Evaluation of satellite precipitation products over Mexico using Google Earth Engine. Issue 4 (18th May 2022)

• Record Type: Journal Article
• Authors: Rincón-Avalos, Pedro; Khouakhi, Abdou; Mendoza-Cano, Oliver; Cruz, Jesús López-De la; Paredes-Bonilla, Karla Michelle
• Is Part Of: Journal of hydroinformatics. Volume 24:Issue 4(2022)
• Page Start: 711
• Page End: 729
• Publication Date: 2022-05-18
• Subjects: Google Earth Engine -- hydrometeorological -- Mexico -- rainfall; Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2022.122
• Languages: English
• ISSNs: 1464-7141

Abstract

Abstract: Satellite-based precipitation products and reanalysis precipitation products have the potential to overcome the lack of information in regions where there are no or insufficient rain gauges to achieve any hydrological study. The Google Earth Engine (GEE) data analysis platform has products in its repository with global coverage that offers different geospatial information capable of measuring the amount of precipitation. However, it is necessary to evaluate the reliability of the products. There are precipitation information biases in Mexico due to the scarce presence of gauging stations, failed operations, access difficulty, and data capture errors. This study evaluates the reliability of satellite and reanalysis precipitation products hosted in the GEE repository against rain gauge observation from 2001 to 2017 using data from 4, 658 stations over Mexico. The evaluation was carried out using statistical indicators comparing the behavior across different topographic, climatic, and temporal conditions. The results exhibit that the performance of the products hosted in GEE seems to depend on elevation conditions for other climatic regions in Mexico. The results show that all products can capture the general precipitation patterns at annual, seasonal, and monthly scales; however, the accuracy of the product is clearly lower at a daily scale. All products are highly biased on low precipitation events. HIGHLIGHTS: The intensity and distribution of precipitation in Mexico depends on topography and climatic regions. ERA5 product in Mexico showed the lowest correlations of the entire region, while CHIRPS is the product with the best score. There is a more significant correlation of products in the regions and seasons with the highest presence of rainfall. Graphical Abstract