Abstract
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.
Highlights
Floods are natural hazards aggravated by both climatic factors and non-climatic factors [1], and result in the destruction and disruption of socio-economic activities, damage to property and infrastructure, loss of lives, and financial loss [2]
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
The 1-unit Lag correlation results show that the serial correlation between data sets at each site varied from −0.002 to 0.516 (−1 = perfect inverse correlation; 1 = perfect correlation; and 0 = no correlation), suggesting the absence of a strong relationship among peak annual discharge at each site
Summary
Floods are natural hazards aggravated by both climatic factors (i.e. climate variability and climate change) and non-climatic factors (e.g. changes in land cover, use, vegetation, etc.) [1], and result in the destruction and disruption of socio-economic activities, damage to property and infrastructure, loss of lives, and financial loss [2]. Knowledge of flood frequency estimates is crucial to ensure socio-economic activities and infrastructural development are planned appropriately to improve resilience [4]. Accurate estimates of flood intensities and frequencies are important for the design of critical infrastructure required to flood risk reduction (dykes, levees, dams, etc.), construction of hydraulic structures (bridges, culverts, drainages), the development of floodplain and urban land-use regulations, emergency management, and disaster risk insurance [5]. To accurately estimate expected flood magnitudes and return periods, networks of gauging stations are typically established to collect hydrological data over a long period. In many developing regions, establishing the optimal number of hydrological stations is usually hampered by challenges such as the high cost associated with gauging equipment. It is essential to explore techniques capable of extracting maximum
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