High-Resolution precipitation datasets in South America and West Africa based on Satellite-Derived Rainfall, enhanced vegetation index and digital elevation model
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Abstract
Mean Annual Precipitation is one of the most important variables used in water
resource management. However, quantifying Mean Annual Precipitation at high spatial
resolution, needed for advanced hydrological analysis, is challenging in developing
countries which often present a sparse gauge network and a highly variable climate. In this
work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial
resolution using different precipitation products from satellite estimates and gauge
observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of
this methodology are given for South America and West Africa. We develop a
downscaling method that exploits the relationship among satellite-derived rainfall, Digital
Elevation Model and Enhanced Vegetation Index. Finally, we validate its performance
using rain gauge measurements: comparable annual precipitation estimates for both South
America and West Africa are retrieved. Validation indicates that high resolution Mean
Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared
Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the
best ensemble of performance statistics for both South America and West Africa. Results also highlight the potential of the presented technique to downscale satellite-derived
rainfall worldwide.
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