Bayesian Model Averaging for Satellite Precipitation Data Fusion: From Accuracy Estimation to Runoff Simulation DOI Creative Commons
Shaowei Ning, Cheng Yang, Yuliang Zhou

et al.

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

Bayesian Model Averaging for Satellite Precipitation Data Fusion: From Accuracy Estimation to Runoff Simulation DOI Creative Commons
Shaowei Ning, Cheng Yang, Yuliang Zhou

et al.

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

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