Constructing a high-precision precipitation dataset on the data-limited Qinghai-Tibet Plateau

Abstract

The primary constraint hindering a comprehensive understanding of the atmospheric, hydrological, and ecological dynamics on the Qinghai-Tibet Plateau (QTP) is the absence of highly accurate precipitation datasets. Given the limited availability of climate observations and the pronounced spatial disparities in this region, traditional interpolation techniques are ill-suited, necessitating the development of novel methods. In this study, we formulated a random forest model leveraging precipitation data from 209 climate stations and multisource satellite datasets spanning the period from 1960 to 2020. These datasets encompassed low spatial resolution TRMM precipitation products and high spatial resolution NDVI data. This model facilitated the creation of a monthly precipitation dataset at a 1 km resolution for the QTP. Our findings showed that (1) the prevailing precipitation datasets for the QTP exhibited notable deficiencies, characterized by low spatial precision (R² = 0.592, p = 0.015) and incongruent temporal trends when compared to observational data (NSE = 0.398); (2) by incorporating multisource satellite data, we achieved a substantial enhancement in precipitation accuracy. For instance, the introduction of NDVI led to a reduction in precipitation estimation errors by 7.9 mm and 15.4 mm in desert and nondesert regions, respectively. The inclusion of TRMM satellite precipitation data not only improved accuracy in areas with limited climate stations by more than 16 % but also reduced cumulative monthly errors by 46.8 mm, and (3) the precipitation dataset generated through this method displayed a 24.8 % increase in accuracy (R² = 0.84) compared to existing datasets for the QTP, aligning well with actual precipitation trends (NSE = 0.806). This study offers an effective approach for acquiring high-precision precipitation datasets in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns. Furthermore, our method significantly enhances the spatial heterogeneity of precipitation products, potentially serving as a model for the development of datasets in data-scarce areas.