INTEGRATION OF LAND COVER DATA INTO THE OPEN SOURCE MODEL SWAT N. Oppelt, H. Rathjens, K. Doernhoefer

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1 INTEGRATION OF LAND COVER DATA INTO THE OPEN SOURCE MODEL SWAT N. Oppelt, H. Rathjens, K. Doernhoefer Kiel University, Dept. for Geography, Ludewig-Meyn-Str. 14, Kiel (Germany) and ABSTRACT Conversion of land is known to alter hydrological processes such as the exchange of energy and water. Therefore it is important to integrate land cover data in hydrological or eco-hydrological model approaches. To integrate remotely sensed input parameters into the ecohydrological model SWAT, we developed a grid-based interface (SWATgrid), which enables the computing of the water balance elements for every model grid cell without losing its geographical reference. We tested SWATgrid in a sub-catchment of the Stoer River, the Buenzener Au. For this study area, we applied conventional SWAT, which is based on the HRU concept, and SWATgrid to compute the water balance between 2003 and Results of Landsat land use classification served as input data. To compute daily discharge at the outlet of the Buenzener Au, SWATgrid showed slightly better accuracy measures (Nash Sutcliffe = 0.60; r² = 0.62) than conventional SWAT (Nash Sutcliffe = 0.57; r² = 0.60). Moreover, the grid-cell interface enabled a spatially distributed modelling based on the Landsat land cover. The grid cells now interact with each other and exchange matter and energy enabling a realistically computation of water balance elements and their distribution patterns. SWATgrid overcomes the difficulties of the conventional SWAT; the user is able both to integrate remote sensing data and to obtain spatially distributed model output. 1. INTRODUCTION Conversion of land is known to alter hydrological processes such as the exchange of energy and water. In Northern Germany a significant land use change can be observed since 2004, i.e. the amendment of the Renewable Energies Act. To investigate the effects of land use change on the water cycle in lowland river catchments, specific properties of lowland areas have to be considered such as flat topographies, low hydraulic gradients and shallow groundwater. In addition, peatland and lakes enlarge the potential for water retention in lowland areas [1]. In large parts of the lowland catchments, natural water balance has been altered by human impacts such as river regulations, pumping stations, and drainage systems (tile drainage or open ditches). The removal of surplus water causes a drawdown of the groundwater level as well as changes in water movement [2]. To address questions related to land use and water management, hydrological models turned out to be useful tools. Water balance and water quality of a catchment can be simulated based on climatic data, topography, soil and land use parameters. Eco-hydrological models have successfully been applied in lowland catchments already [3], [4], [5], [6], [7]. The intensity of the effect of land use on water regime depends on the size, the slope and land use characteristics of the catchment [8]. Ref. [9], for example, described a moderate effect of land use changes on the annual water balance of the small Dietzhoelze catchment in Germany. On the contrary, [10] and [11] showed significant effects of land use changes on catchments in Brazil (Tocatins River) and England (Tyne Basin). Literature also indicates that results depend on the hydrological model used and the physical processes simulated [8]. The Soil and Water Assessment Tool (SWAT, [12]) is one of the most suitable models used to simulate the impact of land use changes on the hydrological cycle in watersheds [13]. Various SWAT applications have been reported in the literature; Ref. [13] and [6] provided comprehensive reviews stating that most applications rely on computation of discharge at the outlet for calibration and validation purposes. Ref. [13] and [6], however, also mentioned the need for integrating spatially distributed information into SWAT. To fill this gap, we developed a model interface to manage input and output data based on grid cells enabling the integration of spatially distributed information such as remote sensing data. 2. STUDY AREA The study area is a sub-catchment of the River Stoer, which is located in the Northern German lowlands (Fig. 1). The Upper Stoer is the tidal-free part of the Stoer catchment; it is situated in the federal state Schleswig- Holstein and belongs to the Elbe River basin. In this study the focus is set on the Buenzener Au, one of the largest tributaries of the Upper Stoer; it is located in the western part of the catchment draining approximately 44 % of the area [2]. The Buenzener Au covers an area of 210 km² and is characterized by flat topography ranging between 50 m in its north-western part to 2 m above sea level at the outlet. The relief gradients in most of the area are smaller than 1 ; only the south-western region shows slopes of more than 3 [14]. Dominant soil types are podzols and planosols, histosols are found in river valleys and depressions. The climate of this area is moderatemaritime with precipitation all over the year. The mean temperature over the 1961 to 1990 period is 8.4 C with Proc. First Sentinel-2 Preparatory Symposium, Frascati, Italy April 2012, ESA SP-707, July 2012

2 an annual precipitation of 785 mm; during the investigation period (2000 to 2010) a mean temperature of 9.5 C and an annual precipitation of 857 mm have been recorded [15]. The gauge Sarlhusen is situated near the outlet of the Buenzener Au, where an average discharge of 2.51 m³s -1 has been measured between 2000 and 2010 [16]. For this period, calculating the water balance results in a discharge of 432 mm; therefore evaporation can be rated at 425 mm. The landscape is characterized by numerous drainage networks. Due to shallow groundwater the fraction of permanent grassland is high (Tab. 1); a considerable amount of land, however, is used for crop production. Since the amendment of the Renewable Energies Act in 2003, the agro-statistical yearbooks report a consistent land cover change for Schleswig-Holstein. According to the statistical yearbooks, grassland was changed to crop land and crop production shifted towards the cultivation of maize; between 2003 and 2010, the percentage of maize cultivation was more than doubled [17]. In the same period, the number of fermentation or biogas plants increased from 20 [18] to 380 [19]; a fact that obviously influenced the cultivation of energy crops and in particular the cultivation of maize. The statistics offer an insight into the land use change in the state Schleswig-Holstein; they are inadequate, however, to serve as input parameter for SWAT. Nevertheless, the results of remotely sensed land use classification provide type and distribution of land use specific area of interest. Table 1. Excerpt of the agro-statistical yearbooks of Schleswig-Holstein for 2003 and 2010 [20] Agriculturally used area [10³ ha] Permanent grassland [%] Cereals [%] Maize [%] Rape & turnip rape [%] Root crops [%] Set aside [%] METHODOLOGY 3.1 Landsat data For the study area, Landsat data were available for 2003, 2009, 2010 and 2011 (Tab. 2). For all data sets, bands 2, 3, 4, 5 and 7 served as spectral input for classification. Original grey-values were converted to radiances using the equations and parameters provided by the Science Data Users Handbook [21]. All images contained clouds; to grant normal distribution they were masked prior to the classification conducted using the image analysis software ENVI 4.2 [22]. For all data sets, the probability threshold was set to Test and validation data are provided by annual field surveys conducted in the study area (Fig. 1). Validation pixels were used to compute confusions matrices and accuracy measures (Tab. 2). Figure 1. Land cover classification results of the Upper Stoer catchment for 2010; the boundary of the Buenzener Au is highlighted The resulting land use classifications exhibit high accuracies. Kappa coefficients exceed 0.9 except for the 2009 classification. The same applies for overall accuracies that are higher than 90 % excluding The selected land use classes could be distinguished accurately; highest errors of omission and commission occurred in the class rape (2010: 16.5 %, 8.99 %) and cereals (2010: 7.7 %, 12.48).

3 Table 2. Accuracy measures of land use classifications Acquisition date Kappa Coefficient Overall Accuracy % % % According to our classifications the increased cultivation of maize was to the detriment of grassland (loss of approx. 6 % between 2003 and 2010) which corresponds well with the statistics. In the western part of the catchment, grassland areas depict fens that mainly contribute to the still dominant land use class grassland (2010: 36 %). Deciduous and evergreen forest (14 % and 4 % respectively) as well as non-agricultural land ( peat bog, water and sealed areas covering 8 %) remained the same. Combing the classification files from 2009 to 2011 using a raster analysis approach in ArcGIS [23] enabled investigation of cropping systems during this period. The analysis focuses on farmland, hence, maize, rape, cereals and grassland; therefore we excluded the land use classes deciduous/evergreen forest, sealed area, water and peat bogs as well as masked and unclassified pixels (grey areas in Fig. 1). Figure 2. Results of the crop rotation analysis between 2009 and 2011; percentages of crop rotation types are shown in the right diagram Fig. 2 presents a crop rotation map for the years 2009 to 2011 indicating a large percentage of mono-cropping as well as indicators for the previously proposed land use change. In this period maize was grown mono-culturally (without any crop rotation) at 16 % of the farmland area. Furthermore, 18 % of cropland changed to maize in 2011 and about 10 % grassland were ploughed up into crop or maize cultivated land. Nevertheless, the catchment is dominated by permanent (2009 to 2011) grassland (36 %) which mainly coincides with the fen mentioned previously. Actual crop rotation as a mix of cereals, rape and maize or presumably cereals-cerealscereals share a minor part in the catchment s cropping systems (2 % and < 1 % respectively). 3.2 The model suite SWAT Since SWAT [12] has been successfully used to estimate anthropogenic, climate and other influences on a wide range of water resources worldwide [24], we chose this open source model suite to simulate the water cycle of the Buenzener Au. SWAT, however, is not able to integrate raster based remote sensing data. SWAT operators primarily use a conventional discretization procedure where the watershed is divided into sub-watersheds and hydrologic response units (HRUs). The HRUs represent percentages of the sub-watershed area and include areas of similar soil, topography and land cover. Information of spatial data such as land cover or soil maps get lost. Using the land use classification of the year 2003 as input for SWAT configuration, a model output can be generated at the outlet of each subcatchment or catchment. To enable simulations over longer periods, SWAT users parameterize crop rotation based on own experience or existing guidelines for best management practice. Based on the Landsat classification results and the Landsat based crop rotation map (Fig. 2), we derived a three year crop rotation system presented in Tab. 3. The conventional SWAT approach was used to carry out a basic calibrated setup. This input parameter set was transferred to the grid based setup using the SWATgrid interface.

4 Table 3. Parameterization of SWAT crop rotation 2003 land use 2004 land use 2005 land use grassland grassland grassland maize maize maize cereals cereals maize rape cereals maize For a detailed description of the alternative interface SWATgrid it is referred to [25]); no further calibration was carried out. Therefore, the model parameter set remained equal to the conventional SWAT approach except for the different discretization. Using SWATgrid the catchment was discretized into 84,273 grid cells with a grid resolution of 50 m x 50 m. To enable a comparison of setups the SWATgrid setup and the conventional setup were applied for the same time period (2004 to 2010). Figure 3. Daily discharge between 2004 and 2010 as observed at gauge Sarlhusen (Q observed) and computed using classic SWAT (Q SWAT) and grid-based SWAT (Q SWATgrid); 4. RESULTS AND DISCSSION SWAT model results of the mean annual water balance components (precipitation: 854 mm, evapotranspiration: 483 mm, total water yield: 363 mm) correspond to values reported by [2]. The model results are realistic and similar for both setups (differences less than 1.5 mm). Fig. 3 presents daily discharge at gauge Sarlhusen between 2004 and 2010, computed with SWAT and SWATgrid. Nash-Sutcliffe-Efficiency (NSE) and coefficient of determination (R²) were calculated as quality measures. For conventional SWAT, comparison of the computed and observed discharge at the outlet showed good results (NSE = 0.57, R²=0.6; Fig. 2). At the outlet, however, consequences of a mono-cropping crop system had no measurable effect on discharge which most probably is due to the masking influence of shallow groundwater. Therefore, focusing on the HRU concept is not a suitable approach for such applications. Compared to the conventional approach the grid-based model setup computes daily discharge slightly better (NSE = 0.60, R²=0.62). Both model interfaces cannot trace peak discharges observed at the gauge, especially in After rainfall events, lag times between peak rainfall and peak discharge at the gauge were observed to be 10 to 12 days. In contrary, computed lag times result in concentration times up to 9 days, resulting in an average difference of two days between computed and observed lag time. The catchment contains large water retention components such as fen areas or peatland which might result in delaying lag times; a detailed analysis of the influence of these components, however, was not able to underpin this hypothesis [26]. Further

5 investigations will be necessary regarding the possibility of varying subsurface catchment areas and/or the influence of the groundwater parameterization in SWAT. Besides a slightly better computing of discharge at the catchment outlet, the grid-cell interface enables a spatially distributed modelling based on the Landsat land cover. The grid cells now interact with each other and exchange matter and energy enabling a computation of water balance elements and their distribution patterns. As an example, Fig. 4 presents the spatial distribution of annual evapotranspiration as well as monthly evapotranspiration rates for different land use classes as computed with SWATgrid. Figure 4: Spatial distribution of mean annual evapotranspiration (left) and course of mean monthly evapotranspiration of reference pixels ( ); the coloured pixels in the left figure present the locations of the reference pixels 5. CONCLUSION AND FUTURE TASKS A primary goal of hydrological modelling is to assess the impact of human activities (i.e. land and water management practices) on a watershed. In Northern Germany a significant land use change can be observed since the amendment of the Renewable Energies Act in 2004, resulting in an increasing cultivation of energy maize. The aim of the study was to assess the impact of land cover and management practices on hydrological processes in a watershed. Based on Landsat classification results we derived a three year crop rotation system that was integrated in the SWAT model. The Buenzener Au catchment, a sub-watershed of the Stoer River, served as a test site to study the impacts of land use changes. As a result, no measurable effect was observed at the watershed outlet probably due to masking influence of shallow groundwater. To describe influences of the land use change in such a low land catchment spatial analysis of hydrological parameters is needed. Therefore, the conventional SWAT approach focusing on the HRU concept is not suitable for such applications. Thus, we used a grid-based approach. Daily discharge derived from SWATgrid corresponded well to the measured discharge at the catchment outlet. Thus, the integration of land use data with high spatial and temporal resolution to asses land cover and management practices in a watershed seems to be promising for an enhanced spatial analysis of hydrological issues within a watershed The integration of remote sensing data into SWAT is not limited to land cover; nevertheless, the most significant impacts on the hydrologic system are caused by land cover change. Due to the advent of Sentinel-2, spatial information comparable to those of Landsat will be available in future and will provide crucial data for hydrologic modelling approaches. 6. ACKNOWLEDGEMENTS The authors would like to thank the Department of Hydrology and Water Resources Management (Kiel University) for their support. We gratefully acknowledge the Schleswig Holstein State Agency for Agriculture, Environment and Rural Areal (LLUR) and Schleswig- Holstein s State Office for Coastal Protection, National Parks and Ocean Protection (LKN) for providing the data sets to set up the model SWAT as well as validation data of gauge Sarlhusen. Thanks are also due for ESA s and NASA s provision and support of Landsat data. References 1. Kieckbusch, J. Schrautzer, J. & Trepel, M. (2006). Spatial heterogeneity of water pathways in degenerated riverine peatlands. Basic Appl. Ecol. 7, pp Schmalz, B. & Fohrer, N. (2009). Comparing model sensitivities of different landscapes using the eco-

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