watch technical report no. 26 simulation of low flows and drought events

8/11/2019 WATCH Technical Report No. 26 Simulation of Low Flows and Drought Events

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Technical Report No. 26

SIMULATION OF LOW FLOWS AND DROUGHT EVENTSIN WATCH TEST BASINS:

IMPACT OF DIFFERENT CLIMATE FORCING DATASETS

Marjolein H.J. van Huijgevoort, Anne F. van Loon, Martin Hanel, Ingjerd Haddeland, Oliver Horvt,Aristeidis Koutroulis, Andrej Machlica, Graham Weedon, Miriam Fendekov, Ioannis Tsanis,Henny A.J. van Lanen

18 March 2011


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Technical Report No . 26 ii


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Technical Report No . 26 ii

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

Title: Simulation of low flows and drought events in WATCH testbasins: impact of climate forcing datasets

Authors: Marjolein H.J. van Huijgevoort, Anne F. van Loon, Martin Hanel,Ingjerd Haddeland, Oliver Horvt, Aristeidis Koutroulis,Andrej Machlica, Graham Weedon, Miriam Fendekov, IoannisTsanis, Henny A.J. van Lanen

Organisations: - Wageningen University - Hydrology and Quantitative WaterManagement 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 Investigateprocesses 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 understandingof the propagation of drought in different hydro-climatological regions,physical catchment structures and different scales, D 4.1.5 Genericmethod to quantify the propagation of a drought, and D 1.3.4 Reporton the uncertainty of the global water cycle of the 20th Century. The

technical report contributes to: (i) M4.1.6a Overview of majorhistorical events; part: drought, and M4.1-7 Analysis of majorhistorical extreme events with metamodel.

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


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Technical Report No . 26 iii


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Technical Report No . 26 iv

Table of Contents

Table of Contents .................................................................... iv

1. Introduction ......................................................................... 1Outline ............................................................................................................. 1

2. Catchment descriptions ..................................................... 32.1. Narsj ........................................................................................................... 52.2. Upper-Metuje ............................................................................................... 52.3. Upper-Szava ............................................................................................... 52.4. Nedoery ...................................................................................................... 62.5. Platis ............................................................................................................. 6

3.

Methodology ........................................................................ 7

3.1. Model descriptions ...................................................................................... 7

3.1.1.

BILAN ..................................................................................................... 73.1.2. FRIER ..................................................................................................... 8

3.1.3. HBV ........................................................................................................ 8HBV-WUR ........................................................................................................ 9HBV-NVE ......................................................................................................... 9HBV-TUC ......................................................................................................... 9

3.2. Drought analysis ....................................................................................... 103.3. Large-scale forcing data ........................................................................... 103.4. Local forcing data...................................................................................... 11

4. Differences between forcing datasets ............................ 13

5.

Results ............................................................................... 17

5.1. Narsj ......................................................................................................... 175.1.1. Modelling .............................................................................................. 17

HBV-WUR model ........................................................................................... 17HBV- NVE model ........................................................................................... 18

5.1.2. Drought analysis ................................................................................... 185.2. Upper-Metuje ............................................................................................. 21

5.2.1. Modelling .............................................................................................. 21HBV-WUR model ........................................................................................... 21BILAN model .................................................................................................. 22

5.2.2. Drought analysis ................................................................................... 22

5.3.

Upper-Szava ............................................................................................. 25

5.3.1. Modelling .............................................................................................. 25HBV-WUR model ........................................................................................... 25BILAN model .................................................................................................. 26

5.3.2. Drought analysis ................................................................................... 265.4. Nedoery .................................................................................................... 29

5.4.2. Modelling .............................................................................................. 29HBV-WUR model ........................................................................................... 29FRIER model ................................................................................................. 30BILAN model .................................................................................................. 30

5.4.3. Drought analysis ................................................................................... 31

5.5.

Platis ........................................................................................................... 35

5.5.2. Modelling .............................................................................................. 35


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Technical Report No . 26 v

HBV-TUC model ............................................................................................ 355.5.3. Drought analysis ................................................................................... 36

5.6. Discussion ................................................................................................. 37

6.

Conclusions....................................................................... 39

Narsj catchment (Norway) ........................................................................... 39

Upper-Metuje (Czech Republic) ..................................................................... 39

Upper-Szava (Czech Republic) ................................................................... 39Nedoery (Slovakia)....................................................................................... 40Platis (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 localand WFD forcing for all catchments ...................................... iii

Annex 3 Influence of (re)calibration on droughtcharacteristics .......................................................................... v


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Technical Report No . 26 1

1. Introduction

Drought is a natural hazard that occurs all over the world that can have large economic, social andenvironmental impacts (Wilhite, 2000). Drought is caused by below-average natural water availabilitydue to low precipitation and/or high evaporation rates. It is characterized as a deviation from normalconditions of the physical system (climate and hydrology), which is reflected in variables such asprecipitation, soil moisture, groundwater, and discharge (Tallaksen and van Lanen, 2004).

For drought analysis, time series of hydrometeorological variables are required. These time seriesshould 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, timeseries of hydrological data can be simulated, e.g. with rainfallrunoff models. However, for this type ofmodel, time series of hydrometeorological data are required for forcing and calibration. To overcome theproblem of lack of local forcing data, global gridded meteorological datasets might be suitable for thistype of hydrological modelling. Over the last decade, global gridded re-analysis meteorological datasetshave been developed based on observations and modelling, e.g. the ERA-40 re-analysis (Uppalaet al.,2005), the Climate Research Unit (CRU) dataset (Mitchell and Jones, 2005). Gridded, large-scale(0.5 x 0.5) meteorological datasets have already been used for soil moisture drought analyses in deUSA and globally (Andreadis et al., 2005;Sheffield and Wood, 2007)and for discharge drought at thecontinental scale (Shukla and Wood, 2008). These drought analyses at large scale gave reasonableresults when compared with broad characteristics derived from observations. However, the suitability oflarge-scale meteorological datasets to force models for drought analysis at catchment scale still needsto be investigated.

The objective of this study is to assess the suitability of large-scale meteorological datasets for droughtanalysis 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 droughtcharacteristics based on simulations using this large-scale forcing dataset with those derived fromsimulations using local, more detailed, forcing data.

Several WATCH test basins were used in this study to test the large-scale forcing dataset. The testbasins are Narsj (Norway), Upper-Metuje (Czech Republic), Upper-Szava (Czech Republic),Nedoery (Slovakia), and Platis (Crete, Greece). In each of the test basins, discharge was simulatedwith 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 werecompared.

OutlineFirst, 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 variablethreshold is explained. Also, a very short description of the large-scale forcing dataset, the WFD, isprovided. The comparison between the two forcing datasets for the test basins can be found inChapter 4. The results of the hydrological modelling and drought analysis with both forcing datasets aregiven in Chapter 5. Conclusions are presented in Chapter 6. Abbreviations used in this report areexplained on page 44. Finally, three Annexes are included, containing 1) figures showing thecomparison 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 moredetailed information about the catchments, the reader is referred to Van Lanen et al. (2008), VanHuijgevoort 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 mostimportant 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-Szava (Czech Republic), d)Narsj (Norway), e)Nedoery (Slovakia), and f)Platis(Crete).

Table 1


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Table1

CatchmentcharacteristicsofthestudiedcatchmentsNarsj(Norway),Upper-MetujeandUpper-Szava(CzechRe

public),Nedoery(Slovakia),andPlat

is(Crete).

Narsj

Upper-Metuje

Upper-Szava

Nedoery

Platis

area(km

2)

119

7

3.6

131.3

181

210

altitude(m

a.m.s.l.)

mean

(min-max)

945

(737

1595)

591

(459-7

80)

628

(487-8

05)

573

(288

1172)

698

(5-2

454)

climatetype()

Dfc

Cfb

Cfb

Dfb

Csa

observa

tionperiod*

1958-2

007

1982-2

005

1963-1

999

1974-2

006

1

974-1

998

tempera

ture(C)**

meanannual

(min;maxmonthly)

0.7

(Jan:

10.1

;Jul:11.9

)

5.9

(Jan:

3.9;Jul:15.5

)

6.8

(Jan:

3.2

;Jul:16.3)

7.6

(Jan:

2.8

;Jul:17.5

)

15

(Jan:

7.4

;Jul:23.7

)

precipita

tion(mm)**

meanannual

(min;maxmonthly)

594

(Mar:27;Jul:81)

746

(Apr:42;Jul:92)

717

(Feb:36;Jun:92)

873

(Feb:52;Jun:96)

930

(Aug:1;Dec:201)

discharg

e(mm/d)**

meanannual

(min;maxmonthly)

2.2

(Mar:0.2

9;May:8.0

)

0

.99

(Oct:0.66;Mar:1.9

)

0.8

2

(Aug:0.4

8;Mar:1.7)

0.9

6

(Aug:0.4

2;Mar:2.1

)

1.6

(Jul-Sep:0;Jan:3.9

)

*usedforcalculationofcatchmentcharact

eristicstemperature,

precipitation,a

nddischarge

**temp

erature,

precipitation,

anddischargedatatakenfromv

arioushydro-me

teorologicalstationsatdifferentlocations(seeFigure1andChapter4&5)


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2.1. Narsj

The Narsj catchment is located in an open mountainous area in Eastern Norway, between Oslo andTrondheim (Figure 1). It is a sub-basin of the Upper-Glomma, which is the headwater catchment of thelargest river in Norway, the Glomma. The Narsj catchment covers an area of 119 km2and 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 Narsj catchment are open area (60.9%), forest (24%), bogs (11.7%), andagriculture (0.4%). Lakes cover 3.1% of the catchment (Hohenrainer, 2008). The Narsj catchment hasa Nordic continental climate with cold winters and relatively warm summers (Kppen-Geiger climateDfc). Mean annual temperature is 0.7C (Table 1). Every winter, snow covers the catchmentcontinuously 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 Narsj is about 594 mm. The flowregime in the catchment is dominated by the snowmelt flood, which on average has its peak in May.The mean annual discharge of Narsj is 2.2 mm day-1. The catchment is dominated by hard rock, whichis covered by glacial deposits and a weathering layer with a variable thickness. During winter, whenprecipitation accumulates as snow, long low-flow periods occur. The presence of many bogs and lakesin the catchment delays the discharge during such dry periods and results in long recessions. Theminimum discharge is usually reached by late winter, just before the snow melt (Hohenrainer, 2008). Insummer, discharge is mainly determined by the catchments fast response to rainfall, resulting in aflashy 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 km2and its meanaltitude is 591 m a.m.s.l., with a minimum of 459 and a maximum of 780 m a.m.s.l. (Rakovecet al.,

2009). Deep valleys, gentle and steep slopes and plateaus are the characteristic elements of thelandscape. 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(Kppen-Geiger climate Cfb) with a mean annual temperature of 5.9C and a mean annual precipitationof 746 mm (Table 1). The mean annual discharge is 0.99 mm day-1. Discharge peaks occur in springdue 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 overlyingrather impermeable Permian-Carboniferous rocks. Groundwater in the Upper-Metuje catchment ischaracterised by deep circulation and high storage. This makes the catchment slowly responding toprecipitation.

2.3. Upper-SzavaThe Upper-Szava catchment is the headwater of the river Szava, which eventually drains into theriver Vltava and subsequently into the river Elbe. The focal area is the Upper-Szava catchmentupstream 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 Szava catchment (Rakovec et al., 2009; Van Lanen et al., 2008). Thecatchment is hilly with gentle slopes and flat wide valleys. The catchment altitude varies from 487 to805 m a.m.s.l., with a mean altitude of 628 m a.m.s.l. (Table 1). The dominant land use types in thecatchment are forest (50%) and cropland and grassland (40%). The bedrock of Upper-Szavacatchment consists predominantly of Proterozoic impermeable metamorphic rocks, which consists ofblack mica migmatite, gneiss and mica schist. There is no extensive groundwater storage (Rakovec etal., 2009). Mean annual precipitation sum in the catchment is 717 mm (Table 1). The lowest monthlyprecipitation is observed in February, the largest in June. The long term mean daily temperature is6.8C. The warmest month is July and the coldest is January. Mean annual discharge is 0.82 mm day-1.


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Floods occur regularly in spring because of snowmelt and low discharges predominantly occur insummer and beginning of autumn (Table 1). In an average year there is snow from November until April,with largest amounts in JanuaryMarch (2530 mm). In the Upper-Szava catchment withdrawal ofsurface water and discharge of waste water into the streams takes place.

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

2.5. Platis

The Platis catchment is located in the south-central part of the island of Crete in Greece and covers anarea of 210 km2 (Figure 1). The mean annual precipitation is estimated to be 930 mm and its meanelevation is 698 m a.m.s.l. which varies from 5 to 2454 m (Figure 1). The climate ranges between sub-humid Mediterranean and semi-arid with long hot and dry summer and relatively humid and cold winterwith a mean annual temperature of 15C (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 forestand semi-natural areas (53.5%) and agricultural areas (46.5%), and a small part of the catchment iscovered with artificial surfaces (0.1%). The hydrogeological bedrock of the area consists of impermeablequartzites and phyllites, as well as permeable carboniferous, limestone formations, neogene, andquaternary deposits (Pavlakis, 2004).


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3. Methodology

As mentioned in the introduction, we used rainfall-runoff models to simulate discharge with local andlarge-scale forcing, with the objective to test the suitability of large-scale forcing data in smallercatchments. 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 aredescribed.

3.1. Model descriptions

In each test basin, different rainfall-runoff models were used (Table 2). For each model, a shortdescription is given below. All models were calibrated separately for both forcing datasets. That isbecause the objective of this research was to investigate whether large-scale forcing datasets aresuitable in catchments where local meteorological observations are not available. So, to comparedrought characteristics in simulations with local and large-scale forcing, both datasets should be usedsimilarly. The effect of not (re)calibrating the models for the large-scale forcing dataset is described in

Section5.6 and Annex 3.Table 2 Overview of models used in each catchment and organisations performing the simulations (forabbreviations see page 44)

Test basin BILAN FRIER HBV

Narsj X(NVE & WUR)

Upper-MetujeX

(TGM-WRI)X

(WUR)

Upper-Szava X(TGM-WRI)

X(WUR)

Nedoery X(UC) X(UC) X(WUR)

Platis X(TUC)

3.1.1. BILANThe structure of the BILAN model is formed by a system of relationships describing basic principles ofwater balance on the land surface, in the zone of aeration (including the effect of vegetation cover), andin groundwater. Air temperature is used as an indicator of energy conditions, which affect significantlythe 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 dischargeseries 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 differentbioclimatic zones from empirical graphs given by Gidrometeoizdat (1976). The saturation deficit iscalculated from data on the air temperature and relative air humidity. It is possible to use potentialevapotranspiration 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. Themodel has six free parameters and uses an optimisation algorithm for calibration in gauged catchments.


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The standard calibration procedure of these parameters consists of two steps. In the first step, thestandard error or the mean absolute error of the simulated runoff is minimized to estimate theparameters significantly affecting the mean runoff. The remaining three parameters affecting the runoffdistribution into its individual components (direct and subsurface runoff and base flow) are thencalibrated 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 interms 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. FRIERFRIER is a physically-oriented rainfall-runoff model with distributed parameters (Horvt, 2007). Themodel divides a catchment into uniform spatial units on a grid scale, in which the water balance issimulated and discharge at the catchments outletis generated. Transformation of the surface runoff inthe catchment is simulated by approximating a diffusive wave model using the geometric and hydrauliccharacteristics of hillslopes and the stream network. The subsurface flow and percolation of each cell iscalculated using Darcys 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 airtemperature in any time step (min. 1 hour), and spatial layers of digital elevation model, soil texture, andland use of the catchment. From these maps, other physio-graphical characteristics are derived asdigital maps: e.g. maps of the soil and land use parameters, flow direction, stream network, slope. Thefilling 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 closeststations, nearest neighbours, lapse rate, or kriging. Potential global radiation can be computed with orwithout the slope orientation of each cell and the shading of its neighbouring cells. The differencebetween 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 ispossible to choose among many methods for the calculation of potential evapotranspiration, which wereselected on the basis of a detailed study (Horvt, 2007).

The routing parameters are generated in a developed extension of the ESRI ArcView GIS program in aGIS interface. Ten global parameters serve to simplify some processes and for the best setting of theinitial values. They are constants for all the cells in the catchment and they can be calibrated. Severalmethods for the calibration of the global parameters and also several objective functions for assessingthe models efficiency (e.g. BIAS, NashSutcliffe) are incorporated in the model.

Output of the FRIER model are time series of the water balance components or spatial maps. The timeseries contain simulated discharge and its three components (overland flow, interflow and base flow) forany time step. The mean quantities for the whole catchment are calculated for each time step, e.g. airtemperature, potential and actual evapotranspiration, rainfall, snowmelt. The output maps can be e.g.layers of total overland flow, interflow, base flow.

3.1.3. HBVThe HBV model (originally developed at SMHI (Bergstrm, 1976; Bergstrm, 1992; Bergstrm andForsman, 1973)) is a rainfall-runoff model, which includes conceptual numerical descriptions ofhydrological processes at the catchment scale using various model routines. HBV can be used as asemi-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


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air temperature (that are corrected during calibration according to an altitude gradient and a snowfallcorrection factor), and daily or monthly potential evaporation regimes. The model is used for floodforecasting in the Nordic countries, and many other purposes. There are many different versions of HBVModel software besides the original SMHI version.

HBV-WURThe model version HBV light (Seibert, 2005), used by WUR (and called HBV-WUR in this report), is alumped version of the HBV model. It does not divide the catchment into subbasins, but makes use ofelevation 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 precipitationwere corrected for elevation according to predefined elevation zones. The model consists of fourroutines, i.e. a distributed snow routine and soil moisture routine, a lumped response routinerepresenting groundwater, and a routing routine. Snow accumulation and melt are calculated by thedegree-day method for a number of elevation (max. 10) and vegetation (max. 3) zones separately. Ineach of these zones, groundwater recharge and actual evapotranspiration are simulated as a function ofactual 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 triangularweighting 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 ofobserved discharge using the genetic calibration algorithm described by Seibert (2000). To give moreweight 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. Thefirst year of data was used as starting up year to initialize the model state.

HBV-NVEThe HBV model version used by NVE is the "Nordic" HBV model (Killingveit and Slthun, 1995;Slthun, 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 explicitlymodelled. Ten equal area height zones from the hypsometric curve for the catchments are defined, andland cover data is distributed by height zone. The model consists of the same four routines as the HBV-WUR model, i.e. a distributed snow routine and soil moisture routine, a lumped response routinerepresenting groundwater, and a routing routine. All processes contribute directly to discharge at theoutlet without internal routing between elevation zones. Processes are represented as linear or simplenon-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 areadjusted to achieve a best fit relative to discharge observed at the catchment outlet. In the applicationsreported 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 parameterestimation routines (Doherty, 2004)based on PEST v. 11.2 were used to calibrate parameters for HBV-NVE model. The HBV-NVE model was calibrated on the Nash-Sutcliffe efficiency (Reff) (Nash andSutcliffe, 1970) and volume bias, see also Lawrence et al.(2009).

HBV-TUCThe version of HBV used in the Platis catchment by TUC (called HBV-TUC in this report) is theIntegrated Hydrological Modelling System (IHMS 5.10.1) HBV 7.1, developed and provided by SwedishMeteorological and Hydrological Institute (SMHI) (Integrated Hydrological Modeling System, 2006). Thismodel 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 potentialevaporation. The evapotranspiration values used are long-term monthly averages. Air temperature,


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vapour pressure, and wind speed are used for calculations of snow accumulation and melt. Dischargeobservations are used to calibrate the model, and to verify and correct the model before a runoffforecast. 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 lnReffand 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 variableof interest (e.g. discharge, precipitation, recharge) is below a predefined threshold (Figure 2). The startof a drought event is indicated by the point in time when the variable falls below the threshold and theevent continues until the threshold is exceeded again. Drought characteristics commonly derived withthis method are beginning, end, duration, deficit volume, and minimum flow during an event (Fleiget al.,2006; Hisdalet al., 2004). The characteristics taken into account in this study are the number, meanduration, 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 flowduration curve was applied. The discrete monthly threshold values were smoothed by applying acentred moving average of 30 days (Van Loon et al., 2010). To eliminate minor drought events, aminimum 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 discussionSection5.6).

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

3.3. Large-scale forcing dataThe 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 19582001. In this study, the daily data were used.For hydrological modelling with these data, the same periods were used as were available for the localforcing data in each catchment. The data have a resolution of 0.5 x 0.5. The WFD originate frommodification (bias-correction) of the ECMWF ERA-40 re-analysis data, which are sub-daily data on aone-degree spatial resolution. The different weather variables have been interpolated and corrected forthe elevation differences between the ERA-40 one-degree grid and the CRU half-degree grid. Forprecipitation, 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 precipitationtotals (Schneideret al., 2008) and finally gauge-catch corrections were applied separately for rainfalland snowfall. Additionally, the interpolated ERA-40 near-surface temperatures were elevation corrected


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and bias-corrected using both CRU monthly average temperatures and CRU monthly average diurnaltemperature 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 usedin 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 forcingin HBV-WUR and HBV-TUC simulations, see Chapter 5) to check the credibility of the large-scaleforcing data. For the comparison of the two forcing datasets, only WFD grid cells that cover thecatchments are used. Time series of catchment average forcing data were computed by calculating theweighted average according to the relative area of the catchment in each grid cell in case of two gridcells covering the area. The area of a WFD grid cell is much larger than the areas of the studiedcatchment (~2500 km2vs. 73-210 km2) and also altitudes are different (Table 3). The grid cell averagesmight not be representative for the catchments, especially in regions with complex orography. Someform of altitude correction can be applied to correct for this difference. In some models used in thisresearch (e.g. HBV-WUR and HBV-NVE), both the local and large-scale forcing datasets are correctedbased on an altitude gradient and a snowfall correction factor. The corrected precipitation values arealso 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 aregiven inTable 3.The differences in mean annual temperature vary from -0.2 C to 2 C (WFD comparedto local forcing data). In most catchments WFD temperatures are higher than temperatures measuredlocally, but this is not consistent for all catchments. The WFD either overestimate or underestimate themean 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 Narsj catchment thedifferences between the datasets are smaller than in the Platis catchment.

InFigure 3 toFigure 7 monthly temperature and precipitation of both WFD and local forcing data areshown for each catchment. Differences between catchments are visible: in the Narsj and Nedoerycatchments 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 orlower than local precipitation throughout the year: in some months the WFD overestimate theprecipitation, in other months precipitation is underestimated. Overall, the differences between theforcing 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 comparisonof WFD with local forcing data (see Chapter5 HBV-WUR and HBV-TUC models for the origin of the local forcingdata)Catchment(overlappingtime period)

Forcingdataset

No ofgridcells

Elevation (ma.m.s.l.)

Meanannual T

(C)

DifferenceT (C)

Meanannual P

(mm)

DifferenceP (mm)

Narsj Local T: 628, P: 713 0.5 586(19582001) WFD 2 785 0.3 -0.2 632 46 (7.8%)

Upper-Metuje Local 490 5.8 754(19812001) WFD 1 446 7.6 1.8 824 70 (9.3%)

Upper-Szava Local T: 530, P: 628 6.7 717(1962-1999) WFD 2 461 7.6 0.9 782 65 (9.1%)

Nedoery Local 573 7.6 849(1973-2001) WFD 1 580 7.1 -0.5 795 -54 (-6.4%)

Platis Local 698 15.1 931(1975-1998) WFD 1 469 17.1 2 836 -95 (-10.2%)


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Figure 3 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasetsin Narsj.

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

Figure 5 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasetsin Upper-Szava.

Figure 6 Long-term average mean monthly temperature and total monthly precipitation for both forcing datasetsin Nedoery.


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


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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 Nash-Sutcliffe values of all model results for all catchments are presented in Annex 2.

5.1.Narsj

5.1.1. Modelling

HBV-WUR modelThe local meteorological data used for the HBV-WUR model of the Narsj catchment are measured atthree stations. Daily precipitation was obtained from two stations (Figure 1), i.e. Ellefsplass andTufsingdal (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 fromthe meteorological station Rros (about 25 km from Narsj). Daily discharge was recorded at the outletof the catchment (gauging station Narsj, Figure 1). For modelling with HBV-WUR, data from all stationswere used for the period 19582001. This entire period was used as calibration period of the model andcalibration was done on the logarithm of the Nash-Sutcliffe efficiency (lnReff). Potentialevapotranspiration was calculated with the Penman-Monteith method (Allenet al., 1998) for both localforcing data and WFD. In case of missing or incorrect meteorological data, assumptions andrecommendations 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 isconsidered. The winter low-flow conditions in Narsj can be simulated quite well with the linear reservoir

in HBV. In a winter situation in Narsj, all precipitation is stored as snow and therefore there is norecharge. This means that the forcing datasets have little influence on the recession, which couldexplain the good performance of the model for both datasets. Hardly any difference was found betweensimulated discharge using the two different forcing datasets (Figure 8). In both cases, peaks are oftenunderestimated (due to calibration on lnReff), but low flows are modelled well.

Table 4Nash-Sutcliffe values for HBV-WUR and HBV-NVE models with local and WFD forcing for the Narsjcatchment (grey columns depict which calibration criterion was used)

Reff lnReff

HBV-WUR local 0.7691 0.9046WFD

0.7814 0.8902HBV-NVE local 0.7214 0.5165WFD 0.7067 0.7884

a)


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b)

Figure 8 Discharge (HBV-WUR model): (a) observed and simulated discharge for Narsj, (b) detail of thedischarge (low-flow range) for the period 19931994 (part of calibration period).

HBV- NVE modelThe local forcing data used for the HBV-NVE model is based on a 1 x 1 km gridded daily temperatureand precipitation dataset of the Norwegian Meteorological Institute (met.no). For each day, thecatchment mean temperature and precipitation are calculated (i.e. the mean of the grid cell within theNarsj catchment) and used as input to the model. PET is calculated based on a temperature indexmethod. 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 1980-1989, 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 validationperiod.

As this model was calibrated on the Nash-Sutcliffe efficiency (Reff), it gives a lower performance on lowflows (see the lower lnReffvalues for HBV-NVE inTable 4). However, the model still shows reasonableresults (Figure 9), especially when timing of low flows is considered. The results of the simulation withlocal 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 Narsj, (b) detail of thedischarge (low-flow range) for the period 19931994 (part of validation period).

5.1.2. Drought analysisFrom the observed and simulated discharge time series, hydrological droughts were identified with thethreshold 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 fromsimulated and observed discharge for each characteristic is also included.

These differences show that for the HBV-WUR model drought characteristics derived from simulationswith WFD are more or less similar to those obtained with local forcing data. When comparing drought


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events in both simulations with drought events in observed discharge, all characteristics correspond well,with deviations of 220%.

The HBV-NVE model gives larger differences between the characteristics derived from the simulationsand 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 yrinstead of 43 yr). For the HBV-NVE model, the drought characteristics derived from the simulations withWFD are closer to the ones of the observations than those from simulations with local forcing data, inparticular 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 low-flow periods than the model run using WFD. However, the threshold level is different for all runs (seeSection3.2 andFigure 11), which influences average drought characteristics (see Section5.6). Anotherreason 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 inFigure 10 for the HBV-WUR model and inFigure 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 withboth datasets, especially the most severe events (e.g. summer 1982). However, there are alsodifferences in duration, deficit, and start of the drought events between simulated and observeddischarge (e.g. winter 1979-1980, and summer 1983).

Table 5 Summary of discharge drought characteristics for Narsj (number, mean duration, and mean deficit ofdroughts) and differences between the two forcing datasets

Period % difference

Number of droughts(-)

Observed discharge 1959-2001 140HBV-WUR with local forcing 128 -8.6

HBV-WUR with WFD 128 -8.6Observed discharge 1961-2001 136HBV-NVE with local forcing 221 62.5HBV-NVE with WFD 157 15.4

Mean duration drought(days)

Observed discharge 1959-2001 23.29HBV-WUR with local forcing 25.83 10.9HBV-WUR with WFD 23.78 2.1Observed discharge 1961-2001 22.24HBV-NVE with local forcing 13.42 -39.7HBV-NVE with WFD 19.66 -11.6

Mean deficit(mm)

Observed discharge 1959-2001 6.59HBV-WUR with local forcing 5.34 -19.0HBV-WUR with WFD 5.52 -16.2Observed discharge 1961-2001 6.11HBV-NVE with local forcing 3.19 -47.8HBV-NVE with WFD 3.84 -37.2


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a)

b)

c)

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

a)

b)

c)

Figure 11 Drought events in Narsj (HBV-NVE model): (a) observed discharge and threshold, (b) simulateddischarge with local forcing data and threshold (c) simulated discharge with WFD and threshold.


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BILAN modelFor the BILAN model of the Upper-Metuje catchment the same local meteorological data were used asfor the HBV-WUR model. That means daily mean temperature and precipitation from Bunicemeteorological station and daily discharge from gauging station MXII (Figure 1) for the period 1981-

2001. Potential evapotranspiration was calculated with the standard method of BILAN (Section3.1.1).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 thecalibration criterion in both steps of the calibration. This has led, however, to unacceptable deviations inmedium and high flows.

A 2-yr time slice of the observed and simulated discharge series is shown inFigure 13.In general, thesimulated discharge is consistent with observed discharge. Low flows are slightly overestimated. Thedifference between the simulation with local forcing and WFD is minimal. The Nash-Sutcliffe efficienciesfor the BILAN model are comparable to those of the HBV-WUR model (Table 6), only lnReffvalues ofBILAN 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 thedischarge (low-flow range) for the period 19931994.

5.2.2. Drought analysisFrom the observed and simulated discharge time series, several drought characteristics weredetermined with the threshold level method (Section3.2), i.e. number, mean duration, and mean deficit

of droughts (Table 7). The relative difference between results from simulated and observed dischargefor each characteristic is also included.

For the HBV-WUR model, drought characteristics derived from simulations with WFD are a bit closer tothose derived from observed discharge (6.5 23.3%), than results from simulations with local forcingdata (25.8 45.1%). This was not expected on the basis of the Nash-Sutcliffe values (upper part ofTable 6). Differences in the shape of the threshold of each time series could be the reason for thisdifference (Figure 14).

For the BILAN model, the differences between the drought characteristics from the simulateddischarges (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 thedifferent calibration criteria (lnReff for HBV-WUR and mean absolute and relative error for BILAN) and


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partly by the fact that the BILAN simulations have very smooth recessions leading to longer droughtsthan in the observations. This is due to the soil module in the daily version of the BILAN model whichdoes not allow the infiltrated water to runoff to subsurface flow until the soil water storage reaches itsmaximal capacity. Therefore, if the cumulative infiltration minus evapotranspiration in relatively dryperiods 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 bemodified in the coming version of the BILAN model. Since the smooth recessions are a consequence ofthe structure of the model, simulations with WFD lead to similar drought characteristics as simulationswith local forcing data.

InFigure 14 andFigure 15,drought events in the time series of observed and simulated discharges areindicated in red for the period November 1991 to November 1996. These figures illustrate that bothmodels 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 areadded (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 inobserved discharge are not reproduced by the smooth hydrographs of the BILAN model, leading tolonger droughts with higher deficits.

Table 7 Summary of discharge drought characteristics for Upper-Metuje (number, mean duration, and meandeficit of droughts) and differences between the two forcing datasets

Period % difference


Observed discharge 1981-2001* 98HBV-WUR with local forcing 69 -29.6HBV-WUR with WFD 82 -16.3

Observed discharge 1980-2001* 100BILAN with local forcing 46 -54.0BILAN with WFD 48 -52.0


Observed discharge 1981-2001* 14.59HBV-WUR with local forcing 21.17 45.1HBV-WUR with WFD 17.99 23.3Observed discharge 1980-2001* 14.91BILAN with local forcing 32.26 116.4BILAN with WFD 31.46 111.0

Mean deficit

(mm)

Observed discharge 1981-2001* 0.93

HBV-WUR with local forcing 1.17 25.8HBV-WUR with WFD 0.87 -6.5Observed discharge 1980-2001* 0.98BILAN with local forcing 2.95 201.0BILAN with WFD 2.93 199.0

* = hydrological year from 1 November to 31 October


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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) simulateddischarge with local forcing data and threshold (c) simulated discharge with WFD and threshold.


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5.3. Upper-Szava

5.3.1. Modelling

HBV-WUR model

The local meteorological data used for the HBV-WUR model of the Upper-Szava catchment aremeasured at several meteorological stations in and around the catchment. Data were obtained for theperiod 1-11-1961 until 30-10-2000. Records of temperature are available for two stations, i.e. dailytemperature from Pibyslav and minimum and maximum daily temperature from Svratouch. Daily data ofprecipitation are available from Pibyslav, Krucemburk, r nad Szavou -Stranov, Kinky, andKadov meteorological stations. Some climatological data (minimum and maximum temperature, windspeed, and solar radiation) are available from Svratouch and they are used to calculate potentialevapotranspiration with the Penman-Monteith method (Allenet al., 1998). In case of missing or incorrectmeteorological data, assumptions and recommendations of Doorenbos & Pruitt (1975) and Allen et al.(1998) were followed. Although Vatn meteorological station has a suitable location close to thecatchment border, we did not make an effort to obtain these data because of the short observationperiod, which started in the beginning of the 1990s. The location of mentioned stations is shown inFigure 1. Daily discharge was measured at station 1550 (Szava u ru nad Szavou). The entireperiod was used as calibration period, with focus on low flows (lnReffas calibration criterion).

The Nash-Sutcliffe values (upper part of Table 8) are reasonable and especially lnReff values arecomparable for both datasets (0.63 and 0.60). A 2-yr time slice of the observed and simulated dischargeseries is shown inFigure 16.In the complete hydrograph (Figure 16a), peaks are not simulated correctlydue to the focus on low flows during calibration. Low flows, however, are represented quite well (Figure16b), 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-Szavacatchment (grey columns depict which calibration criterion was used)

Reff lnReff

HBV-WUR local 0.6115 0.6313WFD 0.5276 0.6048

BILAN local 0.5583 0.5363WFD 0.5499 0.5408

a)

b)

Figure 16 Discharge (HBV-WUR model): (a) observed and simulated discharge for Upper-Szava, (b) detail ofthe discharge (low-flow range) for the period 19931994.


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BILAN modelFor the BILAN model of the Upper-Szava catchment the same local meteorological data were used asfor the HBV-WUR model. That means daily temperature from Pibyslav, precipitation from Pibyslav,Krucemburk, r nad Szavou-Stranov, Kinky, and Kadov meteorological stations and daily

discharge from gauging station 1550 (Szava u ru nad Szavou) for the period 1961-2000(Figure 1). Potential evapotranspiration was calculated with the standard method of BILAN(Section3.1.1). The entire period was used as calibration period. The standard two-step calibrationprocedure (Section 3.1.1) emphasizing the mean and low flows was used. The lnReffwas also tested asthe calibration criterion in both steps of the calibration. This has led, however, to unacceptabledeviations in medium and high flows.

Also for the Upper-Szava catchment, the Nash-Sutcliffe efficiencies for the BILAN model arecomparable to, but slightly lower than those of the HBV-WUR model (Table 8). A 2-yr time slice of theobserved and simulated discharge series is shown inFigure 17.Both peaks and low flows are generallysimulated 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 observeddischarge (for explanation see Section5.2.2.on page22-23).

a)

b)

Figure 17 Discharge (BILAN model): (a) observed and simulated discharge for Upper-Szava, (b) detail of thedischarge (low-flow range) for the period 19931994.

5.3.2. Drought analysisFrom the observed and simulated discharge time series, several drought characteristics were

determined with the threshold level method (Section3.2), i.e. number, mean duration, and mean deficitof droughts (Table 9). The relative difference between results from simulated and observed dischargefor each characteristic is also included.

For the HBV-WUR model, drought characteristics derived from simulations with local forcing data (2-72%) 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) isalmost 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, butclearly the smooth simulated discharges by the BILAN model play a large role as well. InFigure 18 and


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Figure 19 drought events in simulated and observed discharges are indicated in red. Both HBV-WURand BILAN models do not reproduce the small peaks during low-flow periods that interrupt droughts inobserved discharge. Therefore, in both models, the number of drought events is underestimated anddrought 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 inobserved discharge starting in spring 1990 and lasting until autumn 1991. All simulated discharge timeseries represent these droughts quite well looking at timing and total duration. The smaller droughtevents in the observed discharge in 1988 and 1989 are sometimes completely missed or not wellidentified in the simulations. The HBV-WUR model simulated an extra drought in 1992 that is not visiblein the observations. However, droughts determined in simulated discharge with both datasets are verysimilar in both models, especially the severe droughts.

Table 9 Summary of discharge drought characteristics for Upper-Szava (number, mean duration, and meandeficit of droughts) and differences between the two forcing datasets

Period % difference


Observed discharge 1962-2000* 210HBV-WUR with local forcing 124 -41.0HBV-WUR with WFD 111 -47.1Observed discharge 1961-2000* 215BILAN with local forcing 67 -68.8BILAN with WFD 66 -69.3


Observed discharge 1962-2000* 12.58HBV-WUR with local forcing 21.6 71.7HBV-WUR with WFD 25.14 99.8Observed discharge 1961-2000* 12.64

BILAN with local forcing 42.49 236.2BILAN with WFD 44.67 253.4

Mean deficit(mm)

Observed discharge 1962-2000* 1.17HBV-WUR with local forcing 1.19 1.7HBV-WUR with WFD 1.84 57.3Observed discharge 1961-2000* 1.24BILAN with local forcing 3.44 177.4BILAN with WFD 3.37 171.8



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a)

b)

c)

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


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5.4. Nedoery

5.4.2. Modelling

HBV-WUR model

The local meteorological data used for the HBV-WUR model of the Nedoery catchment are measuredat a number of stations in and around the catchment (Figure 1). Daily temperature data were derivedfrom two meteorological stations: Prievidza and Turcianske Teplice, and daily precipitationmeasurements from five stations: Nitrianske Pravno, Chvojnica, Vricko, Slovensk Pravno, and ValaskBel Gapel. Catchment average temperature and precipitation were calculated using Thiessenpolygons (Oosterwijk et al., 2009). Daily discharge is measured at gauging station Nedoery (Figure 1).The modelling period for Nedoery was 19742001. The entire period was used as calibration periodand calibration was done on the logarithm of the Nash-Sutcliffe efficiency (lnReff). Potential evapotrans-piration was calculated with the Penman-Monteith method (Allenet al., 1998)for both local forcing dataand WFD. In case of missing or incorrect meteorological data, assumptions and recommendations ofDoorenbos & Pruitt (1975) and Allen et al.(1998) were followed.

For Nedoery the lnReff(upper part ofTable 10)is similar to that of the Upper-Metuje catchment (Table6)and Upper-Szava catchment (Table 8), but lower than that of the Narsj catchment (Table 4). Thehydrological regime of Nedoery is less regular and more determined by a fast response to rainfall thanthat of Narsj. Model results in Nedoery are therefore more dependent on the quality andrepresentativeness of precipitation measurements. The difference in precipitation between the local andlarge-scale forcing data is quite small (Table 3) and this leads to quite similar simulations of thedischarge with both datasets (Figure 20).

Table 10 Nash-Sutcliffe values for HBV-WUR, BILAN, and FRIER models with local and WFD forcing for theNedoery catchment (grey columns depict which calibration criterion was used) for the period 1981-2001

Reff lnReff

HBV-WUR local 0.6639 0.671WFD 0.6723 0.7338

FRIER local 0.6065 0.6332WFD 0.516 0.5634

BILAN local 0.4108 0.3071WFD 0.2231 -0.1243

a)


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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 frommeteorological station Prievidza was modified according to the mean altitude of the catchment. Thedaily mean air humidity was used from the same station. Discharges were taken from the Nedoerygauging 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 dailydischarges) using the standard method of BILAN (Section 3.1.1). For the simulation with local forcingdata the calibration period was 1981 1986, and for the simulation using WFD the calibration periodwas 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 therun with WFD. Low flows are occasionally simulated reasonably well, but most are underestimated andsmall peaks during a recession are not reproduced by the model. Overall, the response of the BILANmodel is much too smooth (for explanation see Section5.2.2.on page22-23). The simulation with WFDgives a lower agreement than the simulation with local forcing; some peaks are completely missed.

Differences in the forcing data for the Nedoery catchment (Figure 6)can be part of the cause, but theshort 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 Nedoery, (b) detail of thedischarge (low-flow range) for the period 19911992.

5.4.3. Drought analysisFrom the observed and simulated discharge time series, several drought characteristics were

determined with the threshold level method (Section3.2), i.e. number, mean duration, and mean deficitof droughts (Table 11). The relative difference between results from simulated and observed dischargefor each characteristic is also included.

For the HBV-WUR model, drought characteristics derived from simulations with WFD (14-49%) areabout equal or a bit closer to those derived from observed discharge than results from simulations withlocal forcing data (36-55%). However, the differences between droughts in simulations and observationsare much larger than the differences between droughts in the two simulations with different forcing data.


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For the FRIER model, the difference between simulations and observations (22-39%) is smaller than forthe HBV-WUR model1 . The absolute differences between drought characteristics in observed andsimulated discharge are quite similar for both datasets, but the local forcing data lead to anunderestimation of the number of droughts (and hence an overestimation of duration), while the WFDsimulations 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 andobservations (59-224%) is larger than for the HBV-WUR and FRIER models. The number of droughts isunderestimated and the mean duration and mean deficit are highly overestimated. This is due to thesmooth 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 toJanuary 1993 (Figure 23, Figure 24, and Figure 25) show the same pattern as the droughtcharacteristics. 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 ofsimilar magnitude in the same period as the observations, whereas the BILAN model does notreproduce observed drought events or gives droughts when no drought is observed. The differencesbetween drought events in simulations with both datasets are relatively small for all models, but theinfluence of the forcing datasets is clearly visible, for example in 1990 and 1993. This is due to the fastresponse of the Nedoery catchment to rainfall.

Table 11 Summary of discharge drought characteristics for Nedoery (number, mean duration, and mean deficitof droughts) and differences between the two forcing datasets

Period % difference

Number of droughts(-) Observed discharge 1974-2001 161HBV-WUR with local forcing 103 -36.0

HBV-WUR with WFD 102 -36.6Observed discharge 1981-2006 163FRIER with local forcing 127 -22.1Observed discharge 1958-2001 232FRIER with WFD 300 29.3Observed discharge 1981-2001* 117BILAN with local forcing 39 -66.7BILAN with WFD 48 -59


Observed discharge 1974-2001 12.4HBV-WUR with local forcing 19.17 54.6HBV-WUR with WFD 18.44 48.7Observed discharge 1981-2006 11.07FRIER with local forcing 13.87 25.3Observed discharge 1958-2001 13.75FRIER with WFD 9.86 -28.3Observed discharge 1981-2001* 11.48BILAN with local forcing 37.23 224.3BILAN with WFD 30.17 162.8

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 canpartly explain the different results for both forcing datasets.


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Mean deficit(mm)

Observed discharge 1974-2001 1.22HBV-WUR with local forcing 1.77 45.1HBV-WUR with WFD 1.39 13.9Observed discharge 1981-2006 0.95FRIER with local forcing 1.32 38.9Observed discharge 1958-2001 1.35FRIER with WFD 1.07 -20.7Observed discharge 1981-2001* 0.99BILAN with local forcing 2.97 200BILAN with WFD 2.45 147.5


a)

b)

c)

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


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a)

b)

c)

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

a)

b)

c)

Figure 25 Drought events in Nedoery (BILAN model): (a) observed discharge and threshold, (b) simulateddischarge with local forcing data and threshold (c) simulated discharge with WFD and threshold.


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b)

Figure 26 Discharge (HBV-TUC model): (a) observed and simulated discharge for Platis, (b) detail of thedischarge (low-flow range) for the period 19851986.

5.5.3. Drought analysisFrom the observed and simulated discharge time series, several drought characteristics weredetermined with the threshold level method (Section3.2), i.e. number, mean duration, and mean deficitof droughts (Table 13). The relative difference between results from simulated and observed dischargefor each characteristic is also included.

The number of droughts in the simulated time series correspond well to those in the observations (1to -13% difference), mean drought duration is overestimated by the simulations (53-87%), and thesimulations of mean deficit are again quite close to observations (-12 to 22%). The simulation with WFDseems to perform slightly better. This was not according to the expectations based on the Nash-Sutcliffevalues (Table 12) and the hydrographs (Figure 26). This difference is also not spotted in the periodplotted in Figure 27, in which drought events are indicated in red. In the period 1981-1986, droughtevents 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 WFDthan in the observations. In the Platis catchment, the forcing data have an influence on the simulation ofdischarge peaks and therefore on the drought characteristics.

Table 13 Summary of discharge drought characteristics for Platis (number, mean duration, and mean deficit ofdroughts) and differences between the two forcing datasetsPeriod % difference


Observed discharge 1974-1999* 80HBV-TUC with local forcing 69 -13.8HBV-TUC with WFD 81 1.3


Observed discharge 1974-1999* 16.26HBV-TUC with local forcing 30.39 86.9HBV-TUC with WFD 24.9 53.1

Mean deficit(mm)

Observed discharge 1974-1999* 3.2HBV-TUC with local forcing 3.89 21.6HBV-TUC with WFD 2.83 -11.6

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


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a)

b)

c)

Figure 27 Drought events in Platis (HBV-TUC model): (a) observed discharge and threshold, (b) simulateddischarge 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, whichgoverned the choice of what calibration method to use. If the focus was not (only) on low flows anddrought during the calibration process, but f.e. on floods or hydropower production, like in the NVE-HBVmodel, model results for low flows and drought characteristics are less similar to observed low flows anddroughts. For these cases, it is not straight-forward to draw conclusions about the suitability of the WFDfor drought characteristics. However, we considered the differences in calibration procedure in ourperformance 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 beused 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 usinglocal forcing. The influence of calibrating or not calibrating on (low) discharges and droughtcharacteristics has shown to be small (Annex 3). However, difficulties can arise in simulated soilmoisture and groundwater storage, so, preferably, these variables should be used carefully.

For the same reason, the threshold level used for drought analysis (Section3.2)is separately calculatedfor the discharge simulated using local forcing data and discharge simulated using WFD. In catchmentswhere local forcing data is not available, threshold levels would also need to be calculated and can notbe taken from the simulation with local forcing data. A problem could arise if discharges simulated usingWFD have the same dynamics as the ones simulated using local data, but are shifted up or down. Suchan 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 thesummary tables of drought characteristics, but also a number of specific drought events. It depends on


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6. Conclusions

In this report, the results of modelling using both local and large-scale forcing data in the WATCH testbasins, i.e. five small, contrasting catchments in Europe, are given. The objective of this study was toassess 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 (Chapter4).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(Chapter5).

3. In all studied catchments, the difference between simulations with different models is muchlarger than difference between simulations with different forcing data (Chapter5).

4. All models seem to be able to reproduce the most severe events in the observed discharge withboth forcing datasets in all catchments (Chapter5).

Of course, there are differences between catchments and models:

Narsj 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 datasetso 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 datao 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 droughtresults, but large differences with observations, due to:

o Smooth BILAN hydrographs do not reproduce small peaks in observed discharge whichis caused by the structure of the BILAN modelo Different calibration criterion used

- (Severe) drought events are not always captured by the models using both datasets.

Upper-Szava (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 modelo Different calibration criterion used


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