Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes

For reliable prediction of sediment yield in a watershed, fine-scale projections for hydro-climate components were first obtained using the statistical bias correction and downscaling scheme based on the combination of an Artificial Neural Network (ANN), Nonstationary Quantile Mapping (NSQM) and Stochastic Typhoon Synthesis (STS) sub-modules. Successively, the hydrologic runoff and sediment yield from the land surfaces were predicted through the long-term continuous watershed model, Soil and Water Assessment Tool (SWAT), using the bias-corrected and downscaled Regional Climate Model (RCM) output under the Intergovernmental Panel on Climate Change’s (IPCC’s) A1B climate change scenario. The incremental improvement of the combined downscaling process was evaluated successfully during the baseline period, which provides projected confidence for the simulated future scenario. The realistic simulation of sediment yield is closely related to the rainfall event with high intensity and frequency. During the long-term future period, the Coefficient of River Regime (CORR) reaches 353.9 (27.2% increase with respect to baseline). The projection for annual precipitation by 2040 and 2100 is a 25.7% and a 57.2% increase with respect to the baseline period, respectively. In particular, the increasing CORR rate (33.4% and 72.5%) during the flood season is much higher than that for the annual total amount. However, the sediment yield is expected to increase by 27.4% and 121.2% during the same periods, which exhibits steeper trends than the hydrologic runoff. The June, July, August (JJA) season occupies 83.0% annual total sediment yield during the baseline period, which is similar during the projection period. The relative change of sediment yield is 1.9-times higher than that of dam inflows.