%0 Journal Article
%T Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda
%A Ivan Bamweyana
%A Moses Musinguzi
%A Lydia Mazzi Kayondo
%J Atmospheric and Climate Sciences
%P 797-811
%@ 2160-0422
%D 2021
%I Scientific Research Publishing
%R 10.4236/acs.2021.114046
%X <p>
	<span style=\"font-family:;\" \"=\"\">The Ugandan economy is largely dependent on rural-based and rain-fed
agriculture. This creates a critical need to understand the rainfall dynamics
at the local scale. However, the country has a sternly sparse and unreliable
rain gauge network. This research, therefore, set</span><span style=\"font-family:;\" \"=\"\">s</span><span style=\"font-family:;\" \"=\"\"> out to
evaluate the use of </span><span style=\"font-family:;\" \"=\"\">the </span><span style=\"font-family:;\" \"=\"\">CHIRPS satellite gridded dataset as an alternative rainfall estimate for
local modelling of rainfall in Uganda. Complete, continuous and reliable <i>in</i></span><i><span style=\"font-family:;\" \"=\"\"> </span></i><i><span style=\"font-family:;\" \"=\"\">situ</span></i><span style=\"font-family:;\" \"=\"\"> station
observations for the period between 2012 and 2020 were used for the comparison
with CHIRPS satellite data models in the same epoch. Rainfall values within the
minimum 5 km and maximum 20 km radii</span><span style=\"font-family:;\" \"=\"\"> </span><span style=\"font-family:;\" \"=\"\">from the <i>in</i></span><i><span style=\"font-family:;\" \"=\"\"> </span></i><i><span style=\"font-family:;\" \"=\"\">situ</span></i><span style=\"font-family:;\" \"=\"\"> stations
were extracted at a 5 km interval from the interpolated <i>in</i></span><i><span style=\"font-family:;\" \"=\"\"> </span></i><i><span style=\"font-family:;\" \"=\"\">situ</span></i><span style=\"font-family:;\" \"=\"\"> station
surface and the CHIRPS satellite data model for comparison. Results of the 5 km
radius were adopted for the evaluation as it</span><span style=\"font-family:;\" \"=\"\">กฏ</span><span style=\"font-family:;\" \"=\"\">s closer
to the optimal rain gauge coverage of 25 km<sup>2</sup>. They show the R<sup>2</sup> = 0.91, NSE = 0.88, PBias = <span style=\"white-space:nowrap;\"><span style=\"white-space:nowrap;\">-</span></span>0.24 and RSR = 0.35. This attests that the CHIRPS
satellite gridded datasets provide a good approximation and simulation of <i>in</i></span><i><span style=\"font-family:;\" \"=\"\"> </span></i><i><span style=\"font-family:;\" \"=\"\">situ</span></i><span style=\"font-family:;\" \"=\"\"> station
data with high collinearity and minimum deviation. This tallies with related
studies in other regions that have found CHIRPS datasets superior to
interpolation surfaces and sparse rain gauge data in the comprehensive
estimation of rainfall. With a 0.05<span style=\"white-space:nowrap;\">กใ</span> * 0.05<span
%K Spatial Statistics
%K CHIRPS
%K Satellite Gridded Dataset
%K Rainfall Estimates
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=112910