Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Investigation on Spatiotemporal Variability of Drought Events under Different Climate Change Scenarios, Case Study: Rakai District

Version 1 : Received: 23 August 2021 / Approved: 25 August 2021 / Online: 25 August 2021 (10:45:01 CEST)

How to cite: Wasswa, P.; Tanner, J.; Sabiiti, G.; Ojara, M.; Okal, H.; Kato, P.; Namulindwa, H. An Investigation on Spatiotemporal Variability of Drought Events under Different Climate Change Scenarios, Case Study: Rakai District. Preprints 2021, 2021080483 (doi: 10.20944/preprints202108.0483.v1). Wasswa, P.; Tanner, J.; Sabiiti, G.; Ojara, M.; Okal, H.; Kato, P.; Namulindwa, H. An Investigation on Spatiotemporal Variability of Drought Events under Different Climate Change Scenarios, Case Study: Rakai District. Preprints 2021, 2021080483 (doi: 10.20944/preprints202108.0483.v1).

Abstract

Drought occurrences in Rakai district take a strange model and it has been rampantly increasing causing reduced income levels for farmers, reduced farm yields, increased food insecurity and migration, wetland degradation, illness and loss of livestock. The purpose of this study was to investigate past and future characteristics of drought due to climate change in Rakai district. Datasets used include dynamically downscaled daily precipitation and temperature data from Coordinated Regional Climate Downscaling Experiment (CORDEX) at 0.44°×0.44° resolution over the Africa domain. R software (Climpact2 package), was used to generate SPI values, Mann Kendall trend test and Inverse Distance Weighting methods were used to examine temporal and spatial drought characteristics respectively. Results depicted more extreme and severe drought conditions for SPI12 under historical compared to SPI3,Kakuto, Kibanda and Lwanda sub counties were the most drought hot spot areas, positive trends of drought patterns for both time scales were observed, though only significant under SPI12. Projected results revealed extreme and severe drought conditions will be observed under RCP8.5 SPI12, and the least will be under RCP8.5 SPI3 and SPI12. Results further reveal that Kakuto, Kibanda, Kiziba, Kacheera, Kyalulangira, Ddwaniro and Lwanda sub counties will be the most drought hot spot sub counties across all time scales. Generally projected results reveals that the district will experience more drought conditions under RCP8.5 compared to RCP4.5 for time scale SPI12 and therefore urgent actions are needed.

Keywords

Spatio-temporal; Drought; Climate Change; SPI; RCP; Rakai

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