Process-constrained statistical modeling of sediment yield.

[Display omitted] • We proposed a method for fine-tuning stochastic modeling of sediment yield. • Underlying characteristics of sediment transport are used to tune the model. • Various stream regimes are examined to estimate/predict sediment yield. • Various ecoregions are investigated across the conterminous United States. • The effect of watershed properties on the sediment yield process is studied. Sediment transport is a major contributor to a non-point source of pollution impacted by various factors that are modulated by climatic changes and anthropogenic influences. Quantifying and disentangling the contribution of these factors to sediment yield at large scales and across different flow regimes has not been fully explored. Here we present a framework to fine-tune a stochastic sediment yield model by classifying discharge and Suspended Sediment Load (SSL) observations based on the underlying governing processes in unregulated streams with various hydrological regimes. This stochastic model, rooted in copula theory, constructs a joint distribution between discharge and SSL storm events using historical time series of observations, classified based on seasonality, hysteresis patterns, and hydrograph components of the sediment transport processes. We include hydrological, land use, and geological properties of the watersheds to describe and discuss the effects of different factors on applying the underlying dynamics to enhance sediment yield estimation/prediction accuracy. We evaluated the proposed method on 67 streams across the United States. Our results show significant improvements in sediment yield modeling performance. [ABSTRACT FROM AUTHOR]