In developing regions missing data are prevalent in historical hydrological datasets, owing to financial, institutional, operational and technical challenges. If not tackled, these data shortfalls result in uncertainty in flood frequency estimates and consequently flawed catchment management interventions that could exacerbate the impacts of floods. This study presents a comparative analysis of two approaches for infilling missing data in historical annual peak river discharge timeseries required for flood frequency estimation: (i) satellite radar altimetry (RA) and (ii) multiple imputation (MI). These techniques were applied at five gauging stations along the floodprone Niger and Benue rivers within the Niger River Basin. RA and MI enabled the infilling of missing data for conditions where altimetry virtual stations were available and unavailable, respectively. The impact of these approaches on derived flood estimates was assessed, and the return period of a previously unquantified devastating flood event in Nigeria in 2012 was ascertained. This study revealed that the use of RA resulted in reduced uncertainty when compared to MI for data infilling, especially for widely gapped timeseries (>3 years). The two techniques did not differ significantly for data sets with gaps of 1–3 years, hence, both RA and MI can be used interchangeably in such situations. The use of the original in situ data with gaps resulted in higher flood estimates when compared to datasets infilled using RA and MI, and this can be attributed to extrapolation uncertainty. The 2012 flood in Nigeria was quantified as a 1-in-100-year event at the Umaisha gauging station on the Benue River and a 1-in-50-year event at Baro on the Niger River. This suggests that the higher levels of flooding likely emanated from the Kiri and Lagdo dams in Nigeria and Cameroon, respectively, as previously speculated by the media and recent studies. This study demonstrates the potential of RA and MI for providing information to support flood management in developing regions where in situ data is sparse.
Flood modelling and mapping typically entail flood frequency estimation, hydrodynamic modelling and inundation mapping, which require specific datasets that are often unavailable in developing regions due to financial, logistical, technical and organizational challenges. This review discusses fluvial (river) flood modelling and mapping processes and outlines the data requirements of these techniques. This paper explores how open-access remotely sensed and other geospatial datasets can supplement ground-based data and high-resolution commercial satellite imagery in data sparse regions of developing countries. The merits, demerits and uncertainties associated with the application of these datasets, including radar altimetry, digital elevation models, optical and radar images, are discussed. Nigeria, located within the Niger river basin of West Africa is a typical data-sparse country, and it is used as a case study in this review to evaluate the significance of open-access datasets for local and transboundary flood analysis. Hence, this review highlights the vital contribution that open access remotely sensed data can make to flood modelling and mapping and to support flood management strategies in developing regions.
In recent years, flooding has become a recurring problem in many regions including Nigeria, owing to changing climatic conditions, as well as anthropogenic factors such as poor land use management and urbanization that aggravate flood impact. To effectively manage and mitigate flood impact, hydrological data is required, and in many developing regions gauging stations are established, and gauge readers recruited and trained to collect and transmit such data to designated hydrological or water resource management agencies. This study focuses on understanding the challenges associated with hydrological data collection in Nigeria, using the Ogun-Osun River as a typical case, while analytically assessing how these challenges result in uncertainties that propagate unto flood frequency estimates that are used to inform flood management decisions. The findings reveal that (i) capacity and institutional gaps; lack of maintenance of hydrological infrastructure and surrounding landscape; poor data management architecture; and floods events that destroy hydrological equipment and inundate roads thereby restricting access to collected data during peak floods, are some of the challenges associated with hydrological data collection in developing regions; (ii) these conditions result in gaps in and shortened length of annual maximum hydrological time series required for flood frequency estimation, consequently leading to under or overestimation of low and high flood quantiles such as 1-in-2year and 1-in-100year floods, to levels of 0.67 m and 0.9 m respectively for the Ogun Osun River Basin. The need for improved data collation, management and adaptation of new technologies such as radar or sonar by the Ogun-Osun River Basin Development Authority is recommended in this study, to ensure sustainable and improved hydrological data collection, management, transferability and usability for flood management.
This study presents a remote sensing approach of using freely available Landsat 8 satellite Indicators (Land Surface Temperature (LST), Soil Adjusted Vegetation Index (SAVI)) and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) geospatial data to assess the impact of dumpsites on the environment in Benin City, Nigeria. The finding reveals that the average derived LST at the dumpsites were higher than the immediate surrounding, and the average SAVI values were lower than the immediate surrounding. The high values of LST at the dumpsites depict the effect of gases released because of decomposition activities, while low values of SAVI indicate vegetation response to soil and ground water contamination due to leachate infiltration. The average elevation within the dumpsite area derived from SRTM DEM was also applied as a proxy to estimate disposed waste quantity, and related closely with LST that depict biodegradation activities. The result presented here shows that bacterial and fungal counts correlate strongly with the LST and SAVI values at each of the dumpsites R 2 : LST vs Bacteria Count = 0.982, LST vs Fungi Count = 0.951; SAVI vs Bacteria Count = 0.745, SAVI vs Fungi Count = 0.664, thereby suggesting remote sensing can be applied to aid longterm dumpsite monitoring and management.
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, socioeconomic 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 How to cite this paper: Ekeu-Wei, I.T., Blackburn, G.A. and Giovannettone, J. (2020) Accounting for the Effects of Climate Variability in Regional Flood Frequency Estimates in Western Nigeria.
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