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e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40
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Analysing the effects of soil properties changes associatedwith land use changes on the simulated water balance: Acomparison of three hydrological catchment models forscenario analysis
Helge Bormanna,∗, Lutz Breuerb, Thomas Graff c, Johan A. Huismanb
a University of Oldenburg, Department of Biology and Environmental Sciences, Uhlhornsweg 84, 26121 Oldenburg, Germanyb
University of Gießen, Institute for Landscape Ecology and Resources Management, Heinrich-Buff-Ring 26, 35392 Gießen, Germanyc University Potsdam, Institut fur Geookologie, Postfach 60 15 53, 14415 Potsdam, Germanya r t i c l e i n f o
Article history:
Published on line 15 August 2007
Keywords:
Hydrological catchment models
Model comparison
Scenario analysis
Land use change
Soil hydrological properties
a b s t r a c t
This paper presents results of a model comparison study within the LUCHEM framework
(‘assessing the impact of Land Use Change on Hydrology by Ensemble Modelling’) where the
effects of land use change on catchment water balances were assessed with various hydro-
logical catchment models. The motivation for this part of LUCHEM is that it is well known
that land use changes may induce changes in soil chemical and soil physical properties (e.g.
bulk density). Unfortunately the effects of land use change on soil hydraulic properties are
seldom investigated directly, but some information on changes in bulk density is available.
Changes in bulk density can be used as input for pedotransfer functions to derive changes
in soil hydraulic model parameters. In this study, three different catchment models (SWAT,
TOPLATS, WASIM) are compared with respect to their sensitivity to land use change with and
without consideration of associated changes in soil parameterisation. The results reveal that
different models show a different sensitivity to the change in soil parameterisation while
the magnitude of absolute changes in simulated evapotranspiration and discharge is similar.
SWAT calculates largest changes in the water balance in a German mesoscale catchment.
TOPLATS also shows significant changes in the calculated catchment water balances as well
as in the runoff generation while WASIM reacts least sensitive. While TOPLATS and WASIM
show similar patterns with respect to changes in the water flows for all subcatchments and
land use scenarios, SWAT results are similar for the different catchments, but show sce-
nario specific patterns. In relation to the magnitude of the effects on simulated water flows
induced by land use change, the significance of considering soil change effects depends on
both, the scenario definition and on the model sensitivity to soil parameterisation. For two
of the three land use scenarios representing an intensified land use, SWAT and TOPLATS
simulate water balance changes in the same order of magnitude due to both, land use and
soil property changes. Therefore, a consideration of changes in soil properties as part of
land use change scenario analysis is recommended. Future field work needs to aim at the
validation of the assumed dependency of soil hydrologic properties on land use change.
© 2007 Elsevier B.V. All rights reserved.
∗ Corresponding author. Tel.: +49 441 7984459; fax: +49 441 7983769.E-mail address: [email protected] (H. Bormann).
0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2007.07.004
l i n g
30 e c o l o g i c a l m o d e l1. Introduction
Land use change is one of the most conspicuous changes incultural landscapes in many regions of the world. In additionto climate change, land use change is expected to have a strongimpact on the water budget of river catchments (DeFries andEshleman, 2004). The prediction of water fluxes in changingenvironments and under changing boundary conditions is animportant task and requires the use of hydrological catch-ment models. Such models should be evaluated for differentenvironmental conditions (e.g. different climatic conditions,topography, soils and vegetation cover) to be able to esti-mate the changes induced by environmental change. Differentmodel concepts are available to be used for environmen-tal change studies, describing hydrological processes on aphysically basis or by conceptual approaches. In most casesphysically based models are preferred as many of their modelparameters are measurable at the small scale, and hence, pre-dictable if the boundary conditions change. Many studies haveanalysed the effects of land use change on hydrological fluxesin river basins (e.g. Sahin and Hall, 1996; Lørup et al., 1998;Moussa et al., 2002; Niehoff et al., 2002; Costa et al., 2003; Ottand Uhlenbrook, 2004; Wegehenkel, 2004; Fohrer et al., 2005,2002, 2001; Huisman et al., submitted for publication). Mostof these studies analyse the effect of land use change on thecatchment water balance without considering that a changein land use or land cover may also induce a change in otherassociated landscape characteristics such as soil properties.The studies only assume a change in the vegetation cover.This is the case for process-based models in particular. Con-ceptual models such as the SCS Curve number method (SCS,1972) implicitly consider the relations between land use andsoil characteristics.
The effects of land use change scenarios on the catchmentwater balance have for example been analysed in the LUCHEMproject (‘assessing the impact of land use change on hydrol-ogy by ensemble modeling’). Within LUCHEM the effect of landuse change on the catchment water balance was assessed byten different catchment models with a wide variety of modelstructures (Breuer et al., submitted for publication; Huismanet al., submitted for publication). One main finding was thatmost of the models showed similar reactions to the assumedland use changes. But as in other land use scenario studies,soil property changes associated with land use changes werenot considered although it is well known, that land use changemight alter soil hydraulic properties. For example, Bronson etal. (2004), Murty et al. (2002) and Franzluebbers et al. (2000)report an increase of bulk density with a change of land usefrom forest to grassland and to crops. According to Murty etal. (2002) the rate of change in bulk density can be very vari-able. While the variance in bulk density change is very highthere is a statistically significant increase in bulk density after5–10 years when changing land use from forest to crops. How-ever, there are also chronosequence studies such as Breuer etal. (2006) who did not find a significant change in bulk density
caused by land use change. Thus, while studies on bulk densityin relation to land use change are available, we are not aware ofstudies that have systematically analysed and quantified theeffect of land use change on soil hydraulic properties (e.g. sat-2 0 9 ( 2 0 0 7 ) 29–40
urated hydraulic conductivity) or on the soil water retentioncurve. Therefore, this paper introduces a methodology basedon pedotransfer functions to derive a change in soil hydraulicproperties because of a change in bulk density to assess theimportance of considering soil property changes associatedwith land use change. This methodology allows us to anal-yse the effects of changing soil properties in response to landuse change in different land use scenarios by only specify-ing changes in bulk density. Performing such an analysis ofcourse one has to have in mind that soil structural changesare only partly reflected by an analysis based on bulk den-sity only. However, a possibility is presented to perform theanalysis based on data which are available from literature.
This study is designed in the following way. Firstly, threedifferent process-based hydrological catchment models arecalibrated and validated for current conditions in the Dillcatchment in central Germany. The models SWAT, TOPLATSand WASIM already have been applied in other environmen-tal scenario studies. Secondly, the water balances based onland use scenarios with unchanged soil parameters are cal-culated with each of the three models. Thirdly, based on aliterature review, relative changes in bulk density for particu-lar land use changes are estimated. Based on these changes,relative changes in soil hydraulic parameters are derived (e.g.saturated hydraulic conductivity) which depend on the landuse change of each pixel of the soil map. Changed param-eter sets are then used for new water balance calculationswith all three models based on land use scenarios. Compar-ing the three sets of simulations (baseline, land use scenarios,land use scenarios considering a change in soil properties), themodel specific sensitivity to scenario specific soil parametersis revealed. Furthermore, the importance to consider changesin soil properties as part of land use scenario calculation canbe assessed.
2. Material and methods
2.1. Hydrological catchment models
Three different hydrological catchment models are appliedand compared in this study: TOPLATS, WASIM, and SWAT. Themodels are shortly described in the following, while the mainprocess descriptions of each model are summarised in Table 1.
The TOPMODEL-based atmosphere transfer scheme model(TOPLATS; Famiglietti and Wood, 1994a; Peters-Lidard et al.,1997) is a multi-scale, spatially distributed and grid-basedmodel to simulate local to regional-scale catchment water andenergy fluxes. It combines the local-scale and process orientedsoil-vegetation-atmosphere-transfer scheme approach (SVAT)to represent local-scale vertical water fluxes of single grid cellswith the catchment scale TOPMODEL approach (Beven et al.,1995). The topographic index is used to laterally redistributethe water within a catchment. Lateral interaction between thelocal SVATs is not accounted for by the model. Local groundwa-ter levels are adapted after redistribution and are used as lower
boundary conditions for the local SVATs. A channel routingscheme is not included in TOPLATS.WASIM (Schulla, 1997; Niehoff et al., 2002) is a spatiallydistributed, grid-based and process oriented hydrological
e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40 31
Table 1 – Hydrological processes and process descriptions within the three different catchment models used (LAI, leafarea index; PET, potential evapotranspiration)
Process TOPLATS WASIM SWAT
Interception Storage approach: storage capacityproportional to LAI
Storage approach: storage capacityproportional to LAI
Storage approach: storagecapacity proportional to LAI
Actual evapo-transpiration
Reduction of PET (Monteith, 1965) by soilmoisture status
Reduction of PET (e.g. according toMonteith, 1965) by soil moisturestorage
Monteith (1965)
Infiltration Function of soil properties and soil waterstatus (Milly, 1986)
Peschke (1977, 1987) based onGreen and Ampt (1911)
SCS curve number (SCS,1972)
Runoff generation Rainfall rate minus infiltration capacityContributing areas derived fromtopographic index
Horton type runoff SCS curve number
Percolation Gravity driven drainage Function based on soil saturationand saturated conductivity
Cascade flow through soillayers
Capillary rise Capillary rise from water table (Gardner,1958)
Depending on soil saturationdeficit
Function of PET during soilwater deficiency
Interflow – Storage based approach Kinematic storage modelBase flow Exponential decay function Exponential decay function Recession function
ccr2ttg
itdocaierHsirm
itiS(BAaiEiGmia
Snow melt –Routing Travel time
atchment model. It was developed for the calculation oflimate change effects in heterogeneous catchments and rep-esents all relevant hydrological processes (Niehoff et al.,002). The model focuses on the accurate representation ofhe spatial variability of atmospheric boundary conditions. Inhis study the TOPMODEL-based version is applied in whichroundwater is considered as a linear reservoir.
Soil and water assessment tool (SWAT; Arnold et al., 1998)s a semi-distributed hydrological catchment model that con-ains both conceptual and physical based approaches. It waseveloped to predict the impact of land management practicesn water, sediment and agricultural chemical yields in largeomplex watersheds with varying soils, land use and man-gement conditions over long periods of time. The catchments partitioned into a number of sub-basins based on a digitallevation model and is further sub-divided into hydrologicalesponse units (HRU) based on land cover and soil maps. EachRU contains a number of water storage volumes for canopy,
now, soil profile, shallow aquifer and deep aquifer. Thus, themportant water flow processes within catchments can be rep-esented using readily available input data. More details of the
odel specific process descriptions are given in Table 1.These three catchment models were successfully applied
n different climatic and environmental regions. Furthermorehey all have been used for scenario studies focusing on thempacts of climate and land use changes. For example, theWAT model was used for modelling studies in South Africa
Govender and Everson, 2005), Germany (Fohrer et al., 2005),elgium (Romanowicz et al., 2005) and the United States ofmerica (Jayakrishnan et al., 2005). The TOPLATS model waspplied in different regions of the United States of Amer-ca (Famiglietti and Wood, 1994b; Peters-Lidard et al., 1997;ndreny et al., 2000), in West Africa (Bormann et al., 2005),n northern Canada (Pauwels and Wood, 1999a,b) and central
ermany (Bormann, 2006; Seuffert et al., 2002). The WASIModel has been mainly applied in European environments,ncluding lowland regions (Niehoff et al., 2002) as well as alpinereas (Jasper et al., 2002; Gurtz et al., 2003; Verbunt et al., 2003).
Temperature-index approach Degree day approachLinear storage (within a basin),kinematic wave (between basins)
Kinematic wave
2.2. Dill catchment characteristics
The Dill catchment (Fig. 1) is one of the target catchmentsof the collaborative research centre 299 “land use options forperipheral regions”. It has an area of 693 km2 and is situatedin central Germany. The catchment shows the typical topog-raphy of the low mountainous regions with elevation rangingfrom 155 m to 674 m a.s.l. Mean slopes are around 14%. Theclimate is temperate and humid. Mean annual precipitationvaries between 700 and 1100 mm, and the mean annual tem-perature is about 8 ◦C. Land use is dominated by forest (55%)and extensive agriculture (7% crops, 21% pasture, 9% fallowland). During the investigated time period (1980–1998) no landuse change was observed in the Dill catchment. Extensive cul-tivation is typical for this peripheral region where full-timefarming is not profitable due to the unfavourable growing con-ditions and the small average field sizes of 0.7 ha. Dominatingsoils are cambisols, planosols and gleysols. Most soils are thinand underlain by hard rock, which shows higher lateral con-ductivities compared to vertical conductivity. Interflow is thedominant runoff generation process of the Dill area (>70% oftotal stream flow is generated by interflow). The following dataare available for this modelling study:
- spatial data sets of elevation, soil and current land use in25 m resolution,
- three spatially explicit land use scenarios of the agro-economic model ProLand in 25 m resolution (for details seeSection 2.3),
- time series of daily data on weather (2 stations), precipi-tation (15 gauges) and discharge (4 gauges) for the period1980–1998.
Parameterisation of the models was mainly performedbased on the spatial data sets available. Soil profiles were avail-able for each pixel in the catchment (representative profile ofeach mapping unit of the soil map).
32 e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40
men
Fig. 1 – Topography, drainage network and instru2.3. Land use scenarios for the Dill catchment
The land use scenarios investigated in this project are so calledfield size scenarios. The average field size in the Dill catchmentis currently about 0.7 ha in average. This is mainly a result ofthe traditional inheritance system where land is split betweenall children. This heritage system and the geomorphologicboundary conditions of the area led to the characteristic het-erogeneous landscape pattern. The existing land tenure andthe small field sizes currently do not support full-time farm-ing. Therefore, part time farming is common in the catchment.As a result of the small acreages and the high machinery costs,farmers do not use any large machinery. One way of increasingthe farm income and supporting the use of larger machineriesis field aggregation by land consolidation. This may lead to achange in production systems, resulting in a land use distri-bution which is different from the current situation (Weber etal., 2001). Land use scenarios were developed using the Prog-
nosis of Land use (ProLand) model (Kuhlmann et al., 2002).ProLand is an agro-economical model that predicts optimalproduction systems, such as intensive or extensive rangeland,suckler cow production, dairy, cropland, or forest, for a givenTable 2 – Areal proportions of land use classes of the base linecatchment provided by ProLand
Land use data set Forest Pasture
Base line 54.4% 20.5%Scenario: 0.5 ha 56.0% 31.8%Scenario: 1.5 ha 45.9% 17.5%Scenario: 5.0 ha 34.0% 20.6%
The land use distribution for the base line was obtained from multi-temp
tation of the Dill catchment in central Germany.
location. It accounts for different natural, technical, economicand political premises and assumes that farmers act to opti-mize their land rent. The field size scenarios were provided byProLand assuming different boundary conditions for farmingpractices, for example different field size allocations. ProLandpredicts economic variables and provides spatially explicitland use maps. These maps serve as input for the scenarioanalysis. ProLand differentiates between several pasture andcrop production systems, as well as mixed forest. The outputof ProLand is aggregated to land use types comparable to landuse classes derived with remote sensing approaches. The fiveresulting land use classes are: mixed forest, cropland, pasture,water and urban areas. Based on different target field sizes of0.5, 1.5 and 5.0 ha, three different land use maps in 25 m res-olution were provided for scenario analysis. Increasing fieldsizes increases the cost-effectiveness of agriculture as largemachines can be applied on large fields. Therefore, increasingfield size leads to an increase of cropland and a decrease of for-
est. Statistics of land use scenarios compared to the currentconditions are given in Table 2.Table 3 gives an overview of the areal fractions of changesin the Dill catchment for the land use scenarios compared to
use and three different land use scenarios in the Dill
Crops Fallow Water Urban
6.5% 9.1% 0.3% 9.2%2.7% – 0.3% 9.2%
27.1% – 0.3% 9.2%35.9% – 0.3% 9.2%
oral Landsat TM 5 images from 1994 to 1995 (Nohles, 2000).
e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40 33
Table 3 – Areal fractions of different land use changes (compared to the base line) predicted by ProLand model for thedifferent field size scenarios (grassland in this statistics includes fallow land)
Direction of change 0.5 ha scenario 1.5 ha scenario 5.0 ha scenario
No change (including open water and urban areas) 55.1% 42.6% 35.4%Crops to grassland 1.4% 0.6% 0.8%Crops to forest 4.4% 4.1% 3.7%Grassland to crops 1.9% 10.5% 12.7%
17.30.2
19.7
taeasNp6fs
2c
CcieipdtfibTPcsioPw
Grassland to forestForest to cropsForest to grassland
he current situation. For the 0.5 ha scenario, land use changesre predicted in 45% of the region, although the total area ofach land use type does not change considerably except forslight shift from fallow and crops to pasture. For the 1.5 ha
cenario changes are predicted for 57% of the catchment area.et changes from forest to crops and grassland to crops areredicted. For the 5.0 ha scenario, changes are predicted for5% of the catchment area. Again significant net changes fromorest to crops and grassland to crops are predicted as well aslight changes from forest to grassland.
.4. Changes in soil properties induced by land usehange
hanges in soil hydraulic properties due to land use changean be expected to happen but have not been frequently stud-ed in the past (Texeira, 2001). Hence, a strategy is required tostimate changes in soil hydraulic properties from availablenformation. We propose to derive changes in soil hydraulicroperties via pedotransfer functions (PTF). Using PTF, one canerive soil hydraulic properties (e.g. saturated water conduc-ivity, saturated soil water content, residual soil water content)rom readily available soil data such as soil texture, poros-ty, soil organic content and bulk density. Various PFT haveeen compared and evaluated by several studies (Tietje andapkenhinrichs, 1993; Wosten et al., 2001; Jarvis et al., 2002;achepsky and Rawls, 2004). Applying PTF at the local-scalean lead to high parameter uncertainties while at the regionalcale the uncertainty reduces considerably. Using PTF phys-
cally based models can be applied although measurementsn soil hydraulic properties are not available. We used theTF of Rawls and Brakensiek (1985) to estimate saturatedater conductivity (Ksat). Model parameters and the regres-Table 4 – Model parameters and regression equations of the pe
ln[Ksat] = 19.52348P − 8.96847 − 0.028212C + 0.00018107S2 − 0.0094125C2 − 8.3+ 0.02733C2P + 0.001434S2P − 0.0000035C2S
ln[ b] = 5.3396738 + 0.1845038C − 2.48394546P − 0.00213853C2 − 0.04356349S+ 0.00895359C2P − 0.00072472S2P + 0.0000054C2S + 0.5002806P2C
�s = 0.01162 − 0.001473S − 0.002236CP + 0.98402P + 0.0000987C2 + 0.003616SP
�r = − 0.0182482 + 0.00087269S + 0.00513488C + 0.02939286P − 0.00015395C2 −ln[�]= − 7,842,831 + 0.0177544S − 1.062498P − 0.00005304S2 − 0.00273493C2 +
− 0.00000235S2C + 0.00798746C2P − 0.00674491P2C
Notation: b is the bubbling pressure (cm); � the pore size distribution index(%); Ksat the saturated conductivity (cm/h); S the sand (%); C the clay (%); P
% 14.8% 11.7%% 14.9% 21.3%% 12.5% 14.4%
sion equations of the PTF are provided in Table 4. Availablewater capacity was approximated according to Smith (1992):
AWC = �(pF1.8) − �(pF4.2)
= �s − �r
[1 + (63.096/ b)5]�/5
− �s − �r
[1 + (15,849/ b)5]�/5
(1)
where AWC is the available water content (%), b the bubblingpressure (cm), � the pore size distribution index, � the volu-metric soil water content (%), �s the effective saturation of thesoil (%), �r is the residual soil water content (%).
Values for porosity as required in the Rawls and BrakensiekPTF were derived from published bulk density values (assum-ing that soil minerals have a density of 2.65 g/cm3). Using bulkdensity as a proxy variable to estimate changes in soil hydro-logic properties has the advantage that bulk density is aneasy to measure parameter and is reported in many land usechange studies on C and N storage (Table 5). Assumed changesin bulk density associated with land use changes are given inTable 6. They were determined based on the values found inliterature (Table 5) and on the requirement to generate consis-tent changes with respect to different possible paths of change(e.g., a land use change from forest to crops should result inthe same change in bulk density compared to a change fromforest to grassland first and subsequently from grassland tocrops). Other data required by the PTF of Rawls and Brakensiek(1985) such as clay and sand content were assumed to remain
constant under land use changes. Mean changes in Ksat andAWC of the soils in the Dill catchment are given in Fig. 2. It isobvious that Ksat is more sensitive to changes in bulk densitythan AWC.dotransfer function of Rawls and Brakensiek (1985)
95215P2 + 0.077718SP − 0.00298S2P2 − 0.019492C2P2 + 0.0000173S2C
P − 0.61745089CP + 0.00143598S2P2 − 0.00855375C2P2 − 0.00001282S2C
− 0.010859CP − 0.000096C2P − 0.002437P2S + 0.0115395PC
0.0010827SP − 0.00018233C2P2 + 0.00030703C2P − 0.0023584P2C
1.11134946P2 − 0.03088295SP + 0.00026587S2P2 − 0.00610522C2P2
; �s the effective soil saturation (%); �r the residual soil water contentis the porosity.
34 e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40
Table 5 – Changes in bulk density reported in exemplary literature for specific land use changes
Authors Land use change Change in bulk density
Bauer and Black (1981) Grassland to crops 5–20% increase (depth dependent)Bewket and Stroosnijder (2003) Forest to crops 13% increaseBreuer et al. (2006) Crops to grassland No significant differencesBronson et al. (2004) Grassland to crops 3–21% increase (depth dependent)Franzluebbers et al. (2000) Grassland to crops 3–17% increase (depth dependent)
Murty et al. (2002) Forest to grassland 9.5% (±2%) increaseForest to crops
Neill et al. (1997) Forest to grassStrebel et al. (1988) Crops to grass
Table 6 – Changes in bulk density assumed for specificland use changes in this study
Land use change Assumed change in bulk density
Crops to grassland 6.5% decreaseCrops to forest 15% decreaseGrassland to crops 7% increase
Grassland to forest 9% decreaseForest to crops 17% increaseForest to grassland 10% increaseThe original model parameterisation and the new modelparameterisation derived with the PTF were used for a com-parative evaluation of water flows and water balances for eachland use scenario. The model parameters which were alteredby this procedure with respect to the individual models are:bulk density, Ksat and AWC for SWAT and WASIM, and theBrooks and Corey parameters (Ksat, residual water content,saturated water content, pore size distribution index, bubblingpressure) for the TOPLATS model.
A further important land use specific parameter is thetransmissivity coefficient. It is calculated by dividing the sat-urated hydraulic conductivity (Ksat) of the upper soil layer by
Fig. 2 – Mean change in hydraulic model parameters andproperties due to change in bulk density for the upper soilhorizons and land use changes in the Dill catchment.�Ksat = change in saturated conductivity of the soil;�AWC = change in available water capacity of the soil.
17% (±2%) increase
land 0–27% increaseland 15% increase (upper soil horizon)
the f-parameter describing the decay of saturated hydraulicconductivity with depth (Peters-Lidard et al., 1997) whichwas assumed to remain constant. The transmissivity coef-ficient is a spatially distributed input parameter for theTOPMODEL-based models TOPLATS and WASIM. Averagingthe transmissivity coefficients of all grid cells characterisesthe catchment-wide saturated permeability of the soils. Inthis study, for each scenario the transmissivity coefficientwas corrected for all grid cells, and the catchment-wide aver-age transmissivity coefficient was then calculated. It reflectsthe effect of land use change on soil properties as assumedin this study. The average catchment-wide, scenario specificchanges in transmissivity coefficient amount to 1.19, 1.13 and1.09 for the 0.5 ha, the 1.5 and 5.0 ha scenario, respectively.Among the field size scenarios it decreases with increasingfield sizes, which reflects increasing intensity of agriculturaluse within the scenarios. The standardised standard deviationof transmissivity coefficients remains constant for all land usescenarios with a value of 1.01.
3. Results and discussion on the effects ofland use change dependent changes in soilproperties
3.1. Model calibration and validation
Model calibration for the current conditions was performedfor the time period 1983–1989. For the calibration period,all models reproduce the long-term, annual and interannualvariations of discharge well (Fig. 3). WASIM and TOPLATSwere calibrated manually while SWAT was calibrated auto-matically (Eckhardt and Arnold, 2001). The water balance(TOPLATS, WASIM) and the model efficiency after Nash andSutcliffe (1970) (SWAT, TOPLATS, WASIM) were used as objec-tive functions. For the SWAT model the following soil physicalparameters were calibrated: bulk density, saturated hydrologi-cal conductivity of deeper soil layers and the anisotropy factorbetween vertical and horizontal saturated hydraulic conduc-tivity to account for the strong tendency for lateral flow in thecatchment. The parameters influencing baseflow recessionand ground water recharge delay were also calibrated. Themanual calibration of TOPLATS adjusted the stomatal resis-
tances by a constant factor (to reproduce the long-term waterbalance) and to the adjustment of the parameters of the baseflow recession curve. Soil parameters were not calibrated, theywere derived using the PTF of Rawls and Brakensiek (1985). The
e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40 35
F ationc IM.
cbfiWpr
qwadstTtpTtiSwptmshpiod
ig. 3 – Observed vs. simulated monthly discharge for calibratchment models used in this study: SWAT, TOPLATS, WAS
alibration procedure was to reproduce the long-term wateralance first and then to optimize the model efficiency bytting the baseflow recession. The calibrated parameters forASIM were different coefficients of the different flow com-
onents (surface runoff, interflow, macropore flow), base flowecession parameters and a snow melt parameter.
The model validation period was 1990–1998. The modeluality in the calibration and validation periods is evaluatedith a set of three quality measures (bias, model efficiency,
nd coefficient of determination). Discharge simulated by theifferent models in the calibration and validation period ishown in Fig. 3, as monthly averages. Quality measures forhe calibration and validation periods are similar and given inable 7. While model efficiency and coefficient of determina-ion of SWAT and WASIM is slightly higher in the validationeriod than in the calibration period, the performance ofOPLATS is slightly worse. On the other hand, TOPLATS hashe smallest bias. One reason for this differing behaviour liesn the different effort in calibration of the models. WhileWAT was calibrated automatically, WASIM and TOPLATSere calibrated manually. Compared to WASIM (5 calibrationarameters) in TOPLATS only 2 parameters were calibratedo reduce calibration to a minimum and to parameterise the
odel based on available data and transfer functions if pos-ible. In addition to the calibration procedure, the differentydro-climatic conditions of the calibration and validation
eriods are noticeable. Compared to the calibration period,n the validation period precipitation decreases and evap-transpiration increases by about 40 mm/a while dischargeecreases by 80 mm/a!
Table 7 – Quality measures of the three catchment models SWAand validation (1990–1998) for the Dill catchment
Quality measure Period
Bias CalibrationValidation
Nash–Sutcliffe efficiency CalibrationValidation
Coefficient of determination CalibrationValidation
(1983–1989) and validation (1990–1998) period of the three
SWAT reproduces the distribution of rainfall into the threerunoff components well, with interflow as the dominant com-ponent. Based on hydrogeological investigations and baseflowseparation, the portion of baseflow contribution to dischargeis estimated to be between 9 and 16% (Kaviany, 1978). Thecontribution of surface runoff is assumed to be less than10% (Breuer et al., submitted for publication). Nevertheless,exact values are not known. As TOPLATS does not distinguishbetween interflow and groundwater flow, the contribution ofinterflow is divided between the other two flow components(surface runoff and baseflow). WASIM differentiates betweenthree flow components (fast, intermediate, slow) but calcu-lates predominantly the slow flow component. However, theslow flow component of WASIM includes baseflow and slowinterflow.
While the results of SWAT are in agreement with the per-ceptual model based on hydrogeological investigations (58.5%interflow, 23.0% surface runoff, 18.5% baseflow), WASIM andTOPLATS are not exactly, although they show a good agree-ment to the discharge at the stream gauges. WASIM calculatessurface runoff and interflow each with 4% and baseflow with92% while TOPLATS calculates 32% surface runoff and 68%baseflow. This might be explained by the fact that TOPLATSand WASIM are based on the TOPMODEL concept (Beven etal., 1995). TOPMODEL was developed for catchments withsteep slopes and shallow soils that typically result in two
flow components (i.e. no deep groundwater recharge). The Dillcatchment has moderate to steep slopes and mainly shallowsoils, and therefore we argue here that baseflow calculated byTOPLATS and WASIM also includes the interflow. This argu-T, TOPLATS and WASIM during calibration (1983–1989)
SWAT TOPLATS WASIM
5.6% 0.9% 1.6%9.7% 2.7% 2.5%
0.72 0.66 0.700.73 0.61 0.74
0.74 0.71 0.740.77 0.69 0.82
l i n g
36 e c o l o g i c a l m o d e lmentation reconciles these models with the perceptual modelof the Dill catchment: the simulated results now are muchcloser to the hydrogeological observations. It can be concludedthat the model performance of all models is acceptable, alsoconsidering the fact that calibration of those models whichwere calibrated manually (TOPLATS, WASIM) was reduced toa minimum with respect to the number of calibrated param-eters, and that the focus of the calibration was also on thelong-term water balance for these two models. Please notethat for the following interpretation of the results, we use theoriginal flow components provided by the model and not theaggregated flow components discussed above.
3.2. Land use scenarios
Fig. 4 shows the scenario results for the three models with-out considering changes in soil properties. These scenarioresults are analysed in detail by Huisman et al. (submittedfor publication). The tendency between the three scenariosis similar for all models: annual discharge at the catchmentincreases with increasing field size representing intensifiedland use. Compared to the land use of the baseline, TOPLATSpredicts different changes in the water balance (decrease indischarge) as compared to WASIM and SWAT (increase in dis-charge) which behave similar. The main reason that TOPLATSdeviates is the model specific parameterisation of land usetypes. Three land use types only occur in the baseline (conif-erous forest, deciduous forest, fallow), one land use type onlyoccurs in the scenarios (mixed forest). The different parame-
terisation of forest in the baseline and the scenarios alreadyexplains about 75% of the differences identified for TOPLATS.This explanation was found by replacing all forest areas inthe baseline (deciduous and coniferous forest) by mixed for-Fig. 4 – Change in mean annual discharge (1983–1998) inrelation to the baseline scenario as simulated by themodels SWAT, TOPLATS and WASIM. Land use scenariosare predicted by the Proland model and assume differentaverage field size aggregation levels of 0.5, 1.5 and 5.0 ha.
2 0 9 ( 2 0 0 7 ) 29–40
est parameterisation without changing the areal fraction anddistribution. In LUCHEM the vegetation parameters for mixedforest were derived by weighted averaging of the parame-ters of deciduous and coniferous forest, taking into accounttheir areal fractions. TOPLATS is very sensitive to the leaf areaindex, and parameter sensitivity does not behave linearly asassumed by weighted averaging. Thus, the water balance cal-culated for mixed forest does not equal the water balancecalculated for deciduous and coniferous forest.
3.3. Land use scenarios considering soil propertychanges
Fig. 5 presents the differences in the scenario results for thethree models including the changes in soil properties due toland use change compared to the scenarios with unchangedsoil parameterisation. The model results show that all threedifferent models react sensitively to the change in soil proper-ties and soil parameters with respect to division of water intodifferent flow components (surface runoff, interflow, base-flow). Only SWAT and TOPLATS react sensitively with respectto water balance terms (discharge, actual evapotranspira-tion = ET). Obviously, the models show different sensitivitiesto the assumed changes in soil parameterisation. The waterbalance of SWAT (discharge, actual evapotranspiration) reactsmost sensitive. While a consideration of changes in the soilparameterisation leads to scenario specific effects for theSWAT model (increasing ET for 1.5 ha, decreasing ET for 0.5and 5.0 ha scenarios), TOPLATS simulates a decreasing ET forall scenarios. The water balance simulated by WASIM showsalmost no reaction concerning ET for all land use scenarios.
From the change in model parameterisation a decreasing ETwith increasing field size could be expected as simulated byTOPLATS and partly by SWAT (except 1.5 scenario). WASIMdoes not behave like the other two models. The reason isFig. 5 – Differences in the simulated annual water flows(1983–1998) for the three land use change scenarios in theDill basin with and without considering a change in thesoil hydraulic parameters.
n g 2 0 9 ( 2 0 0 7 ) 29–40 37
amppaww
Tbtcpnintcieir
AmpcoemviateeTtssa
aAcibTnrccfTbihaas
Fig. 6 – Differences in the simulated annual water flows(1983–1998) for the three land use change scenarios in thethree sub-basins of the Dill basin (Obere Dill, Dietzholze,Aar) with and without considering a change in the soil
e c o l o g i c a l m o d e l l i
ssumed to be a combination of two aspects. The macroporeodule of WASIM is coupled to the land use and not to soil
roperties. Therefore, land use change has no effect on thisrocess. Furthermore a decrease in porosity in WASIM causesn increase in capillary rise. Consequently ET also increaseshich partly compensates the assumed decrease in availableater capacity of the soils.
With respect to the flow components, for all scenariosOPLATS calculates increasing surface runoff and decreasingaseflow components when changes in soil hydraulic proper-ies are considered. The increase in surface runoff is mainlyaused by an increase in saturation excess runoff. SWAT alsoredicts increasing surface runoff for the 1.5 ha and 5.0 ha sce-arios while interflow decreases. WASIM simulates increasing
nterflow and decreasing baseflow for all land use change sce-arios while surface runoff is almost constant. Altogether, allhree models show a considerable sensitivity of different flowomponents to the assumed soil parameter changes which aren the same order of magnitude for all models while the influ-nce on runoff generation mechanisms and flow componentss model specific. Fast runoff components increase while slowunoff components decrease.
The question arises why the models do behave differently?s shown above, the three models are based on differentodel concepts and model approaches for the three flow com-
onents (Table 1). Due to the different model approaches itan be assumed that changed model parameters such as Ksat
r AWC have a different meaning in the different models. Asxplained above, “interflow” of WASIM does not necessarilyean the same as “interflow” of SWAT model. Similar obser-
ations can also be made for other flow processes. Infiltrations calculated in WASIM using the process-based approachccording to Green and Ampt (1911) while in SWAT the sta-istical SCS curve number method is used. And TOPLATS, forxample, calculates infiltration excess runoff and saturationxcess runoff while WASIM only calculates infiltration excess.hese differences in the models must lead to differences in
he simulation results. But as mentioned, all models show alight shift from slow to fast flow components caused by inten-ified land use followed by (in average) compacted soils. Thisgreement in the general model behaviour could be expected.
Fig. 6 presents the same differences of the scenario resultss shown in Fig. 5, but for three subcatchments of the Dill.gain, all three models react sensitively to the assumedhanges in soil properties while the quantity of the changess subcatchment specific. Changes with respect to the wateralance terms again are highest for the SWAT model whileOPLATS is most sensitive to changes in the flow compo-ents. Water balance components calculated by WASIM do noteact sensitively to changes in soil properties in all three sub-atchments. Concerning runoff generation processes and flowomponents, the same patterns of all models can be observedor the subcatchments as found for the entire Dill basin.OPLATS calculates increasing surface runoff and decreasingaseflow for all scenarios and all subcatchments, the more
ntensive the larger the field sizes are. SWAT simulates the
ighest changes in the water balance for almost all scenariosnd subcatchments. Surface runoff increases for all scenariosnd subcatchments, while interflow increases for the 0.5 hacenario only and decreases for the 1.5 and 5.0 ha scenarios.hydraulic parameters.
Baseflow increases for all subcatchments and scenarios exceptthe 0.5 ha scenario in the Obere Dill catchment. But changesin baseflow are of minor relevance due to the small total con-tribution to stream flow. WASIM calculates almost no changesin the water balance and in surface runoff generation. Inter-flow increases while baseflow decreases for all scenarios andsubcatchments.
3.4. Discussion
From the simulation results presented in the last sectionsit can be deduced that a consideration of soil propertieschanges associated with land use change might help to designscenarios in a plausible way. The models are more or lesssensitive to the assumed changes in soil parameterisation.Changes in the direction of intensified agricultural use lead toa slight decrease in actual evapotranspiration and an increasein discharge (except WASIM) while generation of fast flow
components increases (TOPLATS and SWAT: surface runoff;WASIM: interflow) and slow flow components predominantlydecrease (TOPLATS and WASIM: base flow; SWAT: interflow).Despite these changes in the flow components, a change in
38 e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 29–40
Table 8 – Differences in simulated annual discharge [mm/a] for the Dill catchment caused by land use changes vs.differences in simulated annual discharge [mm/a] caused by soil property changes associated with land use change
Model Change in . . . 0.5 ha scenario 1.5 ha scenario 5.0 ha scenario
SWAT Land use −5.5 15.7 22.7Soil properties 21.4 −12.7 21.0
TOPLATS Land use −105.6 −23.3 −3.5Soil properties 8.6 12.6 9.8
.8
.8
WASIM Land use −8Soil properties −3
the dominant runoff generation processes is not observed forany model. Although the different subcatchments of the Dillbasin vary with respect to the catchment properties (land use,soils, annual rainfall), the results of all catchments show simi-lar patterns in changes concerning the effect of soil propertieschange on the water balance of scenario calculations. There-fore, the results of the Dill catchment can be assumed to berepresentative for other regions with similar catchment char-acteristics.
However, the results also showed that the different hydro-logical catchment models show different sensitivities withrespect to the assumed model parameter changes. The differ-ent model reactions can be explained by the different modelapproaches, different process descriptions (which can inducea different meaning of the same parameters in differentmodels), and different calibration strategies. Based on thesereasons the alteration of a few parameters cannot be expectedto cause exactly the same effects in different catchment mod-els with consist of different approaches. It is important toemphasize that different models show different reactions toboth, a change in vegetation parameterisation (see Section3.2) and a change in soil parameterisation explained by soilchanges going along with land use changes (see Section 3.3).Thus, the choice of the catchment model to be applied for sce-nario analysis may be as crucial as the composition of soundscenarios which is doubtlessly essential. The model perfor-mance of the models should be carefully checked in differentcatchments with different physiographic characteristics to beapplied for scenario analysis.
If the effects of land use change and of an additional changein soil properties change are set into relation, it is worth tonote that – despite the remarks mentioned above – in half ofthe cases both changes show effects in the same order of mag-nitude (Table 8). Only TOPLATS (for the 0.5 ha scenario whichis due to vegetation parameter sensitivity) and WASIM (all sce-narios due to compensating effects) simulate changes in waterbalance which are much larger than the change due to chang-ing soil properties. The directions of the simulated changesin water balance terms (increase or decrease in evapotranspi-ration and discharge) are scenario and model specific. Theypartly go in the same direction inducing that effects add up,but partly the changes also balance themselves, which resultsin reduced effects caused by land use change. Besides the dif-ferent model geometries the reason may be also that most
simulated effects on the water balance (Table 8) are relativelysmall (10–20 mm/a). So uncertainties in model structure andmodel parameterisation probably contribute to the model spe-cific reaction.13.2 32.9−2.1 −1.5
Resuming it can be stated that soil property changes shouldbe considered when analysing the effects of land use change inscenario studies provided that the assumptions made in thisstudy with respect to the change in soil properties and soilparameterisation are valid. The results also raise the ques-tion again whether all models were sufficiently validated forthe purpose of such a study. Quality measures such as theNash–Sutcliffe model efficiency were satisfying with respectto water balance simulations, but the runoff generation mech-anisms could be checked only qualitatively due to missingobservations. With respect to the transferability of the find-ings to other catchments, the individual effects of changes insoil and land use on water fluxes of course depend on thecatchment characteristics and therefore are catchment spe-cific. As shown here, the effects of land use change and achange in soil properties induced by land use on the waterflows are often in the same order of magnitude and there-fore are equally important. Ignoring the effects of a changein soil hydraulic properties probably might cause relevanterrors.
4. Conclusion
Three models have been compared with respect to their sen-sitivity to land use change scenarios on the one hand andsoil property changes associated with land use change on theother hand. Soil property changes were assessed using liter-ature studies on changes on bulk density, and pedotransferfunctions were used to translate the reported changes in bulkdensity into changes in hydraulic model parameterisation.
All three models react sensitive to the assumed changesin soil properties, but they show different sensitivities whensoil property changes due to land use change are considered.Changes in the water balance are small and in the order of afew percent, but runoff generation mechanisms and flow com-ponents are considerably affected by the assumed changesin soil hydraulic properties. In this study, the effects of bothchanges were identified to be in the same order of magnitudefor all three models. Therefore, a consideration of changes insoil properties as part of a land use change scenario analy-sis seems to be required. The results probably depend on soiland land use characteristics within the catchment of inter-est, but the model specific reaction to the assumed changes
in soil properties were similar for all catchments investigated.Therefore, the results are representative for other catchmentsin similar environments. Of course, the relevance of soil prop-erty change also depends on the magnitude of the simulated
n g
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ffects induced by change in plant parameters only and there-ore are scenario specific.
The interpretation of the results of this study is restrictedo model behaviour. What really happens in the field requiresdditional investigations. Models should be validated for landse change situations. Therefore, further field work on the
nterrelation between land use change and soil propertyhange is required to verify the assumed effects of land usehange on soil properties, and to enable modellers to morerecisely predict the effects of land use change on the waterycle. As not all models generated the same behaviour inll investigated catchments, further modelling investigationseem to be required. These studies should focus on a com-arison of further models and different model concepts andn varying catchment characteristics (e.g. soils, land use andlimate).
cknowledgements
he authors thank the “Deutsche Forschungsgemeinschaft”or the funding the collaborative research centre 299 “Landse options for peripheral regions”.
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