Multi-source precipitation fusion method based on machine learning and Bayesian model averaging

Abstract

[Objective] Precipitation input errors are the major source of flood forecasting errors, and integrating multi-source precipitation information is an important approach to reduce such errors. Satellite precipitation products with high spatiotemporal resolution can better capture the spatiotemporal distribution of precipitation events, but their application is limited due to large point estimation errors. A multi-source precipitation fusion method based on machine learning and Bayesian model averaging(BMA) is proposed to improve the accuracy of precipitation data at spatiotemporal scales. [Methods] Firstly, a bilinear interpolation method was used to perform spatial downscaling of three satellite precipitation products(GSMaP, IMERG, and PERSIANN). Then, the light gradient boosting machine(LGBM) algorithm was utilized for precipitation bias correction. Finally, the corrected precipitation products were fused based on the seasonal-scale BMA to obtain precipitation data with higher accuracy. [Results] The river basin upstream of Xiashan station in the upper reaches of Ganjiang River was selected for case validation. The result showed that:(1) the fused precipitation was significantly better than the original satellite products in all six evaluation indicators(with a root mean square error of 4.73 mm, a correlation coefficient of 0.92, and a false alarm ratio of 0.28).(2) Compared with the original satellite products, the spatial accuracy of the fused precipitation data was significantly improved and showed better consistency with ground observation stations.(3) The root mean square error values of both the single-satellite corrected precipitation and the multi-satellite fused precipitation were significantly lower than those of the original satellite products under five precipitation magnitudes: light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm. [Conclusion] Overall, the multi-satellite fused precipitation data integrates the advantages of each corrected precipitation product and performs well across different precipitation magnitudes, providing accurate precipitation input data support for hydrological simulation and forecasting.

Keywords

multi-source precipitation fusion / machine learning / Bayesian model averaging / Ganjiang River Basin / influencing factors