Comparing and contrasting the performance of high-resolution precipitation products via error decomposition and triple collocation: An application to different climate classes of the central Iran

Abstract

Precipitation is a vital pillar in the most of hydro-climatological studies. To measure its stochastic behavior, recent technological advancements have provided new sources of High-Resolution Precipitation Products (HRPPs), which could be utilized to overcome limitations of the ground measurements. However, accuracy of HRPPs is not the same in different regions and climates and therefore, should be assessed prior to any practical application. In this study, monthly datasets of ten HRPPs, known as CHIRPS, CMORPH, ERA5-Land, GPM_3IMERGM, MSWEP V2, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, TerraClimate, and TRMM_3B43, are assessed over Central Plateau watershed located in central Iran during 2005 to 2015. Lack of previous studies as well as remarkable variations in altitude and climate of this watershed, make it a suitable region for studying spatiotemporal pattern of precipitation and evaluating HRPPs. For this purpose, two approaches are implemented; comparing the products with ground measurements and together using Triple Collocation (TC). Through the first approach, an error decomposition scheme is utilized besides the other statistical metrics to further investigate total and seasonal accuracy of the HRPPs; Köppen-Geiger climate classification indicators are used to assess climate-based spatial performance of the HRPPs. According to the results, most of the HRPPs underestimate and overestimate precipitation values in wetter and drier climates, respectively. Additionally, winter contributes more than any other season to the biases of the products. While GPM_3IMERGM is the most accurate HRPP in the region with NRMSE, NSE, and KGE of 0.95, 0.62, and 0.73, respectively, PERSIANN-CCS results in the lowest accuracy with NRMSE, NSE, and KGE of 2.09, −0.82, and − 0.02, respectively.