Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

Abstract

Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km 2 ) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions.

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

“…Therefore, the impacts of NDVI datasets on the downscaling results included two aspects. First, a higher NDVI did not always represent more precipitation in humid zones because of saturated NDVI [75,78]. This saturation effect influences the positive relationship between NDVI and precipitation, which may lead to some errors in the downscaled precipitation datasets.…”

A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature

“…Chen et al [16] and Xu et al [17] constructed a geographically weighted regression model based on the assumption that the rainfall-geospatial factors relationship varies spatially but is similar in a region. Shi et al [18] proposed a downscaling algorithm by introducing a machine learning algorithm known as Random Forests (RF) for detecting the complex precipitation-NDVI and precipitation-DEM relationships. Their validation results indicated that the Random Forests-based downscaling model outperformed compared to the linear regression and the exponential regression models.…”

“…The purpose of this study is to obtain annual total precipitation maps with fine spatial resolution from coarse resolution satellite-based precipitation datasets, for which we proposed a spatial downscaling method based on the researches of Immerzeel et al, Jia et al, and Shi et al [14,15,18]. In this study, we introduced land surface temperature as a factor for enhancing the precipitation-land surface characteristics relationships when downscaling annual total precipitation data.…”

A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

“…There is one issue in this study that needs to be noted: we used a simple spline tension interpolator to interpolate the residual at coarse resolution to 1 km resolution. According to the results of previous downscaling algorithm studies, the residual of the models represents the precipitation that cannot be estimated by the models, and the spline tension interpolator [68] has been used widely in previous downscaling models to acquire interpolated residuals [6,22,23,25]. Additionally, the residual correction significantly improved the accuracy of the downscaled results by SVM.…”

“…Chen et al and Xu et al constructed a geographically weighted regression model based on the assumption that the rainfall-geospatial factors relationship varies spatially but is similar within a region [23,24]. Shi et al [25] developed a downscaling algorithm by introducing a machine learning algorithm termed Random Forests (RF) to detect the complex precipitation-NDVI and precipitation-DEM relationships, and their validation results indicated that the RF-based downscaling model outperformed the linear regression and exponential regression models.…”