Modeling anthropogenic affected sediment transport in a mid-sized European river catchment–extension of the sediment rating curve equation

Abstract Land use and water resource management influence the suspended sediment concentration (SSC) in rivers. Fine sediments are an important driver for river development, even coarse-material-rich In this study, rating curve approach is modified to predict SSC several river-km downstream of a sampling site. Further, prediction improved by adding input, storage, dilution effects through relevant anthropogenic measures model identification approach. Thus, impact most severe measures, damming changes length channel section Rur River, could be identified. describing parameter those on can computed considered future management. approach, particle swarm optimization was used fit parameters permutable test- training data sets identify linear extensions curve. The input consists (1) SSC, which obtained along four times year over approximately two years, (2) discharge from gauges supplemented rainfall-runoff modeling between stations, (3) rainfall meteorological (4) sub-catchment characteristics like erosivity with GIS. Via incorporating deposition response damming, we reduced RMSE (root mean squared error) 152.27 131.83% p-value 0.073 Wilcoxon Signed Rank Test. Further integration led overfitting decreased accuracy. A catchment-wide achieved, but sub-catchments operate different spatial scales connectivity behavior, restricts transferability equation. SSC-Q hystereses provide first indications characteristic sources were discuss behavior within study area. They recommended as part (sub-) catchment characterization further studies.