Intercomparison of gridded global precipitation data for arid and mountainous regions: A case study of Afghanistan

Abstract

Study region: Afghanistan Study focus: Thirteen global gridded precipitation datasets (GGPDs) were evaluated against the historical records, based on the statistical indices and Scored-based system in four temporal and three spatial scales. The evaluation results reveal that across the country, the CFSR, PERSIANN, IMERG, PERSIANN_CCS, and GPCC datasets consistently demonstrate strong performance. The present study addresses the challenge of precipitation data scarcity by investigating reliable alternative data for diverse research and applications in Afghanistan. New hydrological insights for the region: GGPDs are reliable sources for water-related studies in regions with sparse and limited ground observations. This study, compared 13 GGPDs against 75 observation stations across Afghanistan between 2009 and 2020. Statistical analyses at daily, monthly, and seasonal resolutions assessed GGPDs using Pearson's correlation coefficient, root-mean-square error, and bias. To unify these statistical measures, a weighted scoring system was employed. Quantitative spatial assessment of GGPDs against observations considered factors such as river basins, climate zones, and elevations. Precipitation from GGPDs over same coordinates as the stations, were further analysed. Spatial data were generated from observations using inverse distance weighting. Discrepancies between observed and GGPDs were measured at seasonal and annual time scales. Analysis revealed mean annual precipitation values in observations ranging from 70 to 502 mm. Regarding mean annual, PERSIANN_CCS, GPCC, TRMM, and CFSR tended to overestimate, while the PERSIANN and CMORPH-v1 consistently underestimated precipitation across the country. CFSR and PERSIANN demonstrated higher reliability, averaging scores of 72 and 68, respectively. In contrast, GPCP and CMORPH-v1 displayed lower reliability, scoring averages of 56 and 57. The study area covers mountainous region of the world with complicated hydrology. The findings can provide a deeper insight into the performance of the GGPDs over a region of the world where less attention has been paid by the scholars. These findings guide researchers in selecting suitable GGPDs for specific applications, effectively mitigating data scarcity concerns through enhanced temporal and spatial coverage, especially in regions with limited observational data.

Keywords

• Precipitation
• Reanalysis
• Satellite-driven precipitation
• Semi-arid climate
• Gridded precipitation dataset
• Data scarcity