Influence of rainfall data scarcity on non-point source pollution prediction: Implications for physically based models

摘要: Abstract Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially those large-scale catchments. However, few studies explored comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) effects spatial (by removing 11%–67% stations based their locations) results; 2) temporal (10%–60% time series); 3) development a new evaluation method that incorporates information entropy. A case was undertaken using Soil Water Assessment Tool (SWAT) typical China. The results this highlighted importance critical-site often showed greater influences cross-tributary simulations. Higher missing rates above certain threshold as well locations during wet periods resulted poorer simulation results. Compared to traditional indicators, entropy could serve good substitute because it reflects distribution variability heterogeneity. paper reports important implications application Distributed Models Semi-distributed Models, optimal design gauges among large basins.