Technical Report No. 26 SIMULATION OF LOW FLOWS ... - EU WATCH

Technical Report No. 26 SIMULATION OF LOW FLOWS AND DROUGHT EVENTS IN WATCH TEST BASINS: IMPACT OF DIFFERENT CLIMATE FORCING DATASETS

Marjolein H.J. van Huijgevoort, Anne F. van Loon, Martin Hanel, Ingjerd Haddeland, Oliver Horvát, Aristeidis Koutroulis, Andrej Machlica, Graham Weedon, Miriam Fendeková, Ioannis Tsanis, Henny A.J. van Lanen 18 March 2011

Technical Report No. 26

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WATCH is an Integrated Project Funded by the European Commission under the Sixth Framework Programme, Global Change and Ecosystems Thematic Priority Area (contract number: 036946). The WACH project started 01/02/2007 and will continue for 4 years.

Title: Authors:

Simulation of low flows and drought events in WATCH test basins: impact of climate forcing datasets Marjolein H.J. van Huijgevoort, Anne F. van Loon, Martin Hanel, Ingjerd Haddeland, Oliver Horvát, Aristeidis Koutroulis, Andrej Machlica, Graham Weedon, Miriam Fendeková, Ioannis Tsanis, Henny A.J. van Lanen

Organisations:

- Wageningen University - Hydrology and Quantitative Water Management Group (WUR) - Comenius University in Bratislava, Faculty of Natural Sciences, Department of Hydrogeology (UC) - T.G. Masaryk Water Research Institute, v.v.i. (TGM-WRI) - Technical University of Crete - Water Resources Management & Coastal Engineering Laboratory (TUC) - Norwegian Water Resources and Energy Directorate (NVE) - Met Office (JCHMR)

Submission date:

18 March 2011

Function:

This report is an output from Work Block 4 Extremes: frequency, severity and scale, and contributes to: (i) Task 4.1.1 Investigate processes controlling the propagation of drought, and (ii) Task 1.3.4. Evaluating uncertainty of means and extremes.

Deliverable

WATCH deliverables D 4.1.4 Report on the increased understanding of the propagation of drought in different hydro-climatological regions, physical catchment structures and different scales, D 4.1.5 Generic method to quantify the propagation of a drought, and D 1.3.4 Report on the uncertainty of the global water cycle of the 20th Century. The technical report contributes to: (i) M4.1.6a Overview of major historical events; part: drought, and M4.1-7 Analysis of major historical extreme events with metamodel.

Photos cover: upper left: Narsjø catchment Norway (Van Lanen, 2007), upper right: Upper-Metuje catchment Czech Republic (Van Loon, 2006), lower left: Upper-Sázava catchment Czech Republic (Van Loon, 2008), lower right: Nedožery catchment Slovakia (Oosterwijk, 2009).


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Table of Contents Table of Contents ....................................................................iv 1. Introduction .........................................................................1 Outline ............................................................................................................. 1

2. Catchment descriptions .....................................................3 2.1. 2.2. 2.3. 2.4. 2.5.

Narsjø ........................................................................................................... 5 Upper-Metuje ............................................................................................... 5 Upper-Sázava ............................................................................................... 5 Nedožery ...................................................................................................... 6 Platis ............................................................................................................. 6

3. Methodology ........................................................................7 3.1. Model descriptions ...................................................................................... 7 3.1.1. BILAN ..................................................................................................... 7 3.1.2. FRIER ..................................................................................................... 8 3.1.3. HBV ........................................................................................................ 8 HBV-WUR........................................................................................................ 9 HBV-NVE ......................................................................................................... 9 HBV-TUC ......................................................................................................... 9 3.2. Drought analysis ....................................................................................... 10 3.3. Large-scale forcing data ........................................................................... 10 3.4. Local forcing data...................................................................................... 11

4. Differences between forcing datasets ............................13 5. Results ...............................................................................17 5.1. Narsjø ......................................................................................................... 17 5.1.1. Modelling .............................................................................................. 17 HBV-WUR model ........................................................................................... 17 HBV- NVE model ........................................................................................... 18 5.1.2. Drought analysis ................................................................................... 18 5.2. Upper-Metuje ............................................................................................. 21 5.2.1. Modelling .............................................................................................. 21 HBV-WUR model ........................................................................................... 21 BILAN model .................................................................................................. 22 5.2.2. Drought analysis ................................................................................... 22 5.3. Upper-Sázava ............................................................................................. 25 5.3.1. Modelling .............................................................................................. 25 HBV-WUR model ........................................................................................... 25 BILAN model .................................................................................................. 26 5.3.2. Drought analysis ................................................................................... 26 5.4. Nedožery .................................................................................................... 29 5.4.2. Modelling .............................................................................................. 29 HBV-WUR model ........................................................................................... 29 FRIER model ................................................................................................. 30 BILAN model .................................................................................................. 30 5.4.3. Drought analysis ................................................................................... 31 5.5. Platis ........................................................................................................... 35 5.5.2. Modelling .............................................................................................. 35


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HBV-TUC model ............................................................................................ 35 5.5.3. Drought analysis ................................................................................... 36 5.6. Discussion ................................................................................................. 37

6. Conclusions.......................................................................39 Narsjø catchment (Norway) ........................................................................... 39 Upper-Metuje (Czech Republic) ..................................................................... 39 Upper-Sázava (Czech Republic) ................................................................... 39 Nedožery (Slovakia)....................................................................................... 40 Platis (Crete) .................................................................................................. 40

References ..............................................................................41 List of abbreviations ..............................................................44 Annex 1 – Total monthly precipitation for four test basins; original and elevation-corrected by the HBV-WUR model ....i Annex 2 – Nash-Sutcliffe values for all models with local and WFD forcing for all catchments ......................................iii Annex 3 – Influence of (re)calibration on drought characteristics ..........................................................................v


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1. Introduction Drought is a natural hazard that occurs all over the world that can have large economic, social and environmental impacts (Wilhite, 2000). Drought is caused by below-average natural water availability due to low precipitation and/or high evaporation rates. It is characterized as a deviation from normal conditions of the physical system (climate and hydrology), which is reflected in variables such as precipitation, soil moisture, groundwater, and discharge (Tallaksen and van Lanen, 2004). For drought analysis, time series of hydrometeorological variables are required. These time series should be long enough to sufficiently capture climate variability. In many catchments around the world, no or insufficient hydrological and meteorological observations are available. As an alternative, time series of hydrological data can be simulated, e.g. with rainfall–runoff models. However, for this type of model, time series of hydrometeorological data are required for forcing and calibration. To overcome the problem of lack of local forcing data, global gridded meteorological datasets might be suitable for this type of hydrological modelling. Over the last decade, global gridded re-analysis meteorological datasets have been developed based on observations and modelling, e.g. the ERA-40 re-analysis (Uppala et al., 2005), the Climate Research Unit (CRU) dataset (Mitchell and Jones, 2005). Gridded, large-scale (0.5º x 0.5º) meteorological datasets have already been used for soil moisture drought analyses in de USA and globally (Andreadis et al., 2005; Sheffield and Wood, 2007) and for discharge drought at the continental scale (Shukla and Wood, 2008). These drought analyses at large scale gave reasonable results when compared with broad characteristics derived from observations. However, the suitability of large-scale meteorological datasets to force models for drought analysis at catchment scale still needs to be investigated. The objective of this study is to assess the suitability of large-scale meteorological datasets for drought analysis at the catchment scale. To reach this objective, the potential of one of these large-scale forcing datasets, the WATCH Forcing Data (Weedon et al., 2010) was investigated by comparing drought characteristics based on simulations using this large-scale forcing dataset with those derived from simulations using local, more detailed, forcing data. Several WATCH test basins were used in this study to test the large-scale forcing dataset. The test basins are Narsjø (Norway), Upper-Metuje (Czech Republic), Upper-Sázava (Czech Republic), Nedožery (Slovakia), and Platis (Crete, Greece). In each of the test basins, discharge was simulated with one or more rainfall-runoff models using both local forcing data and the WATCH Forcing Data (WFD). The same drought analysis was done on these simulations and drought characteristics were compared. Outline First, a short description of the WATCH test basins used in this study is given (Chapter 2). In Chapter 3, all rainfall-runoff models that were applied are described and drought analysis with the variable threshold is explained. Also, a very short description of the large-scale forcing dataset, the WFD, is provided. The comparison between the two forcing datasets for the test basins can be found in Chapter 4. The results of the hydrological modelling and drought analysis with both forcing datasets are given in Chapter 5. Conclusions are presented in Chapter 6. Abbreviations used in this report are explained on page 44. Finally, three Annexes are included, containing 1) figures showing the comparison between WFD and local corrected precipitation data, 2) an overview table of model results, and 3) the effects of (re)calibration of models using large-scale data.


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2. Catchment descriptions In this chapter, a short description is given of the WATCH test basins used in this study. For more detailed information about the catchments, the reader is referred to Van Lanen et al. (2008), Van Huijgevoort et al. (2010), and Van Loon et al. (2010). Figure 1 shows the location of the test basins in Europe and Table 1 gives an overview of the most important catchment characteristics of the studied test basins.

Figure 1 a) Location of the studied catchments in Europe; and gauging station and meteorological stations in b) Upper-Metuje and c) Upper-Sázava (Czech Republic), d) Narsjø (Norway), e) Nedožery (Slovakia), and f) Platis (Crete). Table 1


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594 (Mar: 27; Jul: 81) 2.2 (Mar: 0.29; May: 8.0)

mean annual (min; max monthly)


precipitation (mm)**

discharge (mm/d)**

0.99 (Oct: 0.66; Mar: 1.9)

746 (Apr: 42; Jul: 92)

5.9 (Jan: –3.9; Jul: 15.5)

1982-2005

Cfb

591 (459-780)

73.6

Upper-Metuje

0.82 (Aug: 0.48; Mar: 1.7)

717 (Feb: 36; Jun: 92)

6.8 (Jan: –3.2; Jul: 16.3)

1963-1999

Cfb

628 (487-805)

131.3

Upper-Sázava

0.96 (Aug: 0.42; Mar: 2.1)

873 (Feb: 52; Jun: 96)

7.6 (Jan: –2.8; Jul: 17.5)

1974-2006

Dfb

573 (288–1172)

181

Nedožery

1.6 (Jul-Sep: 0; Jan: 3.9)

930 (Aug: 1; Dec: 201)

15 (Jan: 7.4; Jul: 23.7)

1974-1998

Csa

698 (5-2454)

210

Platis

* used for calculation of catchment characteristics temperature, precipitation, and discharge ** temperature, precipitation, and discharge data taken from various hydro-meteorological stations at different locations (see Figure 1 and Chapter 4&5)

0.7 (Jan: –10.1; Jul: 11.9)

1958-2007

Dfc

945 (737–1595)


mean (min - max)

119

temperature (ºC)**

observation period*

climate type (–)

altitude (m a.m.s.l.)

area (km2)

Narsjø

Table 1 Catchment characteristics of the studied catchments Narsjø (Norway), Upper-Metuje and Upper-Sázava (Czech Republic), Nedožery (Slovakia), and Platis (Crete).

2.1.

Narsjø

The Narsjø catchment is located in an open mountainous area in Eastern Norway, between Oslo and Trondheim (Figure 1). It is a sub-basin of the Upper-Glomma, which is the headwater catchment of the largest river in Norway, the Glomma. The Narsjø catchment covers an area of 119 km2 and its mean elevation is 945 m a.m.s.l., with a minimum of 737 and a maximum of 1595 m a.m.s.l. (Engeland, 2002). Land use types in the Narsjø catchment are open area (60.9%), forest (24%), bogs (11.7%), and agriculture (0.4%). Lakes cover 3.1% of the catchment (Hohenrainer, 2008). The Narsjø catchment has a Nordic continental climate with cold winters and relatively warm summers (Köppen-Geiger climate Dfc). Mean annual temperature is 0.7°C (Table 1). Every winter, snow covers the catchment continuously for on average 7 months, from approximately the middle of October until the end of May, depending on altitude (Engeland, 2002). Mean annual precipitation in Narsjø is about 594 mm. The flow regime in the catchment is dominated by the snowmelt flood, which on average has its peak in May. The mean annual discharge of Narsjø is 2.2 mm day-1. The catchment is dominated by hard rock, which is covered by glacial deposits and a weathering layer with a variable thickness. During winter, when precipitation accumulates as snow, long low-flow periods occur. The presence of many bogs and lakes in the catchment delays the discharge during such dry periods and results in long recessions. The minimum discharge is usually reached by late winter, just before the snow melt (Hohenrainer, 2008). In summer, discharge is mainly determined by the catchment’s fast response to rainfall, resulting in a flashy hydrograph.

2.2.

Upper-Metuje

The Upper-Metuje catchment is situated in northeast Czech Republic and partly in Poland (approximately 10% of the catchment area) (Figure 1). It is the headwater catchment of the river Metuje, which discharges into the river Elbe. The area of the Upper-Metuje catchment is 73.6 km2 and its mean altitude is 591 m a.m.s.l., with a minimum of 459 and a maximum of 780 m a.m.s.l. (Rakovec et al., 2009). Deep valleys, gentle and steep slopes and plateaus are the characteristic elements of the landscape. Land use of the catchment consists mainly of cropland and grass fields (51%), and forest (46%) (Rakovec et al., 2009). The Upper-Metuje catchment has a Central European continental climate (Köppen-Geiger climate Cfb) with a mean annual temperature of 5.9°C and a mean annual precipitation of 746 mm (Table 1). The mean annual discharge is 0.99 mm day-1. Discharge peaks occur in spring due to melting of snow accumulated in winter, whereas low discharges are mostly observed in summer (Rakovec et al., 2009). The subsurface consists of thick permeable Cretaceous deposits overlying rather impermeable Permian-Carboniferous rocks. Groundwater in the Upper-Metuje catchment is characterised by deep circulation and high storage. This makes the catchment slowly responding to precipitation.

2.3.

Upper-Sázava

The Upper-Sázava catchment is the headwater of the river Sázava, which eventually drains into the river Vltava and subsequently into the river Elbe. The focal area is the Upper-Sázava catchment upstream from Zdar nad Sazavou (Figure 1). It only drains Czech territory and has an area of 131 km 2, which is about 3% of the total Sázava catchment (Rakovec et al., 2009; Van Lanen et al., 2008). The catchment is hilly with gentle slopes and flat wide valleys. The catchment altitude varies from 487 to 805 m a.m.s.l., with a mean altitude of 628 m a.m.s.l. (Table 1). The dominant land use types in the catchment are forest (50%) and cropland and grassland (40%). The bedrock of Upper-Sázava catchment consists predominantly of Proterozoic impermeable metamorphic rocks, which consists of black mica migmatite, gneiss and mica schist. There is no extensive groundwater storage (Rakovec et al., 2009). Mean annual precipitation sum in the catchment is 717 mm (Table 1). The lowest monthly precipitation is observed in February, the largest in June. The long term mean daily temperature is 6.8°C. The warmest month is July and the coldest is January. Mean annual discharge is 0.82 mm day-1. Technical Report No. 26 5

Floods occur regularly in spring because of snowmelt and low discharges predominantly occur in summer and beginning of autumn (Table 1). In an average year there is snow from November until April, with largest amounts in January–March (25–30 mm). In the Upper-Sázava catchment withdrawal of surface water and discharge of waste water into the streams takes place.

2.4.

Nedožery

The Nedožery catchment is located in the upper part of the Nitra catchment (Figure 1). The Nitra discharges into the Vah and, finally into the Danube. The catchment is located in the Prievidza district in central Slovakia. It has an area of 181 km2 and an average altitude of 573 m a.m.s.l. (Table 1). The Nedožery catchment is an asymmetric valley, with the lowest parts in the east and most of the highest parts in the west (Oosterwijk et al., 2009). Two-thirds of the catchment is covered by forest. Other land cover types are agriculture (23%), natural meadow (6%), and urban area (5%) (Oosterwijk et al., 2009). The catchment has a moderately warm, humid continental climate (Köppen-Geiger climate Dfb), with a mean annual precipitation of 873 mm and a mean annual air temperature of 7.6°C. Annual average discharge is 0.96 mm day-1 (Table 1). Maximum discharges occur in spring, minimum discharges occur in summer and autumn (Machlica and Stojkovova, 2008). The largest part of the catchment consists of Mesozoic rocks of the Inner Carpathians. These rocks are located in the northern, eastern and western parts of the catchment. Since the catchment is dominated by hard rock, Nedožery shows a quick response to rainfall.

2.5.

Platis

The Platis catchment is located in the south-central part of the island of Crete in Greece and covers an area of 210 km2 (Figure 1). The mean annual precipitation is estimated to be 930 mm and its mean elevation is 698 m a.m.s.l. which varies from 5 to 2454 m (Figure 1). The climate ranges between subhumid Mediterranean and semi-arid with long hot and dry summer and relatively humid and cold winter with a mean annual temperature of 15°C (Pavlakis, 2004). The mean annual discharge is 1.6 mm day-1. It is estimated that from the total precipitation onto the catchment about 46% evapotranspirates, 19% flows to the sea, and 35% recharges the groundwater. The land cover consists predominantly of forest and semi-natural areas (53.5%) and agricultural areas (46.5%), and a small part of the catchment is covered with artificial surfaces (0.1%). The hydrogeological bedrock of the area consists of impermeable quartzites and phyllites, as well as permeable carboniferous, limestone formations, neogene, and quaternary deposits (Pavlakis, 2004).


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3. Methodology As mentioned in the introduction, we used rainfall-runoff models to simulate discharge with local and large-scale forcing, with the objective to test the suitability of large-scale forcing data in smaller catchments. To analyse drought in these simulated time series, the variable threshold method is used. In this chapter, the rainfall-runoff models, the approach for the drought analysis, and the forcing data are described.

3.1.

Model descriptions

In each test basin, different rainfall-runoff models were used (Table 2). For each model, a short description is given below. All models were calibrated separately for both forcing datasets. That is because the objective of this research was to investigate whether large-scale forcing datasets are suitable in catchments where local meteorological observations are not available. So, to compare drought characteristics in simulations with local and large-scale forcing, both datasets should be used similarly. The effect of not (re)calibrating the models for the large-scale forcing dataset is described in Section 5.6 and Annex 3. Table 2 Overview of models used in each catchment and organisations performing the simulations (for abbreviations see page 44) Test basin BILAN FRIER HBV X Narsjø (NVE & WUR) X X Upper-Metuje (TGM-WRI) (WUR) X X Upper-Sázava (TGM-WRI) (WUR) X X X Nedožery (UC) (UC) (WUR) X Platis (TUC)

3.1.1. BILAN The structure of the BILAN model is formed by a system of relationships describing basic principles of water balance on the land surface, in the zone of aeration (including the effect of vegetation cover), and in groundwater. Air temperature is used as an indicator of energy conditions, which affect significantly the water balance components. The input data of the model are daily series of catchment precipitation, air temperature, and relative air humidity. For the calibration of the model parameters, a daily discharge series at the outlet of the catchment is used. The potential evapotranspiration is estimated from saturation deficit by using functions (in form of tables) that have been derived for individual days (by interpolation between monthly values) and for different bioclimatic zones from empirical graphs given by Gidrometeoizdat (1976). The saturation deficit is calculated from data on the air temperature and relative air humidity. It is possible to use potential evapotranspiration calculated externally, then the abovementioned estimation is bypassed. The model generates daily series of catchment average potential evapotranspiration, actual evaporation, water storage components in the snow cover, zone of aeration (soil), direct runoff storage, and groundwater. The total runoff consists of two components, which are direct runoff and base flow. The model has six free parameters and uses an optimisation algorithm for calibration in gauged catchments. Technical Report No. 26

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The standard calibration procedure of these parameters consists of two steps. In the first step, the standard error or the mean absolute error of the simulated runoff is minimized to estimate the parameters significantly affecting the mean runoff. The remaining three parameters affecting the runoff distribution into its individual components (direct and subsurface runoff and base flow) are then calibrated using the mean of absolute values of relative deviations. It has been demonstrated by experimental calculations that in most cases this calibration procedure ensures an acceptable fit in terms of both mean runoff and low flow runoff, which is formed predominantly by base flow. In addition, different objective functions can be used in both calibration steps. 3.1.2. FRIER FRIER is a physically-oriented rainfall-runoff model with distributed parameters (Horvát, 2007). The model divides a catchment into uniform spatial units on a grid scale, in which the water balance is simulated and discharge at the catchment’s outlet is generated. Transformation of the surface runoff in the catchment is simulated by approximating a diffusive wave model using the geometric and hydraulic characteristics of hillslopes and the stream network. The subsurface flow and percolation of each cell is calculated using Darcy’s law and a method of approximating the kinematic wave model. The necessary input files are time series of discharge at the catchment outlet, total precipitation, and air temperature in any time step (min. 1 hour), and spatial layers of digital elevation model, soil texture, and land use of the catchment. From these maps, other physio-graphical characteristics are derived as digital maps: e.g. maps of the soil and land use parameters, flow direction, stream network, slope. The filling in of missing data is possible but not necessary. Data from the time series in the model can be spatially distributed by the arithmetic mean of closest stations, nearest neighbours, lapse rate, or kriging. Potential global radiation can be computed with or without the slope orientation of each cell and the shading of its neighbouring cells. The difference between short-wave and long-wave solar radiation is expressed by the net radiation balance. In the surface energy balance, they are required for the determination of potential evapotranspiration. It is possible to choose among many methods for the calculation of potential evapotranspiration, which were selected on the basis of a detailed study (Horvát, 2007). The routing parameters are generated in a developed extension of the ESRI ArcView GIS program in a GIS interface. Ten global parameters serve to simplify some processes and for the best setting of the initial values. They are constants for all the cells in the catchment and they can be calibrated. Several methods for the calibration of the global parameters and also several objective functions for assessing the model’s efficiency (e.g. BIAS, Nash–Sutcliffe) are incorporated in the model. Output of the FRIER model are time series of the water balance components or spatial maps. The time series contain simulated discharge and its three components (overland flow, interflow and base flow) for any time step. The mean quantities for the whole catchment are calculated for each time step, e.g. air temperature, potential and actual evapotranspiration, rainfall, snowmelt. The output maps can be e.g. layers of total overland flow, interflow, base flow. 3.1.3. HBV The HBV model (originally developed at SMHI (Bergström, 1976; Bergström, 1992; Bergström and Forsman, 1973)) is a rainfall-runoff model, which includes conceptual numerical descriptions of hydrological processes at the catchment scale using various model routines. HBV can be used as a semi-distributed model by dividing the catchment into subbasins. Each subbasin is then divided into zones according to altitude, lake area, and vegetation. It can also be run as a lumped catchment model, using similar elevation and vegetation zones. The model is normally run on daily values of rainfall and Technical Report No. 26

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air temperature (that are corrected during calibration according to an altitude gradient and a snowfall correction factor), and daily or monthly potential evaporation regimes. The model is used for flood forecasting in the Nordic countries, and many other purposes. There are many different versions of HBV Model software besides the original SMHI version. HBV-WUR The model version “HBV light” (Seibert, 2005), used by WUR (and called HBV-WUR in this report), is a lumped version of the HBV model. It does not divide the catchment into subbasins, but makes use of elevation zones. The HBV-WUR model was forced with observed daily temperature and precipitation, and calculated daily potential evaporation for all catchments (Table 2). Temperature and precipitation were corrected for elevation according to predefined elevation zones. The model consists of four routines, i.e. a distributed snow routine and soil moisture routine, a lumped response routine representing groundwater, and a routing routine. Snow accumulation and melt are calculated by the degree-day method for a number of elevation (max. 10) and vegetation (max. 3) zones separately. In each of these zones, groundwater recharge and actual evapotranspiration are simulated as a function of actual water storage in the soil. Subsequently, the lumped response function, consisting of two linear reservoirs, transforms recharge into discharge. Finally, channel routing is computed by a triangular weighting function. A more comprehensive description of the model can be found in Seibert (2000; 2005) and Oosterwijk et al. (2009). Calibration of the HBV-WUR model was done on time series of observed discharge using the genetic calibration algorithm described by Seibert (2000). To give more weight to low flows, the logarithm of the Nash-Sutcliffe efficiency (lnReff) (Nash and Sutcliffe, 1970; Seibert, 2005) was used to evaluate the agreement between simulated and observed discharge. The first year of data was used as starting up year to initialize the model state. HBV-NVE The HBV model version used by NVE is the "Nordic" HBV model (Killingveit and Sælthun, 1995; Sælthun, 1996). This version (called HBV-NVE in this report) has the same setup as the HBV-WUR model. It is a lumped catchment model in which the spatial structure of the catchment is not explicitly modelled. Ten equal area height zones from the hypsometric curve for the catchments are defined, and land cover data is distributed by height zone. The model consists of the same four routines as the HBVWUR model, i.e. a distributed snow routine and soil moisture routine, a lumped response routine representing groundwater, and a routing routine. All processes contribute directly to discharge at the outlet without internal routing between elevation zones. Processes are represented as linear or simple non-linear relationships, and all are controlled by parameters determined during calibration. The model is driven by daily time series of air temperature and precipitation, and model parameters are adjusted to achieve a best fit relative to discharge observed at the catchment outlet. In the applications reported here, evapotranspiration was estimated by HBV-NVE using the temperature index method, rather than using monthly values as model input. In the HBV-NVE model calibrations, PEST parameter estimation routines (Doherty, 2004) based on PEST v. 11.2 were used to calibrate parameters for HBVNVE model. The HBV-NVE model was calibrated on the Nash-Sutcliffe efficiency (Reff) (Nash and Sutcliffe, 1970) and volume bias, see also Lawrence et al. (2009). HBV-TUC The version of HBV used in the Platis catchment by TUC (called HBV-TUC in this report) is the Integrated Hydrological Modelling System (IHMS 5.10.1) HBV 7.1, developed and provided by Swedish Meteorological and Hydrological Institute (SMHI) (Integrated Hydrological Modeling System, 2006). This model has the same setup and routines as the HBV-WUR and HBV-NVE models. Input data are observations of precipitation, air temperature, vapour pressure, wind speed, and estimates of potential evaporation. The evapotranspiration values used are long-term monthly averages. Air temperature, Technical Report No. 26

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vapour pressure, and wind speed are used for calculations of snow accumulation and melt. Discharge observations are used to calibrate the model, and to verify and correct the model before a runoff forecast. The first year of the simulation period is used as starting up year to initialize the model state. The model was calibrated manually, based on both efficiency criteria lnReff and Reff (Nash and Sutcliffe, 1970). More weight was given to lnReff, to focus on a better performance of low flows, according to the aims of the present study.

3.2.

Drought analysis

To determine drought events from the time series of simulated discharge, the threshold level method (Hisdal et al., 2004; Yevjevich, 1967) was applied. With this method, a drought occurs when the variable of interest (e.g. discharge, precipitation, recharge) is below a predefined threshold (Figure 2). The start of a drought event is indicated by the point in time when the variable falls below the threshold and the event continues until the threshold is exceeded again. Drought characteristics commonly derived with this method are beginning, end, duration, deficit volume, and minimum flow during an event (Fleig et al., 2006; Hisdal et al., 2004). The characteristics taken into account in this study are the number, mean duration, and mean deficit of drought events. Both a fixed and variable (seasonal, monthly, or daily) threshold can be used. In this study, a monthly threshold derived from the 80-percentile of the flow duration curve was applied. The discrete monthly threshold values were smoothed by applying a centred moving average of 30 days (Van Loon et al., 2010). To eliminate minor drought events, a minimum duration of 3 days was used. The thresholds were calculated for each time series of discharge (observed and simulated) separately, so each time series had a different threshold (see for discussion Section 5.6).

Figure 2 Threshold level method with a variable (monthly) threshold (data from Tallaksen and Van Lanen (2004)).

3.3.

Large-scale forcing data

The large-scale forcing dataset used in this study is the WATCH Forcing Data (WFD (Weedon et al., 2010)). It consists of gridded time series of meteorological variables (e.g. rainfall, snowfall, temperature, wind speed), both on a sub-daily and daily basis for 1958–2001. In this study, the daily data were used. For hydrological modelling with these data, the same periods were used as were available for the local forcing data in each catchment. The data have a resolution of 0.5º x 0.5º. The WFD originate from modification (bias-correction) of the ECMWF ERA-40 re-analysis data, which are sub-daily data on a one-degree spatial resolution. The different weather variables have been interpolated and corrected for the elevation differences between the ERA-40 one-degree grid and the CRU half-degree grid. For precipitation, the ERA-40 data were firstly adjusted to have the same number of wet (i.e. rain- or snow-) days as the CRU wet day data. Next the data were bias corrected using monthly GPCC precipitation totals (Schneider et al., 2008) and finally gauge-catch corrections were applied separately for rainfall and snowfall. Additionally, the interpolated ERA-40 near-surface temperatures were elevation corrected Technical Report No. 26

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and bias-corrected using both CRU monthly average temperatures and CRU monthly average diurnal temperature ranges. For more information the reader is referred to Weedon et al. (2010).

3.4.

Local forcing data

The local forcing data is different for each catchment and sometimes even for the different models used in one catchment. The description of the local forcing data that each model uses in a certain catchment, is given in Chapter 5 before the description of the modelling results.


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4. Differences between forcing datasets For each catchment, the WFD are compared with measured local values (same as used as local forcing in HBV-WUR and HBV-TUC simulations, see Chapter 5) to check the credibility of the large-scale forcing data. For the comparison of the two forcing datasets, only WFD grid cells that cover the catchments are used. Time series of catchment average forcing data were computed by calculating the weighted average according to the relative area of the catchment in each grid cell in case of two grid cells covering the area. The area of a WFD grid cell is much larger than the areas of the studied catchment (~2500 km2 vs. 73-210 km2) and also altitudes are different (Table 3). The grid cell averages might not be representative for the catchments, especially in regions with complex orography. Some form of altitude correction can be applied to correct for this difference. In some models used in this research (e.g. HBV-WUR and HBV-NVE), both the local and large-scale forcing datasets are corrected based on an altitude gradient and a snowfall correction factor. The corrected precipitation values are also compared (Annex 1), but show the same results as presented in this paragraph. The differences in mean annual temperature (T) and precipitation (P) between the two datasets are given in Table 3. The differences in mean annual temperature vary from -0.2 ºC to 2 ºC (WFD compared to local forcing data). In most catchments WFD temperatures are higher than temperatures measured locally, but this is not consistent for all catchments. The WFD either overestimate or underestimate the mean annual precipitation by maximally about 10%. Again there is no systematic bias for all catchments. There are some differences between the catchments, for example, in the Narsjø catchment the differences between the datasets are smaller than in the Platis catchment. In Figure 3 to Figure 7 monthly temperature and precipitation of both WFD and local forcing data are shown for each catchment. Differences between catchments are visible: in the Narsjø and Nedožery catchments temperature differences are minimal, but in the other catchments temperature is overestimated by the WFD throughout the year. The WFD precipitation is not consistently higher or lower than local precipitation throughout the year: in some months the WFD overestimate the precipitation, in other months precipitation is underestimated. Overall, the differences between the forcing datasets seem to be acceptable for further hydrological model applications. Table 3 Long-term average mean annual temperature and precipitation for both forcing datasets and comparison of WFD with local forcing data (see Chapter 5 HBV-WUR and HBV-TUC models for the origin of the local forcing data) Catchment Forcing No of Elevation (m Mean Difference Mean Difference (overlapping dataset grid a.m.s.l.) annual T T (ºC) annual P P (mm) time period) cells (ºC) (mm) Narsjø (1958–2001)

Local WFD

2

T: 628, P: 713 785

0.5 0.3

Upper-Metuje (1981–2001)

Local WFD

1

490 446

Upper-Sázava (1962-1999)

Local WFD

2

Nedožery (1973-2001)

Local WFD

Platis (1975-1998)

Local WFD


-0.2

586 632

46 (7.8%)

5.8 7.6

1.8

754 824

70 (9.3%)

T: 530, P: 628 461

6.7 7.6

0.9

717 782

65 (9.1%)

1

573 580

7.6 7.1

-0.5

849 795

-54 (-6.4%)

1

698 469

15.1 17.1

2

931 836

-95 (-10.2%)

13

Figure 3 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasets in Narsjø.

Figure 4 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasets in Upper-Metuje.

Figure 5 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasets in Upper-Sázava.

Figure 6 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasets in Nedožery.


14

Figure 7 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasets in Platis.


15


16

5. Results In each catchment, discharge is simulated with locally measured meteorological data and with the WFD. This chapter gives the results of the modelling and the drought analysis for each test basin. The NashSutcliffe values of all model results for all catchments are presented in Annex 2.

5.1. Narsjø 5.1.1. Modelling HBV-WUR model The local meteorological data used for the HBV-WUR model of the Narsjø catchment are measured at three stations. Daily precipitation was obtained from two stations (Figure 1), i.e. Ellefsplass and Tufsingdal (moved to Tufsingdal-Midtdal in 1991), located on either side of the catchment. Catchment precipitation was calculated by computing the arithmetic mean. Daily mean temperature was taken from the meteorological station Røros (about 25 km from Narsjø). Daily discharge was recorded at the outlet of the catchment (gauging station Narsjø, Figure 1). For modelling with HBV-WUR, data from all stations were used for the period 1958–2001. This entire period was used as calibration period of the model and calibration was done on the logarithm of the Nash-Sutcliffe efficiency (lnReff). Potential evapotranspiration was calculated with the Penman-Monteith method (Allen et al., 1998) for both local forcing data and WFD. In case of missing or incorrect meteorological data, assumptions and recommendations of Doorenbos and Pruitt (1975) and Allen et al. (1998) were followed. The model performs well for both forcing datasets (upper part of Table 4), especially if lnReff is considered. The winter low-flow conditions in Narsjø can be simulated quite well with the linear reservoir in HBV. In a winter situation in Narsjø, all precipitation is stored as snow and therefore there is no recharge. This means that the forcing datasets have little influence on the recession, which could explain the good performance of the model for both datasets. Hardly any difference was found between simulated discharge using the two different forcing datasets (Figure 8). In both cases, peaks are often underestimated (due to calibration on lnReff), but low flows are modelled well. Table 4 Nash-Sutcliffe values for HBV-WUR and HBV-NVE models with local and WFD forcing for the Narsjø catchment (grey columns depict which calibration criterion was used)

HBV-WUR HBV-NVE

local WFD local WFD

Reff

lnReff

0.7691 0.7814 0.7214 0.7067

0.9046 0.8902 0.5165 0.7884

a)


17

b)

Figure 8 Discharge (HBV-WUR model): (a) observed and simulated discharge for Narsjø, (b) detail of the discharge (low-flow range) for the period 1993–1994 (part of calibration period).

HBV- NVE model The local forcing data used for the HBV-NVE model is based on a 1 x 1 km gridded daily temperature and precipitation dataset of the Norwegian Meteorological Institute (met.no). For each day, the catchment mean temperature and precipitation are calculated (i.e. the mean of the grid cell within the Narsjø catchment) and used as input to the model. PET is calculated based on a temperature index method. The overlapping period between this source of local forcing data and WFD at the time of model simulations was 01-09-1961 until 31-12-2001. The calibration period of the HBV-NVE model is 19801989, the validation period is 1990-1999. The calibration criteria used are the Nash-Sutcliffe efficiency (Reff) and volume bias, and the best parameter set is used in the model simulations for the validation period. As this model was calibrated on the Nash-Sutcliffe efficiency (Reff), it gives a lower performance on low flows (see the lower lnReff values for HBV-NVE in Table 4). However, the model still shows reasonable results (Figure 9), especially when timing of low flows is considered. The results of the simulation with local forcing data seem to be better than the ones of the simulation with WFD. a)

b)

Figure 9 Discharge (HBV-NVE model): (a) observed and simulated discharge for Narsjø, (b) detail of the discharge (low-flow range) for the period 1993–1994 (part of validation period).

5.1.2. Drought analysis From the observed and simulated discharge time series, hydrological droughts were identified with the threshold level method (Section 3.2). Several drought characteristics were determined, i.e. number, mean duration, and mean deficit of droughts (Table 5). The relative difference between results from simulated and observed discharge for each characteristic is also included. These differences show that for the HBV-WUR model drought characteristics derived from simulations with WFD are more or less similar to those obtained with local forcing data. When comparing drought Technical Report No. 26

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events in both simulations with drought events in observed discharge, all characteristics correspond well, with deviations of 2–20%. The HBV-NVE model gives larger differences between the characteristics derived from the simulations and those from observed discharge than the HBV-WUR model. This can be caused by the different calibration criterion used by HBV-NVE (Reff instead of lnReff) or the short calibration period used (10 yr instead of 43 yr). For the HBV-NVE model, the drought characteristics derived from the simulations with WFD are closer to the ones of the observations than those from simulations with local forcing data, in particular the number of droughts and duration. This was not expected on the basis of the hydrographs (Figure 9). The model run using local forcing better reproduced the shape of the hydrograph during lowflow periods than the model run using WFD. However, the threshold level is different for all runs (see Section 3.2 and Figure 11), which influences average drought characteristics (see Section 5.6). Another reason for this difference can be the gridded local forcing data used in HBV-NVE. Drought events found in the period 1979-1983 in both local and simulated discharge are shown in Figure 10 for the HBV-WUR model and in Figure 11 for the HBV-NVE model. Overall, drought events (indicated in red) in the observed discharge are reproduced in the simulations of both models and with both datasets, especially the most severe events (e.g. summer 1982). However, there are also differences in duration, deficit, and start of the drought events between simulated and observed discharge (e.g. winter 1979-1980, and summer 1983). Table 5 Summary of discharge drought characteristics for Narsjø (number, mean duration, and mean deficit of droughts) and differences between the two forcing datasets Period % difference Number of droughts (-)

Mean duration drought (days)

Mean deficit (mm)


Observed discharge HBV-WUR with local forcing HBV-WUR with WFD Observed discharge HBV-NVE with local forcing HBV-NVE with WFD

1959-2001


1959-2001


1959-2001

1961-2001

1961-2001

1961-2001

140 128 128 136 221 157 23.29 25.83 23.78 22.24 13.42 19.66 6.59 5.34 5.52 6.11 3.19 3.84

-8.6 -8.6 62.5 15.4 10.9 2.1 -39.7 -11.6 -19.0 -16.2 -47.8 -37.2

19

a)

b)

c)

Figure 10 Drought events in Narsjø (HBV-WUR model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold.

a)

b)

c)

Figure 11 Drought events in Narsjø (HBV-NVE model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold. Technical Report No. 26

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5.2.

Upper-Metuje

5.2.1. Modelling HBV-WUR model The local meteorological data used for the HBV-WUR model of the Upper-Metuje catchment are measured at a representative meteorological station, i.e. the Bučnice station (Figure 1). Daily mean temperature and precipitation measurements were obtained for the period 1981–2001. Daily discharge was measured during the same period at the outlet of the catchment (gauging station MXII, Figure 1). The entire period 1981-2001 was used as calibration period and calibration was done on the logarithm of the Nash-Sutcliffe efficiency (lnReff). Potential evapotranspiration was calculated with the PenmanMonteith method (Allen et al., 1998) for both local forcing data and WFD. In case of missing or incorrect meteorological data, assumptions and recommendations of Doorenbos & Pruitt (1975) and Allen et al. (1998) were followed. The Nash-Sutcliffe efficiency (Reff) for the Upper-Metuje catchment is low (upper part of Table 6), mainly due to a poor simulation of peak flows (Figure 12). However, focus is on low flows, for which the model performance is better reflected by lnReff. This lnReff (0.71 and 0.65) is substantially higher than the Reff (0.48 and 0.35), but still lower than lnReff of the Narsjø catchment (0.90 and 0.89). In Upper-Metuje, low flows show a recession curve typical to a large, multiple aquifer system, which is more difficult to represent with the linear reservoir in HBV. Low flows in Upper-Metuje usually occur in summer and are caused by low precipitation and/or high evaporation rates. The forcing data have a larger influence on simulated discharge than in the Narsjø catchment, especially for the winter season. The differences between the forcing datasets of the Upper-Metuje catchment (Table 3) could cause the difference between the lnReff values of the simulations using both datasets. Table 6 Nash-Sutcliffe values for HBV-WUR and BILAN models with local and WFD forcing for the Upper-Metuje catchment (grey columns depict which calibration criterion was used)

HBV-WUR BILAN

local WFD local WFD

Reff

lnReff

0.4783 0.3489 0.4016 0.3973

0.7111 0.6509 0.5059 0.4892

a)

b)

Figure 12 Discharge (HBV-WUR model): (a) observed and simulated discharge for Upper-Metuje, (b) detail of the discharge (low-flow range) for the period 1993–1994. Technical Report No. 26

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BILAN model For the BILAN model of the Upper-Metuje catchment the same local meteorological data were used as for the HBV-WUR model. That means daily mean temperature and precipitation from Bučnice meteorological station and daily discharge from gauging station MXII (Figure 1) for the period 19812001. Potential evapotranspiration was calculated with the standard method of BILAN (Section 3.1.1). The entire period was used as calibration period. The standard two-step calibration procedure (Section 3.1.1) emphasizing the mean and low flows was used. The lnReff was also tested as the calibration criterion in both steps of the calibration. This has led, however, to unacceptable deviations in medium and high flows. A 2-yr time slice of the observed and simulated discharge series is shown in Figure 13. In general, the simulated discharge is consistent with observed discharge. Low flows are slightly overestimated. The difference between the simulation with local forcing and WFD is minimal. The Nash-Sutcliffe efficiencies for the BILAN model are comparable to those of the HBV-WUR model (Table 6), only lnReff values of BILAN are lower, which might be due to the different objective functions used in calibrating the two models. a)

b)

Figure 13 Discharge (BILAN model): (a) observed and simulated discharge for Upper-Metuje, (b) detail of the discharge (low-flow range) for the period 1993–1994.

5.2.2. Drought analysis From the observed and simulated discharge time series, several drought characteristics were determined with the threshold level method (Section 3.2), i.e. number, mean duration, and mean deficit of droughts (Table 7). The relative difference between results from simulated and observed discharge for each characteristic is also included. For the HBV-WUR model, drought characteristics derived from simulations with WFD are a bit closer to those derived from observed discharge (6.5 – 23.3%), than results from simulations with local forcing data (25.8 – 45.1%). This was not expected on the basis of the Nash-Sutcliffe values (upper part of Table 6). Differences in the shape of the threshold of each time series could be the reason for this difference (Figure 14). For the BILAN model, the differences between the drought characteristics from the simulated discharges (both local and WFD) and those from the observed discharges are larger (min. 52%) than for the HBV-WUR model and similar for both datasets. The larger difference could partly be caused by the different calibration criteria (lnReff for HBV-WUR and mean absolute and relative error for BILAN) and Technical Report No. 26

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partly by the fact that the BILAN simulations have very smooth recessions leading to longer droughts than in the observations. This is due to the soil module in the daily version of the BILAN model which does not allow the infiltrated water to runoff to subsurface flow until the soil water storage reaches its maximal capacity. Therefore, if the cumulative infiltration minus evapotranspiration in relatively dry periods with low intensities of precipitation does not exceed the maximal soil water storage (parameter of the model) the runoff is formed exclusively by the outflow from the groundwater reservoir. This will be modified in the coming version of the BILAN model. Since the smooth recessions are a consequence of the structure of the model, simulations with WFD lead to similar drought characteristics as simulations with local forcing data. In Figure 14 and Figure 15, drought events in the time series of observed and simulated discharges are indicated in red for the period November 1991 to November 1996. These figures illustrate that both models and both datasets have problems with exactly mimicking the droughts in observed discharge: some drought events are missed (e.g. summer 1992 and summer 1994) or extra drought events are added (winter 1993-1994). Again, drought deficit and duration are different in the simulations. However, the timing of most severe drought events in observed discharge is reproduced by the simulations, especially the drought in spring 1996. Furthermore, it is clear that the small interruptions of droughts in observed discharge are not reproduced by the smooth hydrographs of the BILAN model, leading to longer droughts with higher deficits. Table 7 Summary of discharge drought characteristics for Upper-Metuje (number, mean duration, and mean deficit of droughts) and differences between the two forcing datasets Period % difference Number of droughts (-)


Mean deficit (mm)

Observed discharge HBV-WUR with local forcing HBV-WUR with WFD Observed discharge BILAN with local forcing BILAN with WFD

1981-2001*


1981-2001*


1981-2001*

1980-2001*

1980-2001*

1980-2001*

98 69 82 100 46 48 14.59 21.17 17.99 14.91 32.26 31.46 0.93 1.17 0.87 0.98 2.95 2.93

-29.6 -16.3 -54.0 -52.0 45.1 23.3 116.4 111.0 25.8 -6.5 201.0 199.0

* = hydrological year from 1 November to 31 October


23

a)

b)

c)

Figure 14 Drought events in Upper-Metuje (HBV-WUR model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold.

a)

b)

c)

Figure 15 Drought events in Upper-Metuje (BILAN model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold. Technical Report No. 26

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5.3.

Upper-Sázava

5.3.1. Modelling HBV-WUR model The local meteorological data used for the HBV-WUR model of the Upper-Sázava catchment are measured at several meteorological stations in and around the catchment. Data were obtained for the period 1-11-1961 until 30-10-2000. Records of temperature are available for two stations, i.e. daily temperature from Přibyslav and minimum and maximum daily temperature from Svratouch. Daily data of precipitation are available from Přibyslav, Krucemburk, ŽĎár nad Sázavou-Stržanov, Křižánky, and Kadov meteorological stations. Some climatological data (minimum and maximum temperature, wind speed, and solar radiation) are available from Svratouch and they are used to calculate potential evapotranspiration with the Penman-Monteith method (Allen et al., 1998). In case of missing or incorrect meteorological data, assumptions and recommendations of Doorenbos & Pruitt (1975) and Allen et al. (1998) were followed. Although Vatín meteorological station has a suitable location close to the catchment border, we did not make an effort to obtain these data because of the short observation period, which started in the beginning of the 1990s. The location of mentioned stations is shown in Figure 1. Daily discharge was measured at station 1550 (Sázava u ŽĎáru nad Sázavou). The entire period was used as calibration period, with focus on low flows (lnReff as calibration criterion). The Nash-Sutcliffe values (upper part of Table 8) are reasonable and especially lnReff values are comparable for both datasets (0.63 and 0.60). A 2-yr time slice of the observed and simulated discharge series is shown in Figure 16. In the complete hydrograph (Figure 16a), peaks are not simulated correctly due to the focus on low flows during calibration. Low flows, however, are represented quite well (Figure 16b), and both datasets seem to give a similar result. Table 8 Nash-Sutcliffe values for HBV-WUR and BILAN models with local and WFD forcing for the Upper-Sázava catchment (grey columns depict which calibration criterion was used)

HBV-WUR BILAN

local WFD local WFD

Reff

lnReff

0.6115 0.5276 0.5583 0.5499

0.6313 0.6048 0.5363 0.5408

a)

b)

Figure 16 Discharge (HBV-WUR model): (a) observed and simulated discharge for Upper-Sázava, (b) detail of the discharge (low-flow range) for the period 1993–1994. Technical Report No. 26

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BILAN model For the BILAN model of the Upper-Sázava catchment the same local meteorological data were used as for the HBV-WUR model. That means daily temperature from Přibyslav, precipitation from Přibyslav, Krucemburk, ŽĎár nad Sázavou-Stržanov, Křižánky, and Kadov meteorological stations and daily discharge from gauging station 1550 (Sázava u ŽĎáru nad Sázavou) for the period 1961-2000 (Figure 1). Potential evapotranspiration was calculated with the standard method of BILAN (Section 3.1.1). The entire period was used as calibration period. The standard two-step calibration procedure (Section 3.1.1) emphasizing the mean and low flows was used. The lnReff was also tested as the calibration criterion in both steps of the calibration. This has led, however, to unacceptable deviations in medium and high flows. Also for the Upper-Sázava catchment, the Nash-Sutcliffe efficiencies for the BILAN model are comparable to, but slightly lower than those of the HBV-WUR model (Table 8). A 2-yr time slice of the observed and simulated discharge series is shown in Figure 17. Both peaks and low flows are generally simulated well, although the peaky behaviour of the observations is not reproduced by the model. Simulated discharge, both with local forcing and WFD, is much more smoothed than observed discharge (for explanation see Section 5.2.2. on page 22-23). a)

b)

Figure 17 Discharge (BILAN model): (a) observed and simulated discharge for Upper-Sázava, (b) detail of the discharge (low-flow range) for the period 1993–1994.

5.3.2. Drought analysis From the observed and simulated discharge time series, several drought characteristics were determined with the threshold level method (Section 3.2), i.e. number, mean duration, and mean deficit of droughts (Table 9). The relative difference between results from simulated and observed discharge for each characteristic is also included. For the HBV-WUR model, drought characteristics derived from simulations with local forcing data (272%) are a bit closer to those derived from observed discharge than results from simulations with WFD (47-100%). Especially the mean deficit of droughts in the simulation using local forcing (1.19 mm) is almost similar to the mean deficit of droughts in observed discharge (1.17 mm). For the BILAN model, the difference between drought characteristics of simulations and observations (69-253%) is larger than for the HBV-WUR model, but results of BILAN simulations with both datasets are similar. The larger difference could again partly be caused by the different calibration criteria, but clearly the smooth simulated discharges by the BILAN model play a large role as well. In Figure 18 and Technical Report No. 26

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Figure 19 drought events in simulated and observed discharges are indicated in red. Both HBV-WUR and BILAN models do not reproduce the small peaks during low-flow periods that interrupt droughts in observed discharge. Therefore, in both models, the number of drought events is underestimated and drought duration and deficit are overestimated. In BILAN this effect is even stronger than in HBV-WUR. In the period displayed in Figure 18 and Figure 19, there was a series of severe drought events in observed discharge starting in spring 1990 and lasting until autumn 1991. All simulated discharge time series represent these droughts quite well looking at timing and total duration. The smaller drought events in the observed discharge in 1988 and 1989 are sometimes completely missed or not well identified in the simulations. The HBV-WUR model simulated an extra drought in 1992 that is not visible in the observations. However, droughts determined in simulated discharge with both datasets are very similar in both models, especially the severe droughts. Table 9 Summary of discharge drought characteristics for Upper-Sázava (number, mean duration, and mean deficit of droughts) and differences between the two forcing datasets Period % difference Number of droughts (-)


Mean deficit (mm)


1962-2000*


1962-2000*


1962-2000*

1961-2000*

1961-2000*

1961-2000*

210 124 111 215 67 66 12.58 21.6 25.14 12.64 42.49 44.67 1.17 1.19 1.84 1.24 3.44 3.37

-41.0 -47.1 -68.8 -69.3 71.7 99.8 236.2 253.4 1.7 57.3 177.4 171.8

* = hydrological year from 1 November to 31 October


27

a)

b)

c)

Figure 18 Drought events in Upper-Sázava (HBV-WUR model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold.

a)

b)

c)

Figure 19 Drought events in Upper-Sázava (BILAN model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold. Technical Report No. 26

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5.4.

Nedožery

5.4.2. Modelling HBV-WUR model The local meteorological data used for the HBV-WUR model of the Nedožery catchment are measured at a number of stations in and around the catchment (Figure 1). Daily temperature data were derived from two meteorological stations: Prievidza and Turcianske Teplice, and daily precipitation measurements from five stations: Nitrianske Pravno, Chvojnica, Vricko, Slovenské Pravno, and Valaská Belá – Gapel. Catchment average temperature and precipitation were calculated using Thiessen polygons (Oosterwijk et al., 2009). Daily discharge is measured at gauging station Nedožery (Figure 1). The modelling period for Nedožery was 1974–2001. The entire period was used as calibration period and calibration was done on the logarithm of the Nash-Sutcliffe efficiency (lnReff). Potential evapotranspiration was calculated with the Penman-Monteith method (Allen et al., 1998) for both local forcing data and WFD. In case of missing or incorrect meteorological data, assumptions and recommendations of Doorenbos & Pruitt (1975) and Allen et al. (1998) were followed. For Nedožery the lnReff (upper part of Table 10) is similar to that of the Upper-Metuje catchment (Table 6) and Upper-Sázava catchment (Table 8), but lower than that of the Narsjø catchment (Table 4). The hydrological regime of Nedožery is less regular and more determined by a fast response to rainfall than that of Narsjø. Model results in Nedožery are therefore more dependent on the quality and representativeness of precipitation measurements. The difference in precipitation between the local and large-scale forcing data is quite small (Table 3) and this leads to quite similar simulations of the discharge with both datasets (Figure 20). Table 10 Nash-Sutcliffe values for HBV-WUR, BILAN, and FRIER models with local and WFD forcing for the Nedožery catchment (grey columns depict which calibration criterion was used) for the period 1981-2001

HBV-WUR FRIER BILAN

local WFD local WFD local WFD

Reff

lnReff

0.6639 0.6723 0.6065 0.516 0.4108 0.2231

0.671 0.7338 0.6332 0.5634 0.3071 -0.1243

a)


29

b)

Figure 20 Discharge (HBV-WUR model): (a) observed and simulated discharge for Nedožery, (b) detail of the discharge (low-flow range) for the period 1991–1992.

FRIER model Data from 6 rain-gauge (R) stations and 3 meteorological (M) stations (Figure 1) were used for modelling: Nitrianske Pravno (R, inside the catchment), Chvojnica (R, inside), Valaská Belá - Gapel (R, outside the catchment boundary), Slovenské Pravno (R, outside), Vricko (R, outside), Zliechov (R, outside), Prievidza (M, outside), Turčianske Teplice (M, outside) and Krížna (M, outside). Missing data were filled in from the nearest station which measured at that time. Four fictive stations were created in the mountains and near the catchment outlet due to missing meteorological stations in the catchment and missing measurements at all altitude levels, especially at the highest points of the catchment. The highest rain-gauge station is located in the Vricko village at 603 m a. s. l., the highest point in the catchment is the Reváň hill at 1204 m a. s.l.. Kriging method was used for spatial distribution of point measurements. Daily data of observed discharge was taken from Nedožery gauging station. The simulation period was 27 years, from 1981 to 2007. The entire period was used as calibration period and manual calibration was based on the Nash-Sutcliffe efficiency. Potential evapotranspiration was calculated by the Schendel method (Hoelting, 1980) for both local forcing data and WFD. Actual evapotranspiration was determined by the Krickij-Menkel-Rosinskij method (Hoelting, 1980). The Nash-Sutcliffe efficiency (Reff) was 0.61 for the model with local forcing data (Horvát and Machlica, 2009) and 0.52 for the model with WFD (middle part of Table 10). Figure 21 shows that the simulation with WFD is much more peaky that the one with local forcing data. Overall, the FRIER model shows a good agreement with observations for the low flows. a)

b)

Figure 21 Discharge (FRIER model): (a) observed and simulated discharge for Nedožery, (b) detail of the discharge (low-flow range) for the period 1991–1992.

BILAN model The local meteorological data used for the BILAN model of the Nedožery catchment are measured at a number of stations in and around the catchment (Figure 1). Daily precipitations were taken from 6 Technical Report No. 26

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stations: Nitrianske Pravno, Chvojnica, Valaska Bela, Slovenske Pravno, Vricko, and Prievidza. Thiessen polygons were used for calculation of mean precipitation. Daily mean temperature from meteorological station Prievidza was modified according to the mean altitude of the catchment. The daily mean air humidity was used from the same station. Discharges were taken from the Nedožery gauging station and they were converted to the runoff depth. The modelling time period was 1981 2007. Calibration of the BILAN model was done on time series of observed data (mean daily discharges) using the standard method of BILAN (Section 3.1.1). For the simulation with local forcing data the calibration period was 1981 – 1986, and for the simulation using WFD the calibration period was 1958 - 1960. The Nash-Sutcliffe values of the BILAN model are lower than those of HBV-WUR and FRIER (Table 10), especially for low flows (lnReff). Figure 22 shows that peaks are highly underestimated, especially in the run with WFD. Low flows are occasionally simulated reasonably well, but most are underestimated and small peaks during a recession are not reproduced by the model. Overall, the response of the BILAN model is much too smooth (for explanation see Section 5.2.2. on page 22-23). The simulation with WFD gives a lower agreement than the simulation with local forcing; some peaks are completely missed. Differences in the forcing data for the Nedožery catchment (Figure 6) can be part of the cause, but the short calibration period used is probably the main reason for the low performance of BILAN. a)

b)

Figure 22 Discharge (BILAN model): (a) observed and simulated discharge for Nedožery, (b) detail of the discharge (low-flow range) for the period 1991–1992.

5.4.3. Drought analysis From the observed and simulated discharge time series, several drought characteristics were determined with the threshold level method (Section 3.2), i.e. number, mean duration, and mean deficit of droughts (Table 11). The relative difference between results from simulated and observed discharge for each characteristic is also included. For the HBV-WUR model, drought characteristics derived from simulations with WFD (14-49%) are about equal or a bit closer to those derived from observed discharge than results from simulations with local forcing data (36-55%). However, the differences between droughts in simulations and observations are much larger than the differences between droughts in the two simulations with different forcing data.


31

For the FRIER model, the difference between simulations and observations (22-39%) is smaller than for the HBV-WUR model 1 . The absolute differences between drought characteristics in observed and simulated discharge are quite similar for both datasets, but the local forcing data lead to an underestimation of the number of droughts (and hence an overestimation of duration), while the WFD simulations overestimate the number of droughts (and underestimate duration). So, the FRIER model using local forcing data simulates less but more severe drought events than the model run using WFD. For the BILAN model, difference between drought characteristics derived from simulations and observations (59-224%) is larger than for the HBV-WUR and FRIER models. The number of droughts is underestimated and the mean duration and mean deficit are highly overestimated. This is due to the smooth hydrographs of the BILAN model, in which drought events are not interrupted by small peaks. The model using WFD performs slightly better than the one using local forcing data. Drought events in observed and simulated discharge for all models for the period January 1989 to January 1993 (Figure 23, Figure 24, and Figure 25) show the same pattern as the drought characteristics. The FRIER model is peaky and shows short drought events and the BILAN model is smooth and shows long drought events. Both the HBV-WUR and FRIER models give drought events of similar magnitude in the same period as the observations, whereas the BILAN model does not reproduce observed drought events or gives droughts when no drought is observed. The differences between drought events in simulations with both datasets are relatively small for all models, but the influence of the forcing datasets is clearly visible, for example in 1990 and 1993. This is due to the fast response of the Nedožery catchment to rainfall. Table 11 Summary of discharge drought characteristics for Nedožery (number, mean duration, and mean deficit of droughts) and differences between the two forcing datasets Period % difference Number of droughts (-)


Observed discharge HBV-WUR with local forcing HBV-WUR with WFD Observed discharge FRIER with local forcing Observed discharge FRIER with WFD Observed discharge BILAN with local forcing BILAN with WFD

1974-2001


1974-2001

1981-2006 1958-2001 1981-2001*

1981-2006 1958-2001 1981-2001*

161 103 102 163 127 232 300 117 39 48 12.4 19.17 18.44 11.07 13.87 13.75 9.86 11.48 37.23 30.17

-36.0 -36.6 -22.1 29.3 -66.7 -59 54.6 48.7 25.3 -28.3 224.3 162.8

1 Note

that the FRIER model runs with local forcing and WFD use a different simulation period. Therefore, the drought characteristics of the simulations are compared with droughts in observed discharge over two different time periods. This can partly explain the different results for both forcing datasets. Technical Report No. 26

32

Mean deficit (mm)


1974-2001 1981-2006 1958-2001 1981-2001*

1.22 1.77 1.39 0.95 1.32 1.35 1.07 0.99 2.97 2.45

45.1 13.9 38.9 -20.7 200 147.5

* = hydrological year from 1 November to 31 October a)

b)

c)

Figure 23 Drought events in Nedožery (HBV-WUR model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold.


33

a)

b)

c)

Figure 24 Drought events in Nedožery (FRIER model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold.

a)

b)

c)

Figure 25 Drought events in Nedožery (BILAN model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold. Technical Report No. 26

34

5.5.

Platis

5.5.2. Modelling HBV-TUC model The local meteorological data used for the HBV-TUC model of the Platis catchment were obtained from meteorological stations located in and around the catchment. Daily precipitation is measured at three stations located within the basin and eight surrounding stations. Average daily temperature data of the nearby meteorological stations were provided by the Hellenic National Meteorological Service. Timeseries of measured potential evaporation for Platis test basin were available from one nearby meteorological station, at monthly time-step. Mean daily precipitation was derived from IDW interpolation and daily average temperature was calculated from multiple linear regression for the period 1974-1999. Daily potential evaporation (PET) values were estimated through a combination of a locally calibrated Blaney-Cridlle equation (Allen and Pruitt, 1986) and adjustment indices from generic daily WFD PET data. Daily discharge time-series at the outlet of the catchment were used for model calibration. The first year of the period was used as a warm-up period to initialize model states. The model was calibrated manually, based on both efficiency criteria lnReff and Reff. More weight was given to lnReff, to focus on a better performance of low flows (Integrated Hydrological Modeling System, 2006). No data were preserved for validation purposes, in order to maximize the calibration period and, likely, calibration reliability. The HBV-TUC model delivered acceptable calibration results, as Reff for the whole period was 0.76 and lnReff was 0.80 (Table 12). The simulation with local forcing data better reproduces observed discharge than the simulation with WFD (Reff = 0.50 and lnReff = 0.72; also visible in Figure 26). Erroneous rainfall peaks in the WFD cause discharge responses that are not observed, e.g. in the autumn of 1986. The blocky observations in the low-flow range reflect measurement errors, which are quite common in catchments with zero-flow situations. Table 12 Nash-Sutcliffe values for HBV-TUC model with local and WFD forcing for the Platis catchment (grey columns depict which calibration criterion was used)

HBV-TUC

local WFD

Reff

lnReff

0.7602 0.5019

0.8001 0.7201

a)


35

b)

Figure 26 Discharge (HBV-TUC model): (a) observed and simulated discharge for Platis, (b) detail of the discharge (low-flow range) for the period 1985–1986.

5.5.3. Drought analysis From the observed and simulated discharge time series, several drought characteristics were determined with the threshold level method (Section 3.2), i.e. number, mean duration, and mean deficit of droughts (Table 13). The relative difference between results from simulated and observed discharge for each characteristic is also included. The number of droughts in the simulated time series correspond well to those in the observations (1 to -13% difference), mean drought duration is overestimated by the simulations (53-87%), and the simulations of mean deficit are again quite close to observations (-12 to 22%). The simulation with WFD seems to perform slightly better. This was not according to the expectations based on the Nash-Sutcliffe values (Table 12) and the hydrographs (Figure 26). This difference is also not spotted in the period plotted in Figure 27, in which drought events are indicated in red. In the period 1981-1986, drought events in observed discharge are reproduced by both model runs, except for the one in spring 1983. Furthermore, the deficit of the large drought in winter 1985-1986 is lower in the simulation with WFD than in the observations. In the Platis catchment, the forcing data have an influence on the simulation of discharge peaks and therefore on the drought characteristics. Table 13 Summary of discharge drought characteristics for Platis (number, mean duration, and mean deficit of droughts) and differences between the two forcing datasets Period % difference Number of droughts (-)

Observed discharge HBV-TUC with local forcing HBV-TUC with WFD

1974-1999*

80 69 81

-13.8 1.3



1974-1999*

16.26 30.39 24.9

86.9 53.1

Mean deficit (mm)


1974-1999*

3.2 3.89 2.83

21.6 -11.6

* = 31-8-1974 to 30-1-1999


36

a)

b)

c)

Figure 27 Drought events in Platis (HBV-TUC model): (a) observed discharge and threshold, (b) simulated discharge with local forcing data and threshold (c) simulated discharge with WFD and threshold.

5.6.

Discussion

The model simulations of the various partners in the WATCH project had a different focus, which governed the choice of what calibration method to use. If the focus was not (only) on low flows and drought during the calibration process, but f.e. on floods or hydropower production, like in the NVE-HBV model, model results for low flows and drought characteristics are less similar to observed low flows and droughts. For these cases, it is not straight-forward to draw conclusions about the suitability of the WFD for drought characteristics. However, we considered the differences in calibration procedure in our performance assessment. As indicated before (in Section 3.1 and Annex 3), the model parameters of the runs using WFD are (re)calibrated. This is required as the objective is to test whether large-scale forcing datasets can be used at catchment scale instead of local forcing data. In catchments where local forcing data is not available, calibration would also be done as parameter sets can not be taken from a model run using local forcing. The influence of calibrating or not calibrating on (low) discharges and drought characteristics has shown to be small (Annex 3). However, difficulties can arise in simulated soil moisture and groundwater storage, so, preferably, these variables should be used carefully. For the same reason, the threshold level used for drought analysis (Section 3.2) is separately calculated for the discharge simulated using local forcing data and discharge simulated using WFD. In catchments where local forcing data is not available, threshold levels would also need to be calculated and can not be taken from the simulation with local forcing data. A problem could arise if discharges simulated using WFD have the same dynamics as the ones simulated using local data, but are shifted up or down. Such an offset could yield similar values for the drought characteristics (i.e. number of droughts, drought duration, and deficit), as the threshold level would also be shifted. Therefore, we did not only study the summary tables of drought characteristics, but also a number of specific drought events. It depends on Technical Report No. 26

37

the purpose of a drought analysis (e.g. focus on duration or deficits only or also on absolute discharge values) whether the approach with different thresholds for each time series is valid. In this research, the use of different threshold levels for model runs using local forcing and model runs using large-scale forcing is necessary. However, one should bear in mind that if, for example, a pre-defined Ecological Minimum Flow would be used as threshold for all simulations, results might be different. Average drought characteristics may give the an incomplete impression of simulated drought events when simulations are less peaky than observations. More smooth simulated hydrographs result in a low number of droughts and large duration and deficit values, whereas, for a certain severe drought event, timing, total duration, and total volume of the event might be similar to those of the observed drought event. Therefore, it is essential to use a pooling method (Fleig et al., 2006; Tallaksen et al., 1997), or, as we did in this research, also investigate time series of drought events and compare the most severe droughts by visual inspection. Large-scale forcing datasets (like the WFD used in this research) are based on observed meteorological data of many meteorological stations around the world. These stations are not equally spread, but datarich (e.g. North-America and Europe) and data-poor (e.g. Africa and Asia) regions exist. In data-rich areas, where many meteorological stations are located and much observed data is available, large part of the observations is incorporated in the large-scale datasets and, therefore, these datasets show a high reliability. In data-poor areas, the large-scale datasets might be less capable of reproducing local climate. As little data for ground truthing is available in these regions, the reliability is unknown. In this research, we studied catchments in a data-rich region. It is unclear, however, which meteorological stations are used in the WFD for the selected cells. For further research, it is interesting to investigate if the results of the comparison between drought characteristics simulated with both local and large-scale forcing would change if the local forcing data used was not incorporated in the large-scale dataset. This is especially important in data-poor regions, as the large-scale data will be based on less local meteorological data.


38

6. Conclusions In this report, the results of modelling using both local and large-scale forcing data in the WATCH test basins, i.e. five small, contrasting catchments in Europe, are given. The objective of this study was to assess the suitability of large-scale forcing data in smaller catchments. The overall conclusions of this research are: 1. The differences between the WFD and local forcing seem to be acceptable as input for hydrological model applications (Chapter 4). 2. In all studied catchments and for all models, the difference between simulations and observations is much larger than difference between simulations with different forcing data (Chapter 5). 3. In all studied catchments, the difference between simulations with different models is much larger than difference between simulations with different forcing data (Chapter 5). 4. All models seem to be able to reproduce the most severe events in the observed discharge with both forcing datasets in all catchments (Chapter 5). Of course, there are differences between catchments and models: Narsjø catchment (Norway) - HBV-WUR model: simulations with local forcing and WFD give the same low flow and drought results, due to: o High similarity of both datasets o Calibration focussed on low flows, which mainly occur in winter when the forcing data have little influence - HBV-NVE model: drought characteristics of simulations with WFD are more similar to drought characteristics of observations, than simulations with local forcing data, due to: o Gridded local forcing data o Calibration on short period and not focussed on low flows - (Severe) drought events are mostly captured by both models using both datasets. Upper-Metuje (Czech Republic) - HBV-WUR model: simulations with WFD give lower Nash-Sutcliffe values for low flows, but more similar drought characteristics than simulations with local forcing, due to: o Differences in the datasets - BILAN model: simulations with local forcing and WFD give the same low flow and drought results, but large differences with observations, due to: o Smooth BILAN hydrographs do not reproduce small peaks in observed discharge which is caused by the structure of the BILAN model o Different calibration criterion used - (Severe) drought events are not always captured by the models using both datasets. Upper-Sázava (Czech Republic) - HBV-WUR model: simulations with local forcing and WFD give the same low flow and drought results - BILAN model: simulations with local forcing and WFD give the same low flow and drought results, but large differences with observations, due to: o Smooth BILAN hydrographs do not reproduce small peaks in observed discharge which is caused by the structure of the BILAN model o Different calibration criterion used Technical Report No. 26

39

-

(Severe) drought events are captured by both models using both datasets.

Nedožery (Slovakia) - Simulations with local forcing and WFD give similar low flow and drought results, due to the high similarity of both datasets o FRIER model: simulated discharge more peaky than observed discharge o BILAN model: simulated discharge smoother than observed discharge o HBV-WUR model: simulated discharge similar to (or slightly smoother than) observed discharge - Drought characteristics are best reproduced by FRIER using both datasets and severe drought events are captured best by HBV-WUR using both datasets. Platis (Crete) - Simulations with WFD give lower Nash-Sutcliffe values for low flows, but more similar drought characteristics (as compared with observations) than simulations with local forcing, due to: o Differences in the datasets - (Severe) drought events are captured by the model using both datasets. In conclusion, this study demonstrates that the large-scale forcing dataset used here (WFD) is suitable for drought analysis in these small, contrasting catchments in Europe.


40

References Allen, R.G., Pereira, L.S., Raes, D., 1998. Crop evapotranspiration : guidelines for computing crop water requirements. FAO irrigation and drainage papers;56. FAO, Rome. Allen, R.G., Pruitt, W.O., 1986. Rational use of the FAO Blaney-Criddle formula. Journal of Irrigation & Drainage Engineering - ASCE, 112(2): 139-155. Andreadis, K.M., Clark, E.A., Wood, A.W., Hamlet, A.F., Lettenmaier, D.P., 2005. Twentieth-century drought in the conterminous United States. J. Hydrometeorol, 6(6): 985-1001. Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments; PhD thesis; SMHI Reports RHO No. 7, Norrköping, Sweden. Bergström, S., 1992. The HBV model-its structure and applications. Report RH No. 4. In: Swedish Meteorological and Hydrological Institute (SMHI) (Ed.), Norrköping, Sweden, pp. 35. Bergström, S., Forsman, A., 1973. DEVELOPMENT OF A CONCEPTUAL DETERMINISTIC RAINFALL-RUNOFF MODEL. NORDIC HYDROL., 4(3): 1973. Doherty, J., 2004. PEST: Model-independent parameter estimation, User Manual 5th ed. Watermark Numerical Computing, Brisbane, Australia. Doorenbos, J., Pruitt, W.O., 1975. Guidelines for predicting crop water requirements. Irrigation and drainage paper. FAO;no. 24. FAO, Rome. Engeland, K., 2002. ECOMAG - Application to the Upper Glomma catchment. In: Department of Geosciences (Ed.). University of Oslo, Norway. Fleig, A.K., Tallaksen, L.M., Hisdal, H., Demuth, S., 2006. A global evaluation of streamflow drought characteristics. Hydrol. Earth System Sci., 10(4): 535-552. Gidrometeoizdat, 1976. Rekomendatsii po roschotu ispareniia s poverhnosti suchi. In: Gidrometeoizdat (Ed.), St. Peterburg. Hisdal, H., Tallaksen, L.M., Clausen, B., Peters, E., Gustard, A., 2004. Hydrological Drought Characteristics. In: Tallaksen, L.M., van Lanen, H.A.J. (Eds.), Hydrological Drought Processes and Estimation Methods for Streamflow and Groundwater. Elsevier Science B.V, Developments in Water Science, 48, pp. pp. 139-198. Hoelting, B., 1980. Hydrogeologie, Einfuhrunfg in the Allgemeine und Angewandte Hydrogeologie. Ferdinand Enke Verlag, Stuttgart, 340 pp. Hohenrainer, J., 2008. Propagation of drought through the hydrological cycle in two different climatic regions, der Albert-Ludwigs-Universität Freiburg i. Br. . Horvát, O., 2007. Parameterization of Hydrologic Processes in Runoff Modelling, Slovak University of Technology Bratislava, 129 pp pp. Horvát, O., Machlica, A., 2009. Inter-comparison of modelling results of Bilan and FRIER models in the upper Nitra catchment, 21st Conference of young scientists, Slovak Hydrometeorological Institute, Slovak Committee for Hydrology of the IHP, Bratislava, Slovakia, pp. 1-5. Integrated Hydrological Modeling System, 2006. IHMS v5.10, Swedish Meteorological and Hydrological Institute (SMHI). Killingveit, Å., Sælthun, N.R., 1995. Hydrological models. Hydropower Development 7: Hydrology. Trondheim: Norwegian Institute of Technology: 99-128. Lawrence, D., I. Haddeland, Langsholt, E., 2009. Calibration of HBV hydrological models using PEST parameter estimation. Machlica, A., Stojkovova, M., 2008. Groundwater drought in different geological conditions, IOP Journal Conf. Series: Earth and Environmental Science 4, pp. 9. Mitchell, T.D., Jones, P.D., 2005. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology, 25(6): 693-712. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I - A discussion of principles. J. Hydrol., 10(3): 282-290. Technical Report No. 26

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Oosterwijk, J., van Loon, A.F., A. Machlica, Horvát, O., van Lanen, H.A.J., Fendeková, M., 2009. Hydrological drought characteristics of the Nedožery sub catchment, Upper Nitra, Slovakia, based on HBV modelling. In: WATCH Technical report No. 20 (Ed.). http://www.euwatch.org/nl/25222760-Technical_Reports.html. Pavlakis, P., 2004. Hydrological study of Platis basin. Rakovec, O., van Loon, A.F., Horáček, S., Kašpárek, L., van Lanen, H.A.J., Novický, O., 2009. Drought analysis for the Upper Metuje and Upper Sázava catchments (Czech Republic) using the hydrological model HBV. In: WATCH Technical report No. 19 (Ed.). Wageningen University, Wageningen, The Netherlands, T. G. Masaryk Water Research Institute, Prague, Czech Republic http://www.eu-watch.org/nl/25222760-Technical_Reports.html. Sælthun, N.S., 1996. The Nordic HBV Model. Schneider, U., Fuchs, T., Meyer-Christoffer, A., Rudolf, B., 2008. Global Precipitation Analysis Products of the GPCC. In: Global Precipitation Climatology Centre (GPCC) (Ed.). http://www.dwd.de/bvbw/generator/Sites/DWDWWW/Content/Oeffentlichkeit/KU/KU4/KU42/en/ Reports__Publications/GPCC__intro__products__2008,templateId=raw,property=publicationFil e.pdf/GPCC_intro_products_2008.pdf, Deutscher Wetterdienst, Offenbach a. M., Germany. Seibert, J., 2000. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrol. Earth System Sci., 4(2): 215-224. Seibert, J., 2005. HBV light version 2, User's manual. In: Department of Physical Geography and Quaternary Geology (Ed.). Stockholm University, http://people.su.se/~jseib/HBV/HBV_manual_2005.pdf. Sheffield, J., Wood, E.F., 2007. Characteristics of global and regional drought, 1950-2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. Journal of Geophysical Research-Atmospheres, 112: D17. Shukla, S., Wood, A.W., 2008. Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2). Tallaksen, L.M., Madsen, H., Clausen, B., 1997. On the definition and modelling of streamflow drought duration and deficit volume. Hydrol. Sci. J.-J. Sci. Hydrol., 42(1): 15-33. Tallaksen, L.M., van Lanen, H.A.J. (Eds.), 2004. Hydrological drought : processes and estimation methods for streamflow and groundwater. Elsevier Science BV, Developments in water science; 48, The Netherlands. Uppala, S.M., Kallberg, P.W., Simmons, A.J., Andrae, U., Bechtold, V.D., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Van De Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Holm, E., Hoskins, B.J., Isaksen, L., Janssen, P., Jenne, R., McNally, A.P., Mahfouf, J.F., Morcrette, J.J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P., Woollen, J., 2005. The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131(612): 2961-3012. Van Huijgevoort, M.H.J., Anne F. van Loon, O. Rakovec, I. Haddeland, S. Hořácek, H.A.J. van Lanen, 2010. Drought assessment using local and large-scale forcing data in small catchments. In: Servat, E., S. Demuth, A. Dezetter, T. Daniell (Ed.), Global Change: Facing Risks and Threats to Water Resources. IAHS Publ. No. 340, Fez (Maroc). Van Lanen, H.A.J., Tallaksen, L.M., Candel, M., Carrera, J., Crooks, S., Engeland, K., Fendeková, M., Haddeland, I., Hisdal, H., Horacek, S., Bermúdez, J.J., van Loon, A.F., Machlica, A., Navarro, V., Novický, O., Prudhomme, C., 2008. Database with hydrometeorological variables for selected river basins: Metadata Catalogue. In: Technical report No. 4 (Ed.). WATCH. Van Loon, A.F., Fendeková, M., Hisdal, H., Horvát, O., Van Lanen, H.A.J., Machlica, A., Oosterwijk, J., Tallaksen, L.M., 2010. Understanding hydrological winter drought in Europe. In: Servat, E., S. Technical Report No. 26

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Demuth, A. Dezetter, T. Daniell (Ed.), Global Change: Facing Risks and Threats to Water Resources. IAHS Publ. No. 340, Fez (Maroc). Weedon, G.P., Gomes, S., Viterbo, P., Österle, H., Adam, J.C., Bellouin, N., Boucher, O., Best, M., 2010. The WATCH Forcing Data 1958-2001: a meteorological forcing dataset for land surfaceand hydrological-models. In: WATCH Technical report No. 22 (Ed.). http://www.euwatch.org/nl/25222760-Technical_Reports.html. Wilhite, D.A. (Ed.), 2000. DROUGHT A Global Assesment, Vol I &II, Routledge Hazards and Disasters Series, Routledge, London. Yevjevich, V., 1967. An objective approach to definition and investigations of continental hydrologic droughts. In: Hydrology Papers 23 (Ed.). Colorado State University, Fort Collins, USA.


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List of abbreviations BILAN FRIER HBV NVE TGM-WRI TUC UC WATCH WFD WUR

= "Balance" in Czech = Water distribution (Flow, Routing, IUH) model with accent to Evapotranspiration and Radiation methods = Hydrologiska Byråns Vattenbalansavdelning model = Norwegian Water Resources and Energy Directorate, Oslo, Norway = TGM Water Research Institute, Prague, Czech Republic = Technical University of Crete, Greece = Comenius University, Bratislava, Slovakia = EU-FP6 Integrated Project Water and Global Change = WATCH Forcing Data = Wageningen University and Research Centre, Wageningen, the Netherlands


44

Annex 1 – Total monthly precipitation for four test basins; original and elevation-corrected by the HBV-WUR model

Figure A1.1 Total monthly precipitation for both forcing datasets in Narsjø; original and corrected by the HBVWUR model.

Figure A1.2 Total monthly precipitation for both forcing datasets in Upper-Metuje; original and corrected by the HBV-WUR model.


i

Figure A1.3 Total monthly precipitation for both forcing datasets in Upper-Sázava; original and corrected by the HBV-WUR model.

Figure A1.4 Total monthly precipitation for both forcing datasets in Nedožery; original and corrected by the HBVWUR model.


ii

Annex 2 – Nash-Sutcliffe values for all models with local and WFD forcing for all catchments

Narsjø BILAN

local

Upper-Metuje

Reff 0.4016 lnReff 0.5059 WFD Reff 0.3973 lnReff 0.4892 FRIER local Reff lnReff WFD Reff lnReff HBV-WUR local Reff 0.7691 0.4783 lnReff 0.9046 0.7111 WFD Reff 0.7814 0.3489 lnReff 0.8902 0.6509 HBV-NVE local Reff 0.7214 lnReff 0.5165 WFD Reff 0.7067 lnReff 0.7884 HBV-TUC local Reff lnReff WFD Reff lnReff (grey columns depict which calibration criterion was used)


UpperSázava

Nedožery

Platis

0.5583 0.5363 0.5499 0.5408 0.6115 0.6313 0.5276 0.6048 -

0.4108 0.3071 0.2231 -0.1243 0.6065 0.6332 0.516 0.5634 0.6639 0.671 0.6723 0.7338 -

0.7602 0.8001 0.5019 0.7201

iii


iv

Annex 3 – Influence of (re)calibration on drought characteristics The objective of this research is to assess the suitability of large-scale meteorological datasets for drought analysis at catchment scale. To test whether these large-scale forcing datasets can be used instead of local forcing data (e.g. in catchments where local forcing data are not available), these datasets should be used in exactly the same way. That means that if a model run using local forcing data is calibrated, a model run using large-scale forcing data should also be calibrated (recalibration). In the catchments used in this study, both local and large-scale forcing data are available (which enables comparison). Consequently, the parameters obtained with calibration using local forcing data, can also be applied in the model run with large-scale forcing data. This is done only to explore the influence of (re)calibration on the model results. Of course, this can not be done in real-life situations where parameters from a calibration with local forcing data are not available. The HBV-WUR model was developed for four out of five test basins, i.e. Narsjø (Norway), Upper-Metuje (Czech Republic), Upper-Sázava (Czech Republic), and Nedožery (Slovakia). For these catchments three runs were done: 1) using local forcing data, parameters calibrated, 2) using WFD, parameters (re)calibrated, and 3) using WFD, parameters not (re)calibrated, but taken from run with local forcing data. In this Annex the results of this test are presented. The Nash-Sutcliffe values of the runs with WFD that are not calibrated, are lower than the ones of the calibrated runs (Table A3.1). This applies to all catchments and for both Reff and lnReff. The hydrographs of Upper-Metuje and Upper-Sázava (Figure A3.1 and A3.2) reveal that low flows deviate slightly between calibrating and not calibrating. For UpperMetuje the differences are much larger than for Upper-Sázava. Table A3.1 Nash-Sutcliffe values for the HBV-WUR model with local and WFD forcing (calibrated and not calibrated) for all catchments except Platis (grey columns depict which calibration criterion was used) UpperUpperNarsjø Metuje Sázava Nedožery HBV-WUR local_calibrated Reff 0.7691 0.4783 0.6115 0.6639 lnReff 0.9046 0.7111 0.6313 0.671 WFD_calibrated Reff 0.7814 0.3489 0.5276 0.6723 lnReff 0.8902 0.6509 0.6048 0.7338 Reff 0.5583 0.2812 0.4569 0.5897 WFD_not calibrated lnReff 0.8121 0.564 0.5222 0.6761

a)


v

b)

Figure A3.1 Discharge of the Upper-Metuje simulated with HBV-WUR: (a) observed and simulated discharge, (b) detail of the discharge (low-flow range) for the period 1993–1994.

a)

b)

Figure A3.2 Discharge of the Upper-Sázava simulated with HBV-WUR: (a) observed and simulated discharge, (b) detail of the discharge (low-flow range) for the period 1993–1994.

The effect of calibration on the discharge drought characteristics (Table A3.2) is small, especially for the number of droughts and mean duration. Deficit values are slightly more different when the parameters of the run using WFD are not calibrated, but taken from the run with local forcing. Again, differences exist between the catchments, e.g. the Upper-Metuje catchment shows the largest differences. Table A3.2 Summary of discharge drought characteristics for all catchments except Platis (number, mean duration, and mean deficit of droughts) UpperUpperNarsjø Metuje Sázava Nedožery Number of droughts (-)

Observed discharge HBV-WUR with local forcing HBV-WUR with WFD calibrated HBV-WUR with WFD not calibrated

140 128 128 123

98 69 82 68

210 124 111 112

161 103 102 106



23.29 25.83 23.78 25.74

14.59 21.17 17.99 21.25

12.58 21.6 25.14 24.2

12.4 19.17 18.44 18.95

Mean deficit (mm)


6.59 5.34 5.52 3.66

0.93 1.17 0.87 1.51

1.17 1.19 1.84 1.99

1.22 1.77 1.39 1.83


vi

Up to now, we only looked at discharge and not at other hydrological variables, like soil moisture and groundwater storage. Figure shows that calibration can lead to incorrect simulation of the stores of the model. In Upper-Metuje, soil moisture storage in the calibrated run using WFD (the green line in Figure A3.3b) has the same dynamics as soil moisture storage in the run using local forcing (the green line in Figure A3.3a), but much higher values. Groundwater storage in the calibrated run using WFD (the blue line in Figure A3.3b) is not only much higher than groundwater storage in the run using local forcing (the blue line in Figure A3.3b), but also has completely different dynamics. The soil moisture storage and groundwater storage in the not-calibrated run using WFD (Figure A3.3c) are comparable to the ones in the run using local forcing (Figure A3.3a). So, by calibrating the model, possible errors in the large-scale forcing dataset are compensated in the stores. This is not the case in all catchments. In Upper-Sázava, for example, both soil moisture and groundwater storage show the same dynamics for all three runs (Figure A3.4), although slight differences in the exact values are visible. a)

b)

c)

Figure A3.3 Water balance (HBV-WUR model): P, ET, soil moisture, groundwater, observed and simulated discharge for Upper-Metuje, for the period 1981–1985, a) local forcing data, b) WFD calibrated, c) WFD not calibrated. Technical Report No. 26

vii

a)

b)

c)

Figure A3.4 Water balance (HBV-WUR model): P, ET, soil moisture, groundwater, observed and simulated discharge for Upper- Sázava, for the period 1981–1985, a) local forcing data, b) WFD calibrated, c) WFD not calibrated.

The conclusion of this exercise is that the differences between calibrating and not calibrating are relatively small and that the soil moisture and groundwater storage of the calibrated model run using WFD should not be carefully. This is not problematic in this research because our focus is on discharge droughts, but it limits the application of large-scale datasets for other purposes.


viii

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