The study attempts to evaluate the impact of DEM source (AW3D30 DEM, CartoDEM v2 R1, SRTM v4.1 DEM and ASTER GDEM v2), DEM resolution (30 m to 1000 m), resampling approaches (nearest neighbor, bilinear interpolation, cubic convolution, majority) and area threshold (1500 Ha, 10,000 Ha, 25,000 Ha, 35,000 Ha, 50,000 Ha) on hydrological model (SWAT) simulated outputs. A methodological framework by two criteria: (1) DEM quality assessment and (2) river network delineation capability of DEM were developed for identifying best DEM among the considered DEMs for baseline scenario. It is found from the study that AW3D30 DEM best represented the terrain of the catchment among the evaluated topographic models with a least RMSE value of 7.44 m. Further AW3D30 DEM had the best river network extraction capability with a minimum RMSE value of 44.52 m in comparison with reference network. All the DEM scenarios were found to be insensitive for surface runoff. Ground water flow, evapotranspiration, potential evapotranspiration and water yield estimates did not show any sensitivity to DEM scenarios but soil water content showed its sensitivity to area threshold scenario. In water quality estimates, all DEM scenarios were found to be highly sensitive to sediment yields in comparison to total nitrogen and total phosphorus.

该研究试图评估DEM来源（AW3D30 DEM，CartoDEM v2 R1，SRTM v4.1 DEM和ASTER GDEM v2），DEM分辨率（30 m至1000 m），重采样方法（最近邻，双线性插值，三次卷积）的影响，多数）和面积阈值（1500 Ha，10,000 Ha，25,000 Ha，35,000 Ha，50,000 Ha）基于水文模型（SWAT）模拟输出。建立了基于两个标准的方法框架：（1）DEM质量评估和（2）DEM在河网中的划定能力，以在基准情景中考虑的DEM中确定最佳的DEM。从该研究中发现，AW3D30 DEM最能代表所评估地形模型中的流域地形，其最小均方误差（RMSE）值为7.44 m。此外，AW3D30 DEM具有最佳的河网提取能力，最小RMSE值为44。与参考网络相比52 m。发现所有DEM方案都对地表径流不敏感。地下水流量，蒸散量，潜在蒸散量和水产量估算值对DEM情景没有任何敏感性，但土壤含水量对面积阈值情景有敏感性。在水质估计中，与总氮和总磷相比，所有DEM方案都对沉积物产量高度敏感。