Browsing by Author "Ekolu, Job"

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    Hydrodynamic modelling of floods and estimating socio‑economic impacts of floods in Ugandan River Malaba sub‑catchment
    (Earth Systems and Environment : Springer link, 2022-01-01) Mubialiwo, Ambrose; Abebe, Adane; Kawo, Nafyad Serre; Ekolu, Job; Nadarajah, Saralees; Onyutha, Charles
    River Malaba sub-catchment tends to experience dramatic flooding events, with several socio-economic impacts to the nearby communities, such as loss of lives and destructions of physical infrastructure. Analysis of spatiotemporal extents to which settlements, crops and physical infrastructures tend to be inundated are vital for predictive planning of risk-based adaptation measures. This paper presents a case study on flood risk assessment for Ugandan River Malaba sub-catchment. We applied the two-dimensional Hydraulic Engineering Center’s River Analysis System (2D HEC-RAS) for modelling of flooding extents. We considered extreme flow quantiles, lower and upper quantiles corresponding to the 95% confidence interval limits aimed at determining uncertainties in the flooding extents. Spatial extents of inundation on human settlement, land cover and infrastructure were analysed with respect to return periods of extreme flow quantiles. Finally, we estimated economic loss on infrastructure due to flooding. Results from the 2D HEC-RAS model were satisfactorily comparable with the results of observations. Amongst the land use types, cropland exhibited the highest vulnerability with at least 10,234.8 hectare (ha) susceptible to flooding event of 100-year return period (YRP). Inundated built-up land-use exhibited the highest vulnerability percentage increase (90%) between 2- and 100-YRP. In US Dollar, about US$ 33 million and US$ 39 million losses are estimated at 2- and 100-YRP, respectively, due to inundated rice gardens and these indicate a looming high risk of household food insecurity and poverty. Several infrastructure including 15 academic institutions, 12 health facilities, 32 worshiping places remain annually vulnerable to flooding. At least 6 km and 7 km of road network are also susceptible to flooding under extreme flows of return periods 2 and 100 years, respectively. Churches exhibited the highest economic losses of US$ 855,065 and US$ 1,623,832 at 2-YRP and 100-YRP, respectively. This study findings are relevant for planning the development of sustainable flood risk adaptation pathways given the established destructions within the sub-catchment due to flooding.
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    Large-scale climate drivers of drought-to-flood events in Sub-Saharan Africa: insight from CMIP6 large-ensembles
    (EGU General Assembly, 2025-03-14) Dieppois, Bastien; Ekolu, Job ; Rubinato, Matteo ; Onyutha, Charles ; Okia, Clement ; Musinguzi, Denis ; Bogere, Robert ; Mombo, Felister ; Binego, Liliane ; Fried, Jana ; De Wiel, Marco Van
    Sub-Saharan Africa (SSA) is increasingly experiencing unprecedented drought-to-flood events, posing critical challenges to water and food security. These rapid or seasonal transitions between extreme hydroclimatic conditions underline the urgency of advancing climate adaptation strategies and enhancing risk management frameworks in the region. However, the role of large-scale climate variability, such as the El Niño-Southern Oscillation (ENSO), Atlantic Multidecadal Variability (AMV), and Indian Ocean Dipole (IOD), in influencing decadal trends in these events across SSA remains inadequately understood. This study aims to address this gap by evaluating how well eight single-model initial-condition large ensembles (SMILEs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulate the spatiotemporal patterns of drought-to-flood events in SSA. ERA5-Land data is used as the observational reference. We also investigate potential seasonal links between the probability of drought-to-flood events and large-scale modes of climate variability. Drought-to-flood events are defined as the sequential occurrence of a flood following a drought. To capture these events, we employ a variable threshold approach for identifying droughts, while floods are characterized using absolute thresholds (50th to 90th percentiles). To assess potential differences between meteorological and hydrological definitions of drought and flood, we compare results derived from precipitation, soil moisture, and runoff datasets.