TITLE:
Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda
AUTHORS:
Ivan Bamweyana, Moses Musinguzi, Lydia Mazzi Kayondo
KEYWORDS:
Spatial Statistics, CHIRPS, Satellite Gridded Dataset, Rainfall Estimates
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.11 No.4,
October
29,
2021
ABSTRACT: 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, sets out to
evaluate the use of the CHIRPS satellite gridded dataset as an alternative rainfall estimate for
local modelling of rainfall in Uganda. Complete, continuous and reliable in situ 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 from the in situ stations
were extracted at a 5 km interval from the interpolated in situ station
surface and the CHIRPS satellite data model for comparison. Results of the 5 km
radius were adopted for the evaluation as it’s closer
to the optimal rain gauge coverage of 25 km2. They show the R2 = 0.91, NSE = 0.88, PBias = -0.24 and RSR = 0.35. This attests that the CHIRPS
satellite gridded datasets provide a good approximation and simulation of in situ 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° * 0.05° (Latitude, longitude) spatial
resolution, CHIRPS satellite gridded rainfall estimates are therefore able to
provide a comprehensive rainfall estimation at a local scale. Essentially these
results reward research science in regions like Uganda that have sparse rain
gauges networks characterized by incomplete, inconsistent and unreliable data
with an empirically researched alternative source of rainfall estimation data.
It further provides a platform to scientifically interrogate the rainfall
dynamics at a local scale in order to infuse local policy with evidence-based
formulation and application.