Abstract
Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available from a multitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blended multiple source datasets in multi-temporal scales in order to develop a suitable method for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that the Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical Rainfall Measuring Mission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99th percentile of precipitation (R99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R5dmax). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future.
Highlights
Precipitation serves as a crucial factor used by decision makers who are tasked with water allocation as well as in understanding hydrological processes, atmospheric research, and hazard prevention [1,2,3,4]
In a complex terrain, such as the Qinghai-Tibet Plateau, knowing which precipitation datasets can be suitably applied in hydrometeorology and understanding the effects of climatic change are distinct challenges
We evaluated the daily precipitation estimates from 12 precipitation datasets from global/regional reanalysis, satellite-retrieved precipitation, and multiple sources of blended precipitation in 2003–2010
Summary
Precipitation serves as a crucial factor used by decision makers who are tasked with water allocation as well as in understanding hydrological processes, atmospheric research, and hazard prevention [1,2,3,4]. The main objective in this study is to provide further insight into evaluating the reliability of SRP, high-resolution regional reanalysis datasets, and MSBP in basins of the Qinghai-Tibet Plateau on different temporal scales, and to compare their strengths and weaknesses
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