Browsing by Author "Ongoma, Victor"

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    Evaluation of precipitation simulations in CMIP6 models over Uganda
    (John Wiley & Sons, Ltd, 2021-07) Ngoma, Hamida; Wen, Wang; Ayugi, Brian; Babaousmail, Hassen; Karim, Rizwan; Ongoma, Victor
    Abstract This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981–2014. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatology, seasonal rainfall distribution and trend. Statistical metrics used include mean bias error, normalized root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models' performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown inter‐annually. Some models could not capture the rainfall patterns around local‐scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL‐ESM4, CanESM5, CESM2‐WACCM, MRI‐ESM2‐0, NorESM2‐LM, UKESM1‐0‐LL, and CNRM‐CM6‐1. The models CNRM‐CM6‐1, and CNRM‐ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. Caution should be taken when employing the models in seasonal climate change studies as their performance varies from one season to another. The model spread in CMIP6 over the study area also calls for further investigation on the attributions and possible implementation of robust approaches of machine learning to minimize the biases. Evaluation of the general climate models in CMIP6 over Uganda.
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    Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections
    (Atmosphere, 2021) Onyutha, Charles; Asiimwe, Arnold; Ayugi, Brian; Ngoma, Hamida; Ongoma, Victor; Tabari, Hossein
    We used CMIP6 GCMs to quantify climate change impacts on precipitation and potential evapotranspiration (PET) across water management zones (WMZs) in Uganda. Future changes are assessed based on four Shared Socioeconomic Pathways (SSP) scenarios including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 over the periods 2021–2040, 2041–2060, 2061–2080, and 2081–2100. Both precipitation and PET are generally projected to increase across all the WMZs. Annual PET in the 2030s, 2050s, 2070s, 2090s will increase in the ranges 1.1–4.0%, 4.8–7.9%, 5.1–11.8%, and 5.3–17.1%, respectively. For the respective periods, annual precipitation will increase in the ranges 4.0–7.8%, 7.8–12.5%, 7.9–19.9%, and 6.9–26.3%. The lower and upper limits of these change ranges for both precipitation and PET are, respectively, derived under SSP1-2.6 and SSP5-8.5 scenarios. Climate change will impact on PET or precipitation disproportionately across the WMZs. While the eastern WMZ (Kyoga) will experience the largest projected precipitation increase especially towards the end of the century, the southern WMZ (Victoria) exhibited the largest PET increase. Our findings are relevant for understanding hydrological impacts of climate change across Uganda, in the background of global warming. Thus, the water sector should devise and implement adaptation measures to impede future socioeconomic and environmental crises in the country.
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    Projected Changes in Rainfall Over Uganda Based on CMIP6 Models
    (Theoretical and Applied Climatology,, 2022) Ngoma, Hamida; Ayugi, Brian; Onyutha, Charles; Babaousmail, Hassen; Lim Sian, Kenny; Iyakaremye, Vedaste; Mumo, Richard; Ongoma, Victor
    Information about likely future patterns of climate variables is important in climate change mitigation and adaptation efforts. This study investigates future (2021–2100) changes in rainfall based on CMIP6 datasets over Uganda. The projection period was divided into two sub-periods: 2021–2060 (near future) and 2061–2100 (far future), relative to the baseline period (1985–2014). Two emission scenarios: SSP2- 4.5 and SSP5-8.5, were considered. The results reveal a larger decrease (increase) in rainfall during March – April (November – December) under both SSPs. Moreover, an enhanced decline (increase) is projected under SSP2-4.5 (SSP5-8.5). The spatial distribution of future changes in seasonal rainfall reveals a decrease in MAM rainfall in the near future over most parts of the country under both emission scenarios. However, a recovery is exhibited towards the end of the century with more increase in the south-western parts of the country, and a higher magnitude under SSP5-8.5. In contrast, SON rainfall reveals wetter conditions during both timelines and emission scenarios. Maximum (minimum) wet conditions are expected in the north-western parts of the country (around the Lake Victoria basin). The linear trend analysis shows a non-significant (Z = -0.714) decreasing trend for MAM rainfall during the historical period. This pattern is reflected in the near future with z-scores of -0.757 and − 1.281 under SSP2-4.5 and SSP5-8.5, respectively. However, a significant increase for MAM and annual rainfall (z-scores of 2.785 and 3.46, respectively) is projected towards the end of the century under SSP5-8.5. These findings provide guidance to policy makers in devising appropriate adaptation measures to cope with expected changes in the local climate. Given the increase in intensity and frequency of extreme rainfall over the study region, future work should focus on examining projected changes in rainfall extremes under different global warming scenarios with consideration of model performance and independence.