Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins

Rainfall-runoff modeling is a complex nonlinear spatiotemporal prediction problem. However, few studies have considered the spatial characteristics of rainfall-runoff relationship in runoff forecasts based on machine learning. With the emergence of high-resolution Satellite-based Precipitation Products (SPPs) and the continuous improvement of rainfall estimation accuracy, the shortcoming of sparse spatial information for in-situ rainfall monitoring has been made up. Therefore, this study developed a large scale spatiotemporal deep learning rainfall-runoff (SDLRR) forecasting model for hydrological stations in the upper Yangtze River, and evaluated the positive impact of utilizing spatial information of three SPPs on reducing errors of runoff forecasts. The adopted remote sensing precipitation products are bias-corrected Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Integrated Multi-satellite Retrievals for Global Precipitation Measurement data (IMERG) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis data (TMPA). For runoff forecasting at the Luoduxi (LDX) hydrological station, compared to regular Long Short Term Memory Network (LSTM) model, the proposed SDLRR model that utilizing IMERG data as precipitation input (IMERG_SDLRR) improved 15% in terms of Coefficient of Determination (R2) and improved 25% in terms of Root Mean Squared Error (RMSE). Compared to the best performance model among models using area-averaged precipitation as input, IMERG_SDLRR improved 5% in terms of R2 and 11% in terms of RMSE. Good performance was also acquired in the other hydrological stations. For extreme flood forecasts, IMERG_SDLRR decreased Mean Relative Error (MRE) by 0.29 and increased Qualified Rate (QR) by 53% compared to LSTM, and decreased MRE by 0.08 and increased QR by 6% compared to the best performance model using area-averaged precipitation as input. The utilization of IMERG or TMPA spatial information improved the accuracy of runoff forecasting. The accuracy evaluation of SPPs based on the results of spatiotemporal rainfall-runoff forecasts method was also demonstrated. The research is of great significance for developing runoff forecasting methods and optimizing water resources management.