Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda

摘要

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.