Bayesian Model Averaging for Satellite Precipitation Data Fusion: From Accuracy Estimation to Runoff Simulation

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1154 - 1154

Published: March 25, 2025

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood drought risk prediction. This study proposes Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The enhances estimation accuracy for simulations. BMA synthesizes four products—Climate Hazards Group Infrared with Station (CHIRPS), fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of (GSMaP), Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 2020. We evaluated merged dataset’s performance against its constituent datasets Multi-Source Weighted-Ensemble (MSWEP) at daily, monthly, seasonal scales. Evaluation metrics included correlation coefficient (CC), root mean square error (RMSE), Kling–Gupta efficiency (KGE). Variable Infiltration Capacity (VIC) model was further applied assess how these affect runoff results indicate that BMA-merged dataset substantially improves when compared individual inputs. product achieved optimal daily (CC = 0.72, KGE 0.70) showed superior skill, notably reducing biases autumn winter. In applications, BMA-driven VIC effectively replicated observed patterns, demonstrating efficacy regional long-term predictions. highlights BMA’s potential optimizing inputs, providing critical insights sustainable reduction complex basins.

Language: Английский

Does ERA5-Land Effectively Capture Extreme Precipitation in the Yellow River Basin?

Atmosphere, Journal Year: 2024, Volume and Issue: 15(10), P. 1254 - 1254

Published: Oct. 21, 2024

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable for various applications. Using daily precipitation from 301 meteorological stations in the Yellow River Basin 2001 to 2013 as benchmark data, this study aims evaluate ERA5-Land’s capability of monitoring extreme precipitation. The evaluation conducted three perspectives: amount, indices, and characteristics events. results show can effectively capture spatial distribution patterns temporal trends precipitation; however, it also exhibits significant overestimation underestimation errors. significantly overestimates total indices heavy (R95pTOT R99pTOT), with errors reaching up 89%, but underestimates Simple Daily Intensity Index (SDII). tends overestimate duration events slightly average these These findings provide scientific reference optimizing algorithm users selecting data.

Language: Английский

Evaluation of precipitation products for small karst catchment hydrological modeling in data-scarce mountainous regions

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 645, P. 132131 - 132131

Published: Oct. 10, 2024

Language: Английский