Probabilistic Prediction and Forecast of Daily Suspended Sediment Concentration on the Upper Yangtze River

Abstract

AbstractSediment transport in suspension can represent more than 90% of a river's total annual flux of sediment. In the case of the Yangtze River, more than 99% of the sediment supplied to the sea is suspended load. Suspended sediment is thus an important component of the total sediment load, with implications for channel dynamics, landscape evolution, ecology, and human‐related activities. For hydrological management of large basins such as the Yangtze River, knowledge of the processes governing suspended sediment concentration (SSC) is essential. An analysis of the temporal variation of SSC for the Upper Yangtze basin (defined at Pingshan station) is presented here. For this purpose, a database of 50 years of concurrent discharge and SSC measurements, made by the Yangtze River Commission, is used. The analysis is made using a novel probabilistic data‐driven technique, the Generalized Pareto Uncertainty (GPU). This technique allows for the testing of several strategies of prediction and forecast applied to a time series of SSC and streamflow. Changing between local or seasonal variables to feed these strategies, we inferred that although the main driver of the SSC transport is flow (as reported by previous authors), sediment storage is also a major control. Furthermore, the maximum necessary time lag for forecasts made with the data is on the order of one week, which provides one indication of the time scale of the local processes of SSC transport in the Upper Yangtze. In this paper, limitations and data requirements of the GPU methodology are also discussed.