Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

Abstract

As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals' correction was indispensable for the MLRbased algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.

An Improved Spatial–Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China’s Qilian Mountains

“…Second, study areas where water does not restrict plant growth, such as in the rainforest, NDVI is not suitable for application in the models. Third, previous downscaling research was based on the assumption that precipitation was significantly correlated with NDVI or/and DEM in various regions [9,10,15,[47][48][49][50][51]. It is widely acknowledged that the response of vegetation to precipitation and temperature usually lags by about two or three months in different regions and at high elevations in mountainous areas [52,53].…”

“…It is widely acknowledged that the response of vegetation to precipitation and temperature usually lags by about two or three months in different regions and at high elevations in mountainous areas [52,53]. Therefore, the response lags result in unreliable precipitation-PNDVI relationships at monthly scales, and the precipitation-NDVI relationships may be better than precipitation-PNDVI relationships [14,15,18,54]. Consequently, PNDVI is not suitable for downscaling studies at monthly scales because of the differences in plant-growth lag in different regions and different elevations.…”

“…Therefore, the geographically-weighted regression method and moving window regression [13,14] were proposed based on the assumption that non-stationary relationships between precipitation and environmental variables (NDVI and/or DEM) vary across space. Zhan et al [15] compared the accuracy of monthly downscaling results of multiple regression and geographically-weighted regression based on GPM, and the results show that geographically-weighted regression outperformed multiple regression models in the Hengduan Mountains. Recently, researchers have tried to construct the precipitation-variables relationships with different land surface characteristics as explanatory variables, examples are land surface temperature, longitude, and latitude [16][17][18].…”

Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application

“…Soil moisture is related to a number of factors, which include vegetation growth, crop growth, and food production, as well as important parameters in the fields of hydrology, climate research, agriculture, and ecology [6,7]. Consequently, soil moisture has been widely used in various environmental applications, such as hydrological modeling, land surface evapotranspiration simulation, When compared with the global regression model, GWR mainly introduces geographic location information into the regression model [35,36]. However, due to the limitations of the GWR algorithm, it is impossible to effectively screen local environmental variables with the closest soil moisture based on the spatial distribution; as a result, GWR is not applicable for obtaining multiple factors to downscale the soil moisture.…”

Spatial Downscaling of Tropical Rainfall Measuring Mission (TRMM) Annual and Monthly Precipitation Data over the Middle and Lower Reaches of the Yangtze River Basin, China

“…Generally, rainfall observation stations have a relatively higher observation accuracy at the site scale, but it is difficult to completely reproduce the spatiotemporal distribution of precipitation due to sparse or uneven spatial distribution of weather stations. In recent years, a series of satellite remote sensing precipitation products [5][6][7], such as the Global Satellite Mapping of Precipitation (GSMaP) project at 0.1 • × 0.1 • resolution [8], the Global Precipitation Climatology Project (GPCP) at 2.5 • × 2.5 • resolution [9,10], the Tropical Rainfall Measuring Mission (TRMM) [11][12][13], and the Global Precipitation Measurement (GPM) mission with 10 km × 10 km resolution [14], have provided new global and regional precipitation observations. Among them, TRMM data products offer a spatial resolution of 0.25 • × 0.25 • [11].…”