Influence of digital elevation models on the simulation of rainfall-induced landslides in the hillslopes of Guwahati, India

Topographic input parameters such as slope, curvature, and drainage area are derived from a Digital Elevation Model (DEM) representing the spatial elevation, and are widely used as important sources of geospatial information. Based on the chosen DEM and its derived paramters, physically based GIS models, such as TAIGRS, can compute the transient degradation of the hillslope stability due to rainfall infiltration to identify the landslide occurrences in the considered region. Hence, it is inadvertent that the accuracy of the DEM will significantly affect the outcome of the TAIGRS simulations. Obtaining a high-resolution DEM (using LiDAR, dGPS or other such ground based advanced surveying methods) for a large area is an expensive affair and unavailable in most cases. Hence, under such circumstances, DEMs available in public domain vis., CartoDEM, ALOS-AW3D30-DEM, SRTM-DEM, and ASTER-GDEM, serves to be the sole option. Each of these DEMs are obtained from varying sources using different data collection and processing techniques, thereby leading to DEMs of varying resolutions and different accuracies. Although there is ample literature on the effects of the resolution of specific DEMs, the influence of the varying accuracies of DEMs chosen for TAIGRS simulation has not been adequately reported. Considering the landslide scenario of Guwahati city as the study region, the effect of dissimilarity in DEMs on the prediction of the rainfall-induced landslides in the hillslopes of the study area is reported in this paper. Three rainfall events triggering landslides were considered as input into TAIGRS, and the simulation outcome of the different DEMs were compared with reported landslide locations. Significantly different outputs were obtained for the DEMs highlighting the importance of the analysis. Based on two different evaluation methodologies, i.e. the ROC and LRclass, landslide occurrences identified by ALOS-3D DEM provided the best agreements with the reported landslides, followed by those identified by Carlo-DEMs.