Extreme flood events are becoming more frequent and intense in recent times, owing to climate change and other anthropogenic factors. Nigeria, the case-study for this research experiences recurrent flooding, with the ...Extreme flood events are becoming more frequent and intense in recent times, owing to climate change and other anthropogenic factors. Nigeria, the case-study for this research experiences recurrent flooding, with the most disastrous being the 2012 flood event that resulted in unprecedented damage to infrastructure, displacement of people, socio-economic disruption, and loss of lives. To mitigate and minimize the impact of such floods now and in the future, effective planning is required, underpinned by analytics based on reliable data and information. Such data are seldom available in many developing regions, owing to financial, technical, and organizational drawbacks that result in short-length and inadequate historical data that are prone to uncertainties if directly applied for flood frequency estimation. This study applies regional Flood Frequency Analysis (FFA) to curtail deficiencies in historical data, by agglomerating data from various sites with similar hydro-geomorphological characteristics and is governed by a similar probability distribution, differing only by an “index-flood”;as well as accounting for climate variability effect. Data from 17 gauging stations within the Ogun-Osun River Basin in Western Nigeria were analysed, resulting in the delineation of 3 sub-regions, of which 2 were homogeneous and 1 heterogeneous. The Generalized Logistic distribution was fitted to the annual maximum flood series for the 2 homogeneous regions to estimate flood magnitudes and the probability of occurrence while accounting for climate variability. The influence of climate variability on flood estimates in the region was linked to the Madden-Julian Oscillation (MJO) climate indices and resulted in increased flood magnitude for regional and direct flood frequency estimates varying from 0% - 35% and demonstrate that multi-decadal changes in atmospheric conditions influence both small and large floods. The results reveal the value of considering climate variability for flood frequency analysis, especially when non-stationarity is established by homogeneity analysis.展开更多
文摘Extreme flood events are becoming more frequent and intense in recent times, owing to climate change and other anthropogenic factors. Nigeria, the case-study for this research experiences recurrent flooding, with the most disastrous being the 2012 flood event that resulted in unprecedented damage to infrastructure, displacement of people, socio-economic disruption, and loss of lives. To mitigate and minimize the impact of such floods now and in the future, effective planning is required, underpinned by analytics based on reliable data and information. Such data are seldom available in many developing regions, owing to financial, technical, and organizational drawbacks that result in short-length and inadequate historical data that are prone to uncertainties if directly applied for flood frequency estimation. This study applies regional Flood Frequency Analysis (FFA) to curtail deficiencies in historical data, by agglomerating data from various sites with similar hydro-geomorphological characteristics and is governed by a similar probability distribution, differing only by an “index-flood”;as well as accounting for climate variability effect. Data from 17 gauging stations within the Ogun-Osun River Basin in Western Nigeria were analysed, resulting in the delineation of 3 sub-regions, of which 2 were homogeneous and 1 heterogeneous. The Generalized Logistic distribution was fitted to the annual maximum flood series for the 2 homogeneous regions to estimate flood magnitudes and the probability of occurrence while accounting for climate variability. The influence of climate variability on flood estimates in the region was linked to the Madden-Julian Oscillation (MJO) climate indices and resulted in increased flood magnitude for regional and direct flood frequency estimates varying from 0% - 35% and demonstrate that multi-decadal changes in atmospheric conditions influence both small and large floods. The results reveal the value of considering climate variability for flood frequency analysis, especially when non-stationarity is established by homogeneity analysis.
