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Characteristics of Extreme Rainfall Events over Uganda during September to November Rainfall Season

Journal of Geoscience and Environment Protection 2024年第3期12卷 131-152页

作者： Donnata Alupot Guirong Tan Kokou Romaric Posset peter natiko Key Laboratory of Meteorological Disaster Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science and Technology Nanjing China Climate Change Department Pan African University Institute for Water and Energy Sciences (Including Climate Change) C/O Univer-sité Abou Bekr Belkaid Tlemcen Campus Chetouane Tlemcen Algeria Directorate of Weather Forecasting Uganda National Meteorological Authority Port Bell Road Kampala Uganda

Understanding the characteristics of extreme rainfall is crucial for effective flood management planning, as it enables the incorporation of insights from past extreme rainfall patterns and their spatiotemporal distribution. This work investigated the changes in the frequency and pattern of extreme rainfall over Uganda, using daily datasets sourced from Climate Hazard Group InfraRed Precipitation with Station (CHIRPS-v2) for the period 1981 to 2022. The study utilized the extreme weather Indices provided by the Expert Team on Climate Change Detection and Indices (ETCCDI). Attention was directed towards September to November (SON) rainfall season with precise analysis of four indices (Rx1day, Rx5day, R95p, and R99p). The Sequential Mann-Kendall (SQMK) non-parametric test was applied to identify abrupt changes in SON extreme rainfall trends. Results showed that October consistently recorded the highest count of extreme rainfall days across all four indices. The long-term analysis revealed fluctuations in extreme rainfall events across years, with certain periods exhibiting heightened intensity. The analysis portrayed a shift in the decadal variations and region-specific distribution of extreme rainfall, with Eastern Uganda and areas around Lake Victoria standing out compared to other regions. The findings further revealed an increase in extreme rainfall for all indices in the recent decade (2011-2022) with 2019/2020 standing out as the extreme years of SON for the study period. While trendlines suggested a slight increase in intense daily rainfall events, the SQMK tests revealed statistical significance in the trend of prolonged periods of intense daily rainfall. This study contributes to the understanding of the spatiotemporal variability and trends of extreme rainfall events over Uganda during the SON season, which is crucial for the assessment of climate change impacts and adaptation strategies. It provides valuable information for seasonal extreme rainfall forecas

关键词： CHIRPS Variability Region-Specific Early Warning Climate Change

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Spatio-Temporal Variability of Rainfall and Temperature and Their Effects on Pasture Variability over East Africa: Implication on the Cattle Grazing Areas

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Journal of Geoscience and Environment Protection 2024年第3期12卷 54-69页

作者： natiko peter Wang Wen Nyasulu Matthews Raharivelo Sitraka Ny Aina Alupot Donnata School of Atmospheric Science Nanjing University of Information Science & Technology Nanjing China Uganda National Meteorological Authority Directorate of Station Networks Department of Instruments and Installations Kampala Uganda School of Ecology and Applied Meteorology Nanjing University of Information Science & Technology Nanjing China

Understanding the spatiotemporal variability of climatic parameters and their effects on pasture variability is vital for pasture management interventions over East Africa. The present study aims to assess the spatial-temporal variability of rainfall, temperature and Normalized Difference Vegetation Index (NDVI) (which is being used to assess pasture quality and productivity) over the region, between the period of 1982 and 2019. This study used annual mean values for rainfall, temperature and NDVI which were calculated for the period mentioned above. NDVI was derived from National Oceanic and Atmospheric Administration (NOAA) Global Area Cover (GAC) (NOAA-07-GAC) data. The rainfall data was acquired from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) while temperature is ERA5 reanalysis data sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study utilized the empirical orthogonal function (EOF) to identify patterns and dominant relationships between the climate variables. The correlation was calculated between rainfall, temperature and NDVI to assess the relationship among them. A non-parametric Mann-Kendall trends test was used to determine whether annual precipitation, temperature and NDVI had statistically increasing or decreasing trend. Results revealed a positive correlation between rainfall and NDVI while a negative correlation between NDVI and temperature. Positive correlation between rainfall and NDVI indicates that pasture health (quality and productivity), will improve accordingly. A negative correlation between temperature and NDVI indicates that pasture health will decrease with increase in temperature while improving with decreasing temperature. Outcome from this study suggests that changes in climatic variables influence the distribution of pasture in East Africa’s cattle grazing areas. The study hence recommends prioritisation of climatic (weather) information during pasture management over East A

关键词： NDVI Climatic Parameters EOF Pasture East Africa

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