资源环境科技发展态势分析平台

Estimating an accurate spatial distribution of precipitation with high resolution is necessary for hydrological and ecological applications, especially in data-scarce and terrain-complicated river basins. Satellite-based precipitation data have been widely used to measure the spatial patterns of precipitation, but an improvement in accuracy and resolution is needed. In this article, a new statistical downscaling method is proposed to generate improved monthly precipitation fields at a higher spatial resolution of 1km in Heihe River basin (HRB), China. The presented methods employed the geographical weighted regression (GWR) method to explore the non-stationarity between precipitation and its factors, and used the high-accuracy surface modelling method (HASM) to compensate for the errors produced in the GWR downscaling process. The GWR model was first established under five different spatial scales, and the optimal relation between precipitation derived from the Tropical Rainfall Measuring Mission (TRMM) and its influencing factors was found for each month. The errors caused during the scale change were modified by performing HASM as a data merging framework, which considered both the local climate characteristics and meteorological observations. Results showed that the GWR downscaling method could not generate spatial patterns of precipitation similar to those of the original TRMM products. Although the performance of the GWR method after residual interpolations using Kriging, IDW, and tension Spline was improved, there existed significant variations in some regions, and the accuracy of those methods was still not satisfactory. In comparison with the other four models, GWR-HASM showed better performance in reproducing the precipitation field at a high spatial resolution. Results indicate that the proposed downscaling method appears feasible for precipitation estimation in data-scarce river basins.