Quantifying the utility and uncertainty of multi-source fused precipitation products in hydrological simulations

作者：Zhiwen You, Kaixun Wang, Huaiwei Sun, Hong Zhang, Yuanyao Ye, Siyao Qu, Lin Chen, Huixian Li, Feng‐Yu Wang, Hui Qin, Zhanzhang Cai · 发表于：Journal of Hydrology Regional Studies · 年份：2025 · DOI：10.1016/j.ejrh.2025.102620 · 被引用次数：1 · 研究领域：Precipitation Measurement and Analysis、Meteorological Phenomena and Simulations、Soil Moisture and Remote Sensing

Study Region The Juzhang River Basin in Hubei, China. Study Focus Precipitation is crucial information in hydrological modelling, and its uncertainty significantly affects the simulation performance. This study constructs a decision tree-based gridded fusion model to integrate multiple precipitation products, generating multi-source fused precipitation products (MSFPPs). The mesoscale hydrological model (mHM) is driven by these MSFPPs to simulate the hydrology of the Juzhang River Basin. Additionally, data from The Gravity Recovery and Climate Experiment (GRACE) and other sources are incorporated to validate the results and analyze simulation uncertainties arising from different precipitation products. New Hydrological Insights The fusion algorithm notably enhances precipitation data accuracy, with precipitation products (PP) fused by XGBoost (XGBoost-PP) and random forest (RF-PP) showing superior performance, as indicated by improved Probability of Detection (POD), Critical Success Index (CSI), and reduced False Alarm Rate (FAR). MSFPPs effectively improve the accuracy of runoff modelling during the calibration period (NSE>0.70, KGE>0.78) and help reduce uncertainties in simulations of other variables. Precipitation contributes most to uncertainty in runoff simulation, with higher contribution rates in summer and autumn. In response to precipitation errors, terrestrial water storage (TWS) and soil moisture (SM) exhibit high sensitivity, while runoff and evapotranspiration (E...