Soil erosion is a key concern for the environment and natural resources since it leads to a decline in-field productivity and soil quality, resulting in land degradation. In this study, assessment of uncertainty in soil erosion modelling of the Karso watershed, India, was carried out by employing the revised universal soil loss equation (RUSLE) and geospatial technologies to evaluate the effect of multi-source digital elevation models (DEMs) [Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Cartosat and Shuttle Radar Topography Mission (SRTM)] with resampled multi-resolution grids. The rainfall erosivity factor (R) was computed using the mean monthly Tropical Rainfall Measuring Mission rainfall estimates for 1998 to 2012. The slope length factor was derived using the ASTER and Cartosat DEMs at grid sizes of 30 m, 50 m, 100 m, 150 m, 200 m, and 250 m, and for the SRTM DEM at 100 m, 150 m, 200 m and 250 m resolutions for the Karso watershed, Jharkhand, India. Significant differences were obtained in the soil loss estimates across the different DEM sources and resampled grid sizes. The Cartosat DEM with a 200 m grid was found to estimate the soil loss the best out of all the DEM combinations considered. The Cartosat DEM proved to be more reliable than the ASTER and SRTM DEMs. The results indicated that the RUSLE is a scale-dependent model since the model estimates were affected not only by the DEM source but also by its resolution. The prediction of erosion potential by employing the multisource, multiresolution DEMs and the RUSLE helped to identify the soil erosion's spatial pattern within the watershed. The study provided an impact analysis of the uncertainties when selecting the multisource, multiresolution DEMs for soil erosion modelling.

水土流失是环境和自然资源的关键问题，因为它导致田间生产力和土壤质量下降，从而导致土地退化。在这项研究中，通过使用修订的通用土壤流失方程（RUSLE）和地理空间技术评估多源数字高程模型（DEM）的效果，对印度Karso流域的土壤侵蚀模型进行了不确定性评估[先进的星载热发射和反射辐射计（ASTER），Cartosat和航天飞机雷达地形图任务（SRTM），具有重新采样的多分辨率网格。降雨侵蚀力因子（R）是使用1998年至2012年的平均每月热带雨量测量任务降雨量估算值计算得出的。坡长因子是使用ASTER和Cartosat DEM得出的，网格尺寸为30 m，50 m，100 m，150 m，200 m和250 m，对于印度Jharkhand的Karso分水岭，分辨率为100 m，150 m，200 m和250 m的SRTM DEM。在不同的DEM来源和重新采样的网格大小上，土壤流失估计值存在显着差异。发现具有200 m网格的Cartosat DEM可以在所有考虑的DEM组合中最好地估计土壤流失。事实证明，Cartosat DEM比ASTER和SRTM DEM更可靠。结果表明，RUSLE是一个与比例有关的模型，因为模型估计不仅受DEM源而且受其分辨率影响。通过采用多源，多分辨率DEM和RUSLE预测侵蚀潜力有助于确定流域内土壤侵蚀的空间格局。该研究为选择多源，多分辨率DEM进行土壤侵蚀建模提供了不确定性的影响分析。