Sheng Wang

Precipitation is a key input for hydrological modeling, and high-resolution, accurate data are essential for flood forecasting and water resource management. This study presents a Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework to improve the spatial resolution and accuracy of precipitation estimates and enhance simulations of extreme precipitation and hydrological responses. HDMPF combines a Radial Basis Function network and Random Forest for downscaling, and applies Bayesian Model Averaging to fuse multiple satellite precipitation products. The fused dataset was used to drive the Grid-Xin’anjiang model for extreme flood simulations. The results show that HDMPF significantly improves spatiotemporal precipitation accuracy, increasing the KGE to 0.90–0.95 and reducing the RMSE to below 0.3 mm/h. The framework accurately reproduces precipitation cores, peak intensities, flood peaks, timing, and multi-peak hydrographs, demonstrating strong potential for improving basin-scale modeling and flood early warning.

The acquisition of high-precision spatiotemporal precipitation data with long-term continuity plays an irreplaceable role in supporting agricultural modeling, hydrological forecasting, disaster prevention, and climate research. This study evaluates and corrects daily precipitation from satellite products (GPM, PERSIANN-CDR, CMORPH, GSMaP), merged datasets (GPCP, MSWEP, CHIRPS), and reanalysis products (ERA5, GLDAS) over the Huang-Huai-Hai Plain from 2000 to 2020. The study proposes a two-stage “error correction and residual correction” optimization framework. The error correction stage integrates machine learning with statistical methods (RF-DQDM), while the residual correction stage uses ground observations to dynamically adjust systematic biases. Results show that all corrected products outperform their original versions in spatial patterns and statistical metrics. Original precipitation data exhibit significant systematic errors modulated by topography, with eastern lowlands showing smaller errors. After correction, correlation coefficients rise above 0.8, and RMSE reductions average 60%. And product responses diverge significantly. CHIRPS improves from weakest to top performer, while model limitations constrain GLDAS enhancements. This framework establishes a transferable monsoon region optimization paradigm. This study provides a transferable bias correction framework for monsoon regions and builds a homogenized high-precision precipitation benchmark. It also recommends using CHIRPS or ERA5 for extreme rainfall analysis and MSWEP or GPCP for hydrological applications.

Rainfall-triggered flood and landslide hazards pose significant threats to human lives and infrastructure worldwide. This study aims to evaluate the applicability of three satellite rainfall data sets—namely, CMORPH, GPM, and TRMM—for the prediction of flood and landslide hazards using a coupled hydrological-slope stability model. The spatial distribution of annual rainfall from the three satellite data sets was similar to that of gauge rainfall, with an increasing trend from the north to the south of Shaanxi Province. The average annual rainfall of CMORPH was the lowest, while that of TRMM was the highest. The modeled discharges forcing by satellite rainfall generally matched the observed discharges at four hydrological stations for the period 2010–2012, with average correlation coefficients of 0.51, 0.61, and 0.57 for the CMORPH, GPM, and TRMM rainfall, respectively. The exceedance probabilities of modeled discharges for the three satellite rainfall data sets were close to those of the observations, particularly when the discharges were low. Moreover, the landslide prediction results demonstrated that the three satellite rainfall data sets could simulate the spatial distribution of landslide events well; these simulations were consistent with the information in the landslide inventory map. Furthermore, when compared to the classical Intensity-Duration (ID) rainfall threshold method, the physically based slope stability model presented higher global accuracy under all three satellite rainfall data sets. The global accuracy of GPM rainfall was the highest among the three data sets (0.973 for GPM vs. 0.951 for CMORPH and 0.965 for TRMM), indicating that GPM rainfall provides the highest quality compared to CMORPH and TRMM rainfall. These findings provide a crucial basis for the application of satellite rainfall data in the context of flood and landslide prediction.