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.
Highlights
Quantifying the spatial distribution of annual rainfall is crucial for water resource management in developing countries such as those of South America and West Africa
The downscaling method presented below implements the procedure in which, once we have chosen the country and the rainfall dataset: (1) a Geographically Weighted Regression (GWR) is performed among the satellite-derived MAP, DEM and average annual Enhanced Vegetation Index (EVI) at coarse spatial resolution, (2) the residual of the regression—i.e., the amount of satellite-derived precipitation that could not be explained by the model- is interpolated at 1 km resolution and (3) the GWR is applied to DEM and average annual EVI at the original resolution of 1 km, and the high-resolution residual is added
As pointed out in Chen et al [15], even if we find a statistically significant relationship between annual precipitation, DEM and EVI, if we apply uniformly this relationship over the whole country, we will have a large amount of variance that is not explained by the regression
Summary
Quantifying the spatial distribution of annual rainfall is crucial for water resource management in developing countries such as those of South America and West Africa. Precipitation regulates hydrological and agricultural process, and an accurate estimate of the Mean Annual Precipitation (hereafter MAP or annual precipitation) is necessary to different disciplines including, among others, hydrological modeling, agricultural resources management, ecology and meteorology. Beside its importance for an improved knowledge of the water cycle, annual precipitation is crucial for mitigation strategies of natural hazards and Disaster Risk Reduction: the work by Hosking et al [1] on L-moments and regional frequency analysis identified statistically significant relationship between rainfall extremes and annual precipitation (for further reference see Schaefer [2]). Especially in developing countries, are poorly instrumented and it becomes difficult to generate maps of annual precipitation using only data from rain-gauge stations.
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