0.5
0.2 Dec Jan May Sep
BOD (mg l−1 ) 2
NO3 (mg l−1 )
NH4 (mg l−1 )
R2 =1
NSE=1
Dec Jan May Sep
Dec
Temp (◦C) 2
NSE=1
R =1
NSE=1
20
6
MIKE11 results CORIWAQ results
10
4
0
2 Jan May Sep Time
Dec Jan May Sep
Dec
Time
Figure 4.3: Time series comparison for the conceptual model output for reservoir number 29 with the MIKE11 results at chainage 5.29 km
CONCEPTUAL RIVER WATER QUALITY MODEL
DO (mg l−1 ) CORIWAQ
2
R = 0.98
10
ME = −0.07
6
0.4 6
8
0.4 0.6 0.8
CORIWAQ
2
2
4 6 MIKE11
1
0.5
1
1.5
Temp (◦C) 2
R =1
20
ME = −0.22
10
4
ME = −0.08
0.5
2
R =1
1.5 1
10
ME = −0.02
R =1
ME = −0.01
BOD (mg l−1 ) 6
2
2
R =1
0.8 0.6
NO3 (mg l−1 )
NH4 (mg l−1 )
1
8
81
0
0
results bisector mean error
10 20 MIKE11
Figure 4.4: Scatter plot for the conceptual model output for reservoir number 29 with the MIKE11 results at chainage 5.29 km
4.1.4
Global sensitivity analysis of the water quality parameters1
The resulting conceptual model is used for a global sensitivity analysis by means of Monte Carlo simulations to determine the most sensitive parameters that define the water quality transformation processes. It is also investigated whether the model parameter sensitivity depends on the pollutant loading of the river. This provides valuable information in support of the model calibration process as focus can be given to the most sensitive parameters and processes. A sensitivity analysis involves the determination of relationships between the uncertainty in the resulting output of the model and the uncertainty in the individual input parameters [63]. There are 27 parameters that describe the 15 transformation processes. However, there are 3 parameters that should not be calibrated (Table 4.5). 1 This research has been presented at the 36th IAHR World Congress: Keupers, I., and Willems, P. Global sensitivity analysis of transformation processes in a river water quality model by means of conceptualization. In E-proceedings of the 36th IAHR World Congress 28 June - 3 July, 2015, The Hague, the Netherlands (2015).
82
RESULTS AND DISCUSSION
Table 4.5: Parameters of the water quality transformation processes that should not be subject to calibration Name
Value
Reason
kplant kbact lat
0.066 0.109 51.1
determined by stoichiometry [157] determined by stoichiometry [157] determined by the location of the case study
To determine which of the remaining 24 parameters leads to the highest model sensitivity, a global sensitivity analysis is performed. To this end, a Monte Carlo simulation approach is adopted as this has been successfully applied in numerous cases and types of applications before [124]. Instead of using the classical Random Sampling (RS) method for sampling the conceptual model parameter space, the LHS method was used (Appendix E). The parameters included in the sensitivity analysis are listed in Appendix F together with their type of Probability Density Function (PDF) and the upper and lower limits which are determined from literature. From the proposed distributions, 5000 random parameter sets are derived with LHS using the MATLAB script from Minasny [92], which is an implementation of the method of Stein [128]. All these parameter sets are run with the conceptual model. The simulated 90th percentile of DO, BOD, NH4, NO3 and T is calculated as this value is used to determine compliance to water quality standards. The 50th percentile is also calculated to see how the input parameters influence the output concentration on average. A multiple linear regression is performed on the standardized inputs and outputs of the model to obtain the Standardized Regression Coefficients (SRCs) for each of the parameters. The SRC is a relative sensitivity index since the standardization (performed by applying Equation 4.1) removes the unit of measurement of predictor and outcome variables. This enables the comparison of relative effects of variables with different orders of magnitudes.
Z=
x − µx σx
(4.1)
where: Z is the standardized time series x is the input or output time series that needs to be standardized µx is the average value of the time series x σx is the standard deviation of the time series x
CONCEPTUAL RIVER WATER QUALITY MODEL
83
The absolute values of the SRCs allow for a ranking of the parameters as they offer a measure of the effect of a given parameter that is averaged over a set of possible values of the other parameters, hence making it applicable to estimate the global sensitivity for non-linear models as well [125]. This ranking is performed along several points of the river reach to account for the spatial differences in the composition of pollutant loading. Before calculation of the SRCs, it is verified whether the model output (90th and 50th percentile values for the different model output variables) relates monotonically to the input parameters as this is a requirement for a correct interpretation of the multi-linear regression of the model output [125]. The model is run by changing only one parameter at a time while keeping all other parameters constant. The results confirm that there is a monotonic relation between the model output percentiles and the input parameters shown in Table F.1. The effect of changing the input parameter BOD decay rate on the 90th percentile of the DO concentration is shown in Figure 4.5 as a typical example result of this monotonous relationship.
DO (mg l−1 )
9 8.5 8 7.5 0
1
2
3
4
BODdecay rate (d
−1
5
)
Figure 4.5: Monotonic relation between the BOD decay rate and the 90th percentile DO concentration To calculate the SRC for each of the model parameters, 5000 model runs with parameter sets determined by LHS are executed for the 9 year simulation period (2000-2008). The (absolute) values of the SRC are computed for the 90th and 50th percentile values of each model output variable of each of the 30 reservoirs. Only SRC values that are significantly (α = 0.05) different from zero are retained in the further analysis. To assess the quality of the performed regression, the model coefficient of determination, R2 , values are given in Table 4.6 for each of the parameters. These values represent the fraction of the model output variance accounted for by the regression model. The SRCs tell us how this fraction of the output can be decomposed according to the input factors, leaving us ignorant about the rest, where this rest is related to the non-linear part of the model.
84
RESULTS AND DISCUSSION
Table 4.6: R2 values of the multiple linear regression performed on the standardized inputs and outputs of the model
90th percentile 50th percentile
DO
NH4
NO3
BOD
T
0.94 0.93
0.85 0.83
0.76 0.40
0.98 0.91
0.99 0.99
The relative importance of the different water quality parameters on the model output variables - shown by the SRC ranks - does not change much in the longitudinal direction for the most sensitive parameters (Figure 4.6, Figure 4.7). This indicates that the difference in loading does not play an important role in determining were to focus the calibration efforts. The SRC ranking is indeed quite stable for the most important parameters. However, some changes in the ranking occur downstream of industry discharge points, indicating other processes become more important when moving from upstream to downstream. The results show that the first order BOD decay rate (kBOD ) is the most sensitive model parameter as it ranks in the top three of most influential parameters for DO, BOD and NH4 at all locations. When comparing the absolute SRC values for the 90th (Figure 4.8) and 50th percentile values (Figure 4.9), it can be concluded that the sensitivity does not differ much when peak values are considered versus average model outputs. This is an important conclusion as during calibration we are interested in capturing both the average and the peak concentrations well. The model parameter with the highest absolute SRC value among all output variables is the nitrification process parameter. This parameter thus almost uniquely determines the variation observed in the NO3 concentrations and it can be concluded that de-nitrification plays a less important role in this study area. The second most sensitive model parameter is the emitted heat radiation. The model output is double as sensitive to the emitted heat radiation as compared to the maximum absorbed solar radiation, which is expected since there is only incoming solar radiation during the daytime whereas the water is emitting heat radiation during day and night. These parameters do, however, not have a significant effect on the other state variables, thus indicating that it is not required to calibrate these parameters to obtain improved model results for the other state variables. By calibration of the emitted heat radiation parameter, a significant improvement of only the temperature simulation can be obtained. The third most important parameter in terms of absolute SRC value is the sedimentation rate of the BOD. The resuspension rate of BOD does not show to be a highly sensitive parameter, but this can be entirely explained by the low velocities in the modelled river reach. The river flow velocity is on average 0.4 m s−1 , which is in the lower range of the critical flow velocity.
CONCEPTUAL RIVER WATER QUALITY MODEL
DO
industry
industry
Sensitivity Rank (-)
1
industry
kBOD pmax Θreaer Θresp kresp ΘBOD knitr kSOD ΘSOD
3 5 7 9 0
2 industry
kBOD
3
ΘBOD
4
vcritBOD
industry
5 7 9 0
2
NH4
3
2
4
NO3
industry
1
knitr kdenitr Θnitr Θdenitr Θresp kresp HSnitr YN H4
3 5 7 0
industry
Sensitivity Rank (-)
2 industry
4
industry
ksedBOD
0
kBOD pmax YN H4 knitr kresp Θresp ΘBOD Θnitr HSSOD Θdenitr
BOD
1
4
1 Sensitivity Rank (-)
85
2
4
Temp
industry
1
Eaf
2
Emax
0 2 4 Distance from upstream (km) Figure 4.6: SRC based sensitivity ranks of the WQ model parameters along the river reach of the 90th percentile model output values
86
RESULTS AND DISCUSSION
DO
industry
industry
Sensitivity Rank (-)
1
kBOD pmax kresp Θresp Θreaer knitr kSOD ΘBOD ΘSOD
3 5 7 9 0
2 industry
ksedBOD
3
ΘBOD
4
kresp
industry
5 7 9 0
2
NH4
3
2
4
NO3
industry
1
knitr kresp Θresp kdenitr kSOD kBOD Θnitr Eaf
3 5 7 0
industry
Sensitivity Rank (-)
2 industry
4
industry
kBOD
0
kBOD YN H4 pmax knitr kresp Θresp ΘBOD Θnitr HSSOD Θdenitr
BOD
1
4
1 Sensitivity Rank (-)
industry
2
4
Temp
industry
1
Eaf
2
Emax
0 2 4 Distance from upstream (km) Figure 4.7: SRC based sensitivity ranks of the WQ model parameters along the river reach of the 50th percentile model output values
CONCEPTUAL RIVER WATER QUALITY MODEL
NO3
Temp
0.8 0.6 0.4 0.2 0
Emax
ΘBOD
kBOD
1
Eaf
1 0.8 0.6 0.4 0.2 0
BOD
ksedBOD
kBOD pmax Θreaer Θresp kresp ΘBOD knitr kSOD kBOD pmax YN H4 knitr kresp Θresp ΘBOD Θnitr
|SRC|
1 0.8 0.6 0.4 0.2 0
NH4
1 0.8 0.6 0.4 0.2 0
knitr kdenitr Θnitr Θdenitr Θresp kresp
DO
|SRC|
1 0.8 0.6 0.4 0.2 0
87
Figure 4.8: Absolute SRC values of the WQ model parameters for the 90th percentile model output values at the penultimate reservoir When we define critical parameters as parameters with an absolute SRC value larger than 0.2, 9 out of the 24 parameters can be selected (Table 4.7). If more measurements would be available, focus should lie on the quantification of these parameters as this would reduce the uncertainty in the model output the most. If no additional measurement campaigns can be performed for direct quantification, the insights obtained from the sensitivity analysis can be used during model calibration to lower the degrees of freedom and to improve the efficiency in obtaining a global optimum parameter set.
88
RESULTS AND DISCUSSION
0
0
kBOD pmax kresp Θresp Θreaer knitr kSOD ΘBOD NH4
1 0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
kBOD YN H4 pmax knitr kresp Θresp ΘBOD
|SRC|
0.8
1
NO3
Temp
0.8 0.6 0.4 0.2 0
Emax
0.2
Eaf
0.4
0.2
Θresp
0.4
kresp
0.6
ksedBOD
0.6
kBOD
0.8
|SRC|
0.8
1
BOD
1
ΘBOD
DO
knitr kresp Θresp kdenitr kSOD kBOD Θnitr
1
Figure 4.9: Absolute SRC values of the WQ model parameters for the 50th percentile model output values at the penultimate reservoir
Table 4.7: Parameters with an absolute SRC value larger than 0.2 Parameter description Ammonia decay rate (nitrification) First order BOD decay rate Maximum absorbed solar radiation Emitted heat radiation Sedimentation rate of BOD Maximum oxygen production by photosynthesis Ratio of ammonia released at BOD decay Respiration of animals and plants Respiration temperature coefficient
CSO WATER QUALITY MODEL
4.2
89
CSO water quality model2
A summary of the ANN model results obtained by applying the methodology described in Section 3.2 is given in Table 4.8. Since measurements were not available for all water quality variables at all locations, the number of WWTPs on which the analysis is based is also mentioned. It is clear that even with the limited amount of data available, a good model fit of the ANN can be obtained for all the water quality variables considered in this study. An example result for the WWTP of Aalst is shown in Figure 4.10. Table 4.8: Summary of the ANN model results (average ± standard deviation) # WWTPs # samples R2 training R2 test
NH4
NO3
OP
TP
9 203 ± 71 0.85 ± 0.04 0.88 ± 0.04
6 221 ± 40 0.81 ± 0.09 0.79 ± 0.09
8 187 ± 26 0.81 ± 0.06 0.83 ± 0.08
7 167 ± 56 0.79 ± 0.06 0.74 ± 0.06
When investigating which factors determine the accuracy of the ANN model, it is first investigated what the role is of the number of samples available. When the number of water quality sample values was limited to 50 or less, the WWTP was not considered in the analysis. Surprisingly, no clear relation between the number of samples and the model goodness-of-fit evaluated based on the R2 between simulated and measured values during the test period, exists. This means that more samples do not necessarily lead to a higher accuracy for the model after training (Figure 4.11). This means that other, yet unidentified factors are more important. The influence of the different input variables on the model performance is tested. Table 4.9 shows that including all the available variables provides the best model fit. However, only a marginal improvement could be obtained by including the rainfall information and no good model fit could be obtained by only using the rainfall information and omitting the discharge data. This is probably because only a few water quality samples are taken during rainfall events, hence calibration may be improved by focusing on sampling during rainy periods. Finally, since we are interested in the water quality parameters during rain events (i.e. during CSO events) it is also tested if we can get improved results for the predicted concentration during rain events when the neural network is trained not on the basis of all measurements, but only using those measurements that were 2 These results have presented at the 10th UDM conference in Canada: Keupers, I., and Willems, P. CSO water quality generator based on calibration to WWTP influent data. In UDM2015, 10th international Urban Drainage Modelling Conference, September 20-23, Mont-Saint-Anne, Québec, Canada (2015).
90
RESULTS AND DISCUSSION
(a)
Conc [mg l−1 ]
30 20 10 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 Time
Simulated conc [mg l−1 ]
(b) R2 TRAIN = 0.91 R2 VAL = 0.86 R2 TEST = 0.76
30 20
predicted concentration TRAIN data VAL data TEST data bisector
10 0
0
5
10
15
25
20
30
Measured conc [mg l−1 ] Figure 4.10: Time series (a) and scatter plot (b) comparing the model results with measurements of ammonia at the influent of the WWTP of Aalst
Table 4.9: R2 test results when considering different ANN inputs time/rain NH4 NO3 OP TP
0.67 0.62 0.55 0.48
± ± ± ±
0.13 0.18 0.10 0.07
time/discharge 0.86 0.72 0.79 0.70
± ± ± ±
0.06 0.10 0.07 0.06
time/discharge/rain 0.88 0.79 0.83 0.74
± ± ± ±
0.04 0.09 0.08 0.06
CSO WATER QUALITY MODEL
91
1
R2
0.9 0.8 0.7 50
100
150 200 250 Number of samples
300
350
Figure 4.11: Relation between the number of samples available for calibration and the R2 goodness-of-fit statistic of the resulting model for the testing period taken on days were the rain intensity was higher than 1 mm. From Figure 4.12 it is clear that when the neural network that was trained on the basis of all measurements did not give a good agreement (i.e. R2 lower than 0.7 on the x-axis), then a considerable improvement can be obtained by only calibrating the model to this rain events. However, if the model gave a good agreement for rain events when all measurements were used during training, then the model predictive capacity can be considerably worsened by only looking at events on rain days. In these cases, not enough rain events remain to properly train to model to take into account the seasonal variation of the different water quality parameters.
92
RESULTS AND DISCUSSION
R2 rain events when only measurements during rain events are used
1 0.9
model results bisector
0.8 0.7 0.6 0.6
0.65
0.7
0.75 0.8 0.85 0.9 R rain events when all measurements are used for training
0.95
1
2
Figure 4.12: Scatter plot of R2 for rain events simulated with NN trained for all measurements and NN trained only on measurements during rain events
4.3
Impact analysis of CSOs on river water quality by means of an integrated model3
4.3.1
Conceptual water quantity models sewer systems Mol and Geel
Separate models were configured for the urban drainage systems of the cities Mol and Geel, because there are no interactions between both systems. The main goal of these models is to simulate overflow discharges and volumes accurately. The sewer systems of Mol and Geel are divided into 13 Storage Cells (SCs). The division of a model in storage cells is case specific and depends on the intended use of the model. Throttles, pumps and other hydraulic structures usually demarcate these cells. Note that it is not necessary to partition the system at every hydraulic structure, since this would lead to extremely interlaced and complex model topologies. The topology for the conceptual sewer models was chosen after identification of the most important hydraulic structures and after assessing multiple configurations. The aim of such topology is to strike the balance 3 The results for this section have been obtained in cooperation with Vincent Wolfs and were presented at the 10th UDM conference in Canada: Keupers, I., Wolfs, V., Kroll, S., and Willems, P. Impact analysis of CSOs on the receiving river water quality using an integrated conceptual model. In UDM2015, 10th international Urban Drainage Modelling Conference, September 20-23, Mont-Saint-Anne, Québec, Canada (2015).
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL
93
between achieving model parsimony (minimize the number of calculation nodes and increase model robustness) and retaining sufficient accuracy. Figure 4.13 shows the defined conceptual model topology of the sewer system of Mol. In total, there are 80 and 31 connections where flow is estimated between the cells of Mol and Geel respectively, of which 57 and 25 lead to locations where overflow constructions are installed. Many of these connections are congregates of several neighbouring individual pipes and overflow constructions, since this leads again to a more parsimonious model. These connections only aggregate flows of the same urban drainage type (i.e. combined or storm sewer). Thus, different quality parameters can still be linked to each type of overflow. Of these overflow connections, only 24 (Mol) and 6 (Geel) lead to the investigated segment of the Molse Nete River. However, the entire system, thus including all pipes and overflow constructions, is captured in the conceptual model. This was necessary since all storage cells are (directly or indirectly) connected to each other. Because the complete sewer system is modelled, additional rainfall events can also be simulated. Calibration was performed using simulation results of the detailed IWCS models for composite storms of 2 days with frequencies and return periods (denoted as ‘F’ and ‘T’ in the event names) of 20 (F20) and 1 (F01) per year and 5 (T05) and 20 (T20) years. Validation was performed on the events F10, F07, T02 and T10. The time step of these rainfall input data is 300 s. More information on these composite storms can be found in Vaes and Berlamont [139] and Willems [167]. Due to the large number of modelled states in the systems, it is impossible to show goodness-of-fit criteria for every simulated variable. Instead, only the three overflows of each system are presented that spill the highest volumes, since these will deteriorate the water quality most. Table 4.10 lists the relative volume error of the simulated spilled volume of the overflows in the conceptual model compared to the results of the detailed IWCS models, together with the simulated spilled volume of the IWCS results. It is clear that these volumes are emulated accurately by the conceptual model, although there are some discrepancies for the events with higher frequencies. However, the magnitude of these incorrect volumes remains limited. Including additional (and preferably historical) events with lower return periods will put more emphasis on these smaller flows during model configuration and will improve model performance. The average NSE for the volumes of the cells that are linked to investigated outfalls (and thus drive the flows to these outfalls) is 0.81 and even 0.94 for the sewer systems of Mol and Geel, respectively, for all validation events. This shows that the conceptual models have good accuracy. Next, the long term rainfall series is simulated. Tables 4.11 and 4.12 show again the relative volume errors and spilled volume in the IWCS model for all links for which simulation results in the detailed model were stored. Although the results of the conceptual model deviate from those of the detailed IWCS model, the summed spilled volumes in the sewer system of Mol diverge only 2 % (Table 4.11). This also shows the self-correcting nature of the conceptual model due its mechanistic set-up:
Figure 4.13: Division in storage cells (SC) of the conceptual water quantity models of the sewer systems of Mol and Geel
94 RESULTS AND DISCUSSION
(C) (V) (C) (V) (C) (V) (V) (C) 1% (30311) 2% (28961) 1% (27841) 0% (26600) 0% (26132) 0% (25574) 0% (25460 1% (25237)
7% (26710) 7% (25296) 7% (23625) 18% (21421) 10% (16502) -20% (5361) -50% (3131) -100% (11)
Geel F_e -2% (18608) 0% (18860) 0% (19094) 0% (19365) 0% (19459) 0% (19585) 0% (19610) 0% (19661)
Geel F_c -1% (14782) -5% (13326) -9% (11912) -10% (9943) -17% (8035) -26% (3880) -31% (3165) -32% (1766)
Mol O_d 1% (7289) 0% (6185) 0% (5019) 1% (3675) -1% (2536) -4% (299) -4% (264) -54% (195)
Mol F_c -17% (6689) -5% (5918) 3% (5160) 7% (4192) -18% (3379) -12% (1603) -7% (1317) -6% (800)
Mol O_c
0% 193
12% 9200
F_d 153% 16670
F_e 303% 158
I_b 31% 864
O_a -11% 396528
O_c
13% 37331
S_a
3% 36789
S_b
-2% 497740
SUM
Deviation IWCS model [m3 ] 0% 0
F_a 18% 431270
F_e
Table 4.12: Comparison of the spilled volumes by the conceptual model of all overflows for which simulation results of the detailed sewer model of Geel were available for the long term simulation
Deviation IWCS model [m3 ]
F_b
Table 4.11: Comparison of the spilled volumes by the conceptual model of all overflows for which simulation results of the detailed sewer model of Mol were available for the long term simulation
T20 T10 T05 T02 F01 F07 F10 F20
Geel F_d
Table 4.10: Relative volume errors of spilled volumes of the 3 largest overflows compared to the results of the IWCS model results. The spilled volume in the IWCS simulation results is shown between brackets.
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL 95
96
RESULTS AND DISCUSSION
if too much flow is spilled via one link, a neighbouring link connected to the same storage cell will transfer fewer mass and a new equilibrium is reached. In addition, one should note that the spilled volume of most links in Table 4.11 is very low compared to the largest relevant outfalls listed in Table 4.10, but no simulation results of the detailed model were stored for comparison for these connections. The calculation time of the combined conceptual sewer models amounts to 1.33 s in total to simulate a one year period using a single core i7 processor, including routing of the sewer overflow discharges to the investigated part of the Molse Nete River. This is much faster than the original detailed IWCS models: simulating a synthetic storm of 3 days takes approximately 24 and 5 minutes for the sewer models of Mol and Geel, respectively, using the full computational resources of an i5 processor.
4.3.2
Conceptual water quantity model Molse Nete River
Defining the conceptual river model topology is, as for the conceptual sewer models, an iterative process: if the model results are unsatisfactory and applying different emulation model structures does not improve the model performance, alternative (likely refined) model topologies can be tried. In this study, the investigated segment of the Molse Nete River was divided into 9 reservoirs. Each reservoir represents a river reach of about 1 km on average. The boundaries of the reservoirs mostly coincide with bridges or culverts. The characteristics of the river stretches between these bridges do not change abruptly, making lumping on such scales possible. Calibration of the conceptual model was based on the simulation results of the detailed MIKE11 model for the period from 8 March until 11 April 2001 with a time step of 900 s. Data from 12 April 2001 until 31 December 2008 were considered for validation. Since the river is not equipped with controllable hydraulic structures and the influence of variable backwater effects is very limited, Transfer Functions (TFs) are ideally suited to model the flow of the river. Different model structures were tested and the most appropriate TF according to the Akaike Information Criterion (AIC) [3] was retained at each flow calculation point. The AIC aims at striking the balance between model accuracy and model structure complexity (i.e. the number of weights in the TF). Finally, water levels are calculated in the upand downstream parts of each reservoir using piecewise linear rating curves. See Wolfs et al. [177] for details on the approach. The performance of the conceptual model is exceptionally good, with NSE values for all flow and water level results exceeding 0.985 for both the calibration and validation periods. The computational time equals 0.08 s for a one year period with a time step of 300 s as opposed to 7 min required to simulate the same period in the MIKE11 model with a time step of 1 min.
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL
4.3.3
97
ANN water quality model CSOs
A MLP NN was trained for the influent concentrations of the WWTPs of Geel and Mol. These concentrations were applied as approximations of the CSO concentrations of their respective sewer system. Table 4.13 shows that the concentrations of BOD, NH4, OP and PP can be estimated satisfactorily for the sewer system of Mol. For nitrate almost all of the measurements were below the detection limit which is consistent with the composition of household waste in which no nitrate is present. Therefore, these concentrations were set to zero in the integrated model. No measurements of dissolved oxygen are available at the WWTP influent. However, due to the high turbulent nature of the flow at the CSO structures, it is assumed that the oxygen concentration will be close to saturation. Hence the saturation concentration is calculated with the APHA equation [57] and the DO concentration is assumed at 80 % of this value. Table 4.13: Results water quality model sewer system Mol
# samples R2 training R2 validation
BOD
NH4
NO3
OP
PP
103 0.7 0.69
102 0.93 0.94
39 -
102 0.94 0.95
102 0.91 0.92
The same remarks for DO and NO3 can be made for the sewer system of Geel. However, Table 4.14 shows that the ANN model accuracy is not very high, especially during the validation. This can probably be attributed to the large distance of the nearest available rainfall station for the WWTP of Geel, which is almost 11 km away and might not represent peak discharges over the sewer catchment accurately. This is in contrast to the WWTP of Mol where a rainfall station is situated within a radius of 7 km. Table 4.14: Results water quality model sewer system Geel
# samples R2 training R2 validation
BOD
NH4
NO3
OP
PP
155 0.66 0.63
133 0.93 0.85
15 -
132 0.89 0.85
131 0.8 0.63
98
4.3.4
RESULTS AND DISCUSSION
Conceptual water quality model Molse Nete River
The investigated segment of the Molse Nete River was divided into 35 reservoirs. Each reservoir represents a river reach of 330 m on average. Calibration of the conceptual model was based on the simulation results of the detailed ECOLab model for the period 10 January until 31 December 2001 with a time step of 1 h. The Mean Error (ME), the R2 and the NSE were calculated for all state variables for each reservoir. Worst simulation results were obtained for DO, which can be explained by the high temporal variability of the processes that affect the DO concentration. The simulation results still show, however, close agreement between the detailed and the conceptual model results. Lowest R2 and NSE values for all reservoirs in the validation period are 0.98 and 0.94, respectively. This is also shown in Figure 4.14 where two years of simulation results for all state variables are plotted of which the first year, 2002, was used for calibration and the second year, 2003, which was part of the validation period. The computational time is as low as 3.6 s for a one year period when a model time step of 1 h is considered as opposed to 10 h required to simulate the same period in the MIKE11 model with a time step of 20 s.
4.3.5
Impact analysis on simulated percentile values
The impact of the CSOs on the river water quality is assessed by calculating the 90th percentile of the simulation results of the integrated model, as these percentile values of BOD and NH4 should comply with the standards that are prescribed by the Flemish environmental regulations shown in Table 2.7 [154]. The results show that the inclusion of the BOD and NH4 pollutant load coming from the sewer system during CSO events does not affect the conclusion on the overall compliance with the water quality standards. Figure 4.15 (a) shows this for the BOD result. This could have been expected though, since the modelled CSOs are only spilling for 8 % of the time on average. However, when evaluating the impact on the extreme concentrations, i.e. the 99th percentile values shown in Figure 4.15 (b), an increase of almost four times the BOD concentrations can be observed at specific locations (i.e. at distance 2.3 km from the most upstream chainage). The effect of these higher extreme concentrations is visible until the end of the modelled river reach, hence important. However, even for extreme concentrations, the impact of CSO events remains limited on the NH4 concentration (Figure 4.16).
Result
R2 =1
0 Jan 02
1
2
3
May 03
Sep May 02 03 Time
Dec 03
NSE=1
Dec 03
NSE=0.95
BOD (mg l−1 )
Sep 02
R =0.99
0 Jan 02
5
10
DO (mg l−1 )
Sep 02
May 03
Dec 03
Sep May 02 03 Time
Dec 03
NSE=1
OP (mg l−1 ) R2 =1
0 Jan 02
0.5
1
1.5
2
0 Jan 02
2
4
NSE=1
NH4 (mg l−1 ) R =1
2
R2 =1
0 Jan 02
0.2
0.4
0.6
May 03
Dec 03
Sep May 02 03 Time
Dec 03
NSE=1
PP (mg l−1 )
Sep 02
NSE=1
NO3 (mg l−1 ) R =1
0 Jan 02
2
4
6
2
2
Sep May 02 03 Time
Dec 03
NSE=0.98
Temp (◦C) R =0.99
Jan 02
0
10
20
30
MIKE11 results CORIWAQ results
Figure 4.14: Conceptual model versus detailed model simulation results for the conceptual model output reservoir number 24 and MIKE11 results at chainage 9477, excluding the input of the CSOs
Result
2
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL 99
100
RESULTS AND DISCUSSION
(a)
mg l−1 3.4
Without CSO With CSO 0.2%
18.2%
9.4% 0.1%
2.8
9.1%
2.2
21.8% 5.5% 0.5%
2 km
(b)
1.6 mg l−1 8.2
Without CSO With CSO 0.2%
18.2%
9.4% 0.1%
6.2
9.1%
4.2
21.8% 5.5% 0.5%
2 km
2.2
Figure 4.15: 90th percentile (a) and the 99th percentile (b) BOD concentration including and excluding the input of the CSOs (% at the CSO shows the percentage of the time the CSO is active)
IMPACT ANALYSIS OF CSOS ON RIVER WATER QUALITY BY MEANS OF AN INTEGRATED MODEL
(a)
mg l−1 2
Without CSO With CSO 0.2%
18.2%
9.4% 0.1%
1.75 1.5
9.1% 21.8%
1.25
5.5% 0.5%
2 km
(b)
1 mg l−1 5
Without CSO With CSO 0.2%
18.2%
9.4% 0.1%
4 3
9.1% 21.8% 5.5% 0.5%
101
2 2 km
1
Figure 4.16: 90th percentile (a) and the 99th percentile (b) NH4 concentration including and excluding the input of the CSOs (% at the CSO shows the percentage of the time the CSO is active)
Chapter 5
General conclusions and recommendations 5.1 5.1.1
Contributions of this research Impact of vegetation on roughness and simulation of the water levels
The importance of a time varying roughness coefficient for the accurate simulation of the water depths in full hydrodynamic river models has been demonstrated in Section 2.3.2. An increase in NSE from 0.33 to 0.67 can be obtained after applying a calibrated time-varying Manning coefficient. The analysis also confirms that although the roughness increases during the summer due to plant growth, the roughness is diminished again during periods of peak flows. It is thus important to take into account the seasonally changing roughness coefficient when the hydrodynamic model is coupled to a water quality model, which is very sensitive to accurate water depth and water velocity predictions. However, when studying river floods, i.e. when there is only an interest in high peak flows, it might be acceptable to work with a constant roughness coefficient.
5.1.2
Sensitivity of WWTP model results on percentile analysis river water quality
In Section 2.4.3 it has been investigated what the demands are for the WWTP model in an integrated sewer-WWTP-river impact applications, for a selected Belgian case
103
104
GENERAL CONCLUSIONS AND RECOMMENDATIONS
study. The analysis employed magnitude-frequency curves and percentile analysis, because water quality standards often are based on percentiles and because of the importance of water quality peak concentrations in the river. From this inquiry, three major conclusions can be drawn: • The WWTP effluent model results need to be transferred to the river water quality model on a time scale of three hours in order to capture the concentration peak behaviour. A smaller time scale is not required, so overhead can be reduced by not transferring results between the models at each calculation time step. • The accuracy requirements can be obtained from a first order sensitivity analysis; given that higher order sensitivity terms do not show a significant contribution to the relative sensitivity index. • The river water quality model is very sensitivity to changes in the WWTP effluent concentrations with relative sensitivity indices of 0.80 for ammonia and 0.65 for nitrate input concentrations 1 km downstream of the WWTP effluent. Calibration of the WWTP effluent model should thus focus on these two variables.
5.1.3
Conceptual river water quality model
River basin managers require tools to assess the possible water quality status of a water body under different scenarios, such as for example the long term impact of planned measures or the impact of climate change. To this end, detailed, physically based river water quality models are being set up that can simulate the physical and biological transformation processes that take place along the river. However, such detailed models require long computational times. This makes calibration of the model difficult and time consuming. Therefore, it is proposed to set up a conceptual reservoir model based on the detailed model. The methodology to develop the COnceptual RIver WAter Quality model (CORIWAQ) model is described in Section 3.1. The river branch is divided into conceptual reservoirs based on user information and for each of these reservoirs, the same water quality transformation processes are being modelled as in the detailed model to maintain the physically based link and consistency with the detailed model. During calibration only the hydraulic characteristics of the reservoirs need to be determined, i.e. adjustment factors for the average residence time, water depth and percentage velocity exceedance. Therefore, if the river network topology remains unchanged, i.e. no changes to cross-sections or structures are made, the calibrated conceptual water quality model can still be used, even for other input time series or other water quality process parameters.
CONTRIBUTIONS OF THIS RESEARCH
105
Conceptual river water quality models have been proposed by previous researchers [160, 83] but it is the first time that it is explicitly shown that the same accuracy can be obtained with these kind of conceptual models compared to detailed, physically based models. Very high NSE values are obtained indicating a good agreement between the simulated concentrations from the conceptual and the detailed model. A speed-up factor of 1.6 × 104 is obtained without loosing accuracy in model predictions, making it possible to use the conceptual model for long term simulations as required for statistical post-processing of the results. The developed GUI helps the user in building up the conceptual model for catchment based on an existing MIKE11 model. The implemented auto-calibration enables the model set-up and calibration to be completed in less time than the detailed model requires to run for a 1-year simulation period. To test the robustness of the conceptual model developed, and thus to asses the validity of the proposed global sensitivity analysis approach based on the conceptual model as a surrogate model for the detailed model, 10 random parameter sets were ran both with the detailed and the conceptual model. Comparison of the conceptual with the detailed model results show that the degree of similarity between both models is such that the conceptual model can be used as a surrogate model in time consuming applications such as the calibration of parameters of the water quality processes, scenario runs and long term simulations for statistical processing.
5.1.4
Global sensitivity analysis of the model parameters of the river water quality model
A global sensitivity analysis was carried out in Section 4.1.4 to determine the most sensitive model parameters of the full set of 24 water quality related parameters. A Monte Carlo approach was used to calculate the sensitivity of the model output to each of the model parameters, which became practical thanks to the use of the conceptual model. From the parameter distributions identified after a literature review, a sample of 5000 parameter sets is drawn through LHS to ensure efficient stratification. The 90th and 50th percentile values for each of the model output variables were chosen to evaluate the model results, since these are important with regard to compliance to water quality standards. From the global sensitivity analysis it became clear that the first order BOD decay rate is the most important parameter affecting the uncertainty in the model result as it ranks in the top three of most sensitive parameters for DO, BOD and NH4 at all studied river locations. The sedimentation rate of BOD is also a very important parameter in terms of absolute magnitude of sensitivity. These sensitivities show the importance to have good knowledge on the BOD composition along the river. Therefore the calibration process could benefit from measurements identifying this composition and hence these parameters.
106
GENERAL CONCLUSIONS AND RECOMMENDATIONS
The global sensitivity analysis indicates that from the 24 model parameters, only 9 have a significant contribution to the uncertainty in the model output result. Further calibration thus should focus on these 9 parameters while keeping the other parameter values constant at the recommended value. The analysis of the longitudinal profile of sensitivity results and ranks shows that the importance of these 9 parameters remains stable from up- to downstream along the river reach. However, one has to be careful considering these conclusions in other cases, because they may be case specific. For example, the sedimentation rate of BOD was determined to be a very sensitive parameter whereas the BOD concentration was not sensitive to changes in the resuspension rate. These findings can be attributed to the low velocities in the considered river reach, in the lower end of the possible range of critical velocity values that discriminate between sedimentation and resuspension regimes. It is therefore advised to apply such sensitivity analysis in other case studies prior to model calibration.
5.1.5
CSO water quality model
In Section 3.2 a methodology to simulate the water quality at the WWTP influent as a proxy for the water quality nearby CSOs is proposed. For this purpose a MLP NN is used considering the scarce data availability. The independent variables considered are the daily discharge measurement over the last three days, the daily mean and maximum rainfall intensity based on hourly rainfall measurements over the last three days, the antecedent dry spell length and logical variables representing the weekend days and the month of the year. The methodology was tested for 15 WWTPs in Flanders, Belgium. Average correlation coefficients are around 0.8 for all water quality variables (nutrient concentrations) investigated in this study. These are good results, especially when taking in mind the data scarcity. Despite these good results, there is room for further investigations and improvements. A minimum value of 50 time moments with available water quality, discharge and rainfall data was considered for training the ANN model. Surprisingly, no clear relation between the number of samples available for training and the goodness-of-fit of the model was found, which needs further investigation. Finally it was also found that to obtain a good estimation of the water quality concentration during rain events it is important to include all available measurements during the training phase, also those obtained during dry weather periods. Due to the limited number of measurements available on rainy days, the weekly and monthly variation of the different water quality parameters cannot be estimated correctly if not all measurements are accounted for.
FUTURE RESEARCH
5.1.6
107
Impact analysis of CSOs on river water quality with integrated model
As a demonstration application of the integrated conceptual model as decision support system, the impact was studied of the CSOs along the Molse Nete river in Section 4.3. Conceptual models have been developed for the water quantity and quality of the sewer system and the water quantity and quality of the river system. Each sub model has been individually calibrated and validated as this is crucial to ensure each model has a physically correct representation of the considered subsystems. The integrated model was used to simulate the concentration of BOD and NH4 in the river system, once with and once without taking into account the pollutant loading that is coming from the CSOs. It is shown that the CSOs do not have a discernible impact when evaluated for water quality status indicators based on 90th percentile values. However, a strong impact on the river water quality is observed for the extreme events evaluated based on the 99th percentile values. These CSO impacts may be devastating for the river ecological state, which needs further research. It shows the importance to conduct long-term model simulations and statistical analysis of the simulation results including extreme water quality conditions. Thanks to the use of surrogate, conceptual models, such simulations become practically feasible.
5.2 5.2.1
Future research Extension of the river water quality model
Currently, the conceptual river water quality model is implemented based on the processes that are included in the MIKE11-ECOLab model which only takes into account the state variables DO, BOD, NH4, NO3, OP, PP and T. In the water quality template used, the resuspension and sedimentation of BOD and PP is modelled in a simplified manner with the definition of a critical velocity. Future research should focus on explicitly modelling Suspended Solids (SS) such that these two processes can be represented more accurately as it is only the fraction that is adsorbed to particulate solids that will be subject to sedimentation and not the dissolved fraction. It should also be investigated if the simulation of the concentration of particulate phosphorus, which is currently highly underestimated at most observation locations, can be improved by including iron as a state variable in the water quality model. Especially in the region of this case study, groundwater has a high iron concentration which is leaked through to the water course. The dissolved Fe2+ oxidises to Fe3+
108
GENERAL CONCLUSIONS AND RECOMMENDATIONS
in the water column which flocculates as colloidal iron hydroxide Fe(OH)3 . This colloidal iron binds to a large extent with numerous anions including phosphate, significantly influencing the concentration of phosphorus in the water column [96]. When the process of phosphate precipitation is included, pH should also be included as a state variable in the model as the acidity of the water column influences the oxidation state of iron and hence the reaction kinetics of the precipitation process [117]. The modification or extension of processes defined in the currently developed conceptual river water quality model can be easily achieved, without the need for re-calibration to the detailed model results, since the process descriptions are specified in a separate function file that can be adjusted before simulation. This way, different process implementation can also be implemented for the same process to investigate the influence of model structure uncertainty on the simulated concentrations.
5.2.2
Calibration of the water quality parameters
The developed conceptual model has been used for a global sensitivity analysis based on a LHS sample. This has identified 9 sensitive parameters that should be calibrated based on the available data. Since the model has many parameters, several output variables and a complex structure multiple minima in the objective function can be observed. Therefore general optimization methods based on random sampling, e.g. Generalized Likelihood Uncertainty Estimation (GLUE), or local method, e.g. model-independent Parameter ESTimation and Uncertainty Analysis (PEST), are not applicable. Consequently, it is advised to test which calibration method should be used for optimization of the water quality parameters. A possible approach would be to use the ‘ParaSol’ method [143] since this method provides narrow confidence regions on the parameter estimates or to apply the multi-objective calibration method described by Rode et al. [119]. Currently, the available time resolution of the water quality measurements, i.e. monthly, also requires that a new methodology needs to be developed that does not focus on instantaneous fits but can take trends in the data into account. Additionally, before a calibration of the water quality parameters can be carried out, the model input should be optimized further first since it has been shown in a previous study that the water quality model is more sensitive to the input than to the parameters, especially for BOD and NH4 [112]. Finally, the uncertainty of the measured pollutant concentrations is a factor that should be considered explicitly during the calibration phase as well [90]. These measurement errors can be significant for some parameters [111, 17], and are higher for suspended particulate matter than soluble concentration [118]. They should be incorporated in the calibration and validation stage of the river water quality model when
FUTURE RESEARCH
109
this information becomes available. The method proposed by Harmel and Smith could be used for this purpose [60]. This method evaluates paired measured (Oi ) and simulated data(Si ) against the uncertainty boundaries or the probability distribution of measured data rather than against individual data values as is done in the traditional goodness-of-fit indicators that employ error deviations (ei = Oi − Si ) in their calculations.
5.2.3
Pollutant loading from the sewer system
To determine the pollutant loading that is sent to the river during CSO events, it is assumed that the influent loads to the WWTP can serve as a proxy for the pollutant loads at the CSO. This is a very important assumption and should be investigated further. Specific measurement campaigns should be carried out for a range of CSOs with different characteristics to test to which degree this assumption is valid and how further model training can take into account the deviations. Since the river water quality model is quite sensitive to changes in model input concentrations as shown in Section 2.4.3, the uncertainty of the integrated sewer-river water quality model can be strongly reduced by improving the knowledge on the pollutant loading of the sewer system. Currently, an artificial neural network was used to derive the relationship between the input (i.e. rainfall, discharge) and output (i.e. pollutant concentrations). It is recommended to compare this purely black-box method with a more physically based approach, e.g. a grey box model that is able to represent wash-off, resuspension and sewer transports of pollutants in a simplified way [14, 172, 166], and existing WWTP influent generators in order to further analyse the merits of these different approaches. Due to the limited knowledge on the physical-chemical, biological and transport processes occurring in sewer systems, the accuracy of more detailed, white box, sewer water quality models is expected to be limited [164], also because no calibration and validation can be achieved due to limited availability of sewer water quality measurements. It can therefore not be included in the comparative analysis. Comparing the different black box and grey box models will allow to investigate if the total uncertainty in simulating the modelled WWTP influent concentration as up to halve of total variance can be attributed to the water quality model structure [165]. Finally, a further integration of the model with a conceptual WWTP model is recommended to convert the pollutant loading that is sent from the sewer system to the inlet of the WWTP to an outlet concentrations that is discharged into the river system. Currently, constant purification efficiencies are being used to account for this conversion but this approach can clearly be optimized further by including a grey box model in the modelling framework.
110
5.2.4
GENERAL CONCLUSIONS AND RECOMMENDATIONS
Integration with other models
To arrive at a truly integrated model, integration with other models still needs to be achieved. A first attempt has been given in this research by integrating a conceptual hydraulic river model with the river water quality model, a sewer water quantity and a sewer water quality model. However, this should be expanded to many more aspects and sub models, depending on the desired application. For example, it should also include a WWTP model as discussed above. Other possible integrations that require further research are with floodplain models and ecological models.
Appendices
Appendix A
Differential equations of the modelled transformation processes The differential equations that determine the water quality transformation processes are taken from the MIKE11-ECOLab manual as they are included in the water quality level 4 template [43]. In this template, it is chosen to use a quadratic relation to express the dependence on the limiting substance concentration instead of using the expected Monod kinetics. This relationship was maintained for this research to enable comparison between both models but can be changed in the CORIWAQ model in future research.
113
114
DIFFERENTIAL EQUATIONS OF THE MODELLED TRANSFORMATION PROCESSES
A.1
Dissolved Oxygen - DO dcDO = + re-aeration dt + photosynthesis − respiration − BODdecay − SOD − DO used by nitrif ication
dCDO (T (t)−20) =kreaer · Θreaer · CS − CDO (t) dt pM ax π · (hour(t) − 13) + · cos depth(t) 24 · α(t) −
kresp CDO (t)2 (t)−20) · · Θ(T resp depth(t) CDO (t)2 + HSresp (T (t)−20)
− kBOD · ΘBOD −
· CBOD (t) ·
CDO (t)2 CDO (t)2 + HSBOD
kSOD CDO (t)2 (T (t)−20) · ΘSOD · depth(t) CDO (t)2 + HSSOD
− Ynitr · nitrif ication(t) where: CDO is the concentration of DO [mg l−1 ] kreaer is the re-aeration rate at 20 ◦C [d−1 ] Θreaer is the temperature coefficient for re-aeration [-] T is the temperature [◦C] CS is the saturation dissolved oxygen concentration calculated by the experimental APHA equation [57] [mg l−1 ] pmax is the maximum photosynthesis production at noon [gO2 d−1 ]
DISSOLVED OXYGEN - DO
115
depth is the average hydraulic radius in the reservoir [m] α is the relative day length [-] kresp is the respiration rate at 20 ◦C [d−1 ] Θresp is the temperature coefficient for respiration [-] HSresp is the half-saturation oxygen concentration for respiration [mg l−1 ] kBOD is the BOD decay rate at 20 ◦C [d−1 ] ΘBOD is the temperature coefficient for BOD decay [-] HSBOD is the half-saturation oxygen concentration for BOD decay [mg l−1 ] SOD is the Sediment Oxygen Demand [g m−3 d−1 ] kSOD is the SOD rate at 20 ◦C [gDO m−2 d−1 ] ΘSOD is the temperature coefficient for SOD [-] HSSOD is the half-saturation oxygen concentration for SOD [mg l−1 ] knitr is the nitrification rate at 20 ◦C [d−1 ] Θnitr is the temperature coefficient for nitrification [-] HSnitr is the half-saturation oxygen concentration for nitrification [mg l−1 ] Ynitr is the yield factor describing the amount of oxygen used during nitrification [gO2 /gNH4 ]
116
DIFFERENTIAL EQUATIONS OF THE MODELLED TRANSFORMATION PROCESSES
A.2
Ammonia - NH4 dCN H4 = + N H4 f rom BODdecay dt − nitrif ication − plantU ptake − bacteriaU ptake
dCN H4 =YN H4 · BODdecay(t) dt (T (t)−20)
− knitr · CN H4 (t) · Θnitr − kplant ·
·
CN H4 (t)2 CN H4 (t)2 + HSN H4
CDO (t)2 CDO (t)2 + HSnitr pM ax − respiration(t) · 0.8 · depth(t)
− kbact · BODdecay(t) where:
CN H4 is the concentration of NH4 [mg l−1 ] CDO is the concentration of DO [mg l−1 ] YN H4 is the yield factor describing the amount of ammonia released during BOD decay [gNH4 /gBOD ] knitr is the nitrification rate at 20 ◦C [d−1 ] kplant is the rate of ammonia taken up by plants during photosynthesis [gNH4 /gO2 ]
HSN H4 is the half-saturation ammonia concentration for ammonia plant uptake [mg l−1 ] kbact is the rate of ammonia taken up by bacteria during BOD decay [gNH4 /gO2 ]
NITRATE - NO3
A.3
117
Nitrate - NO3 dCN O3 = + nitrif ication dt − de-nitrif ication
dCN O3 CDO (t)2 (T (t)−20) =knitr · CN H4 (t) · Θnitr · dt CDO (t)2 + HSnitr (T (t)−20)
− kdenitr · Θdenitr where:
· CN O3 (t)
CN O3 is the concentration of NO3 [mg l−1 ] CN H4 is the concentration of NH4 [mg l−1 ] CDO is the concentration of DO [mg l−1 ] knitr is the nitrification rate at 20 ◦C [d−1 ] Θnitr is the temperature coefficient for nitrification [-]
HSnitr is the half-saturation oxygen concentration for nitrification [mg l−1 ] kdenitr is the de-nitrification rate at 20 ◦C [d−1 ] Θdenitr is the temperature coefficient for de-nitrification [-]
118
DIFFERENTIAL EQUATIONS OF THE MODELLED TRANSFORMATION PROCESSES
A.4
Biological Oxygen Demand - BOD dCBOD = + resuspension if v > vcritBOD dt − sedimentation if v ≤ vcritBOD − BODdecay
dCBOD kresuspBOD = dt depth(t) −
ksedBOD · CBOD depth(t) (T (t)−20)
− kBOD · ΘBOD
· CBOD (t) ·
CDO (t)2 CDO (t)2 + HSBOD
where: CBOD is the concentration of BOD [mg l−1 ] CDO is the concentration of DO [mg l−1 ] kresuspBOD is the resuspension rate for BOD [g m−2 d−1 ] ksedBOD is the sedimentation rate for BOD [m d−1 ] vcritBOD is the critical velocity for resuspension of BOD [m s−1 ]
ORTHOPHOSPHATE - OP
A.5
119
Orthophosphate - OP dCOP = + P P decay dt − P P f ormation + OP from BODdecay − plantU ptake
dCOP (T (t)−20) =kP P · ΘP P · CP P (t) dt (T (t)−20)
− kOP · ΘOP
· COP (t)
+ YP · BODdecay(t) − kOP plant · (photosynthesis(t) − respiration(t)) where:
COP is the concentration of OP [mg l−1 ] CP P is the concentration of PP [mg l−1 ] kP P is the PP decay rate at 20 ◦C [d−1 ] ΘP P is the temperature coefficient for PP decay [-] kOP is the OP formation rate at 20 ◦C [d−1 ] ΘOP is the temperature coefficient for OP formation [-] YP is the release rate of phosphorus from BOD during decay [gOP /gBOD ]
kOP plant is the rate of OP uptake in plants [gP /gO2 ]
120
DIFFERENTIAL EQUATIONS OF THE MODELLED TRANSFORMATION PROCESSES
A.6
Particulate Phosphorus - PP dCP P = + P P f ormation dt − P P decay + resuspension if v > vcritP P − sedimentation if v ≤ vcritP P
dCP P (T (t)−20) =kOP · ΘOP · COP (t) dt (T (t)−20)
− kP P · ΘP P
where:
· CP P (t)
+
kresuspP P depth(t)
−
ksedP P · CBOD depth(t)
CP P is the concentration of PP [mg l−1 ] COP is the concentration of OP [mg l−1 ]
kresuspP P is the resuspension rate for PP [g m−2 d−1 ] ksedP P is the sedimentation rate for PP [m d−1 ] vcritP P is the critical velocity for resuspension of PP [m s−1 ]
TEMPERATURE
A.7
121
Temperature dT = + incomingRadiation dt − outgoingRadiation
dT Emax = · cos dt depth(t) − where:
π · (hour(t) − 13) 24 · α(t)
Eaf depth(t)
T is the temperature [◦C] Emax is the maximum absorbed solar radiation [W h/(m2 d)] Eaf is the emitted heat radiation [W h/(m2 d)]
Appendix B
Comparison of the river water quality model result with the observations at the 24 measurement locations spread over the Grote Nete catchment
123
Value
Jan 00
5
10
Jan 00
4
6
8
10
12
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.2
0.4
0 Jan 00
1
2
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
0.5
1
1.5
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 ) 2
Jan 00
1
2
3
4
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.1: River water quality model result versus observations at Grote Nete 4 chainage 10033 (identifier 257500)
Value
124 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
4
Jan 00
5
10
−1
Jan 02
Jan Jan 01 02 Time
BOD (mg l
Jan 01 )
Jan 03
Jan 03
Jan 00
0.2
0.4
0 Jan 00
1
2
)
Jan Jan 01 02 Time
−1
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
0 Jan 00
0.5
1
1.5
Jan Jan 01 02 Time
Jan 03
)
Jan 03 −1
Pt (OP + PP) (mg l
Jan 00
2
4
6
NO3 (mg l−1 )
Jan 00
0
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.2: River water quality model result versus observations at Grote Nete 4 chainage 8720 (identifier 258000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 125
Value
Jan 00
2
4
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.2
0.4
0 Jan 00
1
2
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
0 Jan 00
2
4
NO3 (mg l−1 )
Jan 00
0
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.3: River water quality model result versus observations at Grote Nete 4 chainage 7223 (identifier 258500)
Value
126 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
1 Jan 00
2
3
4
Jan 00
5
10
−1
Jan 02
Jan Jan 01 02 Time
BOD (mg l
Jan 01 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
0 Jan 00
0.5
1
)
Jan Jan 01 02 Time
−1
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
0 Jan 00
0.5
1
1.5
Jan Jan 01 02 Time
Jan 03
)
Jan 03 −1
Pt (OP + PP) (mg l
0 Jan 00
1
2
NO3 (mg l−1 )
Jan 00
0
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.4: River water quality model result versus observations at Grote Nete 4 chainage 3034.21 (identifier 260000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 127
Value
Jan 00
1
2
3
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
0 Jan 00
0.5
1
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
Jan 00
1
2
NO3 (mg l−1 )
0 Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.5: River water quality model result versus observations at Grote Nete 3 chainage 9763.01 (identifier 260500)
Value
128 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
1
2
3
4
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0 Jan 00
2
4
6
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
Jan 00
0.05
0.1
0.15
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
0 Jan 00
0.5
1
1.5
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.6: River water quality model result versus observations at Grote Nete 3 chainage 3207.14 (identifier 262000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 129
Value
Jan 00
2
4
4 Jan 00
6
8
10
12
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
Jan 00
0.5
1
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
Jan 00
0.1
0.2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
0 Jan 00
0.5
1
1.5
2
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.7: River water quality model result versus observations at Grote Nete 1 chainage 12358.14 (identifier 262200)
Value
130 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
4
6
Jan 00
4
6
8
10
12
−1
Jan 02
Jan Jan 01 02 Time
BOD (mg l
Jan 01 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
0 Jan 00
1
2
3
)
Jan Jan 01 02 Time
−1
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
NO3 (mg l−1 )
0 Jan 00
0.2
0.4
Jan Jan 01 02 Time
Jan 03
)
Jan 03 −1
Pt (OP + PP) (mg l
0 Jan 00
5
10
Jan 00
0
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.8: River water quality model result versus observations at Grote Nete 1 chainage 781.11 (identifier 2627000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 131
Value
−1
Jan 02
Jan 00
Jan 03
Jan 00
0.05
0.15
0.2
0 Jan 00
2
)
Jan 03
0.1
Jan Jan 01 02 Time
BOD (mg l
Jan 01
2
4
6
4
6
8
Jan 00
5
10
DO (mg l−1 )
)
Jan Jan 01 02 Time
−1
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
)
Jan 03 −1
Pt (OP + PP) (mg l
0 Jan 00
1
2
3
4
NO3 (mg l−1 )
Jan 00
0
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.9: River water quality model result versus observations at Grote Laak chainage 14030 (identifier 325000)
Value
132 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
0 Jan 00
10
20
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 03 Jan 00
0.05
0.1
0.15
Jan 00
Jan 03
2 Jan 00 Jan 02
1
4 Jan 01
2
3
4
6
8
10
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
Jan 00
2
4
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.10: River water quality model result versus observations at Grote Laak chainage 5510 (identifier 326100)
Value
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 133
Value
Jan 00
2
4
6
Jan 00
4
6
8
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0 Jan 00
2
4
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 ) 3
0 Jan 00
2
4
NO3 (mg l−1 )
Jan 00
0
10
20
30
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.11: River water quality model result versus observations at Grote Laak chainage 2910 (identifier 326500)
Value
134 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
4
6
8
Jan 00
4
6
8
−1
Jan 02
Jan Jan 01 02 Time
BOD (mg l
Jan 01 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
Jan 00
1
2
3
−1
Jan Jan 01 02 Time
)
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
)
Jan 03 −1
Pt (OP + PP) (mg l
0 Jan 00
2
4
6
NO3 (mg l−1 )
Jan 00
0
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.12: River water quality model result versus observations at Grote Laak chainage 604.44 (identifier 326900)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 135
Value
Value
Jan 03
Jan Jan 01 02 Time
Jan 03
Jan 00
0.1
0.2
0.3
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
0 Jan 00
1
2
3
Jan 02
Jan Jan 01 02 Time
PP (mg l−1 )
Jan 01
NO3 (mg l−1 )
Jan 03
Jan 03
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Figure B.13: River water quality model result versus observations at Kleine Laak chainage 0 (identifier 328000)
Jan 00
2
4
6
8
10
BOD (mg l−1 )
Jan 00
Jan 02
4 Jan 00 Jan 01
1
2
6
8
10
DO (mg l−1 )
Jan 03
136 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
4
6
Jan 00
5
10
15
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
Jan 03
Jan 03
0 Jan 00
0.2
0.4
0.6
0.8
0 Jan 00
1
2
3
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
Jan 00
1
2
3
4
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.14: River water quality model result versus observations at Molse Nete chainage 13106.94 (identifier 329000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 137
Value
Jan 00
2
4
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
0 Jan 00
0.5
1
0 Jan 00
2
4
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
0.5
1
1.5
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 ) 2
Jan 00
1
2
3
4
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.15: River water quality model result versus observations at Molse Nete chainage 7418.06 (identifier 329800)
Value
138 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
4
6
Jan 00
10
20
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
Jan 03
Jan 03
0 Jan 00
0.5
1
0 Jan 00
2
4
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
Jan 00
2
4
6
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.16: River water quality model result versus observations at Molse Nete chainage 5064.33 (identifier 330200)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 139
Value
Jan 00
2
4
6
Jan 00
5
10
15
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
Jan 00
0.5
1
1.5
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
0.5
1
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
0 Jan 00
1
2
3
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.17: River water quality model result versus observations at Molse Nete chainage 2054.04 (identifier 331000)
Value
140 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
3
4
5
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 03
Jan 01
Jan 02
NO3 (mg l−1 )
Jan 03
0 Jan 00
1
2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 ) 3
0 Jan 00
Jan 03
0 Jan 00 Jan 02
1
1 Jan 01
2
3
2
3
4
NH4 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.18: River water quality model result versus observations at Molse Neet chainage 5884.81 (identifier 333000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 141
4 Jan 00
6
8
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
0.1
0.15
Jan 03
Jan 00
5 · 10−2
Jan 03
0 Jan 00
5
10
15
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
0.5
1
1.5
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
0 Jan 00
2
4
6
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.19: River water quality model result versus observations at Molse Neet chainage 419.66 (identifier 333100)
Jan 00
5
10
Value
Value
142 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
1 Jan 00
2
3
4
Jan 00
4
6
8
10
12
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
Jan 03
Jan 03
Jan 00
0.1
0.2
Jan 00
0.5
1
1.5
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
NO3 (mg l−1 )
Jan 03
Jan 00
0.1
0.2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 ) 0.3
0 Jan 00
1
2
3
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.20: River water quality model result versus observations at Oude Nete chainage 483.45 (identifier 333400)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 143
Value
1 Jan 00
2
3
4
Jan 00
5
10
15
−1
Jan 02
Jan Jan 01 02 Time
BOD (mg l
Jan 01 )
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.1
0.2
0 Jan 00
0.5
1
1.5
−1
Jan Jan 01 02 Time
)
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan Jan 01 02 Time
Jan 03
Jan 00 0.4
0 Jan 00
0.2
0
10
20
0.6
0.8
)
Jan 03 −1
Pt (OP + PP) (mg l
0 Jan 00
1
2
3
NO3 (mg l−1 )
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.21: River water quality model result versus observations at Scheppelijke Neet chainage 7797.30 (identifier 333500)
Value
144 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
−1
Jan 02
Jan 00
2
4
6
Jan Jan 01 02 Time
Jan 03
Jan 00
0.1
0.2
0.3
8
)
Jan 03
0.4
BOD (mg l
Jan 01
0 Jan 00
2
10
Jan 00
5
10
4
)
Jan Jan 01 02 Time
−1
Jan 02
OP (mg l
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
0 Jan 00
2
4
6
0 Jan 00
2
4
)
Jan Jan 01 02 Time
−1
Jan 02
PP (mg l
Jan 01
NO3 (mg l−1 )
Jan 03
Jan 03
0 Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.22: River water quality model result versus observations at Geeploop chainage 1670.64 (identifier 335000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 145
Value
Jan 00
2
4
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
DO (mg l−1 )
Jan 03
Jan 03
Jan 00
0.05
0.1
0.15
0.2
Jan 00
0.5
1
1.5
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
Jan 00
0.1
0.2
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 ) 0.3
0 Jan 00
0.5
1
1.5
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.23: River water quality model result versus observations at Hanskelselsloop chainage 1638.91 (identifier 335880)
Value
146 COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS
Value
Jan 00
2
4
Jan 00
5
10
Jan 02
Jan Jan 01 02 Time
BOD (mg l−1 )
Jan 01
Jan 03
Jan 03
Jan 00
0.1
0.2
0.3
0 Jan 00
0.5
1
1.5
Jan 02
Jan Jan 01 02 Time
OP (mg l−1 )
Jan 01
NH4 (mg l−1 )
Jan 03
Jan 03
Jan 01
Jan 02
Jan 03
0 Jan 00
0.5
1
1.5
Jan Jan 01 02 Time
Jan 03
Pt (OP + PP) (mg l−1 )
0 Jan 00
0.5
1
1.5
NO3 (mg l−1 )
Jan 00
10
20
Jan Jan 01 02 Time
Temp (◦C)
simulated observed below detection
Jan 03
Figure B.24: River water quality model result versus observations at Ongelbergloop chainage 1963.97 (identifier 336000)
Value
DO (mg l−1 )
COMPARISON OF THE RIVER WATER QUALITY MODEL WITH THE OBSERVATIONS 147
Appendix C
Manual for the GUI to set up the CORIWAQ model C.1
Introduction
This user manual describes how the CORIWAQ model can be installed and how it can be used. The CORIWAQ model allows for a semi-automatic set-up of a conceptual river water quality model based on a detailed river water quality model (MIKE11-ECOLab) thus reducing the computational speed by a factor 104 and opening possibilities for sensitivity analysis, calibration and long term simulations for statistical analysis and scenario analyses. The files required during this process are indicated in Figure C.1. A very schematic overview of the concept behind the CORIWAQ model is given in Figure C.2.
C.2 C.2.1
Software Requirements MATLAB
The CORIWAQ model has a GUI that can be called from MATLAB. It has been tested for R2014a. If problems occur with the use of other versions the developer can be contacted so that they can be resolved. All files from the CORIWAQ zip-folder should be extracted to a folder accessible by MATLAB. This folder should then be added to the search path of MATLAB as shown in Figure C.3.
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MIKE11 set-up • *.nwk11 • *.bnd11 • Processed cross-section file
C model Software tool Model structure identification
Input time series • *.bnd11
Calibration
• *.res11 HD simulation
Validation Data structures
• Temperature
• Boundary info • Simulation results
Calibration & validation data
• Model parameters
Data structures
• *.res11 WQ simulation • measurements
Figure C.1: Required files for a semi-automatic conceptual river water quality model set up Boundary input: WWTP
Measurement location: output required
2
3
4
1 Rainfall-runoff link 1
Rainfall-runoff link 2
Figure C.2: River branch represented by linear reservoirs in series with divisions based on key locations of interest
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151
Figure C.3: Setting the search path of MATLAB to the CORIWAQ model folder
C.2.2
MIKE11
The CORIWAQ model is set up based on a MIKE11 river model. Therefore this MIKE by DHI software needs to be installed since it requires some of the Application Programming Interfaces (APIs). However, no license of the MIKE by DHI software is required during the set-up and calibration of the conceptual model. The model set up has been tested for both Release 2011 and Release 2014.
B
C.2.3
MIKE by DHI Release 2011 is NOT a true 64 bit application. Only from Release 2012 and onwards this has changed. Although the conceptual model will still work, this has as a consequence that reading in of some result files (namely larger than 1 GB) will be extremely slow since they cannot be loaded into memory in one time but instead have to be read in time step per time step for each variable of interest. It is therefore advised to upgrade to the latest release of the MIKE by DHI software if possible.
DHI MATLAB toolbox
The DHI MATLAB Toolbox can be downloaded freely online1 . However, since there are some bugs in this version and some desired functionality is missing (e.g. 1 http://www.mikepoweredbydhi.com/download/mike-by-dhi-tools
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impossible to read in water quality result files), the toolbox is provided together with the CORIWAQ model and these updated files should be used. The folder needs to be installed as all other toolboxes in MATLAB by setting the ‘set path’ to include the folder path were the toolbox is located (Figure C.3).
C.2.4
Windows SDK 7.1
Because the conceptual model is written in C, a compiler is required that can compile the *.c file in a *.mex file that can be executed by MATLAB. Since MATLAB 64bit editions do not ship with a built in compiler as was the case for the 32bit editions it is required to download and install a compiler of choice (for an overview of possibilities2 ). Since many difficulties arise when installing this software, it is advised to follow the steps as described here3 very carefully. After successful installation of the Windows Software Development Kit (SDK) 7.1 (and all the other steps as described e.g. the patch), the compiler still needs to be set up in MATLAB. This is done by typing the command: mex -setup c++
in the command window of MATLAB.
C.2.5
Ghostscript
For the production of high quality figures, it is required to install the Ghostscript (Postscript and PDF interpreter/renderer) that can be downloaded freely4 .
C.2.6
[Optional] Microsoft Visual C++ 2010 Express
If there are problems with the conceptual model that was created or when you want to step through the code of the model line by line, it is required to install Microsoft Visual C++ 2010 Express (freely available online5 ) or any other Integrated Development Environment (IDE). After installation, the c-code file can be opened in Microsoft Visual C++ 2010 Express and breakpoints can be placed at the locations of interest after which you can step line by line through the code, similar 2 http://nl.mathworks.com/support/compilers/R2014a/index.html?refresh=true
3 http://nl.mathworks.com/matlabcentral/answers/101105-how-do-i-install-microsoftwindows-sdk-7-1 4 http://www.ghostscript.com/download 5 https://www.visualstudio.com/downloads/download-visual-studio-vs#DownloadFamilies_ 4
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as in MATLAB. However, it is important that before executing the model in MATLAB, the Microsoft Visual C++ 2010 Express is linked to the MATLAB process (Figure C.4), otherwise the breakpoints will not be hit.
Figure C.4: Attaching the MATLAB.exe process to Microsoft Visual C++ 2010 Express
C.3
Opening the CORIWAQ model
To open the CORIWAQ model, CORIWAQ (case-sensitive) should be typed in the command window of MATLAB. This opens the GUI (Figure C.5) which is used from now on for all interactions. Four tab sheets can be discerned and they should be gone through sequentially. However, when the previous tab sheet has been executed successfully on a previous occasion, there is no need to repeat all steps each time and you can skip forward to the tab sheet of interest. All data is related to each other by the Model name which therefore needs to be provided on each tab sheet. These results are stored in the folder ‘.../ModelName_results’. If you wish, you can always save the data entered in the fields by selecting ‘File Save settings’. To start working from this point on a later date just select ‘File Load settings’. The ‘File - Reset’ option resets all fields to their default values and can be used when you want to set up a model for a different reach and want to make sure that no previous settings are kept. In the help menu, more information on the required format of the data is provided by selecting ‘Required input format’. The version information can be found in ‘Help - About’. The first two tab sheets, ‘Setup model blocks’ and ‘Input time series’, are further discussed in section ‘3 Creating a conceptual model semi-automatically’. In the section ‘Calibrating the conceptual model to the detailed model results’ the third
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Figure C.5: GUI of the COnceptual RIver WAter Quality (CORIWAQ) model tab sheet ‘Calibrate residence time’ is explained in more detail. Finally, the fourth tab sheet ‘Run conceptual model’ is discussed in the section ‘Simulating the conceptual model’.
C.4 C.4.1
Creating a conceptual model semi-automatically Division of the river network in reservoir blocks
To divide the river network in reservoir blocks (Figure C.2), navigate to the tab sheet with name ‘Setup model blocks’. You must provide the model name, select the MIKE11 network file (*.nwk11), the MIKE 11 boundary file (*.bnd11) and the push-button ‘Measurement locations’ needs to be pressed to determine at which points results of the conceptual model will be stored (optional). The text file should contain three columns, tab delimited with no header. The first column should contain the measurement station name, the second column the branch name of the river and the third column the chainage within that river branch. When no measurement locations are provided, the results for all reservoirs will be saved by default. When measurement locations are provided and the check box ‘Save results for all reservoirs’ is unchecked, only the results at the requested locations will be stored, thus reducing the size of the result files. Providing measurement locations in combination with saving results for all reservoirs should be selected if you want
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to make sure that the location of the divisions between different reservoir blocks is not only determined based on the network lay-out and the location of boundary inputs, but also on the locations of interest. To detect possible problems with stability, you can provide a minimum ∆x (default 100 m). When the automatic divisions of the river network leads to reservoir blocks with a smaller length than the defined minimum ∆x, an error is issued and you need to look what is causing this small division. Most often, a ‘correction’ to the boundary file is required where two or more boundary inputs need to be taken together at the same chainage.
B
After making changes to the *.bnd11 file, it is important to re-run the MIKE11 simulation with this altered model set up so that the correct values can be compared to each other. Also be careful with point sources at chainage 0 as MIKE11 will not take these boundary inputs into account and the conceptual model will. Therefore you should use the function addBoundariesTogether, also found in the CORIWAQ folder to change the *.bnd11 file to add the loads of these point sources to the open boundary.
A maximum ∆x also needs to be provided (default 500 m). When the automatic reservoir division leads to reservoir with a longer length than the maximum, the block is split in two until the requirement is met. This is done to ensure that the underlying assumption of the CSTR namely that one concentration is representative for the whole block, is valid. Before the conceptual model can be generated, you must first divide the network in reservoirs by pressing the corresponding push-button. When previously saved results are detected, you have the option to re-open the previous divisions and continue working from this point on (default), or you can choose to start from the default division and thus resetting any previous modifications made. The automatic division can be checked visually and if necessary modifications can be made by pressing the push-button ‘Change reservoir division’ First you need to select the division you want to modify (red dots), after confirmation of the selected point you can select the desired calculation node, which are represented by black dots. Structures are also indicated on the figure with red squares since sometimes instabilities can occur at these locations. Do not forget to save the results of the selection after checking the selection by pressing the ‘Save input layout’. After saving the network layout the model code can be generated by pressing the button ‘Create model’. This action generates the c-code that contains the calculations of the transformation of the water quality concentrations and compiles this code into mex-files. After the compilation is finished, a dialogue appears informing you how long it took to compile the code.
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B
C.4.2
Do not forget to update the calculated grid points in the MIKE11 network file after making changes to the network file or the cross-section file (button generate grid points in the *.nwk1 file). The information on the Q, h and C locations is read in from this file so failing to update will lead to problems when trying to read in the MIKE11 result files later on.
Determining boundary data of the model
In the second tab sheet ‘Input time series’, the hydrodynamic and water quality boundary data is read in and stored in *.mat files per year to avoid excessively large files. The model name is filled out automatically from the first tab sheet but can be filled in manually when starting from this point. You also need to indicate for which period you want to read in and store the data by filling out the input start and end date and the storing frequency of the data (default one hour). Linear interpolation is used to find the values at the desired time interval if not available directly. If the simulation time step is smaller than the storing time step, linear interpolation will also be used to find the value at the desired time. The following MIKE11 files needs to be selected: • *.res11 file containing the result of the hydrodynamic simulation • *.txt file that contains the extracted processed cross-section information • *.bnd11 file with the boundary information as used in the first tab sheet Hydrodynamic result file The hydrodynamic result file needs to be provided so that the water level can be read in. From this water levels, the hydraulic radius is calculated based on the information from the processed cross-section file. This hydraulic radius is used as water depth in many of the water quality transformation processes and is thus a very important variable. To have an accurate representation of the average water depth over the reservoir, all h-points are read in and the average over the values that fall within the current reservoir is calculated. Besides water level information, the water quality model also needs information on the velocity in the reservoir. Therefore a *HDAdd.res11 also needs to be available that contains the information of the velocity in all points. The simulated velocity is used for two purposes. Firstly it is required to determine the advective delay (δ, to account for advective transport) which together with the reservoir constant (k, to account for dispersive transport) forms the residence time Tr (Equation C.1).
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The residence time determines how long the water quality processes can act on the pollutants in the reservoir and is thus a crucial parameter in the water quality modelling. Since the magnitude of the reservoir constant is negligible compared to the advective delay, the residence time is approximately equal to the advective delay. The advective delay is calculated as the length of the reservoir divided by the average velocity over the reservoir.
Tr = δ + k where:
(C.1)
Tr is the residence time [s] δ is the advective delay [s] k is the recession constant [s]
Secondly, the velocity information is necessary to determine whether resuspension or sedimentation will occur. Again, all calculated velocities within the reservoir are used. However, instead of calculating an average velocity, the percentage that the velocity exceed the critical velocity threshold is determined since in one reservoir both sedimentation and resuspension can occur, especially if there are structures present that cause a velocity peak. Finally, also the calculated discharge information is used. At bifurcation locations (i.e. were a river branch splits in two branches) it is necessary to know how the loads will be split over the two branches for an accurate simulation of the concentration. Therefore a proportionality factor based on the split of the flow is determined. Processed cross-section file Because the *.xns11 file is a binary format that cannot be read in by MATLAB, the cross-section file needs to be opened in MIKE11 and the processed cross-section information needs to be extracted to a text file (Figure C.6). This text file contains tables that show the wetted area, the hydraulic radius, the top width, the additional flooded area and the resistance factor in function of the water level. In CORIWAQ, only the hydraulic radius table is used, as mentioned before, to calculate the water depth used in the water quality simulations for the simulated water level. Before running the simulation in MIKE11, the user should examine all processed data to check that the hydraulic radius is monotonously increasing with increasing water level as this is one of the key assumptions for the open water hydraulics [44]. Failing to have a monotonically increasing relationship leads to a very significant risk of obtaining instabilities in the simulation for water levels in the range where the
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Figure C.6: Exporting the processed cross-section information from MIKE 11 non-increasing radius values are present. These instabilities in the hydrodynamic simulation results are magnified in the water quality simulation. Boundary data file The boundary data file from MIKE11 contains information on the magnitude of the discharge and loads of pollutant sources entering the river network. The locations of the boundary in this boundary data file need to be the same as the one for which the model was created in the first tab sheet, but the time series to which it references can be different to take into account different scenarios for example.
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B
159
There seems to be a problem with the way the rainfall run-off (RR) discharge is divided over the length of the river. Therefore, it is advised that you do NOT define any RR links in the network file but put all the RR as (lateral) boundary input in the boundary file (tick on ‘Include in HD calculation’). Since the CORIWAQ model only reads the boundary input from the *.bnd11 file and not from the *.nwk11 file this practice is also advised to avoid discrepancies between the RR calculated by MIKE11 and calculated by hand in the dfs0 file provided in the boundary file to include the AD boundaries.
In a first step, the discharge dfs0 time series or constant value is read in and stored in a matrix with a column per boundary input. The same is done for the concentrations of the different state variables but these are stored as loads taking into account their respective discharges and scale factors because this facilitates later calculation since there is only conservation of mass and not of concentration. In a second stage, the information stored per boundary is divided over the reservoirs. For open and point sources this is a straightforward assignment to the reservoir after the inflow of the point source. For lateral sources, the discharge and loads are divided over the different reservoir they span proportionally to the length of the reservoirs. One total discharge and a total load per state variable is stored per reservoir in structures called dischargesFromBoundaries and loadsFromBoundaries respectively, and saved in the yearly mat files. After providing all the necessary data, the push-button ‘Read Hydraulic Info’ can be pressed. An automatic check is performed on the provided date information before running the program. Error dialogues will indicate where the problem occurs and gives a suggestion on improvement. If all data is provided correctly, a progress bar will show where you can monitor the progress and estimate how long the whole process will take (Figure C.7). When all the data has been read in successful a dialogue will appear to inform you (Figure C.8). If this dialogue box does not appear, you cannot continue to the next tab sheet. Some warn dialogues may appear during the process to indicate possible problems when interpreting the results in a later stage but can be ignored at this moment. For example a warning is shown for blocks were the average depth is smaller than 1 cm. This warning is just informative (as an indicator of problems with the MIKE11 simulation) and does not influence the further simulation of the conceptual river water quality model.
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Figure C.7: Progress of reading in the boundary and hydrodynamic information
Figure C.8: Process of reading in boundary data finished successfully
B
Reading in the dfs0 files requires the creation of a COM/ActiveX automation server and bulk loading the dfs0 file. For large files this might give a problem if other processes are open at the same time. Therefore it is advised to have as few as possible other programs open before trying to read in the boundary data. If not enough memory is available the following error message will occur: MATLAB:COM:E0 Invoke Error, Dispatch Exception: Exception occurred.
This can only be resolved by closing MATLAB and re-opening MATLAB and the CORIWAQ model again. In this case you can use File > Save settings and Load settings to avoid having to manually re-enter all the settings.
C.5
Calibrating the conceptual model to the detailed model results
The conceptual model has now been fully set up, but before it can be used in simulation mode, it first needs to be calibrated against the detailed physically
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1 5
3
2 4
Go to last block Go to next block Go to previous block Go to first block Choose block number
6
Figure C.9: GUI calibration of the conceptual river water quality model based water quality model i.e. the AD+ECOLab simulation results (Figure C.9 - 1). Before the calibration process can be started (Figure C.9 - 2) a calibration start and end date should be given. To limit the time required during the calibration process the maximum possible length of the calibration period is equal to one year. The comparison of the conceptual model results to the detailed model results is made by means of a concentration reduction factor fr,M IKE . The concentration reduction factor gives the ratio between the simulated concentration after water quality transformation processes with the simulated concentration when only advection-dispersion processes are modelled and no water quality transformation processes are taken into account (Equation C.2).
fr,M IKE =
Cout Cout,AD
(C.2)
where: fr,M IKE is the theoretical reduction factor [-] Cout is the outgoing concentration after taking into account all processes [mg l−1 ] Cout,AD is the outgoing concentration after taking into account advection and dispersion processes [mg l−1 ]
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The MIKE11 reduction factor can be straightforwardly calculated since the simulated concentration at the outlet of the reservoir (Cout ) is divided by the simulated concentration at the inlet of the reservoir after routing through a linear reservoir to account for advection and dispersion (Cout,AD ). Since the concentration of the water quality variables after transformation processes is equal to the concentration after advection-dispersion plus the change of concentration times the residence time (Equation C.3) the conceptual reduction factor can be easily calculated by means of Equation C.4.
Cout =Cout,AD + fr,CORIW AQ =1 +
dC/ dt
dC/ dt
· Tr
Cout,AD
· Tr
(C.3) (C.4)
The dC/dt is the result of the processes that change the concentration. The same process formulations are used in the conceptual model as in the detailed water quality model. Currently the WQ template level 4 + phosphorus has been implemented and for more information on the processes the user is referred to the MIKE11 manual [43] and to Appendix A. Calibration of the CORIWAQ model can be achieved by applying a correction factor to the theoretically determined advective delay, water depth, or percentage velocity exceedance (Figure C.9 - 3). Since nitrate is the only variable that is not dependent on the water depth or the percentage exceendance velocity, this variable can be used to calibrate the advective delay (and thus the residence time as the recession constant is negligible compared to the advective delay (Equation C.1). Standard it is determined as the length of the reservoir divided by average of the different simulated velocities over the reservoir, but this could be an over- or underestimation of the true advective delay hence the possibility of adjusting this value by a fraction fadvDelay [-]. The adjustment factors for each reservoir can be calculated independent from each other since in calibration mode the input for each reservoir is taken from the results of the detailed model instead of the result of the previous reservoir as is done in simulation mode. This is done to avoid an accumulation of error when going from upstream to downstream during the calibration. Equation C.5 shows the system of linear equations that needs to be solved for each reservoir to estimate the adjustment factor for the advective delay. The MATLAB’s backslash operator (x = A\B) is used to solve it which returns a least-squares solution. This auto-calibration provides first estimates of the fadvDelay such that the user only needs to visually check the obtained result
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and time is saved compared to a manual calibration.
A ∗ x = B with
A= x= B=
Tr,t1 · dCN O3,t / dt CoutAD,N O3,t1 1
Tr,t2 · dC / dt CoutAD,N O3,t 2 .. . T · dC / dt N O3,t2
r,tend
N O3,tend
CoutAD,N O3,tend
(C.5)
fadvDelay fM IKE,N O3,t1 − 1 fM IKE,N O3,t2 − 1 .. . fM IKE,N O3,tend − 1
where: fadvDelay is the adjustment factor for the advective delay [-] Tr is residence time [s] CN O3 is the concentration of nitrate [mg l−1 ] t is the time [s] CoutAD,N O3 is the outgoing nitrate concentration after taking into account advection and dispersion processes [mg l−1 ] fM IKE,N O3 is the reduction factor of the MIKE11 model results for nitrate [-] After calibration of the advective delay, the representative water depth and the percentage exceedance of the critical velocity requires calibration for some reservoirs. Especially BOD and PP are very sensitive to small errors on the estimation of these variable as both the sedimentation and resuspension processes are strongly dependent on these variables. Since changing these variables does not result in a linear problem and requires the simulation to be re-run for each fdepth and fvelExceed , the backslash operator can no longer be used and a different problem solving technique was applied. For this purpose, a LHS was drawn for both parameters to minimize the number of samples that need to be drawn to achieve a high accuracy in the estimation of the parameters [63]. For the distribution of the parameters, a truncated normal distribution (no negative values possible) with a
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mean of 1 and a standard deviation of 1.1 was assumed. The simulation was run for all the parameter sets and the RMSE between the MIKE11 reduction factor and the conceptual reduction factor for BOD is calculated to determine the most optimal parameter set for fdepth and fvelExceed . To resolve how many samples need to be drawn from the parameter space to find a good optimal solution, the optimal parameter value is plotted against the number of samples used (Figure C.10). From this analysis, it was decided that a sample of 250 parameter sets provides a good trade off between a high accuracy in the estimation of the calibration factors and the required calculation time.
fdepth
1.15 1.1 1.05 1
0
200 400 600 Number of runs
800
Figure C.10: Optimal adjustment factor water depth in function of the number of samples drawn from the parameters space for a typical reservoir To facilitate the manual fine-tuning in the calibration process it is possible to switch between different views (Figure C.9 - 4). The default plots both the MIKE11 and conceptual reduction factors as a function of the temperature. If this plot is unclear, it is also possible to plot the reduction factors in function of time. A scatter plot can also be generated were the conceptual reduction factor is plotted against the MIKE11 reduction factor such that systematic deviations from the bisector can be detected. For some reservoirs, when looking at a scatter plot of the theoretical reduction factors versus the reduction factors calculated for the conceptual model two distinct clouds may appear instead of one cloud centred around the bisector (example see Figure C.11). This indicates that for those reservoirs the percentage fraction of resuspension was not determined correctly. If two distinct clouds with different slopes are visible instead of one, this means that the conceptual model identifies two distinct regimes whereas in the detailed model there is only one regime. This requires and extra calibration i.e. an update of the velocities that were used to calculate the percentage exceedance. This can be achieved by pressing the ‘Select CH’ button, next to the fvelExceed editable field. This will open an extra screen that shows the minimum, maximum and average velocity at each calculation node in the reservoir (Figure C.12. There are three possible options and expert judgement and experience with the model is required to choose the correct option. A calculation
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node can be removed from calculation the percentage of velocity exceedance, a constant value can be added to one of the calculation nodes velocities (or subtracted if a negative value is supplied) or the velocities at a certain node can be multiplied by a certain factor. Of course there is also the possibility to restore to the original default values if later it becomes clear that the result worsened after making the adjustments. (b)
(a) 1.4
1.4
fCORIW AQ
1.3
1.3
1.2 1.2
1.1
1.1
1 1
1.2
1.4
1.1
fM IKE11
1.2 1.3 fM IKE11
1.4
; Figure C.11: Scatter plot of theoretical reduction factor versus the conceptual reduction factor for PP for reservoir GroteNete1_ch6562_7191 - a) original velocities - b) updated velocity chainage 6752 When satisfied with the calibration of the reservoir, you can navigate to the next block (Figure C.9 - 5) to check the automatically determined advective delay and water depth. This process needs to be repeated for all reservoirs after which the calibration results can be stored (Figure C.9 - 6). If in a later stage the calibration is deemed unsatisfactory for some blocks you can press the ‘Start Calibration’ push-button again and you will be requested if you want to continue the calibration without losing the previously calibrated information of all the blocks or if you want start from default one values for the adjustment factors.
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Figure C.12: Example of the extra figure showing the minimum, maximum and average velocity at each calculation node of the reservoir
C.6 C.6.1
Simulating the conceptual model Setting the time information
You first have to choose the start and end time of the simulation. There is no limit on the length of the simulated time period with the exception that the input data is available for the period that you want to simulate. The CORIWAQ model will load the input data year per year to avoid memory problems by loading all the data at once. The simulation time step also needs to be provided (default one hour). This will determine the accurateness of the conceptual model simulation although the model is not so sensitive to the time step as the detailed model. Finally also an output time step is required, equal to or larger than the simulation time step. This value does not have to be an integer multiple of the simulation time step as required in the detailed model since linear interpolation will be applied to find the concentration value at the requested output time step.
SIMULATING THE CONCEPTUAL MODEL
C.6.2
167
Selecting the water quality parameters
From ECO Lab parameter file The water quality template level 4 + phosphorus has 33 constants for which the value needs to be provided. The displacement of the solar radiation maximum from 12pm is currently considered to be fixed at 1 and is therefore hard-coded in the model. Of course if the model is applied to a different region, this value needs to be up-dated there. Since the same processes are modelled in CORIWAQ as in MIKE11, the 33 values are also required in the CORIWAQ simulation and to ensure correspondence to the values used for the simulation of the MIKE 11 model they can be read in automatically from the ECOLab parameter file. This is done automatically during the calibration stage (the *.res11 file holds the information of which *.sim11 file was used to generate the results from which the *.ecolab11 file location is extracted) but during the simulation phase you can also choose a different parameter set as explained in the next section. From MATLAB file You can choose to select a *.mat file that contains a variable ‘parametersWQ’ with the order of constants as defined in tabel C.1. There are four more constants than in the detailed model since longitude is also required to calculate the relative day length and since the half saturation concentration of DO is not a constant but instead is dependent on the process (Sediment Oxygen Demand, degradation, nitrification and respiration each have a different half saturation concentration for oxygen). You can generate the variable yourself with the desired values, or you can use the result of a previous calibration run. Table C.1: Necessary order of the parametersWQ variable Symbol 1
kplant
2
kbact
3 4 5 6 7 8 9
vcritBOD vcritP P kP P kdenitr ksedP P kOP HSSOD
Description rate of ammonia taken up by plants during photosynthesis rate of ammonia taken up by bacteria during BOD decay critical velocity for resuspension of BOD critical velocity for resuspension of PP PP decay rate at 20 ◦C de-nitrification rate at 20 ◦C sedimentation rate for PP OP formation rate at 20 ◦C half-saturation oxygen concentration for SOD Continued
Unit gNH4 /gO2 gNH4 /gO2 m s−1 m s−1 d−1 d−1 m d−1 d−1 mg l−1 on next page
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Table C.1 – continued from previous page Symbol 10
HSN H4
11
knitr
12
Ynitr
13 14 15
kOP plant pmax kBOD
16
YN H4
17
YP
18 19 20 21 22 23 24 25 26 27 28 29
kresp kresuspP P kresuspBOD ksedBOD kSOD ΘBOD Θdenitr Θnitr ΘP P ΘOP Θreaer Θresp
30
ΘSOD
31 32 33 34
lat long Emax Eaf
35
HSBOD
36
HSresp
37
HSnitr
Description half-saturation ammonia concentration for ammonia plant uptake nitrification rate at 20 ◦C yield factor describing the amount of oxygen used during nitrification rate of OP uptake in plants maximum photosynthesis production at noon BOD decay rate at 20 ◦C yield factor describing the amount of ammonia released during BOD decay release rate of phosphorus from BOD during decay respiration rate at 20 ◦C resuspension rate for PP resuspension rate for BOD sedimentation rate for BOD Sediment Oxygen Demand rate at 20 ◦C temperature coefficient for BOD decay temperature coefficient for de-nitrification temperature coefficient for nitrification temperature coefficient for PP decay temperature coefficient for OP formation temperature coefficient for re-aeration temperature coefficient for respiration temperature coefficient for Sediment Oxygen Demand Latitude Longitude (deprecated) maximum absorbed solar radiation emitted heat radiation half-saturation oxygen concentration for BOD decay half-saturation oxygen concentration for respiration half-saturation oxygen concentration for nitrification
Unit mg l−1 d−1 gO2 /gNH4 gP /gO2 gO2 d−1 d−1 YN H4 gOP /gBOD d−1 g m−2 d−1 g m−2 d−1 m d−1 gDO m−2 d−1 ° ° W h/(m2 d) W h/(m2 d) mg l−1 mg l−1 mg l−1
SIMULATING THE CONCEPTUAL MODEL
C.6.3
169
Running the model: possible outputs
Standard output Each time the model is run, the model results are automatically stored in a MATLAB format in the folder ‘modelName_results’ with the file name ‘modelName_resultsMatlab.mat’. There is one variable per output node as determined in the first tab sheet. The name of the variables is ‘msStation_nameStation’. When requesting output from all reservoir blocks this nameStation is automatically generated according to reservoirNumber. If only results are stored for measurement locations, the name as provided in the first column of the text file is used. Each variable has 8 columns. In the first column, the time as number is provided (use datestr to get the data in common notation). The other seven columns contain the simulation results in the following order: DO, NH4, NO3, BOD, OP, PP and Temperature. Comparing simulated results to MIKE11 results To check the correspondence of the conceptual model simulation results to the detailed model, you can tick the check box ‘Compare results to results MIKE’. You need to provide the *.res11 file that contains the AD-WQ simulation results of the MIKE11-ECOLab model which is automatically filled in from the calibration tab sheet. In the drop-down menu you can choose if you only want a summary of the statistics for each variable per reservoir (in Excel) or if you also want to generate the figures that show a graphical comparison between the two simulation results. The statistics calculated are the mean error, the correlation coefficient and the NashSutcliffe Efficiency coefficient (NSE). If lower than the threshold value defined in the first tab sheet of the Excel file, the cell will highlight in bright yellow facilitating a quick location of problematic points. The colouring of the cells is performed through a visual basic macro so in order that a change to the threshold values has effect you first need to ‘Enable Content’ as macros are disabled automatically in Excel. The graphical comparison consists of a figure per reservoir were all seven variables are shown on a separate sub plot. Both a figure of a scatter plot (MIKE11 result on the x-axis and conceptual simulated concentration on the y-axis) and a time series pot is generated. The automatically generated file name contains the name of the station followed by the branch name and the chainage followed by _scatterplot or _timeseries. Both the generated Excel file with the statistics and the image files can be found in the folder ‘conceptualVSmike’ which can be found in the more general folder of
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MANUAL FOR THE GUI TO SET UP THE CORIWAQ MODEL
‘modelName_results’. Comparing simulated results to observations You can also choose to compare the conceptual water quality model results to measurements at observation locations by ticking the check box ‘Compare results to observations’. A *.mat file needs to be provided that contains the measurement information. This file needs to contain a structure named obsDataWQ and should have the following fields: .station with the name of the measurement location .measurements with the first column the time and the consecutive columns the measured concentration for DO, NH4, NO3, BOD, OP, PP and the temperature .MIKE11location string with branch name and chainage information of the measurement location in MIKE11 Both the generated Excel file with the statistics and the image files can be found in the folder ‘conceptualVSobs’ which can be found in the more general folder of ‘modelName_results’. Graphic representation of the simulated results You can also choose to generate figures of the conceptual model results so that it can be checked visually if the simulation completed as expected. Again figures are generated for each reservoir with subplots per state variable. The image files can be found in the folder ‘conceptualResults’ which can be found in the more general folder of ‘modelName_results’. Export simulation results to Excel Next to the storing of the simulation results for the selected reservoir in the first reservoir, you can also choose to store these results in Excel format. When you tick the check box ‘Export results to Excel file’, an Excel file with the model results is automatically stored in the folder ‘modelName_results’ with the file name ‘modelName_resultsExcel.xlsx’.
SIMULATING THE CONCEPTUAL MODEL
171
Store simulated processes information To investigate in more detail how the concentrations of the different state variables change, it is possible to also store the intermediate processes results. When you tick the check box ‘Store processes information’ extra variables will be stored in the file name ‘modelName_resultsMatlab.mat’ which can be found in the folder ‘modelName_results’. The name of these variables is ‘msStationProcesses_nameStation’ in analogy to the default results that are stored. The first column contains the time information whereas the order of the next columns is described in table C.2. Table C.2: Order of the processes information Short description 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
BOD decay re-aeration Photosynthesis rate respiration radiation into the water radiation out of the water Sediment Oxygen Demand Sedimentation BOD Resuspension BOD Ammonia release from BOD nitrification Ammonia plant uptake Ammonia bacteria uptake DO consumption from nitrification de-nitrification PP decay PP formation OP release from BOD OP plant uptake PP sedimentation PP resuspension
When the check box ‘Compare results to results MIKE’ is also ticked on when storing the process information, you have the possibility to also compare the simulated processes in the conceptual model to the simulation results of the detailed mode results (statistics, graphs or both). This can provide more insight to the origin of deviations if they exist. A *.WQAdd file needs to be available that contains the simulation results of all 21 processes. This file can be generated by requesting the additional output of the Processes in the *.ecolab11 file as shown in Figure C.13.
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MANUAL FOR THE GUI TO SET UP THE CORIWAQ MODEL
Figure C.13: Generating the *.WQAdd file containing the processes information
B
Be careful when selecting the ADWQ res11 file when you want to compare the processes with the MIKE11 simulation results. The *WQAdd.res11 file will be automatically loaded and since there are 21 processes that are being modelled, this file becomes very large quite fast, causing memory problems when trying to read the file for a long period. A short simulation period for process investigation is therefore recommended.
Appendix D
Accuracy of the CORIWAQ model compared to the MIKE11 model
173
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Figure D.1: NSE values between conceptual and detailed modelled result for DO for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
174 ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Figure D.2: NSE values between conceptual and detailed modelled result for NH4 for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL 175
0.98
0.99
0.99
1
1
Figure D.3: NSE values between conceptual and detailed modelled result for NO3 for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
176 ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL
0.75
0.8
0.85
0.9
0.95
1
Figure D.4: NSE values between conceptual and detailed modelled result for BOD for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL 177
0.95
0.96
0.97
0.98
0.99
1
Figure D.5: NSE values between conceptual and detailed modelled result for OP for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
178 ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Figure D.6: NSE values between conceptual and detailed modelled result for PP for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL 179
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Figure D.7: NSE values between conceptual and detailed modelled result for T for all reservoirs in the catchment during the validation period 2001-2002
5 km
NSE
180 ACCURACY OF THE CORIWAQ MODEL COMPARED TO THE MIKE11 MODEL
Appendix E
Monte Carlo analysis: sampling procedure There are several techniques available for the analysis of uncertainty in complex systems such as Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity, Sobol variance decomposition and fast probability integration [62]. In this research it is opted to use Monte Carlo techniques as the most effective approach to analyse the uncertainty for various combinations for the following reasons: (i) large uncertainties are often present and a sampling-based approach provides a full coverage of the range of each uncertain variable (ii) modification of the model is not required (iii) direct estimates of distribution functions are provided (iv) analyses are conceptually simple and logistically easy to implement (v) analysis procedures can be developed that allow the propagation of results through systems of linked models a variety of sensitivity analysis procedures are available In Monte Carlo analysis, some type of sampling procedure must be used to generate the sample. The simplest procedure is random sampling. With random sampling from uncorrelated variables, each sample element is generated independently of all other sample elements. With random sampling, there is no assurance that a sample element will be generated from any particular subset. In particular, important
181
182
MONTE CARLO ANALYSIS: SAMPLING PROCEDURE
subsets of the parameter space with low probability but high consequences are likely to be missed. Stratified sampling, or importance sampling as it is also sometimes called, provides a way to mitigate this problem by specifying subsets of the parameter space from which sample elements will be selected. Stratified sampling has the advantage of forcing the inclusion of specified subsets of the parameter set while maintaining the probabilistic character of random sampling. A major problem associated with stratified sampling is the necessity of defining the strata and also calculating their probabilities. Latin hypercube sampling can be viewed as a compromise procedure that incorporates many of the desirable features of random sampling and stratified sampling and also produces more stable analysis outcomes than random sampling [62]. The most important advantage of the Latin hypercube sampling method is that fewer samples are required for the same accuracy due to its efficient stratification properties. This is achieved by dividing the cumulative distribution for each variable into N equiprobable intervals after which a value is selected randomly from each interval (Figure E.1).
MONTE CARLO ANALYSIS: SAMPLING PROCEDURE
183
0.8 0.6 0.4
1
0.2 0 −2
−1
0 x1
1
2
Cumulative probability
1 0.8
Probability x1
Cumulative probability
1
0.8 0.6 0.4 0.2 0
0.6
0
0.2
0.4 0.6 0.8 Probability x2
0.4 0.2 0 −2
−1
0 x2
1
2
Figure E.1: Example of LHS: Random stratified sampling of variables x1 and x2 at 5 intervals (left) and random pairing of sampled x1 and x2 forming a Latin hypercube (right)
1
Appendix F
River water quality model parameters and parameters of their probability density function
185
Description
Emitted heat radiation
Eaf
pmax
HSSOD
ΘSOD
kSOD
Θreaer
Θresp
kresp
respiration rate at 20 ◦C temperature coefficient for respiration temperature coefficient for re-aeration Sediment Oxygen Demand rate temperature coefficient for SOD SOD halfsaturation concDO Maximum oxygen production by photosynthesis
Oxygen processes
Maximum absorbed solar radiation
Emax
Temperature processes
Symbol
1.065 1.4 3.5
mg l−1 gO2 d−1
0.75
0
1.01
0.1
0.5
-
gDO m−2 d−1
1
1
1
6000
546
Lowest Value*
1.024
1.08
-
3
8000
2800
Nominal Value
d−1
W h/(m2 d)
W h/(m2 d)
Unit
7
2.8
1.12
1
1.048
1.16
5
10 000
5054
Highest Value*
Uniform
Normal
Normal
Uniform
Normal
Normal
Normal
Normal
Normal
Distribution
[43]
[23]
[184, 43]
[67]
[40]
[67]
[43]
[22]
[94]
Reference(s)
Continued on next page
-
0.71
0.03
-
0.0123
0.04
1.02
1020.5
1150
Standard deviation
Table F.1: Model input parameters included in the sensitivity analysis and parametrization of probability density functions
186 PDF PARAMETRIZATION OF THE RIVER WATER QUALITY MODEL PARAMETERS
Description
vcritBOD
ksedBOD
kresuspBOD
HSBOD
ΘBOD
kBOD
First order BOD decay rate temperature coefficient for BOD decay Degradation halfsaturation concDO Resuspension of organic matter Sedimentation rate of BOD Critical flow velocity
BOD processes
Symbol
0.2 0.8
m d−1 m s−1
0.6
0.05
0
0.5
g m−2 d−1
0.05
0.5
mg l−1
1.01
0.1
Lowest Value*
1.047
0.7
Nominal Value
-
d−1
Unit
Table F.1 – continued from previous page
1
0.8
10
5.25
1.08
5
Highest Value*
Lognormal Normal
[43]
[43]
[80]
[67]
[67]
Reference(s)
0.102 [115] Continued on next page
0.201
-
0.762
Lognormal Uniform
0.018
0.918
Standard deviation
Normal
Lognormal
Distribution
PDF PARAMETRIZATION OF THE RIVER WATER QUALITY MODEL PARAMETERS 187
Description
Ratio of ammonia released at BOD decay Plant-uptake halfsaturation concNH4 Ammonia decay rate (nitrification) temperature coefficient for nitrification Nitrification halfsaturation concDO Oxygen demand by nitrification Denitrification rate temperature coefficient for denitrification 0.002
4.47 0.1
-
1.045
4.37
2
mg l−1 gO2 /gNH4 d−1
0
1.075
-
1
1.05
0.03
1
d−1
0.01
0
Lowest Value*
0.05
0.3
Nominal Value
mg l−1
gNH4 /gBOD
Unit
1.09
0.2
4.57
4
1.1
1.3
0.24
0.6
Highest Value*
Normal
Normal
Normal
Normal
Normal
0.022
0.05
0.05
1.02
0.0128
-
0.055
Lognormal Uniform
0.153
Standard deviation
Normal
Distribution
[23]
[23, 35]
[184, 43]
[80]
[184]
[12]
[23]
[43]
Reference(s)
*
Lower and upper values were assumed to correspond to the 2.5th and 97.5th percentiles of the normal and lognormal distributions. Values outside this interval were not included in the sampling
Θdenitr
kdenitr
Ynitr
HSnitr
Θnitr
knitr
HSN H4
YN H4
Nitrogen processes
Symbol
Table F.1 – continued from previous page
188 PDF PARAMETRIZATION OF THE RIVER WATER QUALITY MODEL PARAMETERS
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Curriculum vitae Ingrid Keupers was born on September 25, 1985 in Asse, Belgium. She finished her high school in science-mathematics at the Sint-Jozef institute in Ternat in 2003. After this she participated in an exchange program organized by WEP Belgium where she spend one semester as a high school exchange student in Texas, US and one semester as a volunteer in a girls orphanage in Antigua, Guatemala. After this experience she started her university studies at the KU Leuven, first starting a bachelor Sociology for which she graduated cum laude in 2007. The year after commencing this bachelor study she also begun a bachelor in Bio-engineering for which she graduated magna cum laude in 2008. For both fields she also obtained a masters degree. In 2009 she graduated magna cum laude in the Master Sociology with a thesis on ‘Poverty and inequality among the elderly. A comparative study of the pension systems in Belgium and the Netherlands’. In 2010 she graduated summa cum laude with congratulations of the Board of Examine in the Master of Water Resources Engineering which is jointly organized by the KU Leuven and the VUB. The master thesis was entitled ‘Water Quality Assessment: Determining Dioxin Potency by Means of the CALUX Bioassay’ and comprised of a mix of laboratory work and programming statistical data processing. In September 2010, she enrolled in the PhD program at the Hydraulics Division of the Department of Civil Engineering at the KU Leuven under the supervision of prof. dr. ir. Patrick Willems. She worked for the European FP7 project Fluxes, Interactions and Environment at the Land-ocean boundary. Downscaling, Assimilation and Coupling (FIELD_AC) where she studied river flows and pollution in coastal areas. In October 2011, she was awarded a 4-year scholarship from the ‘Fonds voor Wetenschappelijk Onderzoek’ (in Dutch) (FWO) to conduct the research presented in this thesis. In addition to her PhD research, she was involved in the Civil Engineering Bachelor and Master programs as teaching assistant for the courses Statistics, Open Channel Flow and Hydraulics, and was the daily supervisor of 9 Master thesis students. She was also the representative of the ABAP personnel in the departmental board for four years.
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Publications by the author Articles in internationally reviewed journals • Keupers, I., Willems, P. (revision submitted). Development of a Conceptual River Water Quality model for scenario analysis and decision support systems. Water Research. • Keupers, I., Nguyen, T., Willems, P. (2015). Modeling the time variance of the river bed roughness coefficient for improved simulation of water levels. International Journal of River Basin Management, 13(2), 167-178. • Keupers, I., Willems, P. (2013). Impact of urban WWTP and CSO fluxes on river peak flow extremes under current and future climate conditions. Water Science and Technology, 67(12), 2670-2676. • Elskens, M., Baston, D., Stumpf, C., Haedrich, J., Keupers, I., Croes, K., Denison, M., Baeyens, W., Goeyens, L. (2011). CALUX measurements: statistical inferences for the dose response curve. Talanta, 85 (4), 1966-1973.
Papers at international scientific conferences and symposia, published in full proceedings • Keupers, I., Willems, P. (2015). CSO water quality generator based on calibration to WWTP influent data. 10th International Conference on Urban Drainage Modeling Mont-Sainte-Anne, Québec, Canada 20-23 September, pp. 97-104. • Keupers, I., Wolfs, V., Kroll, S., Willems, P. (2015). Impact analysis of CSOs on the receiving river water quality using an integrated conceptual model. 10th International Conference on Urban Drainage Modeling MontSainte-Anne, Québec, Canada 20-23 September, pp. 205-218.
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• Keupers, I., Willems, P. (2015). Global sensitivity analysis of transformation processes in a river water quality model by means of conceptualization. 36th IAHR World Congress. IAHR World Congress. The Hague, The Netherlands, 28 June - 3 July 2015, pp. 1-15. • Keupers, I., Wolfs, V., Pham, H., Smets, I., Willems, P. (2014). On the role of the WWTP in integrated sewer-WWTP-river impact modeling. International Conference on Urban Drainage. Sarawak, Malaysia, 7-12 September 2014. • Keupers, I., Willems, P. (2012). Urbanization versus climate change: impact analysis on the river hydrology of the Grote Nete catchment in Belgium. 9th Urban Drainage Modeling Conference, Belgrade, Serbia. Urban Drainage Modeling Conference. Belgrade, Serbia, 3-7 September 2012. • Keupers, I., Willems, P. (2012). Predicting combined sewer overflow discharges as boundary condition for oceanographic models. 9th Urban Drainage Modeling Conference, Belgrade, Serbia. Urban Drainage Modeling Conference. Belgrade, Serbia, 3-7 September 2012. • Liste, M., Grifoll, M., Keupers, I., Monbaliu, J., Espino, M. (2012). Incorporation of Continental and Urban Run-Off into a Coastal Circulation Model. Application to the Catalan Coast. 33rd International Conference on Coastal Engineering ICCE 2012, 1-6 July 2012.
Meeting abstracts at international scientific conferences and symposia • Keupers, I., Willems, P. (2013). Climate change impact on the river water quality of the Grote Nete catchment. Land Use and Water Quality. The Hague, The Netherlands, 10-13 June 2013, Abstract No. 155. • Liste, M., Grifoll, M., Keupers, I., Monbaliu, J. (2012). Numerical simulation of the river and urban discharges into the Sea: Application to the Catalan Coast. Geophysical Research Abstracts: vol. 14. EGU General Assembly 2012. Vienna, Austria, 22-27 April 2012, Abstract No. 8288. • Liste, M., Grifoll, M., Keupers, I., Fernandez, J., Monbaliu, J. (2012). Modeling and observation of freshwater and sediment plumes at the Catalan Coast. The 44th international Liege colloquium on ocean dynamics, 7-11 May 2012. • Van Langenhove, K., Keupers, I., Croes, K., Vandermarken, T., Denison, M., Baston, D., Elskens, M., Baeyens, W. (2011). Pre-validation study
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for dioxin and dioxin-like compound analysis in WWTP sludge with the CALUX bioassay - Emphasis on obtaining extracts suitable for both chemoand bioanalytical determination. Organohalogen Compounds: vol. 73. International Symposium on Halogenated Persistent Organic Pollutants POPs Science in the Heart of Europe. Brussels, Belgium, 21-25 August 2011, 2132-2135.
External reports: reports by order of - or published by - an external organisation • Liste, M., Monbaliu, J., Grifoll, M., Keupers, I., Toorman, E., Qilong, B., Soret, A., Fernandez, J., Carniel, S., Benetazzo, A., Staneva, J., Wahle, K., Bricheno, L., Wolf, J., Lepesqueur, J., Ardhuin, F. (2012). Impact assessment for the improved boundary conditions (at bed, free-surface, land-boundary and offshore-boundary) on coastal hydrodynamics and particulate transport, 88 pp. • Liste, M., Monbaliu, J., Grifoll, M., Espino, M., Keupers, I., Carniel, S., Benetazzo, A., Sclavo, M., Staneva, J., Bricheno, L., Wolf, J. (2011). Methodology to introduce the 3D boundary condition for river discharges (including practical recommendations) for the 4 studied sites as a function of their prevailing conditions and users need. • Keupers, I., Willems, P., Fernandez Sainz, J., Bricheno, L., Wolf, J., Polton, J., Howarth, J., Carniel, S., Staneva, J. (2011). Methodology (including best practice guidelines) on how to identify and incorporate ‘concentrated’ and ‘distributed’ run-off in pre-operational forecasts, based on the input and requirements from our users. FIELD_AC project, D3.1, 90 pp.
Arenberg Doctoral School of Science, Engineering & Technology Faculty of Engineering Science Department of Civil Engineering Hydraulics Kasteelpark Arenberg 40 box 2448, B-3001 Leuven
