Main content area

A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics

Xu, Shiguang, Wu, Chaoyang, Wang, Li, Gonsamo, Alemu, Shen, Yan, Niu, Zheng
Remote sensing of environment 2015 v.162 pp. 119-140
algorithms, atmospheric precipitation, data collection, digital elevation models, mountains, normalized difference vegetation index, rain gauges, regression analysis, remote sensing, satellites, spatial variation, topography, vegetation, China
Remote sensing is an important tool to monitor precipitation over regions with sparse rain gauge networks. To provide high-resolution precipitation estimates over un-gauged areas, great efforts have been taken to downscale low-resolution satellite precipitation datasets using the Normalized Difference Vegetation Index (NDVI) and the Digital Elevation Model (DEM) based on the assumption that precipitation can be simulated by vegetation and topography proxies at various spatial scales. However, the non-stationarity of the relationship between precipitation and vegetation or topography has not been appropriately considered when low-resolution satellite precipitation datasets are downscaled using NDVI and DEM in previous studies. To overcome this limitation, a new downscaling algorithm was proposed in this study by introducing a regional regression model termed as geographically weighted regression (GWR) to explore the spatial heterogeneity of the precipitation–NDVI and precipitation–DEM relationships. The performance of this new downscaling algorithm was assessed by downscaling the latest version of monthly TRMM precipitation datasets (referred to TRMM 3B43 V7) over the eastern Tibetan Plateau and the TianShan Mountains from 0.25° (about 25km) to 1km spatial resolution, and the downscaled precipitation datasets were validated against ground observations measured by rain gauges. The validation results indicate that the high-resolution precipitation datasets obtained through the new algorithm not only performed better than the traditional downscaling algorithms, but also had higher accuracy than the original TRMM 3B43 V7 dataset. Besides, we found that the performance of this new algorithm was largely dependent on the accuracy of the original TRMM 3B43 V7 data. We therefore recommend considering the non-stationarity of the precipitation–NDVI and precipitation–DEM relationships in the downscaling process, and demonstrate the possibility of downscaling satellite precipitation with NDVI and DEM at monthly temporal scale.