Introducing seasonal snow memory into the RUSLE

Abstract Purpose The sediment supply to rivers, lakes, and reservoirs has a great influence on hydro-morphological processes. For instance, long-term predictions of bathymetric change for modeling climate scenarios require an objective calculation procedure load as function catchment characteristics hydro-climatic parameters. Thus, the overarching this study is develop viable assessment methods in data-sparse regions. Methods This uses Revised Universal Soil Loss Equation (RUSLE) SEdiment Delivery Distributed (SEDD) model predict soil erosion transport catchments. novel algorithmic build free datasets, such satellite reanalysis data. Novelty stems from usage freely available datasets introduction seasonal snow memory into RUSLE. In particular, account non-erosive snowfall, its accumulation over months temperature, erosive snowmelt after fell. Results Model accuracy parameters form Pearson’s r Nash–Sutcliffe efficiency indicate that data interpolation with imagery enables chain further improves when added Non-erosivity snowfall makes most significant increase accuracy. Conclusion represent major improvement estimating suspended loads empirical processes should be considered any analysis mountainous