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- W3130976973 abstract "The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020–2050. Historical analysis revealed bimodal annual rainfall patterns for March–May (MAM) and September–November (SON) gradients representing heavier to lighter rainfall events, respectively, over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann–Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall." @default.
- W3130976973 created "2021-03-01" @default.
- W3130976973 creator A5002014436 @default.
- W3130976973 creator A5047749907 @default.
- W3130976973 creator A5064338758 @default.
- W3130976973 creator A5064671845 @default.
- W3130976973 date "2021-02-22" @default.
- W3130976973 modified "2023-10-05" @default.
- W3130976973 title "Assessing current and future spatiotemporal precipitation variability and trends over Uganda, East Africa, based on CHIRPS and regional climate model datasets" @default.
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