Lennart Hagen Schönfelder, SINTEF Energi. Assessment of the Hydrological Model HYPE for. Southern Norway. IBM Report B1-2018-1. Trondheim May 2018.
Abebe Girmay Adera, NTNU Knut Alfredsen, Knut Alfredsen,NTNU NTNU Tor Haakon Bakken, SINTEF Energi
Assessment of the Hydrological Model HYPE for Southern Norway
NTNU Norwegian University of Science and Technology Faculty of Engineering Science and Technology Department of Hydraulic and Environmental Engineering
Report
Lennart Hagen Schönfelder, SINTEF Energi
IBM Report B1-2018-1
Abebe Girmay Adera, NTNU Knut Alfredsen, NTNU Tor Haakon Bakken, SINTEF Energi Lennart Hagen Schönfelder, SINTEF Energi
Assessment of the Hydrological Model HYPE for Southern Norway
IBM Report B1-2018-1
Trondheim May 2018
Norwegian University of Science and Technology (NTNU) Faculty of Engineering Science and Technology Department of Civil and Environmental Engineering
ISBN 978-82-7598- 110-1 ISBN 978-82-7598- 111-8 (electronic)
SAMANDRAG Prosjektet NO-HYPE er finansiert av Miljødirektoratet for å evaluere om den hydrologiske modellen HYPE er eigna for regionale simuleringar i Norge, med fokus på å simulere relevante hydrologiske variable for evaluering av miljøverknader i umålte felt. Rapporten omtalar eit oppsett av HYPE for ein region i Sør-Norge. Modellen er kalibrert basert på 30 målestasjonar lokalisert i uregulerte felti og ytterlegare validert mot stasjonar (ikkje benytta til kalibrering). Som inngangsdata er det brukt interpolerte verdiar for nedbør og temperatur med oppløysing 1 x 1 kilometer. Kalibrering og validering av modellen ga rimelege verdiar av Kling-Gupta Indeksen (KGE), 0.5 – 0.86 og 0.48 – 0.86 for henholdsvis kalibrering og validering. Testing på dei åtte målestasjonane som ikkje er med i kalibreringa ga verdiar mellom 0.57 og 0.80. Vi ser at modellen har størst problem med å treffe dei høge vassføringane, noko som kan ha samanheng med bruken av interpolert nedbør som kan glatte ut dei største nedbørverdiane. For lavvassperioder er modellen rimeleg grei. Utrekning av IHA indeksar for lavvatn viser større variasjon mellom modellerte og observerte data.
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Executive summary The project “No HYPE” was funded by the Norwegian Environmental Agency and carried out by the Norwegian University of Science and Technology (NTNU) and SINTEF Energy Research. This report is an addition to the main project report published by SINTEF Energy Research (Schönfelder et al., 2017). The report by Schönfelder et al. (2017) presented results of the central and southern Norway, and this report summarizes the extended and re-calibration results for the southern region of Norway. For a further description of the project and HYPE, see the SINTEF Energy Research report. In this project we tested the hydrological model HYPE for the southern Norway region with a main objective of assessing the model results for different environmental impact assessment purposes related to the implementation of the EU Water Frame Work Directive (EU WFD). The model requires considerable work particularly for the preparation of input data. Land use and soil type data are used as hydrological response units and these requires pre-processing of inputs from different map sources. The precipitation and temperature input data were obtained from met.no as gridded data with a 1 km by 1 km resolution. Processing of fine resolution data was done by writing scripts in the R statistical programming language. A calibration process of the model was undertaken on 30 discharge stations in unregulated catchments in the region with 4 different multiple objective criteria, Kling and Gupta Efficiency (KGE and bias), Nash-Sutcliffe Efficiency (NSE adjusted with bias; NSE regional, spatial and median; NSE, Kendall’s correlation coefficient and bias) to obtain an optimum set of parameters was implemented as calibration approach, and KGE adjusted for bias was found to be the best one. Thereafter, the parameters were tested on 8 unregulated gauging stations not used in the calibration. The calibration showed a good KGE value ranging from 0.5 to 0.86 for the calibration period and from 0.48 to 0.86 for the validation period. The hydrograph and water balance were also used as evaluation criteria, which showed that the model simulated the low flows better than the peak flows. Thus, the model results can best be used for different environmental applications related to low flows. We did calculations of hydrological alterations using the Index of Hydrological Alterations on low flows between observed and simulated discharge for the southern region of Norway. Furthermore, a hydrological statistics map was made to show the mean annual, mean annual high and low flows on an extended 30 years period for the 38 gauging stations considered in this study.
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Contents SAMANDRAG........................................................................................................................................... 1 Executive summary ................................................................................................................................. 2 List of Figures .......................................................................................................................................... 5 List of Tables ........................................................................................................................................... 6 1) Introduction ........................................................................................................................................ 8 2) The HYPE model .................................................................................................................................. 9 2.1 Model development and structure ............................................................................................... 9 3) Data Preparation and Analysis.......................................................................................................... 11 3.1 Study region ................................................................................................................................ 11 3.2 Precipitation, temperature and streamflow data ....................................................................... 12 4) Results ............................................................................................................................................... 14 4.1 Calibration and validation ........................................................................................................... 14 4.2 Hydrological statistics ................................................................................................................. 18 4.3 Indicators of Hydrological Alteration (IHA indexes).................................................................... 18 5) Discussions ........................................................................................................................................ 20 5.1 Model results .............................................................................................................................. 20 6) Conclusions and Recommendations ................................................................................................. 21 6.1 Conclusions ................................................................................................................................. 21 6.2 Recommendation for future works ............................................................................................ 21 7) References ........................................................................................................................................ 22 A) Appendix ........................................................................................................................................... 24 A.1 Model calibration and validation................................................................................................ 24 A.1.1 Calibration of 30 discharge stations .................................................................................... 24 A.1.2 Tested 8 discharge stations ................................................................................................. 54
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List of Figures FIGURE 1: THE HYPE MODEL STRUCTURE....................................................................................................................... 10 FIGURE 2: ILLUSTRATION OF THE SOUTHERN NORWAY STATIONS, LAKES, FLOW LINES AND SUB BASINS ....................................... 11 FIGURE 3: CALIBRATION AND VALIDATION OF REINSNOSVATN STATION. ............................................................................... 16 FIGURE 4: TESTED CALIBRATION AND VALIDATION FOR NON-CALIBRATED GRYTA STATION ........................................................ 17 FIGURE 5: HYDROLOGICAL STATISTICS MAPS SHOWING MAQ, MAHQ AND MALQ............................................................... 18
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List of Tables TABLE 1: GAUGING STATIONS USED IN THIS STUDY ........................................................................................................... 12 TABLE 2: EVALUATION CRITERIA OF CALIBRATION AND VALIDATION FOR THIRTY GAUGING STATIONS ........................................... 14 TABLE 3: EVALUATION CRITERIA FOR EIGHT TESTED GAUGING STATIONS (UNCALIBRATED STATIONS) ........................................... 15 TABLE 4: IHA INDEXES FOR 7 STATIONS USED FOR CALIBRATION AND 3 NON-CALIBRATED STATIONS .......................................... 19
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1) Introduction Many hydrological models have been used to generate runoff in gauged and ungauged basins in Norway. Both lumped hydrological models, e.g. the HBV model by Killingtveit and Sælthun (1995) and fully distributed hydrological models e.g. the ENKI platform (Kolberg and Bruland, 2012; Hailegeorgis and Alfredsen, 2014) have been used to simulate discharge in different parts of Norway. The European Water Framework Directive (EU WFD) was launched to obtain an integrated water resources management and water status on hydro-morphological, chemical, physical and biological variables for all European waterbodies in the EU member states (Chave, 2001). The hydrological model HYPE was developed during the application of this framework in Sweden at the Swedish Meteorological and Hydrological Institute (SMHI) and it has been improved and updated with observation data from time to time (Lindström et al. 2010; Strömqvist et al. 2012; Bergstrand et al 2014). This was the background for the Norwegian Environmental Agency to establish a project to test the hydrological model HYPE and use it to estimate flow characteristics relevant for environmental assessment in gauged and ungauged basins in Norway. Environmental flow is vital for the planning and operation of many water infrastructures such as hydropower, water supply, fishers and irrigation systems. Computed hydrological indices that shows the low flow at a specific place or region of small-ungauged or gauged catchments is particularly important in this assessment. Besides, it is mandatory for the Norwegian Water Resources Act to compute a low flow index called “the common low flow” (Engeland and Hisdal, 2009). Richter et al (1996) proposed a method to quantify hydrologic alterations due to dam and hydropower operations, land use changes and flow diversions and further tools to compare regulated and unregulated flow conditions. This method has been selected as a tool in the environmental design handbook in Norway and is considered a standard method to evaluate hydrological alterations between regulated and unregulated conditions (Forseth and Harby, 2014). In this project, the hydrological model HYPE was set up for a region covering most of southern Norway. A total of 30 discharge stations located in unregulated catchments were used for calibration (1997 – 2003) and validation (2004 – 2010) of the model. The calibrated and validated model parameters were further compared against 8 other discharge stations located in unregulated catchments. A total of gauged 38 discharge stations were therefore used for calibration and analysis in this study. Thereafter, hydrological statistics showing the mean annual high and low flows were computed on an extended 30 years (1981 – 2010) of period for each station. Besides, indices of hydrological alterations were computed from the simulated discharge and the results showed clear variations on 1 day, 3 day and 7 day minimum annual flows than seasonal (winter, spring, summer and autumn) flows.
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2) The HYPE model 2.1 Model development and structure The HYPE model (HYdrological Predictions for the Environment) is a hydrological model that simulates water flow and substances through soil, river and lakes for small scale and large scale assessments developed at the Swedish Meteorological and Hydrological Institute during 2005 – 2007 (Lindström et al., 2010). The HYPE model has been tested and applied in different countries with promising results, and this was one of the main reasons for testing it in Norway. In the HYPE version considered in this study, the model is used for simulating water flow only and has been test in different catchments located in the southern and central part of Norway. In the following sections, we will summarize the HYPE set up used in this research project. A detailed description of the model is available from http://www.smhi.net/hype/wiki/doku.php?id=start. Model structure In HYPE, the domain is divided into sub basins which are connected by rivers and ground water flow. Each sub basin is divided into classes, which correspond to hydrological response units that are similar to the zones of elevation and vegetation used in the HBV model (Bergström 1976). These classes are the combination of soil type, land use and lakes. The land use and soil for each sub basins are combined in to SLC’s (Soil and Land use Classes). The lakes were included in two different ways, as local lakes or as outlet lakes. The outlet lakes can be provided with regulation schemes or rating curves and the outlet lakes can be added as sub basins with outlets corresponding to the sub basin outlet. For this study, the region has been divided in 8 different soil type and land use classes. Snow accumulation, melt and snow depth The routine for snow accumulation and melt is a simplification of the routine used in the HBV model. Precipitation is assumed to fall as snow below a threshold air temperature. The snow melt is computed using the degree – day method and uses the same temperature threshold as snow fall. The snow depth is estimated from the water content of the snow and a snow density factor that increases with the average age of the snow pack. Alternative snow melt models are also available, but the default snow melt routine of the model is used here. Precipitation, temperature and Evaporation Precipitation and temperature is prepared as average values for each sub basin used in the research project. There are a total of 2743 sub basins in the domain and a gridded 1 by 1 km precipitation and temperature dataset from met.no is used to set up the model. Thereafter, the model uses precipitation and temperature adjustments as a general parameter or region dependent parameter based on elevation differences and adjustment of precipitation for under catch. The potential evaporation used in this application of HYPE is computed from the temperature, but alternative PET models are also available. The threshold temperature used for evaporation to occur is the same as for snow melting. Rivers and Lakes The HYPE model can contain internal stream and main rivers. In addition, the lakes consist of internal and main lakes (Figure 1). The internal streams and lakes receive runoff from sub basins. The internal streams and lakes are lumped together into one river and lake. The main rivers and outlet lakes includes the connected sub basins and receives runoff after it bypassed the internal rivers and lakes. 9
The internal and main lakes are two separate classes. The land use and soil type connected to both internal and main lakes are specified in the GeoClass.txt file. Precipitation, atmospheric deposition and evaporation of rivers are calculated first, and river flow and inflow, transformation processes and outflow of the lakes are computed thereafter. The main lake can be a lake basin and if not they are considered as simple outlet lakes. Depending on the length of the river and a flood wave velocity, the river flow can be delayed and attenuated in time. The lengths of the river are approximated as the square root of the sub-basin area, if not they are given as input. The outflow from the internal and main lakes is computed using rating curve. The linearization concept is applied in solving the rating curve equation (Lindström, 2016). Nutrient simulation, erosion and sediments HYPE can simulate nutrients on their way from precipitation through soil, lakes and rivers to the outlet of the river. Even if we only simulated discharge, it is possible to use the model for simulating the nutrients Nitrogen and Phosphorus divided in to fractions as inorganic nitrogen, organic nitrogen, soluble reactive phosphorous and particulate phosphorus. This can be related to management practices (e.g. fertilizers) in which application date and amount of N and P to be added is specified. HYPE can simulate sediment transport and soil erosion depending on the model for mobilization of soil particles by either rainfall or runoff. The erosion of soil particles is computed first thereafter the amount of soil particles leaving the area will be computed. Model input files Different input files are required for setting up and running the model. These are discharge, precipitation and temperature files in a daily time step (Qobs.txt, Pobs.txt, Tobs.txt), catchment characteristics (Geodata.txt), soil and land use classes (GeoClass.txt), parameters and model settings (Par.txt, info.txt). For automatic calibration additional files that shows the selected model parameters for calibration and optimization is required (optpar.txt). In addition to these files, many other optional files can be added depending on the objective and purpose of the research work.
Figure 1: The HYPE model structure. Left: schematic division of sub-basins (S1, S2, S3) depending on elevation, soil type, vegetation and lake classes. Right: schematic of the combination between soil type and crop simulated. Solid and dashed arrows show fluxes of water and elements (from Lindström et al. 2010) 10
3) Data Preparation and Analysis 3.1 Study region A region in southern Norway has been selected for this study, and a total of 38 discharge stations located in unregulated catchments has been considered for calibration, validation and testing of the model. The location of the study area, discharge stations, model sub basins and lakes within the domain are shown in Figure 2.
Figure 2: Illustration of the southern Norway stations, lakes, flow lines and sub basins
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3.2 Precipitation, temperature and streamflow data In this study, a gridded precipitation and temperature data in a daily time step with 1km by 1km resolution has been used from met.no. The gridded precipitation and temperature data set, seNorge2 is available on a high-resolution grid and it has been evaluated by rainfall – runoff model and snow model (Lussana, C. et al 2017). The gridded netCDF data set was converted into text file format and an average value of each grids falling within sub-basin was taken as input to the model. The gauging stations used in this study are shown in Table 1. All observed stream flow data from gauging stations were obtained from the Norwegian Water Resources and Energy Directorate (NVE). Table 1: Gauging stations used in this study SUBID Water course
Station
41 50 63 104 110 118 122 123 133 147 149 153 155 166 180 194 196 203 207 212 213 219 291 292 298 301 303 304 4091 25094 25356 32054 34039 36751 37560
Bulken (Vangsvatnet) Svartavatn Stordalsvatn Nessedalselv Sogndalsvatn Sula Røykenes Hølen Lena Knappom Nedre Sjodalsvatn Austbygdåi Viksvatn (Hestadfjorden) Borgåi Etna Engeren Vinde-elv Grunke Fiskum Eggedal Hølervatn Hangtjern Reinsnosvatn Sandvenvatn Byttevatn Fornabu Kråkfoss Gryta Bjørnegårdssvingen Myrkdalsvatn Kinne Tannsvatn (Lognvikvatnet) Magnor Frostdalen Storeskar
VOSSO TYSSEELVI ETNE NESSELVI SOGNDALSELVI LÆRDALS OSELVA KINSO GLOMMA GLOMMA GLOMMA SKIENS GAULAR NUMEDALSLÅGEN DRAMMENS TRYSILELVA DRAMMENS DRAMMENS DRAMMENS DRAMMENS DRAMMENS DRAMMENS OPO OPO GAULAR INDRE OFFERDALSELVI GLOMMA NORDMARK SANDVIKSELVA VOSSO VOSSO SKIENS BYÅLVEN-MANGEN LÆRDALS DRAMMENS
Longitude Latitude 6.27 5.90 6.01 6.28 7.01 8.10 5.44 6.75 10.81 12.05 8.94 8.82 5.88 9.01 9.63 12.09 9.08 8.70 9.79 9.43 9.46 9.45 6.66 6.55 6.35 7.55 11.08 10.80 10.51 6.50 6.50 8.06 12.18 8.03 8.33
60.63 60.65 59.68 61.16 61.29 61.17 60.25 60.36 60.67 60.64 61.57 59.99 61.33 60.31 60.95 61.52 61.15 60.95 59.69 60.15 60.71 60.48 59.95 60.05 61.34 61.23 60.12 59.99 59.89 60.80 60.63 59.67 59.95 61.18 60.89
Upstream area (Km2) 1092 72.35 130.73 30 110.93 30.32 50.09 231.42 183.64 1278 480.7 343.6 508.13 93.8 570.26 388.44 269.48 184.46 51.37 310.73 79.51 11.13 120.5 470.23 104.54 53.17 435.21 7.06 190.62 158.28 511.44 118.09 355.66 25.77 119.42 12
43846 43920 44033
FEIGEDALSELVI GLOMMA KROKADALSELVI
Feigumfoss Brustuen Krokenelv
7.45 8.30 7.40
61.38 61.73 61.35
47.99 253.63 45.92
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4) Results 4.1 Calibration and validation The model was calibrated for seven years (1997 – 2003) and then validated for seven years (2004 – 2010). One year was used as model initialization and initial values for free parameters were taken from previous experience or published research Killingtveit and Sælthun (1995). The model parameters were calibrated using an automatic procedure with four different multiple objective criteria (KGE and bias; NSE adjusted with bias; NSE regional, spatial and median; NSE, Kendall’s correlation coefficient and bias) followed by some manual adjustments. The calibration with KGE and bias criteria gave best parameters as compared to the other three multiple objective criteria’s. The calibration followed a stepwise calibration approach by taking one process at a time (general parameters, soil parameters, land use parameters, river and lake parameters) to reduce equifinality so that the errors at one process will not be compensated by other parts of the model (Arheimer and Lindström, 2013). The goodness of fit between the observations and model results was evaluated by computing the Kling Gupta Efficiency (KGE), bias and mean absolute error (MAE). The calibration and validation of all stations and results from the 8 test stations are presented in Table 2 and Table 3 . The evaluation criteria showed that the model produces satisfactory results. Besides the simulated hydrograph shows good agreement with the observed discharges but some of the model results showed that it could not simulated the peak floods. This is shown to be due to the gridded precipitation used, which is low for some catchments. Table 2: Evaluation criteria of calibration and validation for thirty gauging stations KGE
MAE
Bias
Stat.Name
Calibration
Validation
Calibration
Validation
Calibration
Validation
Bulken Stordalsvatn Nessedalselv Sogndalsvatn Lena Nedre Sjodalsvatn Viksvatn Etna Engeren Vinde-elv Grunke Fiskum Eggedal Hølervatn Hangtjern Reinsnosvatn Sandvenvatn Byttevatn Fornabu Kråkfoss Bjørnegårdssvingen Myrkdalsvatn Kinne
0.80 0.76 0.64 0.78 0.80 0.66 0.66 0.81 0.75 0.64 0.75 0.69 0.84 0.71 0.50 0.86 0.83 0.73 0.81 0.79 0.82 0.80 0.74
0.80 0.82 0.61 0.72 0.69 0.54 0.67 0.76 0.71 0.48 0.77 0.69 0.77 0.78 0.56 0.85 0.83 0.70 0.74 0.70 0.79 0.72 0.66
25.18 4.85 1.27 3.37 1.72 7.08 17.87 4.59 2.97 2.53 2.32 0.50 2.97 0.81 0.13 3.46 13.63 3.86 0.96 4.86 2.06 4.75 14.86
26.87 4.61 1.36 3.43 1.59 8.23 17.67 4.91 3.24 3.04 2.71 0.48 3.08 0.82 0.13 3.18 13.92 3.93 0.99 4.46 1.95 5.32 15.65
9.05 -1.96 0.29 -0.91 -0.15 -4.61 -6.43 0.87 -1.30 1.39 0.46 0.23 0.25 -0.35 -0.06 0.12 3.08 -0.74 -0.05 -0.55 0.40 0.74 2.10
4.03 -0.98 0.08 -0.99 -0.68 -7.03 -6.98 1.61 -1.25 2.14 -0.67 0.17 -0.03 -0.16 -0.05 -0.76 0.96 -0.86 -0.16 -1.83 0.24 0.82 -1.63 14
Tannsvatn Magnor Frostdalen Storeskar Feigumfoss Brustuen Krokenelv
0.74 0.72 0.67 0.82 0.82 0.66 0.80
0.86 0.64 0.60 0.84 0.81 0.65 0.64
1.24 2.78 0.49 1.63 0.95 4.53 1.05
1.21 2.78 0.44 1.51 0.88 4.19 1.21
0.45 -1.18 -0.21 0.23 -0.29 -2.94 0.04
0.22 -1.43 -0.24 -0.27 -0.23 -2.87 -0.21
From the calibration results, all stations showed KGE value of 0.64 or better except for one station with the lowest KGE value of 0.5. Similarly, for the validation of the model with exception of one station (that showed a KGE value of 0.48) produced KGE values of 0.54 – 0.86. Thereafter, the model parameters were used to simulate flow in another 8 stations located in unregulated catchments not included in the calibration as a proxy test. The test catchments showed satisfactory results with KGE values of 0.57 – 0.80. These 8 stations are gauged stations selected as a test for the calibration and validation period, and the evaluation criteria results are shown in Table 3. Table 3: Evaluation criteria for eight tested gauging stations (uncalibrated stations) KGE Stat.Name Calibration Validation Svartavatn 0.57 0.53 Sula 0.74 0.80 Røykenes 0.65 0.60 Hølen 0.57 0.79 Knappom 0.67 0.42 Austbygdåi 0.78 0.85 Borgåi 0.66 0.73 Gryta 0.80 0.84
MAE Bias Calibration Validation Calibration Validation 4.49 5.01 0.11 0.16 0.52 0.39 0.18 -0.12 2.12 2.41 -0.17 -0.45 5.83 4.62 3.79 2.10 12.74 14.50 -8.29 -12.20 3.27 3.49 1.39 0.79 0.91 0.93 0.28 0.10 0.09 0.07 0.00 0.00
The simulated hydrograph was also used as visual inspection criteria to evaluate the performance of the model. In most cases, the model simulated the minimum flow better than the peak flows. The flow duration curve of some catchments showed that the model underestimates the peak flows. The hydrograph for selected two catchments (one calibrated and another one from the proxy test catchments) during the calibration and validation periods are shown in Figure 3 and Figure 4. The remaining hydrograph is presented in A) Appendix. The evaluation criteria computed from the simulated discharge are also shown on each figure for every catchments.
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a) Calibration
b) Validation
Figure 3: Calibration and validation of Reinsnosvatn station. On the top left side there is the duration curve showing the high and low flows with results of different goodness of fit criteria. On the top right there is the mean monthly distribution of the observed and simulated flows.
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a) Tested calibration
b) Tested validation Figure 4: Tested calibration and validation for non-calibrated Gryta station. On top left side there is duration curve showing the high and low flows with results of different goodness of fit. On the right top is mean monthly distribution of the observed and simulated flows.
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4.2 Hydrological statistics The mean annual runoff is one of the fundamental hydrological characteristics, which describes the low flow indices (Smakhtin, 2001). The map showing hydrological statistical parameters such as mean annual low discharge (MALQ), mean annual discharge (MAQ) and mean annual high discharge (MAHQ) of all 38 discharge stations has been computed for the extended 30 years period, and the results are shown in Figure 5. Similar research works has been done for the Swedish version of HYPE (Bergstrand et al 2014) and they computed statistical parameters including MALQ, MAQ and MAHQ map for the entire country. Thus, it is important to plot such a map to show the hydrological statistics with different range values for high and low flows.
Figure 5: Hydrological statistics maps showing MAQ, MAHQ and MALQ
4.3 Indicators of Hydrological Alteration (IHA indexes) The IHA indexes are computed based on the method described by Richter et al (1996). The IHA indexes of 10 sample stations were calculated for both observed and simulated flows and the results are shown in Table 4. The IHA indexes computed on a seasonal flows showed better agreement between observed and simulated than the 1-day, 3-day and 7-day minimum annual indexes.
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Table 4: IHA indexes for 7 stations used for calibration and 3 non-calibrated stations. (* are the 3 non-calibrated stations) St. Name
Svartavatn*
Sula*
Lena
Knappom*
Austbygdåi
Etna
Engeren
Reinsnosvatn
Byttevatn
Magnor
Flow Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff. Observed Simulated % of diff.
Average Q (m3/s) 7.59 7.68 -1.20 1.03 1.06 -2.62 2.78 2.34 15.92 24.62 14.32 41.86 8.17 9.19 -12.43 9.99 11.01 -10.18 7.82 6.57 16.04 9.41 9.01 4.25 9.31 8.45 9.29 5.33 3.98 25.38
Annual minimum average (m3/s) 1 day 3 day 7 day 0.35 0.37 0.41 0.08 0.11 0.19 76.49 70.70 54.63 0.05 0.05 0.06 0.02 0.02 0.03 60.00 57.69 52.73 0.26 0.27 0.29 0.00 0.00 0.00 100.00 99.62 99.32 3.76 3.77 4.00 0.00 0.00 0.01 100.00 100.00 99.85 0.52 0.54 0.56 0.15 0.17 0.23 70.66 68.04 59.11 0.79 0.81 0.84 0.12 0.14 0.18 85.42 83.31 78.78 1.69 1.71 1.75 0.26 0.28 0.31 84.78 83.94 82.11 0.73 0.74 0.76 0.20 0.21 0.26 73.25 70.92 66.23 0.62 0.65 0.74 0.27 0.30 0.35 55.74 54.45 52.70 0.60 0.61 0.65 0.00 0.00 0.00 100.00 100.00 99.85
Seasonal average (m3/s) Winter 5.56 7.82 -40.63 0.16 0.16 0.61 1.22 1.24 -1.48 15.14 8.72 42.40 1.82 2.07 -13.65 2.15 2.46 -14.49 4.12 2.48 39.68 3.10 3.86 -24.75 3.52 5.65 -60.65 5.36 4.71 12.15
Spring 8.00 7.87 1.51 0.57 0.54 5.79 4.92 5.45 -10.76 38.01 33.99 10.56 10.22 10.70 -4.71 15.09 18.18 -20.49 9.09 9.91 -9.02 6.31 5.28 16.35 6.87 6.94 -1.14 7.18 7.14 0.60
Summer 8.37 6.67 20.30 2.66 2.78 -4.78 2.05 1.08 47.48 19.34 5.49 71.63 13.71 15.76 -15.02 13.98 15.72 -12.50 10.29 8.29 19.43 18.78 17.21 8.35 17.26 12.76 26.05 3.19 1.44 54.94
7 day lowest average (m3/s) Autumn 8.77 8.73 0.42 0.73 0.74 -1.78 2.98 1.65 44.68 26.91 9.76 63.73 6.92 8.29 -19.89 8.67 7.80 10.07 7.90 5.76 27.18 9.52 9.80 -2.99 9.75 8.84 9.30 5.85 2.89 50.55
Winter 0.53 0.46 14.61 0.07 0.04 50.68 0.49 0.06 87.35 5.41 0.59 89.14 0.61 0.28 54.74 0.91 0.32 64.24 2.00 0.66 66.97 0.96 0.52 45.92 0.92 0.74 20.15 1.52 0.37 75.81
Spring 0.83 0.58 29.58 0.06 0.04 36.21 0.56 0.15 72.43 5.88 1.45 75.29 0.60 0.44 26.21 0.92 0.54 41.15 1.77 0.68 61.56 0.93 0.40 56.68 0.90 0.65 28.09 1.59 0.15 90.36
Summer 1.19 0.79 33.50 0.61 0.34 43.82 0.34 0.00 98.83 5.07 0.02 99.68 2.58 2.01 22.23 2.56 0.70 72.58 4.90 1.02 79.18 6.18 2.36 61.79 5.44 1.86 65.83 0.86 0.01 98.84
Autumn 0.70 0.95 -35.97 0.20 0.10 48.99 0.53 0.02 97.17 6.73 0.06 99.05 1.95 1.07 45.24 2.30 0.64 72.31 4.30 1.17 72.71 1.91 1.18 38.27 1.76 1.48 16.05 1.37 0.04 97.45
Number of pulses High Low 26.40 12.87 14.93 12.07 43.44 6.22 7.13 4.60 6.73 8.87 5.61 -92.76 10.73 9.07 11.00 11.73 -2.49 -29.40 10.00 6.80 8.53 11.13 14.67 -63.72 10.47 6.20 7.33 8.87 29.94 -3.02 7.60 4.13 7.13 8.40 6.14 -50.80 3.67 2.73 4.13 5.00 -12.71 -82.95 10.60 5.00 8.47 9.40 20.12 -88.00 13.73 7.60 8.80 9.33 35.92 -22.80 7.67 5.60 12.40 11.87 -61.73 -52.81
Seasonal low pulses Winter Summer 5.73 5.80 4.67 6.47 18.59 -11.50 3.53 0.27 6.27 1.47 -77.38 -81.80 3.87 4.40 2.87 8.27 25.86 -87.89 2.47 3.67 2.53 7.73 -2.68 -52.58 4.20 1.40 5.33 2.27 -26.98 -61.93 3.00 0.67 4.40 3.00 -46.67 -77.77 2.27 0.27 3.00 1.60 -32.33 -83.31 3.27 1.00 5.60 2.80 -71.41 -64.29 5.07 1.53 4.47 4.07 11.84 -62.31 2.00 3.27 2.47 8.60 -23.35 -62.01
Rises
Falls
34.13 20.00 41.41 11.20 10.07 10.12 15.53 16.40 -5.58 8.27 13.67 -65.32 15.13 10.53 30.40 9.80 8.53 12.93 1.33 2.00 -50.04 12.73 12.27 3.66 19.07 11.27 40.91 7.93 18.60 -57.35
33.27 6.13 81.56 8.27 4.00 51.61 12.73 10.60 16.75 3.53 6.13 -73.59 12.87 2.33 81.87 6.20 2.53 59.15 0.80 0.53 33.38 8.20 2.73 66.67 15.53 1.40 90.99 3.33 10.33 -67.74
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5) Discussions 5.1 Model results The HYPE model has shown satisfactory results in the European E-HYPE version of the model that evaluated the results across EUROPE (Donnelly et al, 2016) showing less performance results on producing daily and extreme flows than annual and seasonal flows. Similar KGE range values to the EHYPE were obtained in the HYPE version we implemented for southern Norway. However, in this study the KGE value ranges from 0.5 to 0.86 for the calibration period and from 0.48 to 0.86 for the validation period. The same set of parameters were tested on non calibrated catchments and showed a KGE (0.57 – 0.80) which can be transferred and applied in predicting flows for ungauged catchments. The hydrological model HYPE has also been tested and assessed for the central of Norway (Schönfelder et al, 2017). In this report, the IHA indexes are calculated for the minimum flows on daily, weekly and seasonal periods. The IHA indexes results from the model for the seasonal periods (e.g. winter, spring, summer and autumn) showed better match with the observed values as compared to the IHA indexes computed on a daily minimum annual flows. Forseth et al (2014) discussed if regulating a river can be considered as a positive impact on the salmon population when it increases the minimum flow of the river system, which is common for river systems with naturally low flow values in the winter season. Even though the research works here are based on unregulated rivers, it is possible to see such impacts in the river systems from the minimum flows computed to estimate the IHA indexes. The results indicates that seasonal variations should be considered in more details than annual evaluations mainly in places like Norway, where the flow is different in winter and summer seasons. Hydrological models calibrated for multiple calibration objective criteria represent low flow periods in a better way than for a single objective criteria (Sengupta et al, 2018), and the hydrological alterations vary seasonally for the wet and dry periods at a regional level. In this study, the HYPE model was calibrated for four different multiple objective functions criteria to obtain the optimum calibrated model parameters. Thereafter, the parameters showed reasonable results that can be applied on ungauged basins.
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6) Conclusions and Recommendations 6.1 Conclusions To sum up, the HYPE model was tested: -
Model calibrated reasonably well
Some discrepancies in simulating high flows
Reasonable for the seasonal and low flows
-
Satisfactory in non calibrated test catchments
-
IHA low flow indices showed some variability when tested.
6.2 Recommendation for future works In this research project, we tried to show results of the hydrological model HYPE for southern Norway. In addition, environmental applications of the model results (e.g. IHA indexes) were computed and acceptable results were obtained. We would like to propose the following recommendations for future further works of the model and its applications:
Test HYPE in other regions (e.g. Northern parts of Norway) or set up HYPE for the whole Norway and evaluate results for different environmental purposes and make statistical parameters and hydrological maps for the entire country.
An assessment of precipitation input to evaluate the discrepancies shown for some catchment in simulating peak flows.
We suggest to extend and add new analysis mainly to evaluate the hydrological outputs with other hydrological models used for ungauged basins of the southern Norway.
The calibration of the model parameters can still be improved with a different optimization and objective functions (e.g. using NSE for log-transformed streamflow as calibration criterion to get a better fit at low flows as shown by Engeland and Hisdal (2009).
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7) References Arheimer, B. and Lindström, G.: Implementing the EU Water Framework Directive in Sweden, in: Runoff Predictions in Ungauged Basins – Synthesis across processes, places and scales, edited by: Blöschl, G., Sivapalan, M., Wagener, T., and Viglione, A., 353–359, Cambridge University Press, Cambridge, UK, 2013. Bergstrand M., Asp S. & Lindström, G. 2014 Nationwide hydrological statistics for Sweden with high resolution using the hydrological model S-HYPE. Hydrology Research 45 (3), 349 356; DOI: 10.2166/nh.2013.010. Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments. SMHI RHO 7. Norrköping. Chantal Donnelly, Jafet C.M. Andersson & Berit Arheimer (2015) Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe, Hydrological Sciences Journal, 61:2, 255-273, DOI: 10.1080/02626667.2015.1027710 Engeland, K. & Hisdal, H. Water Resour Manage (2009) 23: 2567. https://doi.org/10.1007/s11269-0089397-7 Forseth, Torbjørn; Harby, Atle; Ugedal, Ola; Pulg, Ulrich; Fjeldstad, Hans-Petter; Robertsen, Grethe; Barlaup, Bjørn Torgeir; Alfredsen, Knut; Sundt, Håkon; Saltveit, Svein Jakob; Skoglund, Helge; Kvingedal, Eli; Sundt-Hansen, Line Elisabeth Breivik; Finstad, Anders; Einum, Sigurd; Arnekleiv, Jo Vegar. Handbook for environmental design in regulated salmon rivers. Trondheim: Norsk institutt for naturforskning 2014 (ISBN 978-82-426-2638-7) 90 s. NINA temahefte(53) ENERGISINT NINA NTNU UiO UNI Killingtveit Å and Sælthun N. R (1995) Hydrology book. Norwegian Institute of Technology: Division of Hydraulic Engineering, Trondheim. Lindström, G. 2016 Lake water levels for calibration of the S-HYPE model. Hydrology Research 47 (4), 672–682; DOI:10.2166/nh.2016.019. Lindström, G., Pers, C., Rosberg, J., Strömqvist, J. & Arheimer, B. 2010 Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research 41 (3–4), 295–319; DOI:10.2166/nh.2010.007 Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235-249, https://doi.org/10.5194/essd-10-235-2018, 2018. Richter, Brian D.; Baumgartner, Jeffrey V.; Powell, Jennifer; Braun, David P. (1996): A Method for Assessing Hydrologic Alteration within Ecosystems. In Conservation Biology 10 (4), pp. 1163–1174. DOI:10.1046/j.1523-1739.1996.10041163.x. Hailegeorgis, T. and Alfredsen, K. 2015 Comparative evaluation of performances of different conceptualisations of distributed HBV runoff response routines for prediction of hourly streamflow in boreal mountainous catchments. Hydrology Research 46 (4), 607-628, doi:10.2166/nh.2014.051. Kolberg S. A., Bruland O. 2012 ENKI – An open source environmental modelling platform. Geophysical Research Abstracts 14, EGU2012-13630, EGU General Assembly.
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Chave, P. 2001. The EU Water Framework Directive – An Introduction. International Water Association. Sengupta, A., Stein, E. D., McCune, K. S., Mazor, R. D., Adams, S., Bledsoe, B. P., & Konrad, C. (2018). Tools for managing hydrological alteration on a regional scale I: Estimating changes in flow characteristics at ungauged sites. Freshwater Biology, https://doi.org/10.1111/fwb.13074 Smakhtin, V.U., 2001. Low flow hydrology: a review. Journalof Hydrology, 240 (3–4), 147–186. doi:10.1016/S0022-1694 (00)00340-1 Strömqvist, J., Arheimer, B., Dahné, J., Donnelly, C. & Lindström, G. 2012 Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale. Hydrological Sciences Journal 57 (2), 229–247.
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A) Appendix A.1 Model calibration and validation A.1.1 Calibration of 30 discharge stations 1) a) Bulken (Vangsvatnet) calibration (the number 41 corresponds to SUBID which is on Table 1)
b) Bulken (Vangsvatnet) validation
24
2) a) Stordalsvatn calibration
b) Stordalsvatn validation
25
3) a) Nessedalselv calibration
b) Nessedalselv validation
26
4) a) Sogndalsvatn calibration
b) Sogndalsvatn validation
27
5) a) Lena calibration
b) Lena validation
28
6) a) Nedre sjodalsvatn calibration
b) Nedre sjodalsvatn validation
29
7) a) Viksvatn (Hestadfjorden) calibration
b) Viksvatn (Hestadfjorden) validation
30
8) a) Etna calibration
b) Etna validation
31
9) a) Engeren calibration
b) Engeren validation
32
10) a) Vinde-elv calibration
b) Vinde-elv validation
33
11) a) Grunke calibration
b) Grunke validation
34
12) a) Fiskum calibration
b) Fiskum validation
35
13) a) Eggedal calibration
b) Eggedal validation
36
14) a) Hølervatn calibration
b) Hølervatn validation
37
15) a) Hangtjern calibration
b) Hangtjern validation
38
16) a) Reinsnosvatn calibration
b) Reinsnosvatn validation
39
17) a) Sandvenvatn calibration
b) Sandvenvatn validation
40
18) a) Byttevatn calibration
b) Byttevatn validation
41
19) a) Fornabu calibration
b) Fornabu validation
42
20) a) Kråkfoss calibration
b) Kråkfoss validation
43
21) a) Bjørnegårdssvingen calibration
b) Bjørnegårdssvingen validation
44
22) a) Myrkdalsvatn calibration
b) Myrkdalsvatn validation
45
23) a) Kinne calibration
b) Kinne validation
46
24) a) Tannsvatn (Lognvikvatnet) calibration
b) Tannsvatn (Lognvikvatnet) validation
47
25) a) Magnor calibration
b) Magnor validation
48
26) a) Frostdalen calibration
b) Frostdalen validation
49
27) a) Storeskar calibration
b) Storeskar validation
50
28) a) Feigumfoss calibration
b) Feigumfoss validation
51
29) a) Brustuen calibration
b) Brustuen validation
52
30) a) Krokenelv calibration
b) Krokenelv validation
53
A.1.2 Tested 8 discharge stations 1) a) Svartavatn calibration
b) Svartavatn validation
54
2) a) Sula calibration
b) Sula validation
55
3) a) Røykenes calibration
b) Røykenes validation
56
4) a) Hølen calibration
b) Hølen validation
57
5) a) Knappom calibration
b) Knappom validation
58
6) a) Austbygdåi calibration
b) Austbygdåi validation
59
7) a) Borgåi calibration
b) Borgåi validation
60
8) a) Gryta calibration
b) Gryta validation
61
ISBN 978-82-7598- 110-1 ISBN 978-82-7598- 111-8 (electronic)
