Utilization of climate information and soil moisture estimates to provide monthly and sub‐monthly streamflow forecasts

Abstract

ABSTRACTStreamflow forecasts at monthly and sub‐monthly time scales, e.g. 10‐day period, are critical for making decisions to allocate water for different users and mitigate possible flooding. Adaptive forecasting of 10‐day streamflow is still challenging, although persistence prediction models sometimes have good performance when streamflow has strong lag‐correlation. This study proposes a scheme of proving monthly and sub‐monthly flow forecasts at the beginning of the month and updating sub‐monthly forecasts subsequently. It examines a principal component regression method to provide monthly average streamflow and sub‐monthly, e.g. 10‐day, average streamflow forecasts, utilizing gridded precipitation forecasts from climate models and soil moisture estimates from hydrological models. Monthly streamflow forecasts are first obtained. It is then disaggregated to 10‐day streamflow based on historical observations using a nonparametric approach. The disaggregated 10‐day streamflow forecasts are further improved by incorporating streamflow and soil moisture estimates in the previous 10 days. Hence, sub‐monthly flow can be improved adaptively. The proposed approach is demonstrated for monthly and sub‐monthly streamflow forecasts in July at the Yangtze River, the largest river in China. The correlation between monthly streamflow forecasts and observation is 0.46 in leave‐one‐out cross‐validation mode. Updated sub‐monthly streamflow shows better skill than disaggregated sub‐monthly forecasts. To examine the impact of the accuracy of monthly streamflow forecasts on disaggregated 10‐day streamflow, synthetic streamflow time series of different level of forecasting skills were examined. Results show that the higher skill of synthetic monthly streamflow forecasts, the lower forecasts error. The value of soil moisture estimates in proving streamflow forecasts is also examined.