GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 19; p. 3162
• Main Authors: Ullah, Sana, Zuo, Zhengkang, Zhang, Feizhou, Zheng, Jianghua, Huang, Shifeng, Lin, Yi, Iqbal, Imran, Sun, Yiyuan, Yang, Ming, Yan, Lei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Algorithms; Annual precipitation; ASTER (radiometer); Climate change; Datasets; Digital Elevation Models; downscaling; EDBF algorithm; Empirical analysis; geospatial predictor; GPM; Hydrology; Mathematical analysis; Methodology; Precipitation; Rain; Regression analysis; Remote sensing; Rivers; Root-mean-square errors; Spatial discrimination; spatial pattern; Spatial resolution; Thermal emission; Variables; weighted precipitation; China; Huai River
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12193162

To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.