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.

The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

The West African Monsoon, known for its significant rainfall variability, led to the Sahel drought from 1968 to the 1990s, followed by a recovery in rainfall since the 1990s. In response to such variability, this study introduces a statistical approach for reconstructing interannual rainfall variability across seven rainfall regimes, each representing unique climatic zones in West Africa. Initially, a robust catalogue of daily atmospheric circulation pattern classifications over West Africa is established, based on pre‐selected variables from the ERA5 reanalysis and using k‐means clustering. Subsequently, the annual occurrence frequencies of these circulation pattern classifications, along with the annual rainfall conditions in the rainfall regimes, serve as inputs in a multi‐class logistic regression model. This model is designed to identify dry, normal, and wet years, relative to the climatology. The rainfall regimes are determined using k‐means clustering and a quality‐controlled dataset from 971 rainfall stations, with daily observations ranging from 1959 to 2010. These regimes vary from the Sahelian belt, characterised by a short rainy season, to tropical regions exhibiting a bimodal rainfall regime. After comprehensive predictor screening of specific West African Monsoon patterns, such as the Tropical Easterly Jet and the African Easterly Jet, six variables at four different pressure levels under a running split‐sampling cross‐validation, the best models achieve an average proportion correct of 0.57 and a positive Peirce skill score for all regions over West Africa. This shows the performance in reconstructing dry, normal, and wet years for the different rainfall regimes in West Africa. Therefore, this study provides a statistical tool for the reconstruction of annual rainfall anomalies in this challenging region.

A better understanding of the rainfall variability and extremes in tropical regions is crucial for the development of improved statistical and numerical approaches used for climate research and weather prediction. In this study, we present a novel fuzzy rule-based method for classifying atmospheric circulation patterns relevant to heavy rainfall in the Sudan-Sahel region over West Africa. In the first step, we determine large-scale atmospheric patterns to describe important seasonal features of the West African Monsoon like the movement of Saharan Heat Low over the African continent. In the second step, meso-scale monsoon patterns are classified to better describe rainfall variability and extremes during the monsoon period. In addition to a comprehensive predictor screening using more than 30 variables at different atmospheric levels, a detailed sensitivity analysis is performed, which aims to improve the transferability of the classification approach to an independent dataset. Furthermore, crucial aspects of the methodological development of fully automatic classification approaches are addressed. Using mean sea level pressure and stream function fields (700hPa) as final predictor variables, we identified 23 circulation patterns as robust solution to represent key atmospheric processes and rainfall variability in the study region. The two wettest patterns are distinguished by an enhanced Saharan Heat Low and cyclonic rotation near the study region, suggesting the presence of a tropical wave trough and triggering about 50% of the rainfall extremes on 6.5% of the days. The identified atmospheric circulation patterns are currently used to develop a variety of improved statistical approaches for this challenging region, such as pattern-dependent bias correction, geostatistical interpolation, and simulation.

The Sahel region is characterized by significant rainfall variability and has experienced hydrological changes, including a major drought from 1968 to the 1990s, followed by a subsequent period of rainfall recovery since the 1990s. Addressing this variability, this study introduces a statistical approach for predicting interannual rainfall anomalies within the region. Initially, k-means is used to classify daily atmospheric circulation patterns over West Africa, based on key variables like the V-component of wind at 700 hPa and wind speed at 200 hPa. The two high-altitude wind fields are crucial for understanding the monsoon dynamics due to their direct linkage with African Easterly Waves and the Tropical Easterly Jet. Subsequently, the annual occurrence frequencies of the atmospheric circulation patterns, along with the annual rainfall conditions in the Sahel region, are used as inputs for a multi-class logistic regression model. This model is designed to predict dry, normal, or wet years relative to the climatology. Moreover, the research presents the spatial composites of the atmospheric circulation patterns, along with a detailed explanation of the logistic regression model, an analysis of seasonal pattern occurrences, and their meteorological interpretations. The model has shown success in predicting annual rainfall variability, achieving an average proportion correct of 0.77. This level of accuracy establishes the model as a reliable tool for predicting annual rainfall amounts in the Sahel zone, offering significant insights into the climate of the region.