Project

Upgrade and Optimization of the erosion risk map (ERM2) of Switzerland

Goal: In this project a new and optimized erosion risk map (ERM2) with validity for the next 5 to 10 years for the enforcement in environmental and agricultural laws is being produced, based on the famous RUSLE-approach.
Current status
By order of FOAG a high resolution erosion risk map of Switzerland (ERM2) was already generated from CDE (University of Bern) in cooperation with Agroscope (Gisler et al., 2010, 2011, Prasuhn et al., 2010, 2013). This map is scientifically accepted, online available and supported by the federal authorities (FOAG). It involves the agricultural used areas of the valley zone to the mountainous zone (Talzone - Bergzone II) of Switzerland (Prasuhn et al., 2013). The ERM2 is an important tool for the enforcement “Soil Protection in Agriculture” (BAFU and BLW, 2013).
The revised universal soil loss equation (RUSLE) provides a long term soil erosion risk in tons per hectare and year and consists of six factors (Wischmeier & Smith 1965,1978; Renard et al. 1997)
A [t ha-1 a-1]= R*K*L*S*C*P
R = erosivity of rainfall and runoff, K= erodibility of soil, L=slope length factor, S= slope steepness factor, C= cover and management factor, P= support practice factor

Methods: Geoinformatics

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Project log

Pascal Bircher
added an update
Field block map, Flow-path map, Erosion risk crop quantitative, Erosion risk crop qualitative
 
Pascal Bircher
added a research item
The topographic LS-factor is one of the most difficult factors of the Revised Universal Soil Loss Equation (RUSLE) to define in a landscape with varying topography. For the application of the RUSLE not only at the plot but at catchment or landscape level, different multiple flow algorithms (MFA) have been developed and applied in various studies. However, these different MFAs in combination with various convergence values and applied at different resolutions of digital elevation models (DEM) have not been addressed so far. This publication focuses on filling this gap in the context of the agricultural area of Switzerland. To evaluate different factors of slope steepness (S-factor) and slope length (L-factor), we tested four different multiple flow algorithms (MFA) (Deterministic Infinity (DINF), Multiple Flow Direction (MFD), Multiple Triangular Flow Direction (MTFD), Watershed (WAT)) and compared them with the MFA approach (Flow 95 in MUSLE87) used in the existing erosion risk map of Switzerland with a resolution of two metres. The MFAs we tested, used three different convergence settings and two digital terrain models (DEM) – one with a very fine two metre resolution (DEM2) and one with a coarser resolution of 25 m (DEM25) – enabling us to examine the influence of DEM resolution on the LS-factor. In total, we evaluated 21 L-factor variations to assess the significance for the prediction of the potential erosion risk. The calculations were applied at a local (test area Frienisberg, 88.7 ha) and at a regional scale (Lyss, 11,855 ha) in the agricultural Swiss Plateau. Both test areas were segmented into field blocks with an average size of 5 ha (14 field blocks in Frienisberg and 2305 field blocks in Lyss). A field block can contain several fields with different types of agricultural land use and is delineated by surrounding hydrological barriers. For these field blocks, the various L-factors were calculated automatically using Geographic Information Systems (GIS). Finally, the LS-factors were calculated for two selected MFAs. The L-factors calculated with the various MFAs and the high-resolution DEM2 differed negligibly in terms of statistical values (mean values, standard deviation) and in the spatial distribution of the pixels both among each other and in comparison to the L-factor of the existing erosion risk map. As expected, using the coarser DEM25 resulted in considerably lower S-factors but surprisingly in higher L-factors, so that there was little difference in the average LS values between the DEM25 and the DEM2. However, spatial distribution of the L-factor values and the soil erosion risk was much more differentiated in the DEM2 and better reflected the topography compared with the DEM25. Erosion risk hotspots such as slope depressions with concentrated runoff and thalweg erosion could be reliably identified. Moreover, the lower-resolution DEM25 was not well suited to the chosen approach with field blocks of a mean size of 5 ha, as the intersection of polygon and raster data produced edge errors depending on the clipping method. This study showed that a high-resolution DEM was more important for the calculation of the LS-factor and potential soil erosion risk than the choice of MFA, and that the calculation of LS-factors based on field blocks offered a number of advantages mainly in determining the channel network and maximum flow length.
Pascal Bircher
added a project reference
Pascal Bircher
added a research item
Soil erosion is a well-known challenge both from a global perspective and in Switzerland, and it is assessed and discussed in many projects (e.g. national or European erosion risk maps). Meaningful assessment of soil erosion requires models that adequately reflect surface water flows. Various studies have attempted to achieve better modelling results by including multiple flow algorithms in the topographic length and slope factor (LS-factor) of the Revised Universal Soil Loss Equation (RUSLE). The choice of multiple flow algorithms is wide, and many of them have been implemented in programs or tools like Saga-Gis, GrassGis, ArcGIS, ArcView, Taudem, and others. This study compares six different multiple flow algorithms with the aim of identifying a suitable approach to calculating the LS factor for a new soil erosion risk map of Switzerland. The comparison of multiple flow algorithms is part of a broader project to model soil erosion for the entire agriculturally used area in Switzerland and to renew and optimize the current erosion risk map of Switzerland (ERM2). The ERM2 was calculated in 2009, using a high resolution digital elevation model (2 m) and a multiple flow algorithm in ArcView. This map has provided the basis for enforcing soil protection regulations since 2010 and has proved its worth in practice, but it has become outdated (new basic data are now available, e.g. data on land use change, a new rainfall erosivity map, a new digital elevation model, etc.) and is no longer user friendly (ArcView). In a first step towards its renewal, a new data set from the Swiss Federal Office of Topography (Swisstopo) was used to generate the agricultural area based on the existing field block map. A field block is an area consisting of farmland, pastures, and meadows which is bounded by hydrological borders such as streets, forests, villages, surface waters, etc. In our study, we compared the six multiple flow algorithms with the LS factor calculation approach used in the ERM2. A GrassGis algorithm matches the current erosion risk map (ERM2) best, both statistically and visually; modelling results show water flow accumulation and concentration in areas where a ten year field mapping study documented many erosion events. The same field mapping data were also used to check the validity of soil loss predictions using the six different multiple flow algorithms.
Pascal Bircher
added a project goal
In this project a new and optimized erosion risk map (ERM2) with validity for the next 5 to 10 years for the enforcement in environmental and agricultural laws is being produced, based on the famous RUSLE-approach.
Current status
By order of FOAG a high resolution erosion risk map of Switzerland (ERM2) was already generated from CDE (University of Bern) in cooperation with Agroscope (Gisler et al., 2010, 2011, Prasuhn et al., 2010, 2013). This map is scientifically accepted, online available and supported by the federal authorities (FOAG). It involves the agricultural used areas of the valley zone to the mountainous zone (Talzone - Bergzone II) of Switzerland (Prasuhn et al., 2013). The ERM2 is an important tool for the enforcement “Soil Protection in Agriculture” (BAFU and BLW, 2013).
The revised universal soil loss equation (RUSLE) provides a long term soil erosion risk in tons per hectare and year and consists of six factors (Wischmeier & Smith 1965,1978; Renard et al. 1997)
A [t ha-1 a-1]= R*K*L*S*C*P
R = erosivity of rainfall and runoff, K= erodibility of soil, L=slope length factor, S= slope steepness factor, C= cover and management factor, P= support practice factor