Journal of Hydrology 498 (2013) 221–236

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Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

A comparison of hydrological models for assessing the impact ofland use and climate change on discharge in a tropical catchment

0022-1694/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.jhydrol.2013.06.016

⇑ Corresponding author. Address: Department of Geography, University of Bonn,53115 Bonn, Meckenheimer Allee 172, Germany. Tel.: +49 (0)228 732401; fax: +49(0)228 735393.

E-mail addresses: thocor@uni-bonn.de (T. Cornelissen), b.diekkrueger@uni-bonn.de (B. Diekkrüger), sgiertz@uni-bonn.de (S. Giertz).

Thomas Cornelissen ⇑, Bernd Diekkrüger, Simone GiertzDepartment of Geography, University of Bonn, 53115 Bonn, Germany

a r t i c l e i n f o

Article history:Received 30 March 2012Received in revised form 24 May 2013Accepted 10 June 2013Available online 21 June 2013This manuscript was handled by AndrasBardossy, Editor-in-Chief, with theassistance of Axel Bronstert, AssociateEditor

Keywords:Model type intercomparisonModel uncertaintyDischarge predictionClimate change scenariosLand use scenarios

s u m m a r y

This study assesses the suitability of different model types for simulating scenarios of future dischargebehaviour in a West African catchment (2344 km2) in the context of climate and land use change. Thecomparison of models enables the identification of possible sources of uncertainty in hydrological mod-elling of a tropical catchment. All models were calibrated and validated for the period from 1998 to 2005with reasonable quality. The simulation of climate and land use change impacts on discharge behaviourresults in substantial differences caused by model structure and calibration strategy. The semi-distrib-uted conceptual model UHP-HRU is shown to be the most suitable for the simulation of current dischargedynamics because the simulated runoff components most closely match the current perception of hydro-logical processes based on field data interpretation. In addition, the model does not introduce new uncer-tainties into the simulation by imposing high data demands. All models simulate an increase in surfacerunoff due to land use change. The application of climate change scenarios resulted in considerable var-iation between the models and points not only to uncertainties in climate change scenarios but also givesan idea of the possible range of future developments. Overall, this study indicates that the major weak-ness of all hydrological models is their poor representation of the catchment’s soil characteristics andflow processes.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

West Africa and, in particular, the Republic of Benin suffer froma large discrepancy between the amount of renewable water re-sources and the availability of water for domestic, agriculturaland industrial purposes. This discrepancy is the result of physical,socio-economic and institutional constraints (Hadjer et al., 2010).The physical constraints arise from the seasonality of the dis-charge, a result of the yearly shift between the dry and wet sea-sons, whereas the socio-economic and institutional limitationsimply an infrastructure that cannot make sufficient use of theamount of available freshwater. Apart from these limitations, cli-mate and land use change already have a large impact on thehydrological cycle in West African countries (Giertz et al., 2010;Kasei, 2010; Götzinger, 2007; Jung, 2006; Busche et al., 2005). Inview of these challenges and the importance of reliable data onwater availability, a proper estimation of the yearly water balancerequires the application of hydrological models.

For more than a decade, a large number of modelling ap-proaches have been applied to tropical catchments. Examples arethe study by Andersen et al. (2001) on the Senegal River Basin,the study by Güntner (2002) on a catchment in north-eastern Bra-zil and the study by Leemhuis et al. (2007) on a catchment in Indo-nesia. The number of modelling efforts has increased rapidly since2004, with studies undertaken in the White Volta Basin (Kasei,2010; Jung, 2006; Wagner et al., 2006; Ajayi, 2004) and theOuémé Catchment (Benin) (e.g., Götzinger, 2007) and its sub-catchments, including the Donga Catchment (Séguis et al., 2011;Le Lay et al., 2008; Varado et al., 2006) and the Aguima Catchment(Bormann et al., 2005; Giertz et al., 2005), as well as studies thatcompare simulation results for different catchments (Giertz et al.,2010; Hiepe and Diekkrüger, 2007; Bormann and Diekkrüger,2004).

The simulation results of the previously cited studies show thatthe calculated fraction of each discharge component depends onthe model type applied. For example, Hiepe and Diekkrüger(2007) use a time-continuous but semi-distributed model and findthat base flow and surface runoff are the dominant discharge com-ponents in the Térou Catchment, a tributary of the Ouémé River. Incontrast, Giertz et al. (2010) find that interflow is the dominantdischarge component in the Térou Catchment by applying aconceptual model. This assumption is supported by electrical

222 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

conductivity measurements conducted by Giertz (2004) and Hiepe(2008) and hydrochemical measurements conducted by Fass(2004), who find that fast runoff components are predominant.

If hydrological models are applied within a single study, modelcalibration often results in reliable simulations of the past; how-ever, the influence of model choice and model calibration on thesimulation of climate and land use change impacts remains un-clear, even if uncertainties (e.g., soil (Bossa et al., 2012) and climate(Varado et al., 2006)) are considered.

From a theoretical viewpoint, a physical model represents theunderlying hydrologic and land surface processes in greater detailthan conceptual or statistical models (Beven, 2001). However,more parameters and greater calibration effort are required asthe degree of physical representation of relevant processes in amodel increases.

Despite the potential of comparing the results of different mod-els and model types, this method has not yet been widely used inhydrology. For example, Bormann et al. (2009), Huisman et al.(2009) and Viney et al. (2009) apply a multi-model modelling ap-proach to assess the impacts of land use change on the hydrologyof a catchment in Germany. To date, no comparative study of mod-el types has been performed for the Térou Catchment, althoughtime-continuous, semi-distributed and conceptually or physicallybased model types have all been applied to this catchment (Giertzet al., 2010; Hiepe and Diekkrüger, 2007; Sintondji, 2005; Buscheet al., 2005; Bormann and Diekkrüger, 2004). The aforementionedstudies heavily differed in the simulated fraction of interflow, vary-ing from <3% (e.g., Busche et al., 2005) to 60% (Giertz et al., 2010).Thus, the application of these models in impact studies may yieldcompletely different scenario projections. To differentiate betweenthe effects caused by model choice and the effects caused by cli-mate and land use change impacts, different model types, i.e.,physically based and conceptual, are used in this study to improvethe understanding of hydrological processes and to provide newinsights into the influence of land use and climate change on dis-charge behaviour.

This paper addresses the uncertainty in discharge modelling ofa tropical catchment by comparing simulation results based on dif-ferent model types. Furthermore, the paper aims to compare thepredictions of future discharge obtained by applying land useand climate change scenarios. Towards these aims, four differentmodels were applied to simulate the past (1998–2005) and futurescenarios (2001–2049).

2. Research area

2.1. Geographical overview

The Republic of Benin is located in West Africa and extendsfrom the Gulf of Guinea to 12�300 north and from 0�450 to 4� east.With an area of 112,622 km2, Benin is one of the smaller Africancountries. The Ouémé Catchment drains the major part of Benin.The research area, i.e., the Térou Catchment (2344 km2), is a sub-catchment of the Ouémé Catchment (location and major character-istics shown in Fig. 1).

Benin has a diurnal climate, with a mean annual temperature of27.2 �C, ranging between 21.9 �C and 32.6 �C (Ermert and Brücher,2008). There is a strong precipitation gradient of 300 mm/a, withrainfall increasing from northern Benin, which receives1008 mm/a (period 1961–1990) in a unimodal distribution, tosouthern Benin, which receives 1309 mm/a in a bimodal distribu-tion (Ermert and Brücher, 2008). Rainfall is primarily generatedby squall lines and, to a minor degree, heavy thunderstorms, whichform in hot monsoon air masses (Fink et al., 2010).

Fink et al. (2010) note that the high interannual and decadalvariability in rainfall intensities and the number of rainy days inBenin can result in severe droughts, such as the one that occurredin the early 1970s and mid-1980s, during which rainfall decreasedto a minimum of 800 mm/a. In the Térou catchment, the precipita-tion distribution is unimodal, with the rainy season occurring be-tween the beginning of May and the end of October and the dryseason occurring between November and the end of April. Meanannual rainfall rates vary around 1152 mm, with a maximum rain-fall rate of 260 mm in September and a minimum rate of 0 mm inDecember (Ermert and Brücher, 2008).

Due to millennia-long land use, Benin’s potential natural vege-tation, i.e., the tropical dry forest, has been replaced by savannah-type vegetation (Anhuf and Frankenberg, 1991). The commonlyused classification of Benin’s vegetation is based on physiognomiccharacteristics according to the rules of the 1956 Yangambi Confer-ence (Aubréville, 1957). Based on these rules, the Térou Catchmentis primarily covered by savannah vegetation types, whereas 20% ofthe area is covered by light and dense dry forest. Agriculture onlyrepresents 11% of the total land coverage (Judex, 2008).

The land surface of the study area, termed peneplain, wasformed by repeating cycles of the pedimentation process (Runge,1990; Rohdenburg, 1969) creating a specific slope sequence. Theupper parts of a slope are covered by iron crusts embedded instone lines. These structures are layers with an average thicknessof 40 cm (Faust, 1991) consisting of coarse material, includingangular and curved blocks of quartz (Runge, 1990). The stone linescover the saprolith, which is a layer a few metres in depth that con-tains weathered bedrock. The saprolith substrate is characterisedby its high clay content.

In the main portion of the slope, the stone lines are overlaid byhillwash sediment. The sediment was formed by bioturbation as aconsequence of high termite activity and denudative processes.Due to the translocation of clay, the hillwash sediment is charac-terised by the soil texture ‘‘loamy sand’’. The lower slope containsa river bed formed by recent fluvial erosion processes (Runge,1990; Rohdenburg, 1969).

Recent pedogenetic processes are dominated by lessivation(Junge and Skowronek, 2007) and the translocation of iron (Faust,1991), resulting in small-scale differences in hydrologic processes.

2.2. Land use and climate change

Judex (2008) compares the land use classification for the years1991 and 2000 for the Upper Ouémé catchment. During this peri-od, the cultivated area expanded by 70%, and the settlement areaexpanded by 0.8%. In the northern part of the Térou Catchment,the cultivated area increased by 61–100%, whereas it only in-creased by 21–40% in the southern part. This expansion is the re-sult of migration, agro-colonisation and the vegetation dynamicsinduced by cultivation practices.

According to Paeth et al. (2008), the future climate of Benin willbe characterised by increasing mean annual temperatures (up to4 �C) and further drying (at least a 25% reduction in mean annualrainfall) until 2050. Christoph et al. (2010) explain the reductionin rainfall by the weakening of the hydrological cycle, especiallythe recycling of rainfall. They add that the onset of the rainy seasonwill be delayed by more than 10 days by 2025 and that heavy rain-fall events will decrease.

2.3. Current perception of hydrological processes

The current perception of the dominant hydrological processesis primarily based on the results of Bormann et al. (2005), Giertz(2004) and Fass (2004). The brief description given here does notinclude the effect of small-scale inland valleys, locally termed

Fig. 1. Overview of the Ouémé and Térou Catchments: (a) location of Benin in Africa; (b) location of the Ouémé Catchment and the Upper Ouémé Catchment, theadministrative units of Benin and the location of Benin’s capital Porto Novo; (c) location of the Térou Catchment, its relief and its major cities.

T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236 223

bas-fonds. These depressions influence discharge on a local scalebut cannot be considered at the regional scale analysed in thisstudy. The influence of the soil characteristics on the hydrologicalprocesses is shown in Fig. 2 (refer to next page). Hortonian runoffoccurs on iron crusts and cultivated areas but is highly unlikely incatchments with savannah-type vegetation.

This pattern is due to the high infiltration rates of savannahsoils, usually more than 1000 mm per hour (Giertz et al., 2010)due to biological activity. Agricultural land use reduces the second-ary pore system significantly, resulting in increased surface runoff(Germer et al., 2010).

The analysis of hydrological processes by Bormann et al. (2005)depicts a strong scale-dependency of the flow processes. At the lo-cal scale, groundwater flow is of minor importance, whereas fastsurface and subsurface processes dominate. In contrast, groundwa-ter flow plays a major role in the water cycle at the regional scale.Séguis et al. (2011) confirm this analysis. They found that baseflowwas the major source of annual discharge in catchments rangingfrom 10 to 600 km2 in size. Apart from the dependence on scale,the soil characteristics of the catchment suggest that distinguish-ing between interflow and baseflow might be difficult in the Upper

Ouémé Catchment. First, field observations by Giertz et al. (2005)revealed a very high mean density of macropores of (219 per m2)for a savannah, indicating that bypass flow plays a major role inthe water cycle. Séguis et al. (2011) and Giertz (2004) observe thatit is necessary to distinguish between shallow groundwater anddeep groundwater. The deep groundwater does not contribute tothe local water cycle. Séguis et al. (2011) find that the percolationrate to deeper groundwater is between 9% and 17%. Fass (2004) re-ports a fraction of fast runoff components of 73%, based on hydro-chemical measurements for two different small-scale catchments.The challenge is to distinguish the runoff components on the largescale.

The discharge behaviour in the study area is highly seasonal.Discharge begins in June, peaks in September and ends at thebeginning of December. Giertz et al. (2010) show mean dischargeand runoff coefficient values for different subcatchments of theOuémé between 1993 and 2004. The mean discharge of the TérouCatchment is 212 mm/a, with a runoff coefficient of 0.16. Fink et al.(2010) show that mean potential evapotranspiration can vary be-tween 1690 and 2420 mm/a, with lower values during the rainyseason. An analysis of actual evapotranspiration by Giertz et al.

Fig. 2. Hydrological processes on a typical slope in the Ouémé Catchment (Fass, 2004, p. 119).

224 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

(2010) shows simulation results from the UHP-HRU model of dif-ferent subcatchments of the Upper Ouémé Catchment. The resultsyield an estimate of 843 mm/a for the Térou Catchment.

3. Materials and methods

3.1. Comparison of model structures

In this study, four different hydrological models are applied.These models vary in complexity, spatial resolution and processrepresentation. All of the models are time-continuous and are ap-plied at daily time steps. The models’ basic characteristics are de-scribed in a tabular summary (Table 1), while the text providesdetailed descriptions of the most important differences in processrepresentation between the models.

The Water balance-Simulation Model (WaSiM) has beendeveloped by Schulla (1997) to evaluate the influence of climatechange on water balance in low and high mountain ranges. Thisdeterministic, spatially distributed model incorporates physicaland conceptual approaches to describe relevant hydrological pro-cesses. Version 7.9.11 of the model, developed at the end of2007, is used in this study (Schulla and Jasper, 2007).

The Soil Water Assessment Tool (SWAT) is a time-continuoussemi-distributed catchment model developed by the US Agricul-tural Research Service to evaluate the influence of climate, landuse and agricultural cultivation techniques on water quality andsediment yield (Arnold et al., 1998).

The UHP-HRU model is a semi-distributed conceptual model,originally developed by Bormann and Diekkrüger (2004) and ex-tended by Giertz et al. (2006) using the hydrological response unitconcept and by Giertz et al. (2010) using a routing routine.

The GR4J model (Génie Rural à 4 Paramètres Journaliers; Perrinet al., 2003) is a daily lumped rainfall-runoff model with only fourparameters, all of which must be calibrated. In contrast to WaSiM,which uses a grid based spatial discretisation, SWAT and UHP-HRUdivide the catchment into subbasins that are further divided intoHydrological Response Units (HRUs). While SWAT creates fixedHRUs based on superposed land use and soil maps, UHP-HRU al-lows for yearly changing land use. For the subunits that are createdby the superposition of subbasins and soil maps, a share of each

land use type is calculated for each year. This allows a dynamicchange in land use and an adaption in the size and number of HRUsaccording to land use changes. Thus, each HRU is characterised byuniform land coverage, soil properties and land use management,but it is not georeferenced within a subbasin. GR4J does not requirespatial discretisation.

Whereas SWAT and WaSiM divide the soil into numerical lay-ers, UHP-HRU divides the soil into two storage components, i.e.,the root and unsaturated zones, whose recession constants needto be calibrated. Root-zone storage is filled by the amount of rain-fall that is not captured by interception and runoff; unsaturated-zone storage is filled by percolation, which depends on the waterlevel in root-zone storage, its maximum water storage capacity(field capacity) and a recession constant. All models differ in theirrepresentation of interflow. In WaSiM, lateral flow (interflow) is afunction of slope length and inclination, saturated water conduc-tivity, drainable porosity and the amount of drainable water storedin the saturated zone. Interflow is calculated by comparing a max-imum possible interflow rate, depending on the actual water con-tent, with a second interflow rate that is a function of river density,the hydraulic gradient and conductivity. The smaller value of bothinterflow rates is taken as the actual rate. UHP-HRU determines theamount of interflow as a ratio between the actual and maximumwater storage of the unsaturated zone. In SWAT, interflow is calcu-lated using a linear function that consists of slope length and angle,saturated conductivity, drainable porosity and the amount of waterthat is stored in the saturated zone. All models use a linear storageapproach to calculate baseflow, but they differ in their representa-tion of the groundwater layer. SWAT and UHP-HRU simulate onedeep aquifer, which does not contribute to baseflow, and one shal-low aquifer, which is linked to the river system. The shallow aqui-fer is recharged by percolation and reduced by deep percolation tothe deep aquifer and capillary rise to the unsaturated zone. Perco-lation is only calculated if the water content is above field capacity.GR4J consists of two storage components, the production store andthe routing store. Production storage represents soil–water stor-age. If precipitation is larger than potential evapotranspiration,the net rainfall is calculated by subtracting potential evapotranspi-ration from rainfall. The amount of water filling the productionstore from net rainfall is calculated using a parabolic functionresembling infiltration. The remaining portion of the water will

Tabl

e1

Hyd

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lpr

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and

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four

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al.,

1998

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al.,

2010

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03)

Inte

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age

appr

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ch;

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appr

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nct

ion

ofLA

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nsi

dere

dPo

ten

tial

Evap

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ion

Pen

man

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teit

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,197

5)Pe

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975)

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man

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956)

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T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236 225

directly feed the discharge. This process is represented by consid-ering a unit hydrograph approach. If precipitation is lower than po-tential evapotranspiration, the net evapotranspiration rate iscalculated by subtracting rainfall from potential evapotranspira-tion. Actual evapotranspiration is computed from soil water stor-age using a parabolic equation. Percolation from the productionstore is calculated using a power function based on water storage.Total discharge, computed from net precipitation and percolation,is divided into two unit hydrographs with a fixed ratio of 9:1. Agroundwater exchange rate resembling the fast and slow ground-water components is calculated and applied to the amount of rou-ted water. The total discharge is calculated as the superposition ofthe two unit hydrographs (Perrin et al., 2003).

WaSiM requires twelve parameters for each soil mapping unitto simulate soil water dynamics: four parameters for the incorpo-ration of macropores, the saturated hydraulic conductivity and itsrecession for each horizon, the van Genuchten parameters describ-ing the soil water retention function of the horizon and the thick-ness of the horizons. The van Genuchten parameters are calculatedfrom available soil data by applying the pedotransferfunction ofCarsel and Parish (1988). The recession of saturated hydraulic con-ductivity with depth is a calibration parameter. Due to limiteddata, the macropore model is not applied and bypass flow is notsimulated. For the simulation of the evapotranspiration module,WaSiM depends on thirteen different parameters for each landuse class. Five parameters (threshold value for the start of drynessstress, two parameters which describe the reduction of transpira-tion due to oxygen stress, root distribution in the soil and the inter-ception capacity per LAI) do not vary during the year, whereas theother parameters (i.e., evapotranspiration resistance, aerodynamicresistance, leaf area index, vegetation height, albedo and rootdepth) are variable in time. All of these parameter values were ta-ken from literature, except the evapotranspiration resistances thatare associated with agriculture, which had to be calibrated. Forexample, the parameters for the ‘‘wood savannah’’ land use typewere taken from Bronstert et al. (2001), Güntner (2002), Hage-mann (2002) and Steyaert and Knox (2008).

In SWAT, the soil module is parameterised with hydrologicgroups attributed according to soil texture, effective soil depthand shrink-swell potential, soil depth, organic carbon content, soiltexture distribution (all available from measurements) and bulkdensity (estimated by a regression model derived from measuredsoil data from Giertz (2004) and Sintondji (2005)); available watercapacity and saturated hydraulic conductivity were estimatedusing the pedotransfer function of Rawls and Brakensiek (1995).

The original land use classes were attributed to the predefinedland use classes of SWAT, but plant heights and the vegetationparameters affecting the seasonal development of LAI had to beadapted according to data from Mulindabigwi (2006). UHP-HRUrequires the soil depth, water holding capacity and storage con-stants for the root and unsaturated zone for each soil unit. Whilethe storage constants were calibrated, the soil depth and waterholding capacity were taken from measurements (Giertz (2004),Hiepe (2008), Sintondji (2005)). A Curve Number (CN II) is requiredfor each combination of soil and land use type, which was assessedbased on SCS (1972). Other required vegetation parameters areroot depth, LAI, albedo and plant height. These parameters were ta-ken from investigations in the Ouémé catchment by Orthmann(2005) and Mulindabigwi (2006). Missing data were taken fromScurlock et al. (2001).

3.2. Data sources

Table 2 summarises the data used and the data sources. Allmodels were run with a temporal resolution of one day. As WaSiMis the only spatially distributed model considered, only this model

Table 2Data availability for the four models used (DMN = Direction de la Météorologie Nationale; CATCH = Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique; IRD = Institutde Recherche pour le Développement; DGE = Direction Générale de l’Eau; PET = potential evapotranspiration, IMPETUS = An Integrated Approach to the Efficient Management ofScarce Water Resources in West Africa).

WaSiMa SWATb UHP-HRUb GR4Jb

Climate data 1 Station (DMN) 2 Stations (DMN, IMPETUS) Same as SWAT PET is taken from UHP-HRU;Precipitation 4 Stations (DMN) 13 Stations (CATCH, IRD, IMPETUS) 18 Stations (IRD, IMPETUS) Same as UHP-HRUDischarge data DGE; daily data, same for all four modelsElevation Data SRTM-Data; resolution: 90 � 90 m SRTM-Data; resolution: 90 � 90 m SRTM-Data; resolution: 90 � 90 m –Soil data Soil map 1:200,000. Soil properties were provided by Sintondji (2005) and Hiepe (2008): one representative

profile for each soil mapping unit (sheet Djougou; Faurè, 1977)–

Land use classification Judex (2008); classified from LANDSAT images with a resolution of 28.5 � 28.5 m –

a Data are valid for the Térou Catchment.b Data are valid for the Upper Ouémé Catchment; see figure 1 for locations.

226 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

was run with a spatial resolution of 1 km2. SWAT and UHP-HRUuse the Hydrologic Response Unit concept for spatial discretisation,and GR4J does not require spatial data. For the spatial discretisa-tion of the Térou Catchment, SWAT uses 177 HRUs, whereasUHP-HRU uses 281 HRUs.

The distributed catchment precipitation is calculated in differ-ent ways for the four models. The UHP-HRU and the SWAT modelassign one precipitation value for each day to each subbasin. Thismethod may create sharp boundaries between precipitation re-gions. This spatial distribution of precipitation values follows theThiessen polygon interpolation method. For WaSiM, the inversedistance weighting method was assigned, thus resulting in a dis-tinct precipitation value for each grid cell. This approach yieldssmoother patterns. Data for the UHP-HRU model were taken as in-puts for GR4J. Nevertheless, the convective character of precipita-tion can neither be accurately represented by the continuousprecipitation field of the inverse distance weighting method norby the sharp boundaries of the Thiessen polygons. In addition touncertainty resulting from the density of rain gauges, the interpo-lation method may influence model results. Bormann (2005) notesthat the choice of interpolation method has a weaker influence onsimulation results than errors in the precipitation data.

3.3. Calibration and validation procedures

As calibration and validation were done by different authors,the available data were split into calibration and validation periodsinconsistently. Calibration for the UHP-HRU and GR4J model wasperformed for the years 2003 and 2004, while validation was per-formed for 1997 through 2002. The years 2002 through 2005 werechosen as the calibration period for the WaSiM simulation, whilethe years 1998 through 2001 were chosen as the validation periodfor the WASIM simulation. A spin-up time of two years was used todefine proper initial conditions for both the validation and calibra-tion periods. The SWAT model was calibrated for the years 1998through 2001, and validated for 2002 through 2005.

Due to the substantial number of parameters, equifinality mayoccur (Beven, 2001). To reduce the possible parameter space, addi-tional information on the fraction of discharge components may beuseful.

According to Séguis et al. (2011), baseflow is the dominant run-off generation process in the savannah-dominated, 586 km2 largeDonga catchment. Moreover, the measurements made by Giertz(2004) show a fraction of surface runoff of 30%. This value is themean value of the conductivity measurements performed in theupper Aguima Catchment, which has a size of 3.2 km2, is predom-inantly savannah vegetation. The IMPETUS project, which focuseson investigating the effect of global environmental change on thewater cycle in Benin and Morocco and the development of a deci-sion support system, also chose the Aguima Catchment as a repre-sentative catchment in terms of hydrological processes (Giertz,

2004). Thus, the calibration aim of WaSiM is to achieve a tradeoffbetween the simulation of a fraction of surface runoff of approxi-mately 30%, the simulated discharge amount and the quality ofthe simulated discharge dynamics.

The calibration aims were achieved by adjusting the followingparameters, which Kasei (2010), Jung (2006), Schulla and Jasper(2007) and Cullmann et al. (2006) found to be sensitive: the reduc-tion of saturated conductivity with depth (influencing the parti-tioning between the interflow and base flow), the drainagedensity (influencing interflow), the scaling factor and the recessionconstants for baseflow and inter- and overland flow.

The validation and calibration of the SWAT model was per-formed by Hiepe (2008) for the Térou Catchment. The calibrationaim is comparable to that of the WASIM simulation, with theexceptions that a fraction of surface runoff of 45% was assumedto be correct and the Nash–Sutcliffe Coefficient (Nash and Sutcliffe,1970) and Coefficient of Determination reached at least 0.7. Thefraction of surface runoff is different from that of WaSiM becausethe fraction for SWAT is a result of baseflow filtering, which cannotdifferentiate between fast and slow baseflow components. Calibra-tion was performed manually and with the automatic calibrationimplemented in SWAT (Shuffled complex evolution algorithm) atyearly and weekly time steps for the SCS curve number parame-ters, the saturated hydraulic conductivity (values smaller than5 mm per hour were set to 5 mm per hour) and the parametersaffecting the recession of baseflow and surface runoff, as well asfor the percolation to the shallow and deep aquifers and evapora-tion. For each land use, four different SCS curve numbers were cal-ibrated according to their assigned hydrologic group, which wereseparated according to different infiltration rates.

The calibration of the UHP-HRU model was performed manu-ally by Giertz et al. (2010) for root depth, the curve number param-eters and the recession constants for interflow and groundwater.The calibration aims to optimise the simulation of total dischargeand its seasonality and maximise the Nash–Sutcliffe Coefficient(Nash and Sutcliffe, 1970) and the Coefficient of Determination.

GR4J was calibrated automatically for the four model parame-ters: the maximum capacity of the routing store and productionstore, the groundwater exchange coefficient and the time base ofthe first unit hydrograph. The aim of the calibration is to maximisethe Nash–Sutcliffe coefficient (Nash and Sutcliffe, 1970).

3.4. Climate and land use scenarios

The WaSiM, SWAT and UHP-HRU models were run in the firstsimulation step with three land use scenarios; in the second simu-lation step, all four models were run with two climate scenariosconsisting of three ensemble runs each.

For the climate change simulation, the model results of Paethand Diederich (2011) for the REMO model (Regional Model, Jacob,2001) were used. The results are based on IPCC scenarios A1B and

Table 4Mean water balance, mean discharge components, coefficient of determination andNash–Sutcliffe-coefficients for the periods 1998–2001 and 2003–2004 for eachmodel.

WaSiM SWAT UHP-HRU GR4J

1998–2001Measured discharge (mm) 219 219 219 219Simulated discharge (mm) 220 229 210 289Fraction of surface runoff (%) 50 45 21 –Fraction of interflow (%) 26 0 65 –Fraction of baseflow (%) 24 55 14 –Potential evapotranspiration (mm) 2258 1635 1626 1626Actual evapotranspiration (mm) 861 599 760 664Precipitation (mm) 1089 1087 1181 1181Nash–Sutcliffe coefficient 0.81 0.91 0.69 0.75Coefficient of determination 0.83 0.92 0.71 0.81

2003–2004Measured discharge (mm) 222 222 222 222Simulated discharge (mm) 287 212 175 225Fraction of surface runoff (%) 50 43 26 –Fraction of interflow (%) 25 0 62 –Fraction of baseflow (%) 25 57 12 –Potential evapotranspiration (mm) 1931 1479a 1659 1659Actual evapotranspiration (mm) 993 767 872 803Precipitation (mm) 1328 1224 1219 1219Nash–Sutcliffe coefficient 0.77 0.84 0.81 0.86Coefficient of determination 0.89 0.85 0.85 0.90

a Values are the mean values for the period 2002–2005.

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B1 (IPCC, 2007). For each scenario, three ensemble runs were avail-able and were applied to the four hydrological models. The REMOmodel is able to consider the change in the concentration of atmo-spheric greenhouse gases, a change in land coverage and soil deg-radation. The simulation period ranged from 2001 through 2049.Because REMO is a mesoscale model, the simulated precipitationpattern has a resolution of 0.5�, which does not match the localprecipitation data. For this reason, a downscaling method was ap-plied. The method includes a physical downscaling component toincorporate the effects of local topography, a stochastic componentwith random rainfall distributions and a statistical component thatadjusts the statistical characteristics of the simulated precipitationpattern to those of the observed daily rainfall data. The methodmaintains the climate signal from REMO and matches the simu-lated pattern to the observed spatial and temporal rainfall distribu-tion (Christoph et al., 2010). Hiepe (2008) compared the results ofthe REMO simulation for the period 1960–2000 with measureddata at Parakou (see Fig. 1). She concluded that REMO reproducesmonthly rainfall and potential evapotranspiration as well as thefrequency distribution of daily rainfall very well.

Land use was simulated on a yearly basis for the years 2000through 2024 by Judex (2008). Based on socio-economic scenariosdeveloped by the IMPETUS project (Reichert and Jaeger, 2010), Ju-dex (2008) computed annual land use change at a high spatial res-olution. In the first step, he identified local driving forces byanalysing land use changes between 1990 and 2000. He then calcu-lated the demand for arable land, depending in part on populationgrowth and technological development, with a yearly time step. Inthe third step, the location where land use change is expected iscalculated based on soil suitability, protected areas and access toinfrastructure or markets.

Table 3 gives an overview of socio-economic development andthe resulting change in arable land. The values mentioned inTable 3 for the expansion of cultivated land refer to the UpperOuémé Catchment but are also valid for the Térou Catchment.

4. Results

4.1. Calibration and validation

To avoid confusion resulting from the different calibration andvalidation periods, the results are presented for 2 periods: a firstperiod between 1998 and the end of 2001 (calibration period forSWAT and validation period for the three other models) and a sec-ond period between 2003 and 2004 (validation period for SWATand calibration period for the three other models). Table 4 summa-rises the components of the simulated water budget and thefractions of discharge components for each model and givesthe Nash–Sutcliffe-coefficient (Nash and Sutcliffe, 1970) and theCoefficient of Determination for both periods.

A correct interpretation of the results requires knowledge aboutthe climate variability during the two periods. During both periods,the first years (1998, 2003) experienced 1459 mm and 1323 mmprecipitation and were wetter than the other years. The years1999 and 2004 had comparable precipitation amounts (1189 mmand 1115 mm, respectively), but during the first period precipita-tion decreased, declining from 1076 mm in 2000–1000 mm in

Table 3Main assumptions of the land use scenarios LU1, LU2 and LU3 and the resulting change in

LU1 scenario LU

� Economic growth � E� Improved political and socio-economic situation � P� Innovations in the agricultural sector spread due to good administrative

structures� Npro

� Expansion of cultivated areas by 15% � E

2001. This resulted in a sharp decline in precipitation and dis-charge amount during both periods. Considering the precipitationdata of the full calibration and validation periods, SWAT has beencalibrated for drier conditions and all other models have been cal-ibrated for wetter than mean conditions. According to Table 4, bothstatistical measures reach at least 0.75 for all models except for theUHP-HRU, which only reaches 0.69 and 0.71. For WaSiM, UHP-HRUand GR4J, this outcome indicates good agreement between themeasured and simulated discharge data during the validation per-iod. SWAT reaches statistical values higher than 0.9, which indi-cates excellent agreement between the measured and simulateddischarge. The analysis of the water balance components (Table 4)shows that UHP-HRU simulates the lowest fraction of surface run-off and that SWAT and WaSiM calculate the highest values. For theWaSiM and SWAT simulations, surface runoff represents 50% and45%, respectively, and is thus higher than the mean measured frac-tion of 30% surface runoff (Giertz, 2004). The calibration aim for theWaSiM simulation, i.e., the limitation of surface runoff to approxi-mately 30%, is only achieved for three individual years.

Fig. 3 compares the difference between the observed and mea-sured discharge for the applied models during the first period. Themonths from December to June are not shown because no dis-charge was measured and only the UHP-HRU model calculatedlow discharge peaks during the dry period caused by single rainfallevents. In the dry period, the surface runoff that occurs will infil-trate in the river bed. This process is not included in UHP-HRU.Moreover, the UHP-HRU simulation overestimates the dischargeat the beginning of the discharge period, but underestimates thedischarge towards the end of the year (Fig. 3). WaSiM underesti-mates the discharge at the beginning of the discharge period, but

the fraction of arable land between 2000 and 2024 (Judex, 2008).

2 scenario LU3 scenario

conomic stagnation � ‘‘Business as usual’’rotected forests are not controlled � Productivity does not increaseo new technologies that enhanceduction

� Expansion of cultivated area by20%

xpansion of cultivated area by 30%

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228 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

correctly simulates the decrease in discharge at the end of the year.In addition, it is visible from the lower range of differences be-tween the observed and simulated discharge that the models’ per-formance is much better during the drier years (1999, 2000 and2001).

This behaviour can be explained by a delayed response of thedischarge components and the groundwater level to the rainfallevents between the beginning of July and mid-August.

The comparison of the results between the two periods showsthat the performances of WaSiM, UHP-HRU and GR4J, evaluatedin terms of the discharge simulation and statistical measurements,are better during the second period. The large difference betweenthe Nash–Sutcliffe-coefficient and the Coefficient of Determination

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for WaSiM is due to the overestimation of peak discharge rates be-tween July and the end of September, 2003 (Fig. 4). SWAT and Wa-SiM overestimate the discharge rates until the end of August 2003,but after that date, WaSiM shows a slight underestimation. BothUHP-HRU and GR4 J underestimate the discharge during the wholeperiod. In 2004, overestimations by UHP-HRU at the beginning ofthe year and by WaSiM during August are visible. The two figuresalso indicate that the model performance is better during drieryears than during wetter years. According to the data in Table 4,the SWAT and GR4J models simulate nearly the same amount ofdischarge for the period 2003–2004, whereas WaSiM computesthe highest amounts and UHP-HRU simulates the lowest amountsof discharge. The overestimation of the total discharge during the

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calibration period of WaSiM results from the calibration procedure.To achieve the goal of 30% surface runoff, parameters influencingthe amount of inter- and baseflow had to be increased. Thesechanges in the parameter values caused the total discharge amountto increase. This observation also explains the substantial overesti-mation of discharge peaks in 2003. The GR4J simulation showsonly a small overestimation of total discharge during the calibra-tion period, but an overestimation of 70 mm during the validationperiod. This might be a result of the calibration process, as this pro-cess will maximise the Nash–Sutcliffe-coefficient. The simulatedfractions of the discharge components are very similar for all mod-els during both periods, except for the UHP-HRU model, whichsimulates at least a 5% increase in surface runoff during the secondperiod corresponding to the calibration period. As shown in Table 4,WaSiM calculates the highest amount of potential evapotranspira-tion of all models evaluated with 2258 mm/a during the period1998–2001 but only 1931 mm/a during the period 2003–2004.Both values are consistent with Class A pan evapotranspirationmeasurements in Benin reported in Fink et al. (2010).The datacome from the Direction de la Météorologie Nationale (DMN) of

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Benin and consider the oasis effect. According to Fink et al.(2010), potential evapotranspiration can vary from 10–12 mmper day during the dry period and from 2 to 4 mm per day in therainy season. These values correspond to a yearly mean value ofbetween 1690 and 2420 mm. Furthermore, the results of Kasei(2010) show that the potential evapotranspiration calculated byWaSiM for the White Volta Basin, which has a drier climate thanthe Térou Catchment, can vary between 1759 mm (calibration per-iod) and 2272 (validation period).

4.2. Land use scenarios

The climate data from 1998 to 2004 were used to simulate theland use scenarios. To exclude the effects of varying climate, amean value of the model results over the seven simulation yearswas calculated. The three land use scenarios are differentiated bytheir increase in agricultural area, as well as by other factors. Thecomputed discharge is shown as a seven-year mean monthly valuefor all scenarios for total and surface runoff in Figs. 5–7; the valuesfor all water balance components are given in Table 5.

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Table 5Seven-year (1998–2004) mean values of water balance components for the three land use scenarios and the land use for the year 2000 for all models.

2000 2024 LU1 2024 LU2 2024 LU3

UHP-HRUTotal discharge (mm) 188 203 219 210Fraction of surface runoff (%) 22 30 35 32Fraction of interflow (%) 65 60 56 58Fraction of baseflow (%) 13 10 9 10Potential evapotranspiration (mm) 1640b 1634b 1624b 1629b

Actual evapotranspiration (mm) 804 786 778 783Precipitation (mm) 1189a 1186a 1195a 1190a

WaSiMTotal discharge (mm) 186 226 249 231Fraction of surface runoff (%) 42 48 51 49Fraction of interflow (%) 38 30 26 29Fraction of baseflow (%) 20 22 23 22Potential evapotranspiration (mm) 2207 2137 2095 2127Actual evapotranspiration (mm) 921 880 855 874Precipitation (mm) 1165 1165 1165 1165

SWATTotal discharge (mm) 217 224 232 227Fraction of surface runoff (%) 48 51 55 53Fraction of interflow (%) 0 0 0 0Fraction of baseflow (%) 52 49 45 47Potential evapotranspiration (mm) 1564c 1564c 1564c 1564c

Actual evapotranspiration (mm) 724 721 714 719Precipitation (mm) 1152 1152 1152 1152

a The differences in the mean values are due to the use of monthly output data; the maximum difference in monthly precipitation between the scenarios is 2.1 mm;b The Penman model uses albedo data, which changes with changing land use.c No change despite Penman–Monteith because the FAO approach for short-grass is used.

230 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

The GR4J model is not used for land use change projections be-cause it is not able to capture the major impacts of land usechanges due to the missing differentiation between dischargecomponents.

In SWAT, it is not possible to change land use dynamically be-cause it determines HRU delineation. Therefore, land use changescenarios can only be performed by independent model runs,which makes comparing land use change effects difficult.

In UHP-HRU and SWAT, the land use and soil parameters foreach land use type were not modified, but the share and spatialdistribution of the different land use types were changed accordingto the land use scenario maps of Judex (2008). In SWAT, a new landuse map changes the delineation of HRUs and thus directly influ-ences the simulation of the discharge fractions.

In WaSiM, changes in land use are described by vegetationparameters. Changes in the distribution of land use parameters re-sult from the usage of different land use maps for the scenarios andcause a change in potential and actual evapotranspiration and,therefore, in total discharge. Table 5 shows that the changes in po-tential and actual evapotranspiration are pronounced in the Wa-SiM simulation. Comparable to the results for the validation andcalibration periods, WaSiM calculates a significantly higheramount of evapotranspiration than UHP-HRU.

The UHP-HRU model simulates an increase in total dischargeof 12% and an increase in surface runoff of 47% on average for allscenarios compared to the reference period. WaSiM simulates amean discharge increase of 27% and a mean increase in surfacerunoff of 46%. SWAT simulates a mean discharge increase of

T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236 231

5% and a mean increase in surface runoff of 17% (refer toTable 5).

All models agree that the LU2 scenario has the largest impact ondischarge sum and the increase in surface runoff. The simulateddischarge amounts and surface runoff fractions are very similarfor WaSiM and SWAT, except for the base run in 2000 where SWATsimulates a higher discharge amount than the two other models.

Differences in the shape of the surface runoff curve between UHP-HRU and WaSiM are apparent from the comparison of Figs. 5 and 6.In the UHP-HRU simulation, surface runoff starts at the end of March,increases rapidly between June and July and decreases steadily untilOctober. In contrast, surface runoff in the WaSiM simulation starts inJune, increases rapidly until September and decreases to 0 mm inNovember. In SWAT, the surface runoff starts in May, but the peakin surface runoff is simulated for August, while the peak in total dis-charge amount is simulated for September (Fig. 7).

4.3. Climate scenarios

The potential impact of climate change on the dischargedynamics is analysed by comparing the yearly mean sums of (1) to-tal runoff, (2) precipitation, (3) potential and actual evapotranspi-ration and (4) runoff coefficient per decade.

Figs. 8 and 9 show the simulated discharge amount per decadebetween 2001 and 2049 for each model as a mean value for allthree ensemble runs of the A1B and B1 scenarios. It is clearly vis-ible from both figures that both climate scenarios lead to a reduc-tion of the total discharge amount, regardless of model type(Table 6). The decrease in total runoff varies between 28% (UHP-HRU, WaSiM) and 34% (SWAT, GR4J) for the A1B scenario andbetween 28% (SWAT, WaSiM, UHP-HRU) and 33% (GR4 J) for theB1 scenario. UHP-HRU calculates the lowest values (160–88 mmper decade). In contrast, WaSiM calculates the highest values(480–200 mm; Table 6).

The development of the discharge amount for the A1B-scenario(Fig. 8) is generally comparable among the models evaluated,which is not the case for the B1 scenario (Fig. 9). The UHP-HRU

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model simulates a continuous decrease in total discharge betweenthe first and last decades. In contrast, SWAT, GR4J and WaSiM cal-culate a large decrease in discharge amount between 2001–2010and 2011–2020, with a maximum decrease of 132 mm (37%) calcu-lated by the SWAT model.

The differences between the three ensemble runs of the A1B andB1 scenarios are shown in Figs. 8 and 9, which depict the maximumand minimum values of the mean discharge amount for each decade.Two features of these figures are apparent. First, the difference be-tween the maximum and minimum values decreases from the firstto the last decade. Second, the differences between the minimumand maximum values are comparable for the GR4J and the UHP-HRU models. Those models also produce the smallest differences,whereas WaSiM computes the largest differences. It has to be notedthat the variability between the ensembles is higher than the de-crease in discharge for all models. In addition, the variability is alsohigher than the differences between the models except for theGR4J and the UHP-HRU models in the first and last decade.

The variability of the simulated decade-mean precipitation val-ues is much lower than the variability of the discharge (Table 6). Ingeneral, the precipitation amount decreases by 138 mm for theA1B scenario and by 164 mm for the B1 scenario between 2001–2010 and 2040–2049. The value cited is a mean value of all ensem-bles and all models.

Potential evapotranspiration increases in all four models be-tween the first and last decades (Table 6) from 5% to 14% in sce-nario A1B and from 5% to 12% in scenario B1. A comparison ofthe simulated potential evapotranspiration of the first and last dec-ades shows an increase of 105 mm for both scenarios in the UHP-HRU model. WaSiM and SWAT calculate higher increases than theUHP-HRU for both scenarios, whereas WaSiM calculates the high-est increase (267 mm) for the A1B scenario.

The development of actual evapotranspiration shows large dif-ferences between the models. In contrast to WaSiM, which simu-lates higher amounts in the last decade than in the first, SWATsimulates a small decrease and GR4 J and UHP-HRU calculate thehighest decrease in actual evapotranspiration.

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Table 6Decade mean sums of total discharge, precipitation, actual and potential evapotranspiration and runoff coefficient for both climate scenarios and all four models.

WaSiM A1B WaSiM B1 SWAT A1B SWAT B1 UHP-HRU A1B UHP-HRU B1 GR4J A1B GR4J B1

Total discharge (mm)2001–2010 435 496 263 357 124 160 182 2222011–2020 407 390 221 225 121 115 176 1662021–2030 367 369 219 227 109 114 156 1672031–2040 375 401 216 263 112 120 156 1732041–2049 325 373 174 227 88 116 121 148

Precipitation (mm)2001–2010 1234 1282 1221 1351 1168 1231 1168 12312011–2020 1164 1152 1138 1148 1134 1113 1134 11132021–2030 1129 1130 1132 1165 1095 1117 1095 11172031–2040 1138 1155 1131 1206 1112 1122 1112 11222041–2049 1095 1123 1067 1152 1046 1098 1046 1098

Potential evapotranspiration (mm)2001–2010 1849 1803 1835 1795 1992 1959 1992 19592011–2020 1931 1915 1905 1893 2018 2016 2018 20162021–2030 1965 1932 1950 1912 2032 2017 2032 20172031–2040 2005 1948 1988 1945 2059 2039 2059 20392041–2049 2116 2017 2082 1990 2096 2064 2096 2064

Actual evapotranspiration (mm)2001–2010 755 741 761 753 894 892 828 8252011–2020 758 763 743 744 886 882 806 8022021–2030 761 760 745 754 874 879 800 8042031–2040 764 755 755 751 889 879 818 8002041–2049 770 751 749 747 867 864 788 790

Runoff coefficient (–)2001–2010 0.35 0.39 0.22 0.26 0.11 0.13 0.16 0.182011–2020 0.35 0.34 0.19 0.20 0.11 0.10 0.16 0.152021–2030 0.33 0.33 0.19 0.19 0.10 0.10 0.14 0.152031–2040 0.33 0.35 0.19 0.22 0.10 0.11 0.14 0.152041–2049 0.30 0.33 0.16 0.20 0.08 0.11 0.12 0.13

232 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

5. Discussion

Chapter 3.1 revealed that discharge simulations with compara-ble quality measures can be generated by different fractions of dis-charge components. This finding in the analysis of discharge

modelling is known as ‘‘equifinality’’ (Beven, 2001). However, theperformance of the models evaluated for scenario simulations dif-fers substantially.

In the case of the first result presented above, all simulationsconducted with the SWAT model in the Térou Catchment agree on

T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236 233

a very low fraction of interflow (<5%) (Busche et al., 2005; Hiepeand Diekkrüger, 2007; Sintondji, 2005). This simulation result isin contrast to the results of the WaSiM and the UHP-HRU models,which simulate a fraction of interflow of 25% and 63%, respectively.The low fraction of interflow calculated by SWAT can be explainedby the model concept. In this model, the lateral flow depends onthe local slope. As the Térou Catchment is relatively flat, the inter-flow cannot be calculated correctly by the SWAT model (Sintondji,2005). This weakness leads to a fraction of surface runoff of nearly50% in Sintondji’s study because the surface runoff and the base-flow compensate for the missing interflow. The simulations ofthe UHP-HRU model indicate a dominance of interflow becausethe mean fraction for all seven simulation years is 66%. This findingis supported by Giertz (2004) using a discharge simulation per-formed with the physical-based model SIMULAT-H, by Fass(2004) using geochemical methods for the small Aguima Catch-ment (3.2 km2) and by the calibration results in the study by Kasei(2010) undertaken in the White Volta Basin. Kasei (2010) found adominance of lateral flow during the calibration period and a dom-inance of surface runoff during the validation period using the Wa-SiM model.

The WaSiM and SWAT models calculate the same fraction ofsurface runoff (Table 4). In WaSiM, the infiltration process calcula-tion is physically based and is thus dependent on soil parameters.Despite the physical calculation of the infiltration process in Wa-SiM, which is limited in our study due to the usage of daily timesteps, none of the models applied in this study can simulate thesaturation-excess runoff mechanism. This property of the modelsimplies that the Hortonian-type surface runoff fully accounts forthe high fraction of surface runoff simulated by WaSiM. The dom-inance of Hortonian runoff is not consistent with the observationby Giertz et al. (2010) that Hortonian runoff is unlikely for savan-nah vegetation types because of their very high infiltration rates (atleast 1000 mm per hour). On agricultural fields, the secondary poresystem is reduced significantly, which results in increased surfacerunoff (Germer et al., 2010).

It was previously stated that the infiltration process calculationin WaSiM requires the definition of certain soil parameters, amongthem the saturated conductivity, which is also used in the SWATmodel. Güntner (2002) and Le Lay et al. (2008) have noted that soilparameters, especially hydraulic conductivity values, are a sourceof uncertainty that produces problems in the correct representa-tion of infiltration characteristics (e.g., shown by Niehoff (2001)for the WaSiM model). The saturated conductivity values used inthe present study were measured by Sintondji (2005) and are atleast a factor of 10 smaller than the values reported by Giertzet al. (2005). These variations can be explained by different incor-porations of the macropore system during laboratory (performedby Sintondji) and field measurements (done by Giertz). One possi-ble solution to the problem would be to calibrate the saturatedconductivity, as Varado et al. (2006) did in their study and as Hiepe(2008) did for the calibration of the SWAT model. Their solutionwas not included because it would limit the physical representa-tiveness of the WaSiM model, which is already limited by the useof the conceptual groundwater module and the calculation of infil-tration at daily time steps.

The choice of different calibration and validation periods withdifferent durations and different climates clearly limits the compa-rability between the results. This is especially true for the compar-ison between WaSiM or SWAT and UHP-HRU or GR4J because thelatter only use a calibration period of two years, whereas the for-mer use a period of four years. Despite these differences, it isimportant to note that SWAT and WaSiM agree on the fraction ofsurface runoff during both periods. Both models are calibratedand validated for different periods. WaSiM is able to simulateinterflow as a fast subsurface discharge component, and both mod-

els have been calibrated to match different fractions of surface run-off (WaSiM: 30%; SWAT: 45%). As SWAT generally simulates thedischarge curve in a better way, it can be stated either that the cal-ibration of SWAT was more successful or that the distinction be-tween interflow and baseflow does not adequately representslocal conditions.

The UHP-HRU and SWAT models do not need saturated conduc-tivity data as inputs for calculating infiltration because the SCScurve number method is used for calculating runoff (SCS, 1972).

Uncertainties in the simulation of and assumptions for parti-tioning the discharge components and the differences in calibra-tion periods inevitably raise a question. Of the four appliedmodels, which, if any, can correctly simulate the discharge, evenwith an optimal database? The models’ weaknesses are (1) the dis-tinction between interflow and baseflow, (2) the exclusive consid-eration of Hortonian runoff and (3) the calibration affecting theformation of the discharge processes.

Instead of distinguishing between interflow and baseflow, a soilmodule suitable for the Térou Catchment should consist of threezones: one zone on top of the loamy-sand layers or iron crusts,one zone underneath these structures and one zone for the param-eterisation of the nearly impermeable soil layers. If this concept isapplied, it should be possible to differentiate the subsurface runoffaccording to the reaction time to rainfall events in terms of threerecession constants, one for each soil zone.

To avoid the parameterisation of preferential flow, which hasa large impact on the discharge components, the recession con-stant for the first layer should allow variability in time andshould depend on land use. The recession constants influencethe amount of discharge. With a time-varying recession constant,it should be possible to mimic the abrupt replacement of oldwater by water infiltration through macropores (Anderson andBurt, 1990).

It is necessary to ask whether models whose calibrations affectrunoff formation processes are suitable for analysing and under-standing discharge processes. This concern is especially valid if acalibration appears to match statistical measures rather than theperception of the processes.

Chapter 4.2 has outlined that a change in land use has a sub-stantial influence on the discharge dynamics. The idea that an in-crease in the fraction of agricultural land causes an increase indischarge is generally confirmed by Bormann (2005) and has beenverified for Benin by Giertz et al. (2005) and Götzinger (2007). Forexample, Giertz et al. (2005) found a higher amount of total dis-charge (62%) for a catchment dominated by agriculture comparedto a catchment dominated by savannah vegetation, although theprecipitation only differs by 12%.

This model comparison study indicates that the increase in theamount of discharge is due to an increase in surface runoff causedby the lower infiltration rates on agricultural land. This finding issupported by measurements (Giertz et al., 2005) and other mod-el-based land use change impact studies of Giertz et al. (2010),using the UHP-HRU model, and Busche et al. (2005) and Hiepeand Diekkrüger (2007), using SWAT. Busche et al. (2005) computean increase in surface runoff of 17% for the same land use scenarioLU2 (Table 3). The low increase of 17% in surface runoff for the LU2scenario is in contrast to the results of the UHP-HRU and WaSiMmodels, which calculate the highest increases in surface runofffor the LU2 scenario (83%) corresponding to the highest increasein arable land (Table 3).

These results indicate that SWAT is basically suitable for assess-ing land use change; however, Hiepe and Diekkrüger (2007) indi-cate that simulating the effects of land use change scenarios ondischarge dynamics with the SWAT model is time consuming be-cause changing land use leads to a change in the delineation ofHRUs. Although the UHP-HRU model used in this study also applies

234 T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236

the concept of HRUs, changing land use is easier to implement, asUHP-HRU has been programmed to dynamically consider annualland use, whereas SWAT is only able to consider one land usemap per simulation run.

Contradictory results concerning the applicability of a physi-cally based model to simulate land use change effects can be foundin the literature. The results of Bormann et al. (2009) and Pöhler(2006) indicate that WaSiM appears to be unsuitable for analysingchanges in discharge due to changing land use. Bormann et al.(2009) explain this finding in terms of the groundwater-dominatedparameterisation of the WaSiM model, which would not allow sur-face runoff and interflow to react to changes in vegetation cover-age. It would be necessary to define the saturated conductivityvalues in relation to land use and not only to soil types. Thisrequirement is especially applicable to Benin because Giertz et al.(2005) find that saturated conductivity values vary more betweenland use types than between soil types.

In contrast to the results of Pöhler (2006) and Bormann et al.(2009), the simulation results of Niehoff (2001) and Leemhuiset al. (2007) show that the WaSiM model can simulate changesin discharge resulting from changes in land use. In the study byNiehoff (2001), the fraction of land cover types is changed byimplementing different land use maps. This approach causes achange in discharge during flood events. This method is alsoused in the present study. Leemhuis et al. (2007) implementland use change in WaSiM by altering the vegetation parame-terisation relative to the cover of the land use classes. Thismethod resulted in a significant change in total runoff for a sce-nario in which all forest at an altitude less than 1200 m abovesea level was converted into agricultural land. However, no suchchange in total runoff occurred for a simulation using the ob-served land use changes.

It can be concluded that, in addition to model structure and ap-proaches, the sensitivity of simulation models to land use changedepends on model calibration strategies. The climate during thecalibration period, as well as the selection of the objective function,determines the parameters and the applicability of the calibratedparameters for scenario quantification. All models show a decreasein precipitation and total discharge until 2050 for the Térou Catch-ment under both climate scenarios. The mean decrease in precip-itation over all models ranges from 134 mm, or 11% (A1B), to156 mm, or 12% (B1). As all models used the same input data forthe climate scenarios, the differences in calculated precipitationcan be explained by the different regionalisation methods in theapplied models. The mean decrease in total discharge ranges from74 mm (A1B), or 29%, to 93 mm, or 30% (B1).

Basically, all models compared in this study are suitable for asimulation of climate scenarios. Pöhler (2006) remarks that the re-sults of scenarios are influenced more by the quality of the datathan by the model structure of WaSiM.

However, the results of climate scenarios must be interpretedwith caution because the simulated climate data depend on cer-tain assumptions about the possible development of climatewhich manifest in the fact that the variability between ensem-bles is larger than the decrease for all models and larger thanthe difference between the models. This concern is especiallyapplicable to the precipitation data simulated for Benin becausethis region is marked by high interannual and decadal variabilityin precipitation (Fink et al., 2010). During the simulation periodfrom 1998 to 2004, the precipitation rates interpolated by Wa-SiM for the Térou Catchment range from 952 mm (2000) to1475 mm (2003), a difference of 55%. In addition, Holländeret al. (2009) remark that the modeller itself is an intrinsic partof the modelling process and thus a source of uncertainty. Thisuncertainty might explain a large part of the variation in simu-lated future developments.

6. Conclusions

In this study, four different model types were used to simulatecurrent and future discharge of a tropical catchment. The simula-tion quality for the current total discharge and its componentswas shown to vary between model types. This variation was attrib-uted to serious uncertainties in input data, particularly in precipi-tation and saturated conductivity data, calibration strategy,parameterisation and differences in model structure. The resultsof the study suggest the question whether hydrological modelswhose calibration allows for a direct influence on the dischargeamount are a good choice for analysing hydrological processes.

Following the dictum by Box and Draper, 1987 that ‘‘essentially,all models are wrong but some are useful,’’ it is not possible to val-idate models in the strict meaning of this term. The aim of modelcomparison studies is therefore to analyse which models may beuseful for certain research questions.

Comparing the results of land use scenarios revealed thatSWAT, UHP-HRU and WASIM are suitable for assessing land usechange because they provide similar results. In WaSiM, land usechange is simulated by a change in evapotranspiration. It is doubt-ful that the change in evapotranspiration fluxes represents theimportant processes linked with the effects of land use change.For example, the reduction in the saturated conductivity values fol-lowing the expansion of arable land is not considered. AlthoughWaSiM applies the Penman–Monteith approach considering plantproperties (e.g., bulk-stomata resistances, root depths), these prop-erties are predominantly unknown for most of the vegetation typesin West Africa and therefore introduce uncertainty into the model.Thus, this study concurs with the opinion of Bormann et al. (2009)and Pöhler (2006), who doubt that WaSiM is suitable for assessingland use change.

All of the models evaluated (with the exception of the WaSiMmodel) are suitable for the simulation of future discharge becausethey produce comparable results for discharge development. TheWaSiM model produces considerably higher total amounts of dis-charge. Despite the choice of different calibration and validationperiods and methods, chapter 4.1 showed that the fractions of dis-charge components do not vary between the two periods we havechosen for the comparison of discharge results. Additionally, onlythe UHP-HRU model is able to simulate the dominance of fast sub-surface flow components that we assume to be the dominant dis-charge component, based on the assumption that themeasurements by Giertz (2004) are representative of processesat larger scales. Thus, according to this study, the UHP-HRU modelis the most suitable for discharge simulation in Benin because itexhibits the best tradeoffs between parameterisation and calibra-tion efforts and physical representativeness.

In contrast, the GR4J model reproduces the discharge curve verywell but does not contribute to the understanding of the underly-ing processes. This finding supports the opinion that any statisti-cally driven calibration, even of a physical model, would lead toa poor representation of processes, especially in data-sparse re-gions such as Benin.

Acknowledgements

The authors would like to thank the Federal German Ministry ofEducation and Research (BMBF, Grant No. 01 LW 06001B) as wellas the Ministry of Innovation, Science, Research and Technology(MIWFT) of the federal state of North Rhine-Westfalia (Grant No.313-21200200) for the funding of the IMPETUS project in theframework of the GLOWA-program. Many thanks to our partnersin Benin and all colleagues of the IMPETUS project, who provideddata and assistance. We further thank Dr. Constanze Leemhuis

T. Cornelissen et al. / Journal of Hydrology 498 (2013) 221–236 235

and Dr. Susannah Moore for their support and Dr. Claudia Hiepe forproviding SWAT model results. Finally, we acknowledge usefulcomments from anonymous referees on an earlier version of thispaper.

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