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

Abstract

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

Proposing Optimal Locations for Runoff Harvesting and Water Management Structures in the Hami Qeshan Watershed, Iraq

“…In future investigations, it might be possible to integrate the spatial optimization models recommended by Tong and Murray [36] into dam site selection. Another probable area of future research would be to estimate the annual soil loss that negatively impacts the functionality and lifespan of the proposed dams [70]. Decision-makers must also consider the additional greenhouse gases emitted due to dam construction that could result in human-induced climate change complications.…”

“…Due to the uneven distribution of rain gauges in the Hami Qeshan Watershed, the monthly Tropical Rainfall Measuring Mission (TRMM) 3B43-V7 dataset [67] was used for developing the precipitation layer. Also, TRMM possesses a spatial resolution of 0.25° × 0.25° [68] and has been verified and applied in different studies [69][70][71][72]. Preselected dam site data in HQW were collected from the Ministry of Agriculture and Water Resources (MAWR), IKR [73].…”

Estimating the Soil Erosion Response to Land-Use Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Heilongjiang Province, China

“…The flexibility of the empirical model in areas of data scarcity enhances its reception and application. Among them, the revised universal soil loss equation (RUSLE) model has been widely recognized and applied in the field of soil erosion worldwide because of its extensive use of data, high data accessibility, sufficient information basis, and wide area coverage [19][20][21][22][23].…”