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Understanding the spatial and temporal distribution of precipitation is important in agriculture, water management resources, and flood disaster management. The present study analyzed the changes in rainfall concentration over East Africa (EA). Three matrices—the precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP)—were used to examine the changes in rainfall during 1981–2021. The changes in spatial variance annually and during two seasons, namely, "long rains" (March to May [MAM]) and "short rain" (October to December [OND]), were estimated using an empirical orthogonal function (EOF). The study employed the robust statistical metrics of the Theil–Sen estimator to detect the magnitude of change and modified Mann–Kendall (MMK) to examine possible changes in rainfall concentration. The localized variation of the power series within the series for PCI, PCD, and PCP variability was performed using the continuous wavelet transform. The findings showed that the concentration of rainfall patterns of EA occurred in four months of the total months in a year over most parts, with the western sides experiencing uniform rainfall events throughout the year. The EOF analysis revealed a homogeneous negative pattern during the MAM season over the whole region for PCD, PCI, and PCP for the first mode, which signified reduced rainfall events. Moreover, the MMK analysis showed evidence of declining trends in the PCD annually and during the MAM season, while the opposite tendency was noted for the OND season where an upward trend in the PCD was observed. Interestingly, areas adjacent to Lake Victoria in Uganda and Lake Tanganyika in Tanzania showed increasing trends in the PCD for annual and seasonal time scales. The analysis to characterize the rainfall cycle and possible return period, considering the indices of PCD, PCI, and PCP, showed higher variability during the year 2000, while much variability was presented in the PCP for the annual period. During the MAM and OND seasons, a 1-year band as a dominant period of variability was observed in all the indices. Overall, the findings of the present study are crucial in detecting the observed changes in rainfall concentration for avoiding the loss of life and property, as well as for coping with potential changes in water resources. [ABSTRACT FROM AUTHOR]

Maize crop (Zea mays) is one of the staple foods in the East African (EA) region. However, the suitability of its production area is threatened by projected climate change. The Multimodel Ensemble (MME) from eight Coupled Model Intercomparison Project 5 (CMIP5) models was used in this paper to show climate change between the recent past (1970–2000) and the future (2041–2060), i.e., the mid-twenty-first century. The climatic suitability of maize crop production areas is evaluated based on these climate datasets and the current maize crop presence points using Maximum entropy models (MaxEnt). The MME projection showed a slight increase in precipitation under both RCP4.5 and RCP8.5 in certain places and a reduction in most of southern Tanzania. The temperature projection showed that the minimum temperature would increase by 0.3 to 2.95 °C and 0.3 to 3.2 °C under RCP4.5 and 8.5, respectively. Moreover, the maximum temperature would increase by 1.0 to 3.0 °C and 1.2 to 3.6 °C under RCP4.5 and 8.5 respectively. The impacts of these projected changes in climate on maize production areas are the reduction in the suitability of the crop, especially around central and western Tanzania, mid-northern and western Uganda, and parts of western Kenya by 20–40%, and patches of EA will experience a reduction of as high as 40–60%, especially in northern Uganda, and western Kenya. The projected changes in temperature and precipitation present a significant negative change in maize crop suitability. Thus, food security and the efforts towards the elimination of hunger in EA by the mid-twenty-first century will be hampered significantly. We recommend crop diversification to suit the new future environments, modernizing maize farming programs through the adoption of new technologies including irrigation, and climate-smart agricultural practices, etc. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The economy of Uganda depends heavily on rainfed agriculture. In this study, daily observed rainfall datasets from 9 weather stations with length varying within 1955 and 2017 were used to generate the probability of rainfall and dry spells occurrence using a Markov chain approach. The length of the maximum dry spell was obtained using the direct method based on the definition of a dry day that rainfall is less than 0.85 mm (R < 0.85 mm) and the length of a dry spell is the sum of the number of dry days in a sequence. Mann–Kendall's statistics (MK) was used to assess the trends in the length of maximum dry spells and Sen's slope test to estimate the magnitude of change (Q2) in days/per month. MK test results show increasing trends in the length of the maximum dry spells in March at 5 stations, while an insignificant decrease in the length of maximum dry spells is revealed for remaining stations. For the month of April and May, the length of a maximum dry spell is observed to be decreasing across most stations although not statistically significant at the 5% significance level during their respective study periods. The probability of 8 days dry spell is high across all the stations (38–69%) in March, April, and August. This could strongly be related to the changing climate in the region. Negative impacts due to increased length of dry spells could be mitigated through well-timed planting of crops, use of irrigation, and growing of heat-/drought-tolerant crop varieties to match the changing weather and climate patterns. [ABSTRACT FROM AUTHOR]

Uganda in East Africa is experiencing highly variable rainfall which is exacerbated by temperatures warming at faster rates. This study analyzed rainfall and temperature patterns in comparison with the potential evaporation transpiration rates (PETs) for parts of Central, Western, Southern, and Southwestern Uganda for varying periods from 1981 to 2022. For rainfall onset date (OD), threshold of 0.85 mm for a rainy day, rainfall of 20 mm accumulated over 5 days with at least 3 rain days, and dry spell not exceeding 9 days in the next 30 days were used. The rainfall cessation dates (RCDs) are determined when water balance (WB) falls below 5 mm in 7 days in the last month of the expected season (May and December) for the first and second season, respectively. Standardized rainfall anomaly was utilized to show seasonal and annual rainfall variability. Pearson's correlation (r) coefficient was used to show the relationship between weather variables (rainfall, temperature) and PET at five rainfall stations. Results showed highly varied onset and cessation dates for March-May (MAM) seasonal rainfall compared to those of September-December (SOND). Results showed highly variable onset and cessation of rainfall over the region and statistically significantly increasing trends in both maximum and minimum temperatures across the region, with the highest rate of increase of maximum and minimum temperature of 0.70 and 0.65 °C per decade respectively. Moreover, the maximum temperature and PET showed strong positive correlation coefficient (r) that ranged from 0.76 to 0.90 across the regions, which likely contribute to excess evaporation from the surfaces, soil moisture deficits that negatively affect plant biomass production, low crop yields and food insecurity. PET and rainfall revealed insignificant statistical negative correlation as indicated by the correlation coefficient ranging from - 0.04 to - 0.22. We recommend water management and conservation practices such as mulching, zero tillage, agroforestry, planting drought-resistant crops, and using affordable irrigation systems during period of water deficit., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)