A spatiotemporal geostatistical downscaling-integration framework of rain gauge and satellite-based precipitation datasets in the Pearl River basin, China

High-resolution and -precision precipitation datasets are important for hydrological modeling, drought and flood monitoring, and water resource management. Remote sensing provides spatially continuous precipitation datasets for wide areas; however, their use in hydrology studies of local regions and watersheds has been limited because of the associated coarse resolution and low precision. Based on the geostatistical theory, a spatiotemporal downscaling-integration framework of rain gauge and satellite-based precipitation datasets has been proposed in this study. First, the area-to-point kriging (ATPK) interpolation with the scale effect considered is used to downscale the Tropical Rainfall Measuring Mission (TRMM) product. The comparison experiment between the downscaled TRMM precipitation estimates and the rain gauge observations shows that the R2 is 0.854, and the mean error (ME) and root mean square error (RMSE) are 5.44 mm and 50.11 mm, respectively. The accuracy of the downscaled TRMM precipitation estimates is slightly higher than the original TRMM data. The downscaling process causes no significant estimation error, while preserving the spatial pattern of the original TRMM precipitation data. Then, a spatiotemporal regression kriging (STRK) model is constructed for estimation and generation of monthly precipitation datasets at a spatial resolution of 1 km for the Pearl River Basin from 2001–2013, by integrating rain gauge data, downscaled TRMM precipitation results and multi-source raster datasets of auxiliary variables such as DEM and NDVI. The cross-validation results show that R2 reaches 0.872, and the ME and RMSE are -4.13 mm and 46.64 mm, respectively. The STRK model is found to enable exploitation of the large-scale coverage of remote sensing products and the high-precision characteristics of rain gauge observations. These findings can facilitate the characterization of the spatiotemporal pattern of precipitation and improve small-scale hydrological modeling.