Cross-validation methods for multi-source precipitation datasets over the sparse-gauge region: applicability and uncertainty

Evaluating the accuracy of various precipitation datasets over ungauged or even sparse-gauge areas is a challenging task. Cross-validation methods can evaluate three or more datasets based on the error independence from input data, without relying on ground reference. Here, the triple collocation (TC) method is employed to evaluate multi-source precipitation datasets: gauge-based CGDPA, model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP-NRT, and GSMaP-MVK over the Tibetan Plateau (TP). TC-based results show that ERA5 has better performances than satellite-only precipitation products over mountainous regions with complex terrains. For purely satellite-derived products, IMERG products outperform GSMaP products. Considering the potential existence of error dependency among input datasets, caution should be exercised. Thus, it is necessary to introduce an alternative cross-validation method (generalized Three-Cornered Hat) and explore the applicability of cross-validation from the perspective of error independence. We found that cross-validation methods have high applicability in most TP regions with sparse-gauge density (accounting for about 80.1% of the total area). Additionally, we conducted simulation experiments to discuss the applicability and robustness of TC. The simulation results substantiated that cross-validation can serve as a robust evaluation method over sparse-gauge regions. Although it is generally known that the cross-validation methods can be served in sparse-gauge regions, the application condition of different evaluation methods for precipitation products is identified quantitatively in TP now.