GIS-Based RUSLE Reservoir Sedimentation Estimates: Temporally Variable C-Factors, Sediment Delivery Ratio, and Adjustment for Stream Channel and Bank Sediment Sources

Abstract

The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 < R2 ≤ 0.99; p-value < 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety.