Sensitivity of a soil-vegetation-atmosphere-transfer scheme to input data resolution and data classification

Journal of Hydrology (2008) 351, 154–169

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Sensitivity of a soil-vegetation-atmosphere-transferscheme to input data resolution and dataclassification

Helge Bormann *

University of Oldenburg, Department of Biology and Environmental Sciences, Uhlhornsweg 84, 26121 Oldenburg, Germany

Received 19 October 2006; received in revised form 12 November 2007; accepted 5 December 2007

00do

KEYWORDSInput data;Data resolution;Data classification;Sensitivity;SVAT scheme;Regional hydrologicalmodelling

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* Tel.: +49 441 7984459; faxE-mail address: helge.borm

r ª 200.011

: +49 44ann@un

Summary This paper presents modelling of the effects of input data resolution and clas-sification of a regionally applied soil-vegetation-atmosphere-transfer (SVAT) scheme.Most SVAT schemes were developed at local scales but often are applied at regional scalesto simulate regional water balances and to predict effects of environmental changes oncatchment hydrology. Applying models at different scales requires investigating sensitivityto the available input data. In this study, investigated input data include soil maps, veg-etation classifications, topographic information and weather data of varying temporal andspatial resolutions. Target quantities are simulated water fluxes such as evapotranspira-tion rates, groundwater recharge and runoff generation rates. Model sensitivity is esti-mated with respect to water balances and water flows, focusing on different timeperiods (months, years). The soil vegetation atmosphere transfer scheme SIMULAT isapplied to two different catchments representing different environments where data setsof varying data quality and resolution are available. Results show that, on an annual timescale, SIMULAT is most sensitive to aggregation of soil information and mis-classification invegetation data. On the monthly time scale, SIMULAT is also very sensitive to disaggrega-tion of precipitation data. The sensitivity to spatial distribution of land-use data and spa-tio-temporal resolution of weather data is low. Based on the investigations, a ranking ofthe sensitivity of the model to resolution and classification of different input data sets isproposed. Minimum requirements concerning data resolution for regional scale SVATapplications are derived.ª 2007 Elsevier B.V. All rights reserved.

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Sensitivity of a soil-vegetation-atmosphere-transfer scheme to input data resolution and data classification 155

Introduction

Analyses of uncertainty in hydrological modelling have be-come (and still are) a hot topic in hydrological sciences.One of the first tools for analysing uncertainty was theGeneralised Likelihood Uncertainty Estimation (GLUE)developed by Beven and Binley (1992). GLUE and similarmethods focused on parameter uncertainty and were ap-plied to different simulation models and in different situa-tions and regions. Refsgaard and Storm (1996) identifieddifferent sources of errors and uncertainty: systematic ver-sus random errors in input data, errors in model parametersand errors in model structure. They called the differencesbetween measured and simulated values ‘‘uncertainties’’,and they discussed which uncertainty components couldbe minimised by calibration. Many studies followed the clas-sification of uncertainty sources proposed by Refsgaard andStorm (1996) and focused on the analysis of model parame-ter uncertainty (e.g., Haan, 1989; Kuczera and Mroczkow-ski, 1998; Eckhardt et al., 2003).

Later, modelling studies analysed the effects of modelstructure in more detail (e.g., Van der Perk, 1997; Buttset al., 2004; Sieber and Uhlenbrock, 2005) and the implieduncertainty followed by model comparison projects suchas the Distributed Model Intercomparison Project (DMIP;Smith et al., 2004). Recently, the methods have been devel-oped further to explicitly analyse uncertainties that arecaused by model structure and model parameters (e.g.,GLUE; Beven and Binley, 1992). Implicitly, GLUE also consid-ers input data uncertainty, but it cannot be explicitly distin-guished from parametric uncertainty. Up to now there is nomethod available to explicitly analyse all uncertaintysources defined by Refsgaard and Storm (1996). Thus, stud-ies are required to analyse as many sources of uncertainty aspossible in order to enable modellers to weigh the impor-tance of the individual sources of uncertainty.

The effect of input data uncertainty, in particular, needsto be quantified given that regional modelling of waterfluxes and water balances requires several different datasets. On the one hand, these data are inevitably a simplifi-cation of the real catchment properties (e.g., digital eleva-tion models, digital soil maps). These simplifications causeuncertainties with respect to simulated water flows andwater balances, which are dependent on the target scalein time and space. On the other hand, many models weredeveloped at local scales but often are applied to simulateregional-scale water balances. These models were devel-oped based on local-scale knowledge and then were appliedusing readily available input data. Applying models at differ-ent scales and using different sources of input data requirean investigation of the model sensitivity to the available in-put data.

Many authors have investigated the effects of single in-put data sets on model results. They investigated the influ-ence of the quality and resolution of soil properties (e.g.,Finke et al., 1996; Bormann et al., 1999a,b; Christiaensand Feyen, 2001; Minasny and McBratney, 2002), topography(e.g., Wolock and Price, 1994; Zhang and Montgomery,1994; Holmes et al., 2000), weather data (e.g., Bormannet al., 1996; Worlen et al., 1999) and rainfall data (e.g. Belland Moore, 2000; Andreassian et al., 2001). Kuo et al. (1999)

and Bormann (2006) studied the effect of spatial aggrega-tion of all spatial input data sets required by catchmentmodels (soils, landuse, and topography), but they did notanalyse the separate effects of the individual input datasources. Thus an integrative analysis of all these aspectsof input data uncertainty has not been carried out, althoughincreasing the size of an investigated catchment mostlyleads to a decreasing data availability, which may have animpact on the achieved simulation results.

In this paper, all input data sets required by regionalhydrological models (weather data, soil information, topog-raphy representation, and vegetation data sets) are used fora comprehensive sensitivity analysis. The impact of dataresolution on simulated water flows and water balances isanalysed using all different resolutions of input data forone local and one regional scale catchment in northernGermany. The data set with highest resolution is thereby as-sumed to be the best one. Measurement errors are notinvestigated; they rather are assumed to be of the samemagnitude for the different data resolutions. The soil-vege-tation-atmosphere-transfer (SVAT) scheme SIMULAT is ap-plied to identify these effects. SIMULAT (Diekkruger andArning, 1995; Richter et al., 1996) is a 1-dimensional modeldeveloped to simulate local-scale water fluxes. It has beenextended to calculate regional-scale water flows as well(Bormann, 2001; Stephan, 2003; Giertz, 2004). The ap-proach carried out in this study is a sequential sensitivityanalysis, focusing on the effects of varying resolution andclassification of input data and model parameters derivedfrom input data. One of the input data sets is changed ata time with all others are kept constant. This topic is rele-vant because data availability decreases with increasingscale, and data quality is not necessarily homogenous forlarge regions. A SVAT scheme was selected for this studyfor two reasons: (1) SVAT schemes are applied on a rangeof scales (from homogenous site scale up to regional catch-ment scale), and (2) SVAT schemes can be easily coupled to,or already are integrated into, atmospheric models. Theyare suitable for climate impact studies for long-term analy-sis of water-balance changes at regional scales.

Materials and methods

SIMULAT model

The hydrological site model SIMULAT (Diekkruger and Arn-ing, 1995; Bormann, 2001) is a physically based and contin-uous hydrological SVAT scheme which has been developedto simulate local-scale hydrological processes. The modelinternal time step is variable but should consider the diurnalvariations of weather for the calculation of evapotranspira-tion and infiltration. In this study, an hourly time step wasused. Soil water flow was calculated on a daily time step,and model output was also provided in daily resolution. SIM-ULAT focuses on vertical water fluxes and is a typical SVATscheme applied to calculate coupled water and nutrientfluxes; alternatively, SIMULAT can be used to provide thelower boundary condition for meteorological models repre-senting the hydrological processes at the land’s surface.Basic equations included in SIMULAT (see also Table 1) arethe Richards’ equation to calculate soil water flux and the

Table 1 Basic hydrological processes and model approaches of the SIMULAT model

Process Approach

Interception Storage approach: storage capacity proportional to leaf area indexPotential evapotranspiration (PET) Penman Monteith equation (Monteith, 1965): plant specific parameters

characterising plant propertiesActual evapotranspiration Reduction of PET by actual soil moisture status (Feddes et al., 1978; Ritchie, 1972)Snow melt Degree day approachInfiltration Semianalytic solution of the Richards’ equation (Smith and Parlange, 1978)Infiltration excess runoff Difference between rainfall rate and infiltration rateSoil water flow Richards’ equationInterflow Darcy’s lawLower boundary condition(groundwater recharge)

Free drainage

Base flow Outflow of a linear groundwater reservoir (subcatchment specific)

156 H. Bormann

Penman–Monteith equation the potential evapotranspira-tion (PET). Actual evapotranspiration (ETA) was calculatedfrom PET taking into account the actual soil moisture status(approaches of Feddes et al. (1978) for transpiration andRitchie (1972) for evaporation). Further processes consid-ered by SIMULAT were the separation of rainfall into surfacerunoff and infiltration (performed by a semi-analytical solu-tion of the Richards’ equation (Smith and Parlange, 1978)),interflow (based on Darcy’s law), groundwater recharge andthe snowmelt (degree-day approach). A plant growth modelis not included; instead, mean seasonal development ofplant parameters necessary for the Penman-Monteith equa-tion were estimated by linear interpolation of values givenfrom the literature (Table 2). At the site scale, which isthe scale for which SVAT schemes were developed, runoffwas calculated by accumulating all three runoff components(surface runoff, interflow, and groundwater recharge) foreach time step. At the scale of local and regional catch-ments, surface runoff and interflow were routed by a con-

Table 2 Main model parameters of the SIMULAT model

Model parameters References and exemplary p

Leaf area index (LAI) Function representing plant1996)

Interception storage capacity Storage capacity per leaf arMinimum stomata resistance Plant specific values (LopmPlant height Linear function representinAerodynamic resistance Determined from wind spee

logarithmic wind profile (LoRooting depth Linear function representing

et al., 1996)Albedo Plant and soil specific valueEmpirical soil factor Ritchie (1972)Critical soil suctions Feddes et al. (1978)Saturated hydraulic conductivity Pedotransfer function of RaSaturated water content Pedotransfer function of RaResidual water content Pedotransfer function of RaPore size distribution index Pedotransfer function of RaBubbling pressure Pedotransfer function of RaStandard deviation in saturatedhydraulic conductivity

Double value of saturated h

centration-time-based approach. Base flow was generatedby one linear groundwater storage per subcatchment, whichcollected groundwater recharge from all SVATs within a sub-catchment. With the exception of linear groundwater stor-age constants, no calibration of the SIMULAT model wasperformed. SIMULAT is a physically based model, and there-fore all model parameters are measurable at the local scale.Model parameters were taken from the literature (Table 2)or derived by transfer functions such as the pedotransferfunction of Rawls and Brakensiek (1985). Diekkruger et al.(1995) showed that SIMULAT simulated the water dynamicsof agricultural sites with the same quality as calibrated sitemodels.

Different concepts have been developed, tested and val-idated to upscale SIMULAT to the regional scale. Stephan(2003) developed a strategy to apply the model for hetero-geneous grid cells taking into account the subgrid variabil-ity. His investigation demonstrated that spatial variabilityof topography and vegetation must be maintained, whereas

arameter values

specific seasonal development (Lopmeier, 1983; Richter et al.,

ea (0.2 mm per LAI)eier, 1983; Braden, 1995; Richter et al., 1996)g plant specific seasonal development (Richter et al., 1996)d and plant height assuming a stable atmosphere and apmeier, 1983; Richter et al., 1996)plant specific seasonal development of rooting depth (Richter

s (Lopmeier, 1983)

wls and Brakensiek (1985)wls and Brakensiek (1985)wls and Brakensiek (1985)wls and Brakensiek (1985)wls and Brakensiek (1985)ydraulic conductivity, based on Cosby et al. (1984)

Table 3 Description of the study catchments Nienwohlde(local scale) and Leine (regional scale)

Catchmentcharacteristics

Nienwohlde Leine

Location Northern Germany Northern GermanySize 16 km2 990 km2

Topography Lowland Low mountainrange

Elevation 60–110 masl 100–500 maslPrecipitation 675 mm/a 600–900 mm/aSoils Sandy and loamy soils Silty, loamy and

clayey soilsLand use Agriculture 63% agriculture

(valleys, slopes)and 30% forest(mountains)

Table 4 Data sets available in the study catchmentsNienwohlde and Leine

Data Nienwohlde Leine

Weather data 1 local station 2 stationshourly resolution half-hourly to 3 h

resolution1988–1991 1981–1989

Rainfall data 1 local gauge 1 gauge (hourly resolution)hourly resolution 34 gauges (daily resolution)1988–1991 6 rainfall regions from

regionalisation anddisaggregation(hourly resolution),1981–1989

Stream gauges – 9 gaugesdaily resolution1981–1989

Soil data 1:5000 1:50,0001:50,0001:200,000

Topography – 31 m gridLanduse data Local mapping 30 m grid

Multi-temporal(Landsat based)Single image(Landsat based)


soil can be represented by effective parameters. Spatialvariability of weather data was not investigated. Bormannet al. (1999b) approximated the regional water fluxes ofthe German Leine catchment by assuming the region to bea large number of independent homogenous areas. Thisassumption could be justified by analysing the size of thehomogenous areas compared to the drainage network den-sity of this catchment. Applying SIMULAT to the dataset ofthe Leine (see Section ‘‘Study catchments and data’’),every homogenous area adjoined a river channel. Thereforelateral interactions between the simulation units could beneglected, and flow paths of neighbouring sites were as-sumed to be independent. The routing of lateral flow com-ponents (overland flow, base flow and interflow) to theoutlet of the drainage basin was carried out based upon con-centration time.

SIMULAT has been successfully applied to simulate local-(Diekkruger and Arning, 1995; Richter et al., 1996; Kuhn,1998; Aden and Diekkruger, 2000) and regional-scale (Richteret al., 1996; Bormann et al., 1999a,b; Stephan, 2003) waterfluxes in different regions in central Europe as well as in lo-cal-scale catchments in the sub-humid tropics of West Africa(Giertz, 2004; Bormann et al., 2005; Giertz et al., 2006).

Study catchments and data

This study was performed in two catchments in northernGermany:

(1) The local-scale Nienwohlde catchment (16 km2, 53�N,10.5�E) is located in the northern German lowlands,which is dominated by sandy and loamy soils underagricultural use.

(2) The regional-scale Leine catchment (990 km2, 51.5�N,10�E) is located in the northern German hilly region,which is characterized by silty agricultural soils inthe valleys and thin, loamy to clayey soils in the for-ested mountain areas.

Both catchments are characterised by a temperate, hu-mid climate and annual precipitation amounts between600 and 900 mm. Soil hydraulic conductivity is high in thesandy parts of the Nienwohlde catchment, whereas conduc-tivity varies widely in the Leine catchment. The dominantrunoff-generation process in the Nienwohlde catchment isbase flow, whereas both fast and slow runoff processesare important in the Leine catchment. Urban areas are smallin both catchments. The physiographic characteristics ofthe catchments are summarized in Table 3. More detailedcatchment descriptions are given by Richter et al. (1996)and Kuhn (1998) for the Nienwohlde catchment and by Bor-mann (2001), Stephan (2003) for the Leine catchment.

For the Nienwohlde catchment, a database of a long-term field campaign was available (Richter et al., 1996).Only a digital elevation model was not available. Due tothe high conductivity of sandy soils and the flat terrain,overland flow is not generated, and lateral outflow of thecatchment mainly occurs by groundwater flow. Thus, gaugedata could not be used to validate the SIMULAT model.

For the Leine catchment, standard data sets were avail-able, similar to those available for most other catchments incentral Europe. Time series of weather, precipitation and

stream flow were available as well as data sets on soil types,vegetation cover and topography (digital elevation model).All data sets available for both catchments are summarisedin Table 4. Model implementation was similar for bothcatchments. Thus, analyses for both catchments should leadto comparable results, given the similar physiographiccatchment characteristics (e.g., climate, land-use). Fur-thermore, both catchments were selected for this studybecause differently resolved data sets were available forthe two basins. In combination, they represent most ofthe possible situations of data availability in central Europe,and northern Germany in particular.

158 H. Bormann

Comparative approach of analysing sensitivity toinput data

The results presented in this paper are based mainly on thecomparison of model results using input data of differentresolutions. A sequential approach was used in which eachof the input data sets was altered (e.g., using a soil mapat 1:50,000 instead of 1:5000) while keeping the rest ofthe input data constant. A model-to-model comparisonwas performed assuming that the deviations between themodel results were caused by the altered inputs only.Uncertainties in parameters and model structure were notinvestigated.

This input-data-driven sensitivity analysis resulted in acomparative ranking of the relevance of spatio-temporalresolution for the model used. The sensitivity of SIMULATto changes in input data was measured by the root meansquared (RMS) difference between a reference simulation(qreference) and the simulations based on different resolutionof an input data source (qresolution). The RMS difference ofthe simulated water flows was calculated for monthly andannual values of runoff and actual evapotranspiration(ETA). Therefore n is the number of years or months,respectively, which were compared. Based on the absoluteRMS Eq. (1) a relative RMS (RMSrel; Eq. (2)) was defined usingthe average flow of the investigated period qreference:

RMSabs ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n

Xni¼1ðqreference; i � qresolution; iÞ

2

sð1Þ

RMSrel ¼RMSabsqreference

ð2Þ

The reference simulation was based on the best data setsavailable for a given catchment. Measurement errors werenot considered; they rather were assumed to be of the samemagnitude for differently resolved data sets. Therefore, forthe Nienwohlde catchment, the local weather station, the

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Figure 1 Regional scale validation of the SIMULAT model for thecomparison between observed and simulated daily discharge.

1:5000 soil map and a local land-use map were used; forthe Leine catchment, two weather stations, the 1:50,000soil map, a multi-temporal satellite-based land-use-classifi-cation and the digital elevation model with a resolution of31 m were used.

Model validation

On the local scale, SIMULAT has been validated by severalstudies in comparable environments (Diekkruger and Arning,1995; Richter et al., 1996; Kuhn, 1998; Aden and Diekkru-ger, 2000). Furthermore, Diekkruger et al. (1995) statedthat SIMULAT simulated the soil water dynamics of agricul-tural sites without any model calibration as well as compa-rable agroecosystem models which were calibrated.Therefore in this study, SIMULAT was assumed to work wellon the local scale using the best available data. On theregional scale, SIMULAT was applied to the Leine catchmentfor a nine-year period (1981–1989). The first year (1981)was used as a spin-up period. For the remaining eight-yearperiod (validation), a good agreement between observedand simulated stream flow was observed (Fig. 1), keepingin mind that the model was not calibrated and driven usingparameters from the literature only (Table 2). Values of thequality measures applied for the validation period were �2%bias in annual stream flow and 0.80 for the model efficiency(Nash and Sutcliffe, 1970), and the coefficient of determi-nation (r2) calculated from daily stream flow values. Thetiming of large floods was well represented by the model,although the peak flows were partly under- or overesti-mated. A few small stream flow peaks were overestimatedby the simulation. The recession curve did not exactlyreproduce the observed low-flow periods of the hydrograph.Possible reasons are a nonlinear behaviour of the groundwa-ter storage or anthropogenic influences such as treatmentplants which were not considered in this model application.

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Nevertheless, despite the minor deficiencies in the simu-lated discharge, the statistical quality measures of the sim-ulations on a regional scale were satisfying, considering thefocus on the simulation of water balances. The seasonal,interannual and long-term dynamics in discharge were wellrepresented despite that SIMULAT was not calibrated. Theparameter sets selected for this study therefore could beassumed to be adequate for the sensitivity analysis.

Table 5 Statistical characteristics of observed (4 yearshourly data) and disaggregated weather data (arithmeticmean = mean, standard deviation = standard dev.)

Data statistics Observed Disaggregated

Mean Standarddev.

Mean Standarddev.

Temperature (�C) 9.06 7.18 9.11 7.25Relative humidity (%) 74.16 18.60 74.83 18.00Wind speed (m/s) 2.63 1.91 2.63 1.50Solar radiation (W/m2) 115.6 188.7 117.1 171.4Precipitation (mm/h) 0.070 0.362 0.070 0.513

Weather data from Nienwohlde and precipitation data of theLeine catchment are used.

Results of the sensitivity analysis

The sensitivity analysis was divided into three parts com-prising model sensitivity on temporal data resolution, spa-tial data resolution and distribution and data classification.

Temporal resolution of weather data

For local-scale applications of SVAT schemes, hourly mea-surements from a local weather station are required toaccount for the non-linearities in runoff generation andevapotranspiration processes. Regional-scale applicationsoften suffer from reduced data availability. Disaggregationof weather data is an appropriate method, in particularfor regions where data is scarce and only daily data isavailable. Disaggregation rules can be used which reproducethe sub-daily behaviour of weather elements (temperature,air humidity, and radiation) or are based on statistical char-acteristics (precipitation). In this study, precipitation datawere analysed separately from other weather data (temper-ature, air humidity, global radiation, and wind speed)because the spatial as well as temporal variability of precip-itation was considerably higher. The German WeatherService (DWD) accounts for this variability by operating dif-ferent observational networks.

Disaggregation of temperature, humidity and globalradiationA four-year dataset (1988–1991) from the Nienwohldeweather station was used to quantify the effect inducedby temporal resolution of weather data. To investigatethe power of disaggregation algorithms, hourly tempera-ture, global radiation, air humidity and wind speed data firstwere aggregated to daily values and then disaggregated tohourly values according to Bormann et al. (1996). Model re-sults based on hourly measured data could then be com-pared to those based on hourly disaggregated data.

The disaggregation of temperature assumed a sine curvebetween minimum and maximum temperatures occurring at4 a.m. and 4 p.m., respectively. Absolute humidity of theair was assumed to be constant for each day and was disag-gregated to relative humidity values based on disaggregatedtemperatures and temperature-specific saturation. Windspeed was assumed to be constant over the whole day,and global radiation was distributed between sunrise andsunset by a sine curve. Precipitation was not altered in thispart of the study.

The statistics of observed and disaggregated weatherdata are given in Table 5. Mean values as well as standarddeviations were comparable, confirming that meanbehaviour and variability were well represented by the dis-aggregation approach. Fig. 2 shows observed and disaggre-

gated temperature for June 1988, which showed littledifference, while observed and disaggregated air humidityfor a few days showed significant differences. Some ex-tremes were over- or under-estimated. Nevertheless, onaverage, observed and disaggregated values were quite sim-ilar, whereas maximum values at night were overpredicted.Global radiation was well represented although the assump-tion of a sine curve led to a slight underestimation of max-imum values at noon. Values in the morning and afternoonhours were slightly overestimated.

Applying SIMULAT to quantify the impact of temporaldisaggregation of weather data on simulated water flowsrevealed slightly different cumulative curves for potentialevapotranspiration (Fig. 3), resulting in a relative RMS of4.2% per year. The impact on the actual evapotranspirationwas considerably reduced to a relative RMS of 0.9% peryear by limited soil water availability during the vegetationperiod. These results were obtained for different soil pro-files representing the most frequent soil texture classes ofthe catchment (soil classification discussed below).Monthly relative RMS was higher (4.1%). For runoff genera-tion, the sensitivity was slightly higher than for actualevapotranspiration (relative RMS of 2.6% for annual and6.1% for monthly runoff rates). But also for runoff rates,the uncertainty varied only slightly between all differentsoil-texture classes (relative RMS of 1.8–3.6% for annualrunoff).

Disaggregation of precipitation dataUsually, precipitation data from local weather stations usedfor process-based SVAT modelling are hourly or even higherin temporal resolution. But at the regional scale, the stan-dard networks of precipitation gauges (e.g., German Weath-er Service, DWD) mostly collect daily data. Therefore, inthis section it is investigated whether hourly precipitationdata can be derived from daily data using a stochastic disag-gregation method without a significant information losscompared to hourly measurements. Disaggregation of dailyto hourly rainfall rates was performed using the method ofArnold and Williams (1989), which analyses the internalstructure of storms (based on highly resolved data) assumingthat precipitation intensities of storms are exponentiallydistributed. Events are characterized by maximum inten-sity, duration and time of occurrence of maximum intensity.The model generates half-hourly values from daily data

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Figure 2 Comparison between observed and disaggregated temperature for June 1988 (Nienwohlde weather station).

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Figure 3 Simulated cumulative potential (ETP) and actual evapotranspiration (ETA) for a typical year (1989) for Nienwohlde basedon hourly observed (obs.) and from daily data disaggregated (dis.) hourly weather data.

160 H. Bormann

based on the analysis of storm structure and a stochasticcomponent. The method can be used to disaggregate dailydata from precipitation gauges in the same region of thegauge used for data analysis, assuming the same structureof precipitation events.

A 10 year dataset of the Leine catchment was used wherehalf-hourly data were available for one rain gauge andwhere daily data were available for 33 additional gauges.The stochastic disaggregation was performed 15 times.

The statistical characteristics of the original and the disag-gregated rainfall series are shown in Table 5. Mean precipi-tation values were identical while standard deviation ofdisaggregated rainfall was 1.47 times higher than the stan-dard deviation of observed rainfall (Table 5). There wasno significant over- or under-estimation in the long-termanalysis of actual evapotranspiration and runoff (Fig. 4).Values of the relative RMS of annual water flows were on�1% for actual evapotranspiration and 2.4% for runoff. But

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Figure 4 Effect of disaggregation of precipitation (=precip.) on monthly stream flow (Q: a) and on monthly actualevapotranspiration (ETA: b) of the Leine catchment. Different colours/shades of grey represent different disaggregationrealisations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of thisarticle.)


a few of the 15 different realisations of disaggregated pre-cipitation considerably over- or under-estimated the inten-sity of single rainfall events and therefore also over- orunder-estimated the calculated monthly runoff (Fig. 4a).

Spatial resolution and distribution of input data

For regional model applications, data availability generallydecreases with increasing scale. Interpolation of weatherdata is required, coarse maps instead of detailed ones areused. Sometimes even only statistical data without a spatialdistribution is available. In this section, the model sensitiv-ity to the spatial transfer of weather data, to the resolutionof a digital elevation model, to the scale of the soil map,and to the use of agricultural land-use statistics is analysed.

Spatial distribution of weather stationsUsually, local weather stations are used to drive local SVATschemes. Applying SVAT schemes at a regional scale, local

station data is not available for each simulation unit.Therefore interpolation schemes are integrated into manyregional-scale models. In this study, the sensitivity ofSIMULAT to the use of data of a nearest weather stationinstead of a local station was investigated. This comparisonwas based upon an application of the Thiessen polygonmethod. Spatial transferability of weather data from oneweather station to neighbouring sites within a radius of50 km was investigated in flat terrain of the northern Ger-man lowlands (Nienwohlde). Data from four additionalweather stations within a radius of 50 km were available.As the terrain was flat, elevation gradients in temperaturecould be neglected.

The transfer of temperature, radiation, humidity andwind speed did not cause considerable deviations in the sim-ulated water balances so long as local precipitation ratesand intensities were available. In the temperate and humidclimate of northern Germany, local rainfall governs the soilwater status which governs the actual evapotranspiration

162 H. Bormann

rates during the vegetation period. Spatial variability ofweather (temperature, humidity, and radiation) is smalldue to the flat terrain. Thus, the resulting relative RMS er-rors in annual actual evapotranspiration and runoff weresmaller than 1%. Model sensitivity was therefore smallerthan the sensitivity to temporal data disaggregation.

Spatial resolution of the digital elevation modelRunoff pathways are determined based on digital elevationmodels (DEM). As in SIMULAT, neighbouring grid cells do notinfluence each other, because topography is used to calcu-late concentration times only. In addition, topographyaffects incoming solar radiation, which limits the energyavailable for evapotranspiration and determines the gener-ation of interflow. In Germany, two different standard datasets are available, representing topography at 12.5 m(DGM5) and 50 m (DGM50) resolutions. While the DGM5 isonly locally available, the DGM50 covers most parts of thearea. Nevertheless, comparable data sets are not availablefor large, international river basins. Thus, the best datasetcovering the entire catchment often is globally availabledata with a resolution of 1 km (USGS). Thieken et al.(1999) found that decreasing spatial resolution smoothesthe terrain representation and changes the terrain charac-teristics. They also found that aggregating the grid size ofa DEM by a factor of ten halved the average slope of alocal-scale hilly catchment. This value was confirmed byBormann (2006) for the regional-scale Dill catchment in cen-tral Germany. Thieken et al. (1999) also found an increase inthe primary coordinates in the flow-direction grid (N, E, S,and W) at the expense of the diagonal directions (NE, SE,NW, and SW) by up to 10% for an aggregation of the DEMby a factor of 10.

To assess the potential impact of smoothed terrain onthe water balance, a sensitivity analysis with respect toaggregated digital elevation models was performed. Basedon the results of Thieken et al. (1999) and Bormann(2006), the sensitivity of the SIMULAT model was calculatedfor the Leine catchment. Focusing on the effect of changesin in-coming solar radiation on the water balance, smoothedterrain can have an impact on the grid-cell water balance.SIMULAT reacted very sensitively to changes in local cli-mate. Aggregating the DEM by factors of 2–80 (e.g., aggre-gation from 25 m to 50–2000 m) led to a relative RMS errorof 1–17% for actual evapotranspiration and 2–55% for run-off generation (Table 6). These values, of course, repre-sented the sensitivity of the model to different grid sizes,only. Integrating water fluxes for catchments, the catch-ment-wide RMS error decreased considerably, as confirmedby Bormann (2006). He investigated the sensitivity of theTOPLATS model to systematic aggregation of input data setsfor the regional-scale Dill catchment. TOPLATS (Famiglietti

Table 6 Effects of aggregation of topography (Dill catchment)water fluxes due to changes in terrain attributes (slope) compare

Relative RMS in 50 m 75 m 100 m 150 m

Annual ETA (%) 0.9 2.0 3.2 5.3Annual runoff (%) 2.3 5.0 8.0 13.4

ETA = actual evapotranspiration.

and Wood, 1994; Peters-Lidard et al., 1997) is a grid-basedcatchment model which combines the process-based SVATapproach for single grid cells with the regional-scale TOP-MODEL approach. TOPLATS therefore accounts for lateralexchange of water within the catchment. He found thatthe catchment-wide relative RMS error for the runoff (sumof fast and slow runoff components) was smaller than 1%for grid sizes between 50 m and 500 m. For single flow com-ponents, he found increased values of relative RMS error.The relative RMS error of surface runoff was smaller than2% up to a grid size of 200 m and smaller than 4% up to a gridsize of 500 m, while the relative RMS error for base flow wassmaller than 1% up to a grid size of 300 m and smaller than2.5% up to a grid size of 500 m. The results showed that therunoff remained constant up to a grid size of 500 m, whilesaturation excess runoff increased slightly and base flow de-creased slightly with increasing grid size. These results arein good agreement with the effects of topography aggrega-tion on simulated water balances reported by Stephan(2003), who reported relative deviations in annual waterflows of 0.7–2.9% for actual evapotranspiration and1.1–3.4% for runoff using the SIMULAT model.

In summary, merging the results of all three studies, thesensitivity of SVAT schemes at the site scale is reduced by afactor of 10 for regional-scale (symmetric) catchments. Thereason is that deviations in water flows of different azi-muths show opposite signs and counterbalance. In addition,the spatial structure of fluxes may be affected, which canlead to misinterpretations of subcatchment water fluxes.

Spatial scale of the soil mapIn Germany, a large number of differently scaled digital soilmaps are available for hydrological modelling purposes,including: 1:5000 (partial coverage of agricultural areas),1:25,000 and 1:50,000 (partial coverage of West Germany),1:100,000 (partial coverage of East Germany) and 1:200,000(total coverage of Germany). The comparison of the sensi-tivity to different soil maps is important for the followingreason: At the site scale (where SVAT schemes were devel-oped) are detailed soil maps or even measurements avail-able, while at regional scales (where SVAT schemes oftenare applied), only coarse soil maps are available.

The sensitivity of SIMULAT to changes in soil informationwas investigated by using differently scaled soil maps as in-put data and comparing the simulation results. The Nien-wohlde catchment was selected for investigation becausemost different soil related data sets were available for thiscatchment: the 1:5000, 1:50,000 and 1:200,000 soil mapsand the 1:25,000 geological map. The geological map couldbe included into the study because it comprised informationabout the upper two meters of the substrate, which wascomparable to the information on soil texture from the soil

on the maximum detected deviations in site scale simulatedd to a grid size of 25 m

200 m 300 m 500 m 1 km 2 km

6.0 8.8 11.9 14.8 16.817.6 29.2 39.4 49.1 55.5


maps. In this study, the 1:5000 soil map was used as refer-ence as it fitted best to the scale for which SVAT schemeswere developed. The resulting water fluxes were comparedto those based on the other available soil maps. The repre-sentative profiles for every mapping unit were used for cal-culation of water fluxes.

The application of the four different data sets led to sim-ilar annual water flows (actual evapotranspiration andgroundwater recharge). The regional-scale sensitivity ofSIMULAT to scale of the soil map was described by a relativeRMS errors between 0.3% (1:50,000) and 2.6% (1:200,000)for actual evapotranspiration and between 1.0% (1:50,000)and 9.0% (1:200,000) for groundwater recharge. Increasingthe difference in map scale considerably led to an increasein relative RMS error. Therefore, highest relative RMS errorswere detected using the 1:200,000 soil map compared tothe 1:5000 soil map. The 1:25,000 geological map induceddeviations comparable to the 1:50,000 soil map whencompared to the 1:5000 soil map (0.8% relative RMS forevapotranspiration and 2.7% relative RMS for runoff). Whilecatchment-wide sensitivities were moderate, the sensitivi-ties at the scale of homogenous simulation units (site scale)were much larger. For evapotranspiration, maximum rela-tive RMS errors of >16% were calculated; for groundwaterrecharge, maximum relative RMS errors were estimated tobe on the order of 50%.

In addition to the map scales investigated in this study,Vachaud and Chen (2002b) investigated the sensitivity ofthe ANSWERS model, whose core module is very similar tothe model structure of SIMULAT. They compared simulatedwater and nutrient fluxes based on a 1:250,000 soil map(complemented by field surveys) to simulation results basedon a 1:1,000,000 soil map. They found that using the coarserdataset caused an important bias in the simulated waterbalance as well as nutrient fluxes. They assumed that themain reason was the different textural limits of the soil clas-ses at different map scales. Such a bias was not identified inthis study, probably because the soil maps used in this studywere based on the same German soil texture classificationsystem.

Spatial distribution of agricultural land-use statisticsCompared to the local scale, regional-scale vegetation map-ping campaigns cannot be performed any more. Thereforethe effect of different statistical and satellite-based datasets on simulated water fluxes was analysed. The sensitivityof SIMULAT to unknown spatial allocation of land-use withina catchment was investigated for the Leine catchment. Ifspatial allocation of catchment properties is unknown, forexample in the case of agricultural statistics, those proper-ties can be distributed in space either randomly or based oncorrelation to other catchment characteristics. In bothcases, the areal fractions of the different input data setsmust be maintained. This study focused on random distribu-tions and therefore calculated all permutations of land-use,soil properties and topographic characteristics. A randomdistribution was selected based on the findings of Bormannet al. (in press), who showed for the Dill catchment thatthree spatially distributed catchment models (TOPLATS,WASIM, and SWAT) indicated only limited sensitivity toland-use redistribution. In that study, the differences be-tween random and topography-correlated redistribution

variants were negligible. These findings are consistent withthe results of Vachaud and Chen (2002b), who demonstratedfor a French regional-scale catchment that model simula-tions using the ANSWERS model based on spatially distrib-uted and stochastic land-use data yielded identical results.

While sensitivity of annual water flows was small (rela-tive RMS error of 0.5% for evapotranspiration and 2.8% forrunoff), sensitivity of monthly water flows increased signif-icantly up to relative RMS error of 10.8% (runoff) and 3.1%(evapotranspiration). This increased sensitivity for monthlytime scales could be explained by plant-specific differencesin seasonal development, inducing bare-soil conditions inthe different seasons. The relative RMS errors for runoffon the annual time scale correlated well with the values de-rived by Bormann et al. (in press) for the Dill catchment(relative RMS errors of 0–3.4%).

Data classification

When hydrological models are applied at the local scale,exact data from measurements and field surveys can be used(e.g., particle size percentages describing the soil texture).At the regional scale mostly classified data sets are avail-able only (e.g. soil maps, land-use classes). In this study,the sensitivity of SIMULAT to the use of classified data wasanalysed with regard to soil texture and land-use data.

Representation of a soil-texture class by the center ofgravity of the classSimulating soil water fluxes at regional scales requires theuse of digital soil maps, which often are represented byone representative soil profile for each mapping unit. Theseprofiles are composed of a number of soil layers which haveattributes such as texture class and bulk density class; incontrast, at the local scale, the soil composition and bulkdensity can be determined exactly. Using soil maps requiresthe use of model parameters from the literature or theapplication of pedotransfer functions. Pedotransfer func-tions translate readily available soil data (e.g., soil texture,porosity, and organic matter content) into soil hydraulicparameters which are required by hydrological models,but which generally are not provided by soil maps (e.g., sat-urated hydraulic conductivity, saturated soil water con-tent). Applying pedotransfer functions is widely acceptedin regional-scale hydrological modelling (Diekkruger et al.,1995; Tietje and Tapkenhinrichs, 1993). Nonetheless, theproblem arises that one texture class comprises many possi-ble texture compositions, while one set of ‘‘representa-tive’’ parameters is needed to represent the properties ofthis texture class. Therefore, this study investigated thesensitivity of SIMULAT to assuming the centre of gravity(CG) of a soil texture class to be representative for theclass, as proposed by Vachaud and Chen (2002a). This anal-ysis calculated water flows for the CG as well as for a set ofmodel runs equally distributed over the texture class usingthe SIMULAT model. To illustrate the simulated water flows,block-kriging was used to generate isolines of the simulationresults. Sensitivity was then expressed as relative RMS error.This analysis was performed for seven different textureclasses: loamy clay, clayey loam, silty loam, sandy loam,clayey silt, sandy silt and loamy sand, following the German

164 H. Bormann

texture classification. Sample results are shown for twoselected texture classes (clayey loam: 30 model runs; andsandy loam: 12 model runs). The analysis is performed forannual and monthly evapotranspiration and runoff.

Analysing the annual results, it is obvious that, for sometexture classes, the relative water flows were symmetricallydistributed within the texture class, isolines were approxi-mately linear. Deviations from the water balance of theCG were small and equally distributed. This was the casefor the texture classes sandy loam (Fig. 5) and silty loamfor both simulated water flows, runoff (Fig. 5a) and actual

Figure 5 Variability of simulated water fluxes within the soil texevapotranspiration (b) in [%] compared to the CG. Weather and rai

Figure 6 Variability of simulated water fluxes within a soilevapotranspiration (b) on a clayey loam [in %]. Weather and rainfa

evapotranspiration (Fig. 5b). In these two cases, the CGwas representative of the mean water flows of the textureclass. Focusing on the clayey loam (Fig. 6), the isolines forthe actual evapotranspiration were still roughly symmetri-cal and linear, but for the runoff the isolines were stronglynonlinear and the CG was no more representative for thistexture class. Thus, sensitivity is strongly dependent onthe particular texture class. The texture-class-specific sen-sitivities are summarized in Table 7. Obviously, the sensitiv-ities of calculated actual evapotranspiration were muchsmaller than for runoff, which confirms the findings of

ture class sandy loam: relative simulated annual runoff (a) andnfall data from the Leine catchment are used.

texture class – relative simulated annual runoff (a) andll data from the Leine catchment are used.


Vachaud and Chen (2002a). In particular, large sensitivitieswere detected for those texture classes predominantly con-sisting of large or small particle sizes (sand, clay) and for

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Jan-83 Jul-83 Jan-84 Jul-84

Time [M

Rel

ativ

e Q

[-]

sandy loam realisation max mi

0

2

4

6

8

10

12

14

16

18

20


Time [M

Rel

ativ

e Q

[-]

a

b

Figure 7 Monthly simulated relative rates of runoff (Q) for differenset in relation to the runoff of the CG. Weather and rainfall data f

Table 7 Classification based on relative RMS of the representadifferent possible realisations of the soil texture class

Texture class Relative RMS inmonthly ETA (%)

Relative RMS iannual ETA (%

Sandy loam 2.1 1.1Silty loam 1.8 0.6Clayey loam 2.1 1.0Loamy sand 8.8 3.8Loamy clay 3.5 1.5Sandy silt 5.3 2.2Clayey silt 3.0 1.5

Realisations are distributed equally over the texture class. ETA = actu

those texture classes which were dominated by one particleclass (sand, clay, and silt). In contrast, Vachaud and Chen(2002a) found for a French catchment that the within-soil-


onth-year]

n


onth-year]

loamy clay realisation max min

t realisations of a sandy loam (a) and a loamy clay (b) which arerom the Leine catchment are used.

tion of a texture class by the center of gravity compared to

n)

Relative RMS inmonthly runoff (%)

Relative RMSin annual runoff (%)

11.5 2.39.2 2.3

41.2 5.747.7 16.371.0 18.321.8 4.413.8 4.5

al evapotranspiration.

Table 8 Overview about detected effects of data resolu-tion and classification on simulated regional scale waterbalances annual time scale

Source of uncertainty Relative RMSof annual ETA (%)

Relative RMSof annualrunoff (%)

Disaggregation ofweather data

0.9 2.6

Disaggregation ofrainfall data

0.8 2.4

Spatial transfer of <1.0 <1.0

166 H. Bormann

class variability had no effect on the water flows simulatedby the ANSWERS model for soils with saturated hydraulicconductivities (Ksat) larger than 10 cm/d. In that study awithin-soil-class variability became an important uncer-tainty source only for soil classes with Ksat < 10 cm/d. Thesefindings could not be confirmed by this study. Also for sandysoils with high hydraulic conductivities high model sensitiv-ities were determined.

Focusing on the uncertainty of monthly water flows andtherefore also seasonal development of uncertainty, actualevapotranspiration was uncertain especially in summertime, while runoff was uncertain over the whole season(Fig. 7). The relative monthly actual evapotranspirationrates of all model runs were approximately equally distrib-uted around 1.0, while runoff was equally distributed onlyfor the sandy loam (Fig. 7a). Runoff rates simulated forthe clayey loam were unequally distributed. The deviationsof simulated runoff rates, surprisingly, reached their highestvalues in winter time (Fig. 7b) and, as a result, the uncer-tainties of monthly water flows also were significantly high-est for clayey and loamy soils (see also Table 7).

Satellite-based land-use classificationsFor the analysis of different land-use classifications, twodifferent Landsat-based land-use classifications were avail-able for the Leine catchment. The first classification wasbased on a multi-temporal analysis, and the second classifi-cation was based on a single satellite image. The Leinecatchment was chosen for this investigation for two rea-sons: (1) the availability of different land-use classifica-tions, and (2) the balanced partition of the catchmentinto different land-use classes (e.g., agriculture, grasslandand forest), which is not the case for the Nienwohlde catch-ment. Using the SIMULAT model, catchment water balancewas calculated based on the different land-use data sets,while all other data were held constant. The identified devi-ation between simulations based on both data sets was arelative RMS error of 4.1% in annual actual evapotranspira-tion and a relative RMS error of 9.8% in runoff. Sensitivitieson a monthly time scale were considerably higher (relativeRMS error of 14.1% for evapotranspiration, 28.2% for run-off). As the multi-temporal classification was assumed tobe plausible, the classification based on a single Landsat-scene was called ‘‘mis-classification’’. Compared to thesensitivity of SIMULAT with respect to unknown spatial dis-tribution of land-use (use of land-use statistics), the de-tected sensitivity to mis-classification was considerablyhigher and relevant at annual and monthly time scales.

weather dataScale of the soil map 0.3–2.6 1.0–9.0Use of soil texture

classes1.7 7.7

Different land-useclassifications

4.1 9.8

Spatial allocation ofland-use

0.5 2.8

DEM aggregation(25 to up to 500 m)

<1.0 <1.0

Data of two catchments are used: weather and soil data (Nien-wohlde), rainfall, topography and land-use data (Leine). ETA =actual evapotranspiration.

Evaluation of sensitivities and sensitivityranking

As no method is available for conducting an integrative anal-ysis of model sensitivity with respect to data resolution andclassification, all different sources of input data were ana-lysed separately. Based on these analyses, a comparisonof the magnitudes of resulting model sensitivities could beperformed. The detected sensitivities were scale-depen-dent in space and time and could be ranked as always thesame sensitivity measure was applied (relative RMS, Eq(2)). Because the focus here was on the regional scale and

on water-balance calculations, maximum site-scale devia-tions and event-scale sensitivities in water flows were notdiscussed. Instead, annual water flows and seasonal varia-tions analysing monthly water flows were considered.

Data sets of two catchments were used for this analysis.This was necessary in order to use a data base as extensiveas possible for this study. However, the question ariseswhether the detected sensitivities from both catchmentsare comparable. For the following reasons, the results areassumed to be comparable. Climate (e.g., mean tempera-ture, rainfall regime) and land-use are similar in both catch-ments. Topography does not play a major role due to theSVAT model concept and the focus on water balance. Soilproperties play an important role but were varied systemat-ically within the whole spectrum of possible soils during thesensitivity analysis.

All sensitivities detected in the previous sections aresummarised in Table 8. Sensitivities of annual water flowsgenerally were considerably smaller than sensitivities onthe monthly time scale. An explanation is that short timedeviations consist of under- and over-estimations. Integrat-ing the water flows over a longer time period therefore re-duces the deviations. The relative RMS error in actualevapotranspiration is generally smaller than the relativeRMS error in runoff. The reason lies in the water balanceof the catchments. In the climate of northern Germany, an-nual evapotranspiration exceeds annual runoff. Thereforerelative changes are less significant as both fluxes dependon each other through the water balance. They rather showsimilar absolute values but different signs.

SIMULAT is most sensitive to soil information and precip-itation data. This sensitivity does not depend on theselected time period (years, months) and is valid for allanalysed water flows. Furthermore, mis-classification of

Table 9 Sensitivity ranking of the SIMULAT model due to data resolution and data classification of different input data

High model sensitivity Low model sensitivitySoils ! Precipitation and Landuse ! Weather ! Topography


land-use also can induce high errors in simulated waterflows. This is also stated by Stephan (2003) who analysedthe sensitivity of SIMULAT to aggregation of input data toheterogeneous grid cells. He found that SIMULAT was simi-larly sensitive to aggregation of land-use and soil informa-tion. High deviations in water flows due to aggregation ofland-use data were caused by changes in land-use fractionsdue to aggregation which is comparable to the results iden-tified for ‘‘mis-classification’’ of land-use in this study.

Uncertainties in soil data have a strong influence on run-off generation mechanisms; storage of soil water for theevapotranspiration process is less influenced. The scale ofthe underlying soil map causes large small-scale deviationsin simulated water flows depending on variability of soilproperties. Nevertheless these uncertainties also need tobe considered at regional scale. Disaggregation of precipita-tion data has a major influence, particular at shorter timeperiods, as the stochastic components of the disaggregationmodels lead to high model sensitivities at the event scale.For longer time periods, such as weeks and months, thedeviations tend to compensate. Therefore, for water bal-ance calculations at an annual time scale, the relativeRMS error reduces to values smaller than those for soil dataand land-use mis-classifications.

Disaggregation of weather data, spatial allocation ofland-use while maintaining the areal fractions and theaggregation of topography data only have a minor impacton simulated water balances. This can be explained by themodel concept of SVAT schemes. SVAT schemes mostly donot take into account neighbourhood relations. Maintainingthe exact areal fractions of properties is decisive and seemsto be more important than the consideration of nonlinearrelations between the different properties is (e.g., soil,land-use). The limited sensitivity of SIMULAT to disaggrega-tion and transfer of weather data can be explained by themoderate topography of the lowlands in northern Germany.Focusing on alpine catchments, topography-induced uncer-tainty in weather data should become much more relevant.Based on this analysis, a ranking of model sensitivity due todata resolution and classification can be derived (Table 9).

Finally, it should be mentioned that the results pre-sented and discussed above are dependent on model philos-ophy (SVAT schemes in this investigation) and catchmentcharacteristics. Results therefore are transferable at leastto other SVAT models, which are widely applied in regio-nal-scale modelling studies. Nevertheless, the systematicsof the results also are transferable to other catchment mod-els containing similar process descriptions driving the modelsensitivity. With respect to a generalisation of the findingsto other catchments, the results here are transferable,whereas the dominant hydrological processes do notchange. For instance, model sensitivity to data availabilityof a base-flow-dominated catchment in the lowlands arenot transferable to a surface-runoff-dominated catchmentin the mountains because different processes (and process

descriptions) can be assumed to react with different sensi-tivity to a change in data availability. In this case, the sen-sitivity analysis should be repeated.

Minimum standards for regional SVAT applications aimingat water-balance calculations in comparable environmentscan be defined as follows, based on the findings in thisstudy: daily data on weather and rainfall (if hourly dataare available for a test period), a soil map at the scale1:50,000, a multi-temporal satellite-based vegetation clas-sification (or alternatively a detailed land-use statistics)and a digital elevation model of at least 500 m resolutionare required for the calculation of the regional-scale waterbalance.

Conclusions

This study presents a comparative analysis of model sensi-tivity to input data resolution and classification with respectto water-balance terms. The analysis is based on model sim-ulations of a validated SVAT scheme using different inputdata sets. It provides a method to compare model-specificsensitivities to input data resolution and data classification,with the goal of identifying weak points in the data-modelsetup. This analysis considers temperate climate conditionsand covers different soil conditions and vegetation types re-lated to lowland and hilly areas in central Europe. Hence,the results are transferable to models with a similar modelphilosophy and catchments with similar physiographiccharacteristics.

The magnitude of the impacts of varying data resolutionand classification on the simulated water flows provides ad-vice for model application. High data resolution and bestdata quality available are used for reference simulationsrepresenting the modelling conditions at the local scale.The investigation reveals that the applied SVAT schemeshows different sensitivities to different data sets. Rainfallintensities as well as soil and land-use data cause the high-est deviations at monthly time scales, whereas soil andland-use data have largest impact on the long-term waterbalance. The SIMULAT model is the most sensitive to dataclassification, especially in the case of non-correspondingsoil or land-use coverage. The effect of temporal resolutionwas moderate only due to the knowledge of the systematicbehaviour of selected state variables. Spatial resolution ofinput data had only minor effects on simulated water flows.

Admittedly, the ranking of input data sets with respect tomodel sensitivity is model-type-specific and also depends onthe catchment-specific climatic characteristics. It is there-fore not directly transferable to other model types and cli-mates. But the process descriptions included in thephysically based SVAT scheme used in this study also areused by many other catchment models. Hence, the system-atics of model sensitivity to data resolution and classifica-tion is of value at least for similar models.

168 H. Bormann

Acknowledgements

The author thanks the ‘‘Deutsche Forschungsgemeinschaft’’for funding the research programs ‘‘Regionalisation inhydrology’’, ‘‘Regional simulation in the hydrology – quan-tification of errors and uncertainties’’. Anonymous refereesand Nicholas Pinter improved this paper by very construc-tive comments.

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