Search Results (13)

Reliable precipitation forecasts are critical for hydrological modelling and flood early warning in West African river basins, where rainfall is dominated by highly variable monsoon-driven convection. This study evaluates and improves the precipitation forecasting skill of six numerical weather prediction (NWP) models over the Ouémé River basin in Benin, with particular emphasis on lead-time dependence, basin-scale effects, and the added value of statistical bias correction. Daily precipitation forecasts, over the period 1985–2015 across lead times of one to seven days, are assessed across six sub-basins using complementary continuous and event-based verification metrics. The results indicate that precipitation forecast skill varies with model choice, forecast horizon, and spatial scale. Among the raw forecasts, the ECMWF and UK Met Office models consistently outperform the other systems with KGE values reaching 0.5. ECMWF exhibits the highest overall skill at short to medium lead times, while the UK Met Office model shows relatively low volumetric bias across most sub-basins (Pbias less than 25%). For some models, forecast performance improves with increasing basin size, reflecting the smoothing effect of spatial aggregation, although this relationship remains model-specific. Distribution-based methods outperform regression-based approaches, with empirical quantile mapping providing the most robust and consistent improvements across lead times and sub-basins. Following bias correction, Empirical quantile mapping achieved median Likelihood Ratio values of approximately 6 during validation, with upper-range values reaching 15–18 across sub-basins for both ECMWF and UK Met Office forecasts. This represents a substantial improvement over raw predictions whose distributions remained consistently bounded below 10 throughout the calibration and validation phases (more than 50% improvement). Overall, the combination of ECMWF or UK Met Office precipitation forecasts with empirical quantile mapping offers a reliable framework for improving precipitation inputs to hydrological models and flood early warning systems in the Ouémé basin. The findings highlight the importance of multi-criteria evaluation and appropriate bias correction when applying NWP precipitation forecasts in monsoon-influenced hydrological environments and flood forecasting. Full article

This study evaluates the impact of precipitation ensembles on flood hazards in the Ouémé River Basin by coupling the hydrological HBV and hydrodynamic HEC–RAS model. Both models were calibrated and validated to simulate hydrological and hydraulic processes. Meteorological and hydrometric data from 1994 to 2016, along with flood maps and DEM are used. Evapotranspiration data are calculated using Hargreaves–Samani formula. The coupling HBV–HEC–RAS models enabled the generation of ensemble hydrographs, flood maps, flood probability maps and additional statistics in West Africa for the first time, offering a comprehensive understanding of flood dynamics under uncertainty. Ensemble hydrographs and maps obtained enhance decision-making by showing discharge scenarios, spatial flood variability, prediction reliability, and probabilities, supporting targeted flood management and resource planning under uncertainty. The findings underline the need for a comprehensive strategy to mitigate both common and rare flood events while accounting for spatial uncertainties inherent in hydrological and hydraulic modeling. Full article

Reliable rainfall data are critical for managing hydrometeorological hazards in West Africa, yet they are often sparse and temporally inconsistent. The current study assessed the accuracy of four near real-time satellite-based rainfall data, namely IMERGv7 Late, IMERGv6 Early, GSMAP-NRT and PERSIANN-DIR Now, for rainfall estimation and hydrological modeling in the Ouémé basin. These datasets were compared with ground-based rainfall data, bias-corrected and used to calibrate and validate the hydrological model HBV light. While they demonstrated qualitative accuracy, their quantitative estimation shows obvious discrepancies on a daily scale, varying across subdomains. The original IMERGv7 product outperforms others in capturing the rainfall pattern and amount (KGE > 0.6), while GSMAP performs moderately (KGE ≈ 0.51) and IMERGv6 and PERSIANN show lower reliability with KGE < 0.5. Quantile mapping emerges as the most effective bias-correction method, improving the performance of all satellite products, with RMSE reductions ≤ 15%. The results of hydrological simulations demonstrate the potential of satellite-based rainfall, particularly IMERGv7 and corrected IMERGv6 (NSE > 0.75), for near real-time flood monitoring and water management in the study area. This study underscores their suitability as valuable alternatives to ground-based data for flood management decision making in the Ouémé basin. Full article

The availability of reliable and quantified information on the spatiotemporal distribution of irrigated land at the river basin scale is an essential step towards sustainable management of water resources. This research aims to assess the spatiotemporal extent of irrigated land in the Ouémé River basin using Landsat multi-temporal images and ground truth data. A methodology was built around the use of supervised classification and the application of an algorithm based on the logical expression and thresholding of a combination of surface temperature (Ts) and normalized difference vegetation index (NDVI). The findings of the supervised classification showed that agricultural areas were 16,003 km², 19,732 km², and 22,850 km² for the years 2014, 2018, and 2022, respectively. The irrigated land areas were 755 km², 1143 km², and 1883 km² for the same years, respectively. A significant increase in irrigated areas was recorded throughout the study period. The overall accuracy values of 79%, 82%, and 83% obtained during validation of the irrigated land maps indicate a good performance of the algorithm. The results suggest a promising application of the algorithm to obtain up-to-date information on the distribution of irrigated land in several regions of Africa. Full article

The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of t + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t + 3 days. The values of these metrics remain stable for forecast horizons of t + 1 day, t + 2 days, and t + 3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of t + 3 days is chosen for predicting future daily water levels. Full article

Since the beginning of the 2000s, most of the West-African countries, particularly Benin, have experienced an increased frequency of extreme flood events. In this study, we focus on the case of the Ouémé river basin in Benin. To investigate flood events in this basin for early warning, the coupled atmosphere–hydrology model system WRF-Hydro is used, and analyzed for the period 2008–2010. Such a coupled model allows exploration of the contribution of atmospheric components into the flood event, and its ability to simulate and predict accurate streamflow. The potential of WRF-Hydro to correctly simulate streamflow in the Ouémé river basin is assessed by forcing the model with operational analysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric and land surface processes are resolved at a spatial resolution of 5 km. The additional surface and subsurface water flow routing are computed at a resolution of 500 m. Key parameters of the hydrological module of WRF-Hydro are calibrated offline and tested online with the coupled WRF-Hydro. The uncertainty of atmospheric modeling on coupled results is assessed with the stochastic kinetic energy backscatter scheme (SKEBS). WRF-Hydro is able to simulate the discharge in the Ouémé river in offline and fully coupled modes with a Kling–Gupta efficiency (KGE) around 0.70 and 0.76, respectively. In the fully coupled mode, the model captures the flood event that occurred in 2010. A stochastic perturbation ensemble of ten members for three rain seasons shows that the coupled model performance in terms of KGE ranges from 0.14 to 0.79. Additionally, an assessment of the soil moisture has been developed. This ability to realistically reproduce observed discharge in the Ouémé river basin demonstrates the potential of the coupled WRF-Hydro modeling system for future flood forecasting applications. Full article

The Atchakpa freshwater reservoir (Ouémé Basin, Benin) was found to harbour an unexpected population of a cichlid species that was presumed to be Sarotherodon melanotheron. This species became dominant in the reservoir and became the main fisheries target species. We applied DNA barcoding to identify this population. Besides specimens from the reservoir, we also sequenced S. melanotheron from its native range in Benin at the lower Ouémé and Sô Rivers, and from Lake Nokoué, and Porto-Novo Lagoon. High sequence similarity indicated that all specimens were conspecific. Hence, we cannot exclude that a natural range extension led to the presence of the species in the reservoir. A comparison with sequences from NCBI GenBank confirmed that all samples belonged to the subspecies S. m. melanotheron, which is native to Benin. This comparison also showed that this subspecies was previously introduced in the Philippines. We call for further studies to investigate the socioeconomic, ecological and environmental impacts of the species in the Atchakpa reservoir. Full article

This study assessed the performance of eight general circulation models (GCMs) implemented in the upper Ouémé River basin in Benin Republic (West Africa) during the Fifth Assessment Report on Climate Change. Historical rainfall simulations of the climate model of Rossby Regional Centre (RCA4) driven by eight Coupled Model Intercomparison Project (CMIP5) GCMs over a 55-year period (1951 to 2005) are evaluated using the observational data set. Apart from daily rainfall, other rainfall parameters calculated from observed and simulated rainfall were compared. U-test and other statistical criteria (R², MBE, MAE, RMSE and standard of standard deviations) were used. According to the results, the simulations correctly reproduce the interannual variability of precipitation in the upper Ouémé River basin. However, the models tend to produce drizzle. Especially, the overestimation of April, May and November rains not only explains the overestimation of seasonal and annual cumulative rainfall but also the early onset of the rainy season and its late withdrawal. However, we noted that this overestimation magnitude varies from one model to another. As for extreme rainfall indices, the models reproduced them poorly. The CanESM2, CNRM-CM5 and EC-EARTH models perform well for daily rainfall. A trade-off is formulated to select the common MPI-ESM-LR, GFDL-ESM2M, NorESM1-M and CanESM2 models for different rainfall parameters for the reliable projection of rainfall in the area. However, the MPI-ESM-LR model is a valuable tool for studying future climate change. Full article

This study evaluates the impacts of land use and climate changes on daily discharge in Ouémé river basin at Bétérou outlet. Observed rainfall and temperature over 2002–2008 and land use data of 2003 and 2007 were used. Corrected rainfall and temperature data, under RCP4.5 and RCP8.5 scenarios from regional climate model REMO were considered. Two land use scenarios from RIVERTWIN project were used. The first one, Land Use A (LUA), is characterized by stronger economic development, controlled urbanization, implementation of large-scale irrigation schemes, and 3.2% population growth per year. The other one, Land Use B (LUB), is characterized by a weak national economy, uncontrolled settlement, and farmland development as well as 3.5% population growth per year. Four climate and land use combined scenarios (LUA + RCP4.5, LUA + RCP8.5; LUB + RCP4.5, and LUB + RCP8.5) were used for forcing LISFLOOD hydrological model to estimate future discharges at 2050. As a result, during calibration and validation, the LISFLOOD model showed high ability to reproduce historical flows of Ouémé River at Bétérou outlet with Nash–Sutcliffe efficiencies greater than 90%. Future discharges simulations show general increase for all land use and climate combined scenarios for all time horizons until 2050. The increase is more exacerbated under the combined scenarios using LUB than the ones using LUA. Increase of river discharge varies between 7.1% and 52% compared to the mean of the reference period 2002–2004. These findings highlight growing challenges for water resources managers and planners. Moreover, they emphasize the need to address potential climate and land use changes’ impact on water resources. Then, developing water management plans, strategies to reduce flooding risks must be considered. Full article

Understanding the impacts of climate change on water resources is of utmost importance to successful water management and further adaptations strategies. The objective of this paper is to assess the impacts of climate change on river discharge dynamics in Oueme River basin in Benin. To this end, this paper used the distribution based scaling approach to improve usability of regional climate model projections for hydrological climate change impacts studies. Hydrological simulations in Bétérou and Bonou sub-catchments of the Oueme River were carried out with a lumped conceptual hydrological model. The main contribution of this paper is to use the hydrological model based on the least action principle (HyMoLAP), which is designed to minimize uncertainties related to the rainfall-runoff process and scaling law, for this assessment. The bias correction approach allows reducing the differences between the observed rainfall and the regional climate model (HIRHAM5 and RCA4) rainfall data. Corrected and raw HIRHAM5 and RCA4 rainfall data were compared with the observed rainfall using Mean Absolute Error (MAE) and Root Mean Square error (RMSE). The results of the bias correction show a decrease in the RMSE and MAE of the raw HIRHAM5 and RCA4 rainfall data of approximately 91% to 98% in both catchments. The results of the simulation indicate that the HyMoLAP is suitable for modelling river discharge in the Oueme River basin. For the future projection based on RCP4.5 scenarios, the projected mean annual river discharge by using HIRHAM5 and RCA4 in Bétérou and Bonou decrease with the magnitude ranging respectively from −25% to −39% and −20% to −37% in the three time horizons 2020s (2011–2040), 2050s (2041–2070) and 2080s (2071–2100), representing the early, middle and late of 21st century. As regards the future projection based on RCP8.5 scenarios, the projected mean annual river discharge by using HIRHAM5 and RCA4 in Bétérou and Bonou decrease with the magnitude ranging respectively from −15% to −34% and −18% to −36% in the three time horizons. The model uncertainties projections indicated that the entire discharge distribution shifted toward more extreme events (such as drought) compared to the baseline period. Full article

This study analyzed the trends of extreme daily rainfall indices over the Ouémé basin using the observed data from 1950 to 2014 and the projected rainfall of regional climate model REMO (REgional MOdel) for the period 2015–2050. For future trends analysis, two Intergovernmental Panel on Climate Change (IPCC) new scenarios are considered, namely RCP4.5 and RCP8.5. The indices considered are number of heavy rainfall days, number of very heavy rainfall days, consecutive dry days, consecutive wet days, daily maximum rainfall, five-day maximum rainfall, annual wet-day total rainfall, simple daily intensity index, very wet days, and extremely wet days. These indices were calculated at annual and seasonal scales. The Mann-Kendall non-parametric test and the parametric linear regression approach were used for trends detection. As result, significant declining in the number of heavy and very heavy rainfall days, heavy and extremely heavy rainfall, consecutive wet days and annual wet-day rainfall total were detected in most stations for the historical period as well as the future period following the scenario RCP8.5. Furthermore, few stations presented significant trends for the scenario RCP4.5 and the high proportion of stations with the inconsistence trends invites the planners to get ready for an uncertain future climate following this scenario. Full article

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. Full article

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. Full article