Insights for Estimating and Predicting Reservoir Sedimentation Using the RUSLE-SDR Approach: A Case of Darbandikhan Lake Basin, Iraq–Iran

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime functionality of dams. Darbandikhan Lake is one example a dam lake Zagros region that was filled late 1961. Since then, has received considerable amount sediments from upstream area basin. Interestingly, series dams have been constructed (13 dams), leading to change rate arriving at main reservoir. This motivated us evaluate different combination equations estimate Revised Universal Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), Reservoir Sedimentation (RSed). Sets Digital Elevation Model (DEM) gathered by Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring (TRMM), Harmonized World Database (HWSD), AQUA eMODIS NDVI V6 data, situ surveys echo-sounding bathymetry, other ancillary data were employed for this purpose. In research, RSed, five models SDR two most sensitive factors affecting soil-loss estimation tested (i.e., rainfall erosivity (R) cover management factor (C)) propose proper RUSLE-SDR model suitable RSed modeling areas. Thereafter, using field measurement bathymetric survey Basin (DLB) validated. The results show six ninety scenarios errors <20%. best scenario out Scenario #18, which an error <1%, 0.46458 km3·yr?1. Moreover, study advises Modified Fournier index (MIF) R factor. Avoiding Index Connectivity (IC) calculating land C obtain better estimates highly recommended.