Modelling topographic potential for erosion and deposition using GIS

Abstract

Abstract. Modelling of erosion and deposition in complex terrain wilhin a geographical information system (GIs) requires a high resolution digital elevation model (DEM), reliable estimation of topographic parameters, and formulation of erosion models adequate for digital representation of spatially distributed parameters. Regularized spline with tension was integrated within a GIs for computation of DEMs and topographic parameters from digitized contours or other point elevation data. For construction of flow lines and computation of upslope contributing areas an algorithm based on vector-grid approach was developed. The spatial distribution of areas with topographic potential for erosion or deposition was then modelled using the approach based on the unit stream power and directional derivatives of surface representing the sediment transport capacity. The methods presented are illustrated on study areas in central Illinois and the Yakima Ridge, Washington.

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

“…The DEM (Figure 2) was provided by the Royal Jordanian Geographic Centre (RJGC). The following equation was adopted to compute the LS factor [54]: …”

Digital elevation model uncertainty and hazard analysis using a geophysical flow model

“…Let Z (U) be a continuous random field used to characterize unknown elevation errors (differences). The random field function is implemented in the function r.random.surface (Ehlschlaeger & Goodchild 1994) of the Geographic Resources Analysis Support System (GRASS) GIS (Mitasova et al 1996), and generates fields obtained using a normal distribution (mean of 0.0 and variance of 1.0). The random field function derives its spatial dependence from the use of a distance-based decay filter function.…”

“…A digital representation of a terrain surface is an approximation of reality and is often subject to significant error (Mitasova et al 1996). The error is usually not known in terms of both magnitude and spatial distribution.…”

“…Usually, these terrain representations are digital elevation models (DEMs). For this type of study, terrain elevation is rightly recognized as the most essential and fundamental of variables in geographical analysis (Mitasova et al 1996;Atkinson 2002;Wechsler & Kroll 2006;Stefanescu et al, in press). Dalbey et al (2008) introduced procedures for constructing hazard maps using ensembles of CFD model simulations (the TITAN2D code; Patra et al (2005)) of such flows constructed by establishing probability distributions of input uncertainties in flow initiation (location and volumes) and by sampling them.…”

Modelling soil erosion risk in a mountainous watershed of Mid-Himalaya by integrating RUSLE model with GIS

“…Erosion increases with slope steepness but, in contrast to the L-factor representing the effects of slope length, the RUSLE makes no differentiation between rill and inter-rill erosion in the S-factor that computes the effect of slope steepness on soil loss (Renard et al, 1997;Krishna Bahadur, 2009). SRTM DEM with 30m resolution was used to compute LS factor using the spatial analyst and hydrology toolkits in ArcGIS software, following the method described by Moore and Burch (1986) and Mitasova et al (1996).…”