Effect of DEM resolution on SWAT outputs of runoff, sediment and nutrients

Abstract

Abstract. Digital Elevation Models (DEMs) have been successfully used in a large range of environmental issues. Several methods such as digital contour interpolation and remote sensing have allowed the generation of DEMs, some of which are now freely available for almost the entire globe. The Soil and Water Assessment Tool (SWAT) is a widely used semi-distributed model operating at the watershed level and has previously been shown to be very sensitive to the quality of the input topographic information. The objective of this study was to evaluate the impact of DEMs generated from different data sources, respectively DLG5m (local Digital Line Graph, 5 m interval), ASTER30m (1 arc-s ASTER Global DEM Version 1, approximately 30 m resolution), and SRTM90m (3 arc-s SRTM Version 4, approximately 90 m resolution), on SWAT predictions for runoff, sediment, total phosphor (TP) and total nitrogen (TN). Eleven resolutions, from 5 m to 140 m, were considered in this study. Results indicate that the predictions of TPs and TNs decreased substantially with coarser resampled resolution. Slightly decreased trends could be found in the predicted sediments when DEMs were resampled to coarser resolutions. Predicted runoffs were not sensitive to resampled resolutions. The predicted outputs based on DLG5m were more sensitive to resampled resolutions than those based on ASTER30m and SRTM90m. At original resolutions, the predicted outputs based on ASTER30m and SRTM90m were similar, but the predicted TNs and TPs based on ASTER30m and SRTM90m were much lower than the one based on DLG5m. For the predicted TNs and TPs, which were substantially sensitive to DEM resolutions, the output accuracies of SWAT derived from ASTER30m and SRTM90m could be improved by down-scaled resampling, but they could not improve on finer DEM (DLG5m) at the same resolution. This study helps GIS environmental model users to understand the sensitivities of SWAT to DEM resolution, and choose feasible DEM data for environmental models.