An improved statistical downscaling scheme of Tropical Rainfall Measuring Mission precipitation in the Heihe River basin, China. (26th March 2018)

• Record Type: Journal Article
• Authors: Zhao, Na; Yue, Tianxiang; Chen, Chuanfa; Zhao, Mingwei; Fan, Zemeng
• Is Part Of: International journal of climatology. Volume 38:Number 8(2018)
• Page Start: 3309
• Page End: 3322
• Publication Date: 2018-03-26
• Subjects: downscaling -- Heihe River basin -- precipitation -- TRMM; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5502
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract : 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 1 km 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. Abstract : The GWR method with the residual interpolations by Kriging, IDW, and tension Spline did not improve the accuracy of the final downscaled results. The introduction of the secondary factors that influence the local precipitation is necessary for the residual correction. The GWR‐HASM downscaling framework significantly improved the accuracy of and detailed information on the precipitation distribution. Flow chart is the statistical downscaling method used in this research.