A two-step machine learning framework for incorporating spatial information into multi-source precipitation merging over high-alpine regions

Accurate precipitation estimation is of vital importance for hydrological simulation and water resources management. However, large uncertainties existed in  precipitation datasets in high-alpine regions due to the scare gauged observations and complex terrains. Data fusion technologies are widely applied to integrate advantages of multi-source precipitation datasets, but the spatial information of precipitation is usually negelected. To overcome this limitation, this study developed a two-step machine learning framework for merging multi-source precipitation datasets based on the 2D convolutional neural network (CNN) incorporating Neighboring spatial information, hereafter referred to as nCNN. The framework employs a hybrid classification-regression model to merge three gridded precipitation products (i.e., ERA5-Land, TPReanalysis and GPM) and gauged observations over a high alpine watershed in China during the period 2001-2019. Two merged precipitation datasets were generated by CNN and the proposed nCNN framework, respectively. The results show that the proposed framework effectively integrates the advantages of multiple datasets. The CNN and nCNN merged precipitation datasets have similar spatial distribution with the original products but differ in precipitation amounts. Precipitation amounts of merged data are much closer to gauged observations than original precipitation products. Both merged datasets outperform original products in terms of statistical and categorical indices evaluated based on 25 independently meteorological stations with complete time period (covering 2001-2019). However, the nCNN merged dataset exhibits superior performance over the CNN merged dataset in capturing precipitation amounts and detecting precipitation event, especially for moderate (5~10 mm/d) and heavy precipitation (>10 mm/d). Compared with the CNN merged result, the nCNN framework reduces the station-averaged root mean square error (RMSE) from 4.25 mm/d to 3.74 mm/d for moderate precipitation and from 9.43 mm/d to 8.57 mm/d for heavy precipitation, while increasing the station-averaged critical success index (CSI) by 0.03 and 0.04, respectively. Overall, this study highlights the importance of incorporating spatial information in precipitation merging, especially for high-alpine regions.