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International Journal of Geographical Information Science

Publication date: 2005-08-01
Volume: 19 Pages: 809 - 829
Publisher: Taylor & Francis

Author:

Lewis, LA
Verstraeten, Gert ; Zhu, HL

Keywords:

rusle, ls factor, homogeneous patches, soil loss, sediment yield, erosion, Science & Technology, Social Sciences, Technology, Physical Sciences, Computer Science, Information Systems, Geography, Geography, Physical, Information Science & Library Science, Computer Science, Physical Geography, RUSLE, LS factor, SEDIMENT YIELD, EROSION, 0406 Physical Geography and Environmental Geoscience, 0806 Information Systems, 0909 Geomatic Engineering, Geological & Geomatics Engineering, 3304 Urban and regional planning, 3709 Physical geography and environmental geoscience, 4013 Geomatic engineering

Abstract:

The RUSLE (Revised Universal Soil Loss Equation) is integrated within a GIS framework to calculate soil loss spatially. For this module, algorithms and procedures were developed to derive the slope length factor (L) and steepness factor (S) from a DEM, then integrated with the R, K, C, and P factors to develop homogeneous patches (sub-units) within each field or river basin. Soil loss is determined for each patch within a study unit, and then combined to determine the fields' or river basin's average annual and total soil loss. Two case studies are presented. The first case study, in central Massachusetts, compares estimated soil loss values obtained for individual fields using the Idrisi RUSLE module to USDA-NRCS RUSLE field data. While soil loss results were similar, the RUSLE module allows fields to be partitioned into more similar units than practical in the field. This permits detailed spatial analysis of soil-loss patterns. The second case study compares soil-loss estimates for a catchment in southwestern Flanders, Belgium. This model-model comparison contrasts the results from the RUSLE module to the WATEM model-a grid cell based model based on the USLE/RUSLE but conceptualized in a multi-flow context. Results between the predicted soil losses utilizing the two different approaches are significantly correlated. However, estimated soil losses are consistently higher for the WATEM model. This likely reflects the differences between how the two models compute L as well as the contribution of ephemeral gullies and flow convergence which are incorporated in WATEM but not in RUSLE.