Sensitivity of simulated hydrological fluxes towards changes in soil properties in response to land use change

Physics and Chemistry of the Earth 29 (2004) 749–758 www.elsevier.com/locate/pce

Sensitivity of simulated hydrological fluxes towards changes in soil properties in response to land use change J.A. Huisman *, L. Breuer, H.-G. Frede Institute of Landscape Ecology and Resources Management, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 26, Giessen 35392, Germany Accepted 24 May 2004

Abstract Current model studies on the impact of land use change on water resources often simulate changes in land use without considering changes in the soil properties due to the change in land use. In this study, an artificial study catchment representing the Dill catchment (Germany) was used within the eco-hydrological model SWAT-G to study the sensitivity of SWAT-G simulations towards changes in soil properties during land use change. Since there is little information on these soil–vegetation interactions, we performed a model sensitivity study to investigate the impact of changes in the depth of the top soil layer, bulk density, saturated hydraulic conductivity and available water content on several simulated hydrological fluxes. To assess the significance of the simulated changes due to the changing soil properties, we compared the model sensitivity with the uncertainty in the hydrological fluxes due to the uncertainty in the parameterization of the plant parameters. The results showed that the changes in soil properties due to a land use transition from cropland to pasture only have a minor impact on the simulated mean annual, summer and winter runoff and actual evapotranspiration. Soil–vegetation interactions have a stronger impact on the simulated mean surface runoff, although the absolute contribution of this flux is small in our conceptualization of the Dill catchment. A comparison of the sensitivity and uncertainty of the simulated hydrological fluxes led to the conclusion that changes in soil properties due to land use change are relatively unimportant in our model of the Dill catchment in the light of the low sensitivity of the dominating hydrological fluxes and the large output uncertainty due to the plant parameter uncertainty. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Land use change; Sensitivity analysis; Uncertainty analysis; SWAT

1. Introduction Assessing the impact of land use changes on hydrological fluxes, such as surface runoff, evapotranspiration, total runoff or ground water recharge, is an important aspect of water resources management. Within the framework of the Collaborative Research Centre (CRC) 299 at the University of Giessen (Germany), several efforts have been made to quantify the effects of pos* Corresponding author. Tel.: +49 641 9937385; fax: +49 641 9937389. E-mail address: [email protected] (J.A. Huisman).

1474-7065/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2004.05.012

sible land use and management options on hydrological and nutrient fluxes in the Dill catchment, Germany. For example, Fohrer et al. (2002) investigated the potential impacts of different mean field sizes on the regional land use distribution and the associated changes in hydrological fluxes. With an adapted version of the eco-hydrological model Soil Water Assessment Tool (SWAT-G, Eckhardt et al., 2002), they found that an increase of mean field size from 0.5 to 2.0 ha caused a slight increase in surface runoff and interflow. In a consecutive study, Eckhardt et al. (2003) analyzed the significance of the observed changes in the SWAT-G simulations with respect to the uncertainty in the plant parameter parameterization. Within the light of the relatively large

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uncertainty in the input parameters, they found that 10– 20% of land use change is required to observe significant differences for total runoff and other hydrological components. In these earlier studies, it was assumed that soil properties do not change as a consequence of changes in land use. Clearly, this does not seem to be a realistic assumption from a soil physical point of view. For example, for tropical soils, some studies have been published where changes of bulk density due to land use change were recorded (Reiners et al., 1994; Neill et al., 1997). In these studies, conversion from forest to pasture and cropland resulted in an increase of bulk density of approximately 17% and 12%, respectively (Murty et al., 2002). Unfortunately, there is only limited quantitative information available on changes in bulk density in response to land use change for temperate regions. Studies on changes in other soil physical properties, such as available water content, saturated hydraulic conductivity and soil depth, are even more scarce. Therefore, as a first step in establishing the relevance of changing soil properties in response to land use change, the aim of this study is to assess the sensitivity of the hydrological fluxes simulated with SWAT-G towards these changes. As a reference, we compared the sensitivity of the simulated fluxes due to these soil–vegetation interactions with the uncertainty in the simulated hydrological fluxes due to the uncertainty in the parameterization of the plant parameters. Four one-at-atime sensitivity analyses were performed for saturated hydraulic conductivity, dry bulk density, available water content and depth of the top soil layer. A multi-factorial sensitivity analysis where all soil properties varied simultaneously was also performed. A comparison of the results of these sensitivity analyses with the uncertainty in the hydrological fluxes due to plant parameter uncertainty gives an indication of the significance of including changes in soil properties in model studies of land use change impact within the Dill catchment.

2. Description of SWAT-G In this study, we used the version SWAT-G from the conceptual distributed catchment model Soil Water Assessment Tool (SWAT, Arnold et al., 1998; Eckhardt et al., 2002). In SWAT-G, the catchment is partitioned into a number of sub-basins based on a digital elevation model and is further sub-divided into Hydrological Response Units (HRU) based on land cover and soil maps. Each HRU contains a number of water storage volumes: canopy, snow, soil profile, shallow aquifer and deep aquifer. Precipitation falling on the HRU is reduced by canopy interception, which is modeled as a function of the reference evapotranspiration (ET), the maximum canopy storage and the ratio between actual

and maximum leaf area index (LAI). The actual LAI and plant growth is simulated with a simplified version of the EPIC model (Williams et al., 1984). Growth only occurs if the daily mean temperature exceeds a plantspecific base temperature (Tbase). The temperature excess above this base temperature is accumulated over time and is counted in so called heat units. The actual LAI is simulated as a function of accumulated heat units, and varies between a plant specific minimum and maximum LAI. After correction for canopy interception, the precipitation reaching the soil surface is divided into infiltration and surface runoff according to the SCS curve number approach. The SCS curve number is a function of the soil permeability, land use and the antecedent soil water conditions (USDA-SCS, 1972). After infiltration, vertical water flow through a user defined number of soil layers is simulated with a cascade flow approach. Water only flows when the water content of a soil layer exceeds the field capacity of the soil layer, where the amount of water above field capacity is referred to as soil water excess. The field capacity is defined as the sum of the wilting point, calculated from textural information, and the available water content (AWC). The maximum amount of water that a soil layer can contain equals the porosity, which is estimated from the dry bulk density (BD). The amount of water that flows out of a soil layer per day is determined from the soil water excess and the saturated hydraulic conductivity (Ksat). Soil water leaving the lowest soil layer recharges the shallow ground water aquifer. The shallow aquifer contributes base flow to the main channel, assuming linear outflow based on a ground water recession coefficient. A fraction of the ground water recharge can be routed to the deep aquifer. Deep aquifer water is permanently removed from the system. Soil water is not only subject to vertical flow, but is also influenced by plant uptake, soil evaporation and lateral flow. Plant uptake is simulated as a function of reference ET, LAI and maximum rooting depth with adjustments for water stress. Reference ET is calculated according to the Penman–Monteith method, which includes both an aerodynamic resistance determined by the canopy height and a canopy resistance determined by LAI and stomatal conductance. Soil evaporation is modeled as a function of reference ET and a soil cover index dependent on the above ground biomass. In SWAT-G, lateral flow is modeled with a kinematic storage model developed by Sloan and Moore (1984). In the original SWAT model, the amount of lateral flow only depends on the soil water excess, the length and slope of the hillslope and an isotropic (vertical) Ksat. This approach resulted in an underestimation of the lateral flow in the Dill catchment because of the anisotropic nature of Ksat due to the solifluction that the soils on the slopes have experienced in the past. Therefore, Eckhardt et al.

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(2002) included an anisotropy factor between horizontal and vertical hydraulic conductivity to increase the amount of lateral flow and to improve the quality of the simulations.

3. Methodology An artificial study catchment (Eckhardt et al., 2003) was used to study the sensitivity of the hydrological fluxes within the SWAT-G model towards changes in soil properties in response to land use change. The artificial catchment is a V-shaped valley (Fig. 1) with a base area of 2 km2, hillslopes of 15% covered with shallow cambisol soils over a hard bedrock layer (90% of the area) and deeper gley soils along the stream (10% of the area). Despite these strong simplifications, the artificial catchment does represent the main features of the Dill catchment, as was shown by Eckhardt et al. (2003). The weather input data for the model runs with the artificial catchment are those from the Dillenburg weather station (50°44 0 N, 8°16 0 E), which has a mean annual air temperature of 6.6 °C and a mean annual precipitation of 880 mm. Two different land cover types are considered in this study: pasture (PAST) and cropland (CROP). Cropland has a typical crop sequence of summer barley (Hordeum vulgare), winter rape (Brassica napus) and winter wheat (Triticum aestivum). We focus on these two vegetation types, because transitions between cropland and pasture are most relevant for land use change studies in the Dill catchment (Fohrer et al., 2002). For each land cover type, SWAT-G requires a number of input parameters, which are given in Table 1. Generally, there is significant uncertainty in these input parameters, which translates in a significant output uncertainty in the simulated hydrological fluxes, as was already shown by Eckhardt et al. (2003). To quantify the impact of this input uncertainty, 5000 Monte Carlo simulations were performed for each land cover type. In these simulations, each

Fig. 1. Schematic of the artificial catchment (Eckhardt et al., 2003).

plant parameter was drawn randomly from a normal distribution described by the mean and standard deviation provided in Table 1. For canopy storage, albedo, maximum leaf area index, maximum stomatal conductance and height, the mean and standard deviation are based on an extensive literature review of Breuer et al. (2003). Despite the considerable size of this database, it is difficult to derive reasonable standard deviations and, therefore, we have assumed that the minimum and maximum value for each plant parameter within the database correspond with ± three standard deviations and that the mean corresponds with (minimum + maximum)/2. For SCS curve number for moisture condition II, base temperature, minimum leaf area index, root depth and biomass energy ratio, the standard deviations were based on local expert knowledge. Root depth was restricted to 0.4–0.9 m to avoid water uptake from the bedrock layer underlying the shallow cambisol soils. To study the sensitivity of hydrological fluxes simulated with SWAT-G towards changes in soil properties in response to land use change, we did one-at-a-time sensitivity analyses for four soil properties (Table 2).

Table 1 Mean and standard deviation of plant parameters for pasture (PAST) and cropland with a 3-year rotation of winter barley (BARL), winter rape (WRAP) and winter wheat (WWHT) used in the Monte Carlo simulations Parameter

PAST

BARL

WRAP

WWHT

Mean

Std

Mean

Std

Mean

Std

Mean

Std

SCS curve number (II) Max. canopy storage Albedo Base temperature Max. leaf area index Min. leaf area index Max. stom. cond. Max. height Max. root depth Biomass energy ratio

61.00 1.55 0.24 3.50 8.35 1.50 6.85 1.85 0.65 20.00

2.000 0.283 0.012 0.167 2.617 0.167 1.883 0.483 0.083 1.667

75.00 2.45 0.24 6.50 2.10 0.00 6.15 1.10 0.65 30.00

2.333 0.183 0.007 0.500 0.100 0.000 0.717 0.167 0.083 1.667

75.00 2.45 0.23 5.00 3.20 0.00 7.85 1.20 0.65 20.00

2.333 0.183 0.012 0.333 0.467 0.000 1.583 0.067 0.083 1.667

75.00 2.45 0.20 5.00 3.45 0.00 8.80 1.10 0.65 30.00

2.333 0.183 0.013 0.333 0.383 0.000 1.267 0.167 0.083 1.667

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Table 2 Sensitivity analyses for changes in soil properties in response to a land use transition from cropland to pasture Description scenario I II III IV V

10% Increase in saturated hydraulic conductivity (Ksat) 10% Decrease in dry bulk density (BD) 10% Increase in available water content (AWC) 10% Increase in depth of top soil layer Multi-factorial sensitivity analysis with I–IV

In sensitivity analysis I–IV, we assessed the sensitivity to a 10% increase in saturated hydraulic conductivity (I), a 10% decrease in dry bulk density (II), a 10% increase in available water capacity (III) and a 10% increase in the depth of the top soil layer (IV), respectively. In analysis I–III, the soil properties were changed to a depth of 1.10 m for the cambisol and to a depth of 0.80 m for the gley soil. In the case of analysis IV, only the depth of the upper soil layer was changed (0.50 m for the cambisol and 0.30 m for the gley soil), while the depth of the entire profile was kept constant. Sensitivity analysis V is a multi-factorial sensitivity analysis where all soil properties are changed simultaneously in the same manner as in analysis I–IV. Each of the 5000 model runs in the combined sensitivity and uncertainty analysis started at 1.1.1989 and ended at 31.10.1994. The first ten months were used as a warm-up period, and the remaining five hydrological years are used to calculate the sensitivity and the uncertainty in mean annual total runoff, actual ET, surface runoff and ground water recharge. To study intra-annual variability, we also calculated mean summer and mean winter values by separating the hydrological year in two periods: November–April (winter) and May–October (summer). To determine whether two land cover types are significantly different with respect to hydrological flux y, we calculated the distinction level Sy(A, B) according to Eckhardt et al. (2003). This distinction level can also be used to quantify the significance of the sensitivity of the simulated hydrological fluxes compared to the uncertainty in these fluxes. In the following, it is assumed that the potential range of the output variable y is separated into N intervals yi. By normalizing the number of runs in each interval by the total number of runs, approximate values for the probability p[yi] for finding the value in that particular interval are obtained. Let P[yi(A)] denote the probability that the value for land cover A falls in the interval yi and let P[yi(B)] denote the same probability for land cover B. The probability P[yi(A, B)] that two model runs will fall in the same interval is calculated by multiplying P[yi(A)] and P[yi(B)]. The probability that two independent realizations of land cover A and B will yield the same result, Py(A, B), can be obtained by summing P[yi(A, B)] over

all intervals. The distinction level can now be calculated as S y ðA; BÞ ¼ 1 

P y ðA; BÞ ; maxfP y ðA; AÞ; P y ðB; BÞg

ð1Þ

where Py(A, A) and Py(B, B) are probabilities that two independent realizations of the same land cover type will yield identical results. The normalization by max{Py(A, A), Py(B, B)} is required to assure that two identical frequency distributions have a Sy(A, B) of 0. Two frequency distributions that do not overlap have a Sy(A, B) of 1. We assume that two land cover types are significantly different with respect to y when Sy(A, B) exceeds 0.90 (Eckhardt et al., 2003). To some extent, the results of the distinction level measure depend on the user-defined interval width. This dependency is strongest with a wide interval width where most model runs fall in two or three intervals. Therefore, we took care to assure that the interval width was chosen such that 10 or more intervals contained model runs.

4. Results and discussion Fig. 2 presents the normalized frequency distributions of simulated annual total runoff, actual ET, surface runoff and ground water recharge for the artificial catchment covered by pasture or cropland. It can be seen that the differences between total runoff, actual ET and surface runoff simulated for pasture and cropland are much larger than the uncertainty in these simulated fluxes. In the case of ground water recharge, the two frequency distribution overlap completely, and the probability that two realizations from these frequency distributions produce a similar result is relatively high, as expressed by the relatively low distinction level of 0.80. It should be noted that the non-parametric Mann–Whitney test that could be used instead of the distinction level indicates that the frequency distributions of simulated ground water recharge for pasture and cropland are significantly different (P < 0.001) despite the fact that the frequency distributions completely overlap. This is caused by the large number of 5000 Monte Carlo simulations, which means that even small differences in the median of the distributions will result in a significant test result. In this study, it is more important to test whether two realizations from the frequency distributions (i.e. paired-catchments, two land use change scenarios) are likely to produce similar results than to test whether the medians of the distributions are significantly different. Therefore, we use the distinction level throughout the paper. To better understand the differences in the mean daily values for the simulated hydrological fluxes, Fig. 3 presents the range of values simulated on each day for daily total runoff, actual ET, surface runoff and ground water

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Fig. 2. Normalized frequency distributions for total runoff, actual evapotranspiration, surface runoff and ground water recharge assuming a uniform land cover of pasture (PAST) and cropland (CROP).

recharge for one year (1999) given the uncertainty in the plant parameter parameterizations. The bounds of the black and gray area are the minimum and maximum value simulated on each day for cropland and pasture, respectively. It can be seen that the total runoff of cropland significantly differs from the total runoff of pasture in September and October. This is caused by the crop harvest in July, which strongly reduces the actual ET of cropland (Fig. 3b). This reduction in simulated actual ET allows an early replenishment of the soil water stores, which results in higher runoff later in the year. The impact of the harvest is still present at the end of the year, where the simulated total runoff of cropland is near the upper bound of the total runoff simulated for pasture. Fig. 3b shows that the uncertainty in the actual ET for pasture fluctuates strongly within one year. A pronounced period with large uncertainty (mid-July to mid-August) is related to a long dry period, while periods with low uncertainty are generally associated with wet parts of the year. This indicates that the large uncertainty in the simulated mean annual actual ET for pasture (Fig. 2b) is related to periods with water stress. Fig. 3c presents the uncertainty in simulated daily surface runoff. Although the small uncertainty bounds make it hard to recognize, close inspection of Fig. 3c showed that the harvest in July caused an increase in simulated surface runoff for cropland in the subsequent months (August–October), since exposed soil is more susceptible to surface runoff. Fig. 3d shows that ground

water recharge for pasture and cropland are mainly limited to the late fall and winter (Fig. 3d), although the impact of harvesting can also be recognized here. It also shows that the uncertainty in the ground water recharge simulations for pasture is too large to distinguish it from the ground water recharge simulations for cropland, as was already illustrated in Fig 2d. To investigate the impact of changing soil properties in response to land use change, we simulated the stepwise transition from initially uniform cropland to pasture in the artificial catchment (0%, 5%, 10%, 20%, 30% and 50% of catchment area covered with pasture). Cropland was replaced by pasture in strips perpendicular to the stream, to ensure that both soil types had an equal amount of land use change (see Fig. 1). Fig. 4 presents the relative changes in the mean of 5000 Monte Carlo simulations for mean total runoff, actual ET, surface runoff and ground water recharge in response to a land use transition from cropland to pasture. The changes in Fig. 4 are relative to the absolute values for cropland presented in Table 3. Fig. 4 also presents the sensitivity of the simulated hydrological fluxes towards changes in soil properties in response to land use change. Table 4 presents the absolute mean annual values corresponding with the baseline run (NC) and the sensitivity analysis I–V (first column of Fig. 4). Fig. 4 and Table 4 show that the sensitivity of mean annual total runoff and actual ET towards changes in soil properties in response to a land use transition from pasture to cropland is small in this study. For example,

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Fig. 3. Minimum and maximum values of total runoff, actual evapotranspiration, surface runoff and ground water recharge simulated on each day of 1999 given the uncertainty in the land cover parameterization given in Table 1. The black area indicates the range of values simulated for cropland and the gray area indicates the range of values simulated for pasture.

in the case of the multi-factorial sensitivity analysis (V) and 50% conversion from cropland to pasture, the changes in soil properties only result in a decrease of 7.0 mm (1.3%) in total runoff on an annual basis as compared with the baseline run (NC). It can also be seen that the mean summer and mean winter total runoff and actual ET are also insensitive. This relative insensitivity can be explained by the process conceptualization in the SWAT-G model, the properties of the artificial catchment and the properties of the Dill catchment it represents. In our conceptualization of the Dill catchment, total runoff is dominated by up to 75% of lateral flow due to the relatively impermeable bedrock layer underlying the soil profile. Therefore, total runoff is determined by the perching of the water at the bedrock layer, which is independent of the shallow soil properties that might be affected by land use change. Actual ET is also insensitive to changes in the surface soil properties because the plants can use the deeper soil water perching at the bedrock layer when the water availability in the upper soil layers changes. Surface runoff shows a stronger sensitivity towards changes in soil properties (Fig. 4 and Table 4). A decrease in bulk density of the upper soil layers (II) and an increase in depth of the top soil layer (IV) both cause a relatively strong decrease in simulated mean surface runoff on a yearly basis. An increase in available water content (III) causes an increase in simulated surface run-

off as compared with the baseline run (NC). Coincidentally, this increase in surface runoff in sensitivity analysis III approximately matches the decrease in surface runoff due to the conversion from cropland to pasture and, therefore, the relative change in surface runoff is small. In the case of the multi-factorial sensitivity analysis V and 50% conversion, the changes in soil properties result in a decrease of 12.5 mm (25.2%) in surface runoff on an annual basis as compared with the baseline run (NC). Fig. 4 also shows that the sensitivity of the simulated mean annual surface runoff differs from the sensitivity of the mean summer and winter surface runoff. Simulated surface runoff is relatively insensitive in the summer period, but shows a strong sensitivity in the winter period. It can also be seen that the insensitivity of surface runoff towards changes in available water content on an annual basis is caused by the opposite effects of a decrease in simulated surface runoff in the summer period and an increase in the winter period. The sensitivity of surface runoff to changes in surface soil properties can be explained through the definition of the curve number approach used in the SWAT-G model. A decrease in bulk density increases the pore volume, and without changes in the other soil properties this means a lower relative saturation, a lower curve number and, therefore, less surface runoff. An increase in available water content increases the relative saturation at field capacity, which results in a higher curve number

J.A. Huisman et al. / Physics and Chemistry of the Earth 29 (2004) 749–758 Annual

Runoff

Relative Change [%]

No change I II III IV V

-20

0

-20

-20

Relative Change [%]

5% 10% 20% 30% 50%

-40 0%

30

20

20

20

10

10

10

0

0

0

0%

5% 10% 20% 30% 50%

5% 10% 20% 30% 50%

0%

20

20

20

0

0

0

-20

-20

-20

-40

-40

Relative Change [%]

-40 0%

30

0%

Groundwater Recharge

5% 10% 20% 30% 50%

30

0%

Relative Change [%]

Surface Runoff

Actual Evapotranspiration

-40 0%

Winter

Summer 0

0

5% 10% 20% 30% 50%

0%

20

20

20

0

0

0

-20

-20

-20

-40 0%

5% 10% 20% 30% 50%

-40 0%

Percentage Pasture

5% 10% 20% 30% 50%

5% 10% 20% 30% 50%

-40

0%

5% 10% 20% 30% 50%

755

5% 10% 20% 30% 50%

Percentage Pasture

-40 0%

5% 10% 20% 30% 50%

5% 10% 20% 30% 50%

Percentage Pasture

Fig. 4. Relative changes in mean total runoff, actual evapotranspiration, surface runoff and groundwater recharge caused by a transition from cropland to pasture for three time periods: annual, summer (May–October) and winter (November–April). Results of sensitivity analyses I–V (Table 2) where soil properties were changed in response to land use change are also shown.

Table 3 Simulated mean total runoff, actual evapotranspiration, surface runoff and ground water recharge of cropland for three time periods: annual, summer (May–October) and winter (November–April)

Total runoff Actual evapotranspiration Surface runoff Groundwater recharge

Annual (mm/day)

Summer (mm/day)

Winter (mm/day)

1.6353 0.8025 0.1623 0.2884

0.7604 1.2788 0.1072 0.0830

2.5389 0.3193 0.2183 0.4967

and more surface runoff. In our conceptualization of the Dill catchment, soils are saturated more often in the winter period than in the summer period and, therefore, the sensitivity of surface runoff is higher in winter than in summer. Ground water recharge, which is the water leaving the soil profile and entering the shallow aquifer, shows the most complicated reaction towards changes in soil properties. Fig. 4 and Table 4 show that a decrease in

bulk density of the upper soil layers (II) resulted in a moderate increase of mean annual ground water recharge. An increase in the available water content (III) resulted in a moderate decrease of the mean annual ground water recharge. Due to these opposite effects, the results of the multi-factorial sensitivity analysis (V) are similar to the results of the baseline run. As in the case of surface runoff, the mean summer ground water recharge is relatively insensitive to changes in soil

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Table 4 Changes in mean annual runoff, surface runoff, ground water recharge and actual evapotranspiration in the case of a transition from cropland to pasture (percentages indicate area covered with pasture) 0%

5%

10%

20%

30%

50%

Total runoff NC I II III IV V

596.9 596.9 596.9 596.9 596.9 596.9

59.2 59.2 59.2 59.2 59.2 59.2

5%

10%

20%

30%

50%

304.9 304.8 304.9 305.4 305.5 306.1

316.9 316.7 317.0 317.9 318.1 319.4

328.8 328.7 329.0 330.4 330.8 332.6

352.8 352.6 353.1 355.5 356.0 359.1

105.4 104.9 107.3 103.7 105.7 105.7

105.7 104.4 109.4 102.0 106.2 106.3

105.9 104.0 111.4 100.4 106.7 106.7

106.3 103.2 115.5 97.2 107.4 107.7

Actual evapotranspiration 590.8 590.9 590.7 590.6 590.5 590.1

584.7 584.9 584.5 584.3 584.1 583.3

572.5 572.8 572.0 571.7 571.2 569.8

560.3 560.7 559.5 559.1 558.3 556.2

536.0 536.6 534.5 533.8 532.6 529.0

Surface runoff NC I II III IV V

0%

292.9 292.9 292.9 292.9 292.9 292.9

298.9 298.9 298.9 299.2 299.2 299.5

Ground water recharge

58.3 58.2 57.0 59.3 57.8 57.0

57.3 57.2 54.7 59.5 56.4 54.8

55.4 55.1 50.2 59.7 53.5 50.4

53.5 53.0 45.7 59.9 50.7 46.0

49.7 48.8 36.6 60.3 44.9 37.2

105.3 105.3 105.3 105.3 105.3 105.3

105.4 105.0 106.3 104.5 105.5 105.5

Sensitivity analyses I–V are described in Table 2 and the no change (NC) run is the baseline run where the soil properties remain constant during land use change.

Runoff

S(CROP,PAST)

Annual 1.0

1.0

0.8

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.6 0.4 0.2

S(CROP,PAST)

5% 10% 20% 30% 50%

0.0 0%

5% 10% 20% 30% 50%

0.0 0%

1.0

1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

5% 10% 20% 30% 50%

0.0 0%

5% 10% 20% 30% 50%

0.0 0%

1.0

1.0

1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

0.0 0%

S(CROP,PAST)

No Change I II III IV V

1.0

0.0 0%

S(CROP,PAST)

Surface Runoff

Actual Evapotranspiration

0.0 0%

Groundwater Recharge

Winter

Summer

1.0

5% 10% 20% 30% 50%

0.0 0%

5% 10% 20% 30% 50%

0.0 0%

1.0

1.0

1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

0.0 0%

0.0 0%

5% 10% 20% 30% 50%

Percentage Pasture

5% 10% 20% 30% 50%

Percentage Pasture

0.0 0%

5% 10% 20% 30% 50%

5% 10% 20% 30% 50%

5% 10% 20% 30% 50%

5% 10% 20% 30% 50%

Percentage Pasture

Fig. 5. Impact of changes in soil properties in response to land use change on the distinction level for mean total runoff, mean actual evapotranspiration, mean surface runoff and mean groundwater recharge for three time periods: annual, summer (May–October) and winter (November–April).

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properties and the differentiated sensitivity of the annual means is caused by the higher sensitivity in the winter period. The sensitivity of the ground water recharge to changes in dry bulk density and available water content is determined by the process conceptualization of vertical water flow. In SWAT-G, vertical water flow occurs when the soil water content exceeds field capacity. Increasing the available water content, increases the field capacity, which leads to less vertical water flow and a decrease in ground water recharge. A decrease in the dry bulk density implies a higher soil porosity and an increase in the pore volume available for vertical water flow, which results in an increase in ground water recharge. Again, the sensitivity of ground water recharge in the winter period is higher because the soils reach saturation more often in this period. Fig. 5 presents the result for the distinction level. In this study, this measure is used to determine whether two land covers result in significantly different hydrological fluxes and to quantify the significance of the sensitivity of the simulated hydrological fluxes (Fig. 4) as compared with the uncertainty in these fluxes (Figs. 2 and 3). A small difference in the distinction level indicates that the changes in the sensitivity analysis are small compared to the uncertainty, whereas a large difference indicates that the changes in the sensitivity analysis are in the same order of magnitude as the uncertainty. Fig. 5 shows that 10– 20% of cropland must be converted to pasture before two SWAT-G simulations will result in significantly different mean annual, summer or winter total runoff or actual ET. This amount of land use change was also reported by Eckhardt et al. (2003) for mean annual total runoff. The similarity of the distinction level for sensitivity analysis I–V indicates that the changes in total runoff and actual ET due to changing soil properties in response to land use change are small compared to the uncertainty given in Fig. 2a and 2b. If changes in soil properties are not considered, the distinction level for surface runoff also indicates that 10–20% of cropland must be converted to pasture before two SWAT-G simulations will result in significantly different mean annual values for surface runoff. Fig. 5 shows that this percentage is sensitive to changes in soil properties in response to land use change. Due to the large uncertainty in the mean annual ground water recharge for pasture (Fig. 2b), the distinction level indicates that two SWAT-G simulations will not result in significantly different mean annual values. However, the changes in the distinction level indicate that the changes in the sensitivity analysis are in the same order of magnitude as the output uncertainty due to uncertainty in the plant parameter values.

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5. Conclusions We assessed the sensitivity of the hydrological fluxes simulated with SWAT-G towards changing soil properties during land use change with an artificial catchment representing the Dill catchment, Germany. Sensitivity analyses were performed for four soil properties: saturated hydraulic conductivity, dry bulk density, available water content and soil depth. To assess the significance of the observed sensitivity, we compared them with the uncertainty in the hydrological fluxes due to the uncertainty in the parameterization of the plant parameters. This comparison showed that changes in soil properties during land use change only have a minor impact on simulated mean annual, summer and winter runoff and actual ET. There was a stronger impact on the simulated mean surface runoff and ground water recharge, although the absolute contributions of these fluxes are only small in our conceptualization of the Dill catchment. It was concluded that changes in soil properties due to land use change are relatively unimportant in our model of the Dill catchment in the light of the low sensitivity of the dominating hydrological fluxes and the large output uncertainty due to the plant parameter uncertainty. It is important to realize that the results presented in this paper are dependent on the process conceptualization in the SWAT-G model, the properties of the artificial catchment and the properties of the Dill catchment it represents. Our conceptualization of the Dill catchment is characterized by a large percentage of lateral flow due to the relatively impermeable bedrock layer underlying the soil profile and relatively little surface runoff. Therefore, the simulated hydrological response is mainly determined by the properties of the bedrock layer, which are unaffected by changes in surface soil properties. Of course, other systems where changes in surface soil properties have a much higher impact on the hydrological response simulated with SWAT-G can easily be imagined. For example, simulations of surface runoff dominated systems, such as those commonly found in semi-arid regions, may respond much more sensitive to changes in soil properties in response to land use change.

Acknowledgment This study was supported by the Deutsche Forschungsgemeinschaft within the scope of the Collaborative Research Centre (SFB) 299.

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