Flood risk assessment and mapping in the Hadejia River Basin, Nigeria, using hydro-geomorphic approach and multi-criterion decision-making method

Flood risk management is crucial for climate change resilience. The Hadejia River basin is known for severe and frequent floods, which have destroyed houses and farmlands and claimed many lives. This study developed a GIS-based flood risk and vulnerability mapping assessment using the Analytical Hierarchical Process (AHP) to outline scenarios that reduce risk and vulnerability associated with floods in the Hadejia River basin. The risk mapping of the basin integrated seven hydro-geomorphological indicators influencing extreme events (elevation, mean annual rainfall, slope, distance from rivers, soil type, and drainage density) and six socio-economic vulnerability indicators (population density, female population density, literacy rate, land use, employment rate, and road network) using a multi-criterion analysis. The average annual rainfall data of 36 years (1982–2018) were used for flood plain mapping in this study. Combining the flood hazard and socio-economic vulnerability indices of the basin revealed high-to-very high flood risk in the downstream and central upstream portions of the basin, which cover about 43.4% of the basin area. The local areas of Auyo, Guri, Hadejia, Ringim, Kafin Hausa, and Jahun were identified as zones at a very high flood risk. The study also revealed that flood hazard and vulnerability indicators have different influences on flood risk. The validated results resonate with the records of previous flood distribution studies of the basin. This research study is significantly important for developing strategic measures and policy revision through which the government and relief agencies may reduce the negative impact of floods in the Hadejia River basin

Change in Heavy Rainfall Characteristics over the Ouémé River Basin, Benin Republic, West Africa

Climate change has severe impacts on natural resources, food production and consequently on food security especially in developing countries. Likely accentuated by climate change, flooding is one of the disasters that affects people and destroies agricultural land and products. At different governance levels and scales, appropriate responses are needed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. A methodology for detecting change was applied to heavy daily rainfall of 34 stations across the Ouémé basin, Benin, in order to assess potential change in its characteristics. The spatial variability of the detected changes in return periods was analyzed using the kriging interpolation method. For this analysis, up to 92 years (1921–2012) of rainfall data were used. Three homogeneous regions were found by the cluster analysis. For all studied return periods, 82% of the stations showed statistically significant change in daily precipitation, among which 57% exhibited a positive change and 43% negative change. A positive change is associated with an increase in heavy rainfall over the area of concern. An analysis of the interpolated change in heavy rainfall of different return periods revealed an east-west gradient from negative to positive along the lower Ouémé basin (Region 2). From the middle to the upper Ouémé (Region 1 and 3), a decreasing tendency of heavy rainfall is dominant mainly for the non-homogeneous period. This result of the complex pattern of changes could be veritable information for decision makers and consequently for development of appropriate adaptation measures

Change in Heavy Rainfall Characteristics over the Ouémé River Basin, Benin Republic, West Africa

Climate change has severe impacts on natural resources, food production and consequently on food security especially in developing countries. Likely accentuated by climate change, flooding is one of the disasters that affects people and destroies agricultural land and products. At different governance levels and scales, appropriate responses are needed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. A methodology for detecting change was applied to heavy daily rainfall of 34 stations across the Ouémé basin, Benin, in order to assess potential change in its characteristics. The spatial variability of the detected changes in return periods was analyzed using the kriging interpolation method. For this analysis, up to 92 years (1921–2012) of rainfall data were used. Three homogeneous regions were found by the cluster analysis. For all studied return periods, 82% of the stations showed statistically significant change in daily precipitation, among which 57% exhibited a positive change and 43% negative change. A positive change is associated with an increase in heavy rainfall over the area of concern. An analysis of the interpolated change in heavy rainfall of different return periods revealed an east-west gradient from negative to positive along the lower Ouémé basin (Region 2). From the middle to the upper Ouémé (Region 1 and 3), a decreasing tendency of heavy rainfall is dominant mainly for the non-homogeneous period. This result of the complex pattern of changes could be veritable information for decision makers and consequently for development of appropriate adaptation measures

Modelling non-stationary extreme streamflow in Peninsular Malaysia

Global change has raised concerns among hydrologists about the use of the stationary assumption (independent and identically distributed flood series) in infrastructure-designed methods. This confirms the necessity of evaluating the stationary or non-stationary behaviour of hydrological variables before deriving flood plans for infrastructure projects and flood mitigation. Trends were evaluated in the annual maximal streamflow of 49 stations in Peninsular Malaysia, using the Mann Kendall and Spearman Rho trend tests. Three models, a stationary model (GEV0) and two non-stationary models, with: 1) location parameter; 2) location and log-transformed scale parameters as a linear function of time (GEV2), were considered for stations with significant trend. It was found that a quarter of the analysed stations show statistically significant trends in their annual maximal streamflow. These results indicate the importance of taking into consideration the non-stationary behaviour of the flood series in order to improve the quality of flood estimation

Non-Stationary Flood Frequency Analysis in the Ouémé River Basin, Benin Republic

A statistical model to predict the probability and magnitude of floods in non-stationary conditions is presented. The model uses a time-dependent and/or covariate-dependent generalized extreme value (GEV) distribution to fit the annual maximal (AM) discharge, and it is applied to five gauging stations in the Ouémé River Basin in Benin Republic, West Africa. Different combinations of the model parameters, which vary with respect to time and/or climate covariates, were explored with the stationary model based on three criteria of goodness of fit. The non-stationary model more adequately explains a substantial amount of variation in the data. The GEV-1 model, which incorporates a linear trend in its location parameter, surpasses the other models. Non-stationary return levels for different return periods have been proposed for the study area. This case study tested the hypothesis of stationarity in estimating flood events in the basin and it demonstrated the strong need to account for changes over time when performing flood frequency analyses

Hydroclimatic trends, breakpoints and future projection analysis of the Ogun River Basin in West Africa

Trend analysis is important to understand the performance and features of hydrological variables over a long-time scale. This study analyses the hydroclimatic trends in precipitation, temperature (minimum and maximum) data from seven synoptic stations and river discharge from three outlets that were investigated between 1984–2019 and projected between 2020–2060 over the Ogun River Basin. The results of the trend analysis showed a non-significant positive trend in precipitation and a significant positive trend (p,0.05 and p,0.01 significant trends) in maximum and minimum temperatures. The discharge reveals a non-significant positive trend on the annual scale while a significant decreasing trend in the dry season. The annual rainfall projection is expected to increase by 1.3% under RCP 2.6 and 1.4% under RCP 8.5 by 2060. The mean annual temperature is expected to increase between 1.5-2.5 °C under RCP 2.6 and 2-3.5 °C under RCP 8.5 by 2060, respectively. The variations in discharge without significant changes in rainfall suggested other variables were influencing the discharge. These could be changes in river basin physical elements such as alterations in the dynamics of land use land cover changes. The findings of this study can be used for strategizing adaptation and mitigation measures for water resources management. HIGHLIGHTS To explore seasonal trends.; Impacts of climate change on the discharge.; Effects of temperature on the streamflow.; Impact of low discharge on water resources.; Effects of anthropogenic activities on water resources management.

Non-Stationary Flood Discharge Frequency Analysis in West Africa

With climate change and intensification of the hydrological cycle, the stationarity of hydrological variables is becoming questionable, requiring appropriate flood assessment models. Frequency analysis is widely used for flood forecasting. This study aims to determine the most suitable models (stationary and non-stationary) for estimating the maximum flows observed at some stations spread across West Africa. A statistical analysis of the annual maximum flows in terms of homogeneity, stationarity, and independence was carried out through the Pettitt, modified Mann–Kendall, and Wald–Wolfowitz tests, respectively, to identify the stations whose flows are non-stationary. After that, the best-correlated climate covariates with the annual maximum flows of the non-stationary stations were determined. The covariates explored are the climatic indices of sea surface temperatures (SST). Finally, different non-stationary GEV models were derived by varying the scale and position parameters of the best-correlated index for each station. The results indicate that 56% of the annual maximum flow series are non-stationary. As per the Bayes information criterion (BIC) values, the performance of the non-stationary models (GEV, generalized extreme values) is largely greater than that of the stationary models. These good performances of non-stationary models using climatic indices open perspectives for the prediction of extreme flows in the study area

Non-Stationary Flood Frequency Analysis in the Ouémé River Basin, Benin Republic

A statistical model to predict the probability and magnitude of floods in non-stationary conditions is presented. The model uses a time-dependent and/or covariate-dependent generalized extreme value (GEV) distribution to fit the annual maximal (AM) discharge, and it is applied to five gauging stations in the Ouémé River Basin in Benin Republic, West Africa. Different combinations of the model parameters, which vary with respect to time and/or climate covariates, were explored with the stationary model based on three criteria of goodness of fit. The non-stationary model more adequately explains a substantial amount of variation in the data. The GEV-1 model, which incorporates a linear trend in its location parameter, surpasses the other models. Non-stationary return levels for different return periods have been proposed for the study area. This case study tested the hypothesis of stationarity in estimating flood events in the basin and it demonstrated the strong need to account for changes over time when performing flood frequency analyses

Chapitre 15. Fonctionnement hydrologique et hydraulique du bas-fond réaménagé de Bankandi

Contexte de l’étude Le projet Generia intervient au Burkina Faso dans la province du Ioba, région du Sud-Ouest, où une diversité de modèles d’aménagements de bas-fonds a été mise en œuvre pour une maîtrise partielle de l’eau. Le modèle le plus courant est celui des « diguettes en courbes de niveau » (DCN), généralement renforcées par empierrement avec possibilité de réguler l’eau par des pertuis. Conçu dans les années 1970 et 1980 pour faire face aux sécheresses survenues à cette période, ce ..

Assessing the Return Periods and Hydroclimatic Parameters for Rainwater Drainage in the Coastal City of Cotonou in Benin under Climate Variability

Cotonou, the economic capital of Benin, is suffering from the impacts of climate change, particularly evident through recurrent floods. To effectively manage these floods and address this issue, it is crucial to have a deep understanding of return periods and hydroclimatic parameters (such as intensity-duration-frequency (IDF) curves and related coefficients), which are essential for designing stormwater drainage structures. Determining return periods and these parameters requires statistical analysis of extreme events, and this analysis needs to be regularly updated in response to climate change. The objective of this study was to determine the necessary return periods and hydroclimatic parameters to improve stormwater drainage systems in the city and its surroundings areas. This required annual maximum precipitation series of 1, 2, 3, 6, 12, and 24 h for 20 years length (1999–2018) as well as flood record data. The intensity series, derived by dividing the amount of rainfall by its duration, was adjusted using Gumbel’s law. IDF curves were constructed based on Montana and Talbot models, and their coefficients were determined according to the corresponding return periods. In 2010, which witnessed devastating floods in the country, the return period for the most intense rainfall events was 40 years, followed by 2013 with a return period of 13.4 years. Consequently, the commonly used 10-year return period for the design of stormwater drainage structures in Cotonou is insufficient. The Talbot model produced the lowest mean square errors for each quantile series and coefficients of determination closest to one, indicating that the parameters obtained from this model are well suited for designing hydraulic structures in Cotonou. The hydroclimatic parameters presented in this study will contribute to the improved design of hydraulic structures in the city of Cotonou