A Copula‐Based Regression Method to Predict Sediment Concentration and Load in Rivers Using Streamflow Data

Abstract Pollution and deposition of sediments in rivers and streams are critical environmental, ecological, navigational, and recreational concerns. While several well‐known watershed models such as SWAT (Soil and Water Assessment Tool) and HSPF (Hydrological Simulation Program‐FORTRAN) are applied to predict sediment concentrations and loads in rivers and streams, their application requires the acquisition of a large amount of climatic, GIS, land use, and watershed input data, which is very time‐consuming and cost‐prohibitive. This study developed a copula‐based regression method to predict sediment concentrations and loads in rivers using streamflow data, which is difficult to achieve using traditional methods. Predicted sediment concentrations from the method were verified and validated by field measurements from three US Geological Survey (USGS) gauge stations across the US, as well as by statistical metrics. Prerequisites, advantages, uncertainties, and limitations of the method were presented and discussed. Results revealed that sediment loads were not proportional to watershed drainage area, indicating that land use and anthropogenic activities also play an important role in sediment load. Overall, no significant increasing or decreasing trends of annual sediment loads were found at study sites in New York and Florida over the 11‐year period from 2011 to 2021. This study suggests that the copula‐based regression approach, which is time‐saving and cost‐effective compared to the traditional watershed models, is a promising alternative for predicting sediment concentrations and loads using streamflow data when a good dependence (or correlation) exists between the sediment content and streamflow after copula transformation.