Optimal water management in irrigation relies, among other things, on soil properties, which can be modified by agronomic interventions such as composting. It is in this context that the present study was carried out in 2024 at the experimental site of the Institut National de l'Eau to determine the optimal compost dose and burial depth with an effect on soil hydrodynamic properties. A completely randomized experimental block design was used, comprising control plots (without compost) and plots treated with three doses of compost (T1 = 3 kg m−2, T2 = 4 kg m−2, T3 = 5 kg m−2), each incorporated at three depths (10, 20 and 30 cm). Statistical analyses were carried out using R.4.4.1 software. Results showed that the application of 5 kg compost at 10 cm (T3 P10) improved water retention (0.466 vs. 0.210 cm3 cm−3−1−1) and reduced bulk density (1.06 g cm−3−3−2, optimizes soil hydrodynamic properties, contributing to more efficient water management during irrigation under natural conditions for a sandy-loam soil type.
Urban flooding has worsened significantly in Cotonou (major flood events in 2010, 2018, and 2020), yet this deterioration occurs against a backdrop of largely stable extreme precipitation. This study analyses the temporal variability of extreme precipitation indices in Cotonou over 40 years (1984–2024) in order to explain this paradox. Based on daily rainfall data from the Cotonou synoptic station, eleven standardised ETCCDI climate indices were calculated at four temporal scales (annual, monthly, seasonal, and decadal). Monotonic trends were detected using the Mann-Kendall test and Sen's slope estimator, while structural breakpoints were identified through a combined application of the Pettitt and Buishand tests. Contrary to widespread expectations of intensifying extremes, the results reveal that only the Simple Daily Intensity Index (SDII) presents a significant downward trend (Sen's slope = −0.084 mm d−1 yr−1, p=0.034). Three structural breakpoints were detected during the 2008–2012 period based on the Buishand criterion (Q>1.45): SDII in 2011, CWD in 2008, and R50mm in 2012. The frequency and intensity of extreme events (R95p, R99p, R95pTOT, R99pTOT) show no significant trend at any of the temporal scales considered. These findings demonstrate that the worsening of urban flood impacts in Cotonou results primarily from accelerated urbanisation – population growth of +140 % and soil impermeability of 70 %–80 % – rather than from climate change intensification, a pattern consistent with observations in other rapidly urbanising West African coastal cities.
The agroecosystems of the rural continuum of the Sudano-Sahelian region of the Volta River Basin are undergoing severe degradation, resulting in serious declines in ecosystem service capacities and weakening community livelihoods, as agriculture remains the primary source of income in the region. This degradation is mainly driven by poor management of hydroclimatic risks, non-adapted agricultural practices characterized by intensive use of chemical fertilizers and pesticides, and insufficient consideration of spatial heterogeneity in decision-making processes. The objectives of this study are (i) to identify a Network of 30 m resolution Grid Cells (NGC) representative of the spatial heterogeneity of the studied agroecosystem – the Dassari headwater catchment (550 km2), a tributary of the Volta River in northern Benin – and (ii) to regionalize plot-scale socioeconomic data to support improved decision-making. The NGC was derived by combining iterative principal component analysis (IPCA) with a Conditioned Latin Hypercube Sampling (CLHS) approach using 37 satellite-derived variables (e.g. Normalized Difference Vegetation Index, saturation index, coloration index), resulting in the selection of 150 grid cells. Field and laboratory investigations provided soil properties (e.g. texture, carbon, field capacity, nitrogen content) and socioeconomic data (e.g. harvest quantity, input costs, total production cost, and gross income), which were standardized and analyzed for major crops (millet, sorghum, maize, and cotton). Results show that natural spatial disparities among NGC cells translate into additional labor and input costs that may be unsustainable for farmers. Multiple linear regression models were developed to relate socioeconomic indicators to soil and remote sensing variables for each crop type, and the resulting regional models proved robust, with predicted and observed values closely aligned within the 95 % confidence interval, coefficients of determination exceeding 70 %, and p-values below 0.01.
Commercial water tanker services provide an important alternative source of drinking water in rapidly urbanizing cities where piped water supply is inadequate. However, concerns remain regarding water quality deterioration during transportation and storage. This study assessed the physicochemical and microbial quality of water supplied by commercial tanker operators in the Tamale Metropolis, Ghana, and examined operational practices influencing water safety. A total of 54 water samples were collected from nine tanker operators and analysed using standard APHA methods, while structured questionnaires were administered to 50 operators to evaluate water sources, cleaning frequency, and hygiene practices. The results showed mean values of 30.16 ± 0.66 °C for temperature, 7.48 ± 0.31 for pH, 269.1 ± 0.04 NTU for turbidity, 126.49 ± 9.28 µS cm−1± 0.007 ppt for salinity, and 0.52 ± 0.42 mg L−1± 104.90 and 40.59 ± 84.96 CFU/100 mL, respectively. While most physicochemical parameters complied with WHO (World Health Organization) drinking-water guidelines, turbidity exceeded the recommended limit in all samples, and oxidation–reduction potential values indicated inadequate disinfection. Total coliforms and faecal coliforms were detected in 38.9 % and 29.6 % of the samples, respectively, confirming post-treatment contamination during transportation and storage. Survey findings revealed that although most operators sourced water from the Ghana Water Company Limited, variations in cleaning frequency and hygiene practices likely contributed to the deterioration in water quality. These findings highlight the need for strengthened regulatory oversight, routine tanker inspection and disinfection, improved operator hygiene practices, and continuous water quality monitoring to safeguard public health.
Sustainable water governance is essential for maintaining water security in river basins subject to growing pressures from agriculture, livestock and climate variability. This study investigates how governance awareness, community participation and water-use attitudes influence water management along the Gambia River. A structured household survey of 290 respondents from 15 riverine villages in three communes was combined with key informant interviews and focus group discussions to examine regulatory awareness, participation in management structures and sources of water-related conflict. The results show generally low awareness of water regulations and limited involvement in local water management organisations, which constrains effective governance and compliance. Conflicts over water use, particularly between crop farmers and livestock herders, are widespread and are reinforced by resource scarcity, weak institutional coordination and limited administrative presence. Qualitative evidence highlights the important role of community-based organisations, notably the Gambia River Valley Producers Cooperative (COPROVAG), in mediating conflicts, facilitating coordination and promoting more equitable water sharing. The findings indicate that strengthening regulatory communication, improving institutional–community linkages and supporting locally rooted governance structures are key to improving water management in data-scarce river basins.
This study investigates the impact of cylindrical obstacle concentration (C) on flow dynamics in low-slope channels using ANSYS Fluent. Cylinders (D=L=−12 s−2. Wall shear stress rises to 6.5 Pa at 64 % C
Understanding extreme floods is crucial for water resource management and risk prevention in West Africa. However, the statistical modelling of maximum annual flows in south-East Senegal is still largely unexplored. The present study addresses this gap by analyzing and modelling the maximum annual flow using the generalized extreme value (GEV) distribution. Several statistical methods were compared to fit the model, including maximum likelihood estimation (MLE), probability-weighted moments (PWM), elementary percentiles (EP), and quantile least squares (QLS). The comparative performance of these methods is evaluated using the Average Standardized Absolute Error (ASAE) criterion, as well as probability and quantile plots. The results indicate that the probability-weighted moments method provides the best fit. Goodness-of-fit tests confirm that the Gumbel distribution, a special case of the GEV, is more appropriate for representing maximum annual flows in the study area. The 100-year return level of the maximum annual flow is estimated at 1751.242 m3 s−1, indicating that such a flow is likely to occur on average once every 100 years. This study highlights the value of extreme value models for flood analysis in regions with high hydro-climatic variability. The results provide useful insights for understanding hydrological risks in southeastern Senegal and can support future work in hydrological modeling and water resource management planning.
Fish farming plays a key role in food security and local economic development in Benin. However, production systems remain largely dependent on manual management practices that constrain productivity, resource optimization, and operational efficiency. This study focuses on the design, development, and functional assessment of a semi-automated above-ground fish tank management system intended for small-scale aquaculture applications. The proposed system is based on a modular low-cost architecture integrating multiple sensors for the real-time monitoring of key water quality parameters, including temperature, pH, dissolved oxygen, and turbidity, along with an automated feeding mechanism and an autonomous corrective control strategy. System control and communication are ensured through a microcontroller-based platform combining Arduino MEGA and ESP8266 modules, while a dedicated web-based interface enables remote data acquisition, visualization, and basic decision support. A functional prototype was developed and evaluated through laboratory and operational tests focusing on system responsiveness, sensor data reliability, communication performance, and control execution. Although no experimental comparison with conventional management systems was conducted, the results demonstrate the technical feasibility and operational stability of continuous monitoring, and automated control under controlled conditions. Overall, the proposed platform offers a scalable, adaptable, and affordable technological framework that lays the groundwork for future large-scale experimental validation and the deployment of smart aquaculture solutions in resource-constrained environments.
This study aims to improve river flow prediction in a region where hydrological data are limited, which is essential for water management and flood preparedness. We combined a traditional rainfall–runoff model with data-driven learning methods to correct systematic simulation errors. Results show that the combined approach predicts river flow more accurately than the traditional model alone. These findings highlight a practical way to improve water resource planning in data-limited regions.
Accurate streamflow forecasting is essential for water resource management, yet remains challenging in data-scarce regions. This study develops and evaluates a hybrid modeling framework for the Ouémé River basin (Benin) that combines the conceptual GR4J hydrological model with machine learning (ML) via a residual-correction strategy. The approach integrates GR4J with three ML techniques Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks to correct systematic errors in simulated streamflow. Models were trained on daily data from 1996–2012 and validated on an independent period (2013–2020) using performance metrics including Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). Results show that all hybrid models (GR4J–RF, GR4J–XGB, and GR4J–LSTM) outperform the standalone GR4J model. The tree-based hybrids (GR4J–RF and GR4J–XGB) achieved the highest performance gains, with validation KGE values of 0.80 and 0.79, respectively, compared to 0.75 for GR4J alone. While the GR4J–LSTM hybrid also improved baseline results, it exhibited comparatively lower performance, attributed to the data-intensive nature of deep learning in a limited-data context. The study demonstrates that hybrid residual-correction frameworks, particularly those employing efficient tree-based ML, offer a robust and practical pathway for enhancing streamflow prediction in data-scarce catchments.
Understanding hydroclimatic variability and climate teleconnections in a warming world is crucial for drought-prone regions like West Africa, where economies heavily depend on rain-fed agriculture. This study investigates the spatiotemporal dynamics of hydrological and meteorological droughts in the Beninese Part of the Niger River Basin (BPNRB). Using the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Consecutive Dry Days Index (CDD), and the Streamflow Drought Index (SDI), the study assessed drought variability on 3- and 12-month timescales. Statistical methods were employed to explore drought patterns. The findings reveal a recovery phase in the 1990s following severe droughts in the 1970s and 1980s. Significant trends include an increase in CDD values during the rainy season, with an average of 18 dry spell days, and a pronounced upward trend in SDI, indicating intensifying hydrological droughts. These changes suggest growing pressure on both surface and groundwater resources. These findings provide critical guidance for water resource management, promoting efficient irrigation practices and developing preparedness plans. This research offers a robust scientific foundation for designing proactive drought management strategies and enhancing water and food security in Northern Benin.
This study focus on maps soil loss in the Zou catchment in central Benin to help protect farmland. Using satellite images, rainfall records and soil data, we identified areas where soil lost is most at risk. Most of the basin shows low soil loss risk, while small zones on steep slopes are more exposed. The results show the important for better land care and future work with higher-resolution satellite data.
Soil degradation constitutes a major constraint for sustainable agricultural development, particularly in extension zones where land resources are under increasing pressure. This study evaluates soil loss and erosion sensitivity within the Zou watershed at the Atchérigbé outlet. The analysis integrated SRTM Digital Elevation Models (30 m, 2023), Landsat OLI TIRS 09 imagery (scenes 192-054 and 191-055, from 2018 to 2023), rainfall data from 1986 to 2021 provided by METEO BENIN, and FAO soil data. Soil loss was quantified using the Universal Soil Loss Equation (USLE), which combines rainfall erosivity (R), soil erodibility (K), the topographic factor (LS), and the vegetation cover factor (C) derived from supervised land cover classification. Five classes of erosion sensitivity were identified with in average 54 t ha−1 yr−1. Very low and low sensitivity zones dominate the watershed, covering 97 % and 2 % of the area, respectively. Medium-sensitivity areas (0.21 %) form a longitudinal band across the basin, while high-sensitivity areas (0.29 %) occur mainly in the western sector. Very high sensitivity zones (0.013 %) are localized in slope failure sites. These spatial patterns underscore the heterogeneous nature of erosion processes and their potential impacts on agricultural productivity, biodiversity, and ecosystem stability. While the USLE provides a robust framework for large-scale erosion assessment, its application remains constrained by sensitivity to input data accuracy and limited suitability under extreme conditions. Nevertheless, the results offer critical insights for watershed management. To mitigate ongoing land degradation, the study emphasizes the necessity of compensatory reforestation and the adoption of sustainable land management practices.
The study assesses the vulnerability of family farms to rainfall-induced flooding in the municipality of Kandi, northern Benin, using the IPCC vulnerability framework based on exposure, sensitivity, and adaptive capacity. Data were collected from 80 agricultural producers across ten villages through structured interviews and analyzed across five types of capital: human, physical, financial, social, and natural. Results reveal significant disparities between farm categories. Small farms (≤ 5 ha) exhibit very high vulnerability (4.5/5), marked by low education levels (38.9 % uneducated), limited mechanization (83.3 % without access), and poor credit availability (77.8 % excluded). Medium farms (5–20 ha) show moderate vulnerability (≈ 3.0/5), with 61% having access to mechanization and 46.3% to credit, but still constrained by low income diversification. Large farms (>20 ha) demonstrate low vulnerability (≈ 1.5/5), benefiting from strong assets: 76.2 % mechanized, 71.4 % with credit access, 90.5 % participating in cooperatives, and 57.1 % cultivating fertile soils. The analysis highlights an inverse correlation between farm size and flood vulnerability, reflecting structural inequalities in access to productive and adaptive resources. Strengthening human and financial capital among smallholders – through literacy, agricultural training, microfinance, and cooperative mechanization – is crucial to enhance resilience. This research contributes to climate vulnerability literature by integrating socio-economic and biophysical indicators into a composite framework, emphasizing the multidimensional nature of vulnerability and the need for inclusive adaptation policies in northern Benin.
In Benin, the Lower Ouémé Valley is a region of intense agricultural activity whose soil, hydrological, and climatic conditions are favourable to rice cultivation. Despite this potential, water management remains one of the challenges facing rice production in the area. The development of new land management approaches and agricultural practices is an ideal alternative for improving water management for agricultural production in a context of climate variability. Among these, the Smart-Valleys (SV) land management approach and the System of Rice Intensification (SRI) have been introduced to make rice production profitable in several regions of West Africa. This study aims to compare two land management approaches (Smart-valleys vs. Conventional) and three levels of irrigation, namely Low Variable Irrigation (IR1), Low Constant Irrigation (IR2), and Intermittent Irrigation (IR3) for efficient water management in rice production in the lower Ouémé valley. The experimental design consists of split plots with two replicates per treatment, where the management approach and irrigation levels represent the primary and secondary factors, respectively. The results of the experiment show that Smart-valleys (SV) management has a positive effect on yield (p-value = 0.01) and water productivity (p-value < 0.001). As for irrigation, the IR3 method yields the best performance in terms of water productivity (p-value < 0.001). In addition, the SV-IR2 combination maximizes paddy rice yields (8.5 t ha−1, an increase of 4.7 t ha−1), while the SV-IR3 combination optimizes water productivity (1.4 kg m−3, an increase of 1.06 kg m−3). This highlights the importance of an integrated approach that combines appropriate land management and optimal irrigation strategies to maximize water efficiency in rice production systems. An economic analysis of the different treatments will help identify the best approach to combine yields, water use, and profitability.
The Lake Nokoué and Porto-Novo Lagoon complex represents the most important Lagoon system in Benin and is influenced by saltwater intrusion during Low water period via Cotonou channel. The main objective was to determine the composition and structure of benthic macroinvertebrates based on their preferred habitat. Samples were collected from 15 stations between November 2022 and October 2023, following the fluctuations of the hydrological regime notably High water (October and November), Low water (January and March) and Slight rise water (June and August). The results reveal a total of 79 species across the entire studied Lagoon complex. For Lake Nokoué, the following abundances were obtained for: strictly freshwater taxa (4.27 %), strictly brackish taxa (44.58 %), and strictly saltwater taxa (11.86 %). In the Porto-Novo Lagoon, the following abundances were recorded for: strictly freshwater taxa (7.78 %), strictly brackish taxa (6.96 %), and strictly saltwater taxa (16.83 %). Regarding the hydrological regime, a predominance of strictly freshwater taxa was noted during the High water period in Lake Nokoué. Furthermore, this predominance was recorded during both High and Low water periods within the Porto-Novo Lagoon. Thus, the variation of these taxa was found to be more pronounced in Lake Nokoué (p<0.01) than in the Porto-Novo Lagoon (p>0.05).
Lake Toho is one of the important freshwater ecosystem in southern Benin, under intense human activities. This study aims to assess the spatial and temporal variations of water physicochemical parameters in Lake Toho. A total of 54 water samples were taken during three campagns at 6 stations from February to June 2024. Water depth and Secchi Disk Depth (SDD) were recorded in situ using a Secchi disk. Turbidity was determined using a turbidimeter. Parameters such as temperature, pH, dissolved oxygen, oxygen saturation, salinity, Electrical Conductivity (EC), and Total Dissolved Solids (TDS) were measured using a probe multiparameter. Nutrient concentrations were quantified using specific reagents and a spectrophotometer. Analyses of variance revealed spatial and temporal significant variations (p < 0.05) for water depth (1.71–2.24 m), SDD (21.33–26.44 cm), pH (7.06–7.68), temperature (30.4–32.25 °C) and suspended solids (22.56–27.22 mg L−1). EC (470.56–508.72 µS cm−1), salinity (0.22–0.24 PSU), nitrate (7.29–10.66 mg L−1), ammonium (0.18–0.28 mg L−1) and orthophosphate (0.08-0.13 mg L−1) were showed only very significant variation between months (p < 0.001). Significant correlations (p < 0.001) were observed between SDD and turbidity, between nitrate and pH, water depth, salinity, EC and TDS. The nutrient enrichment observed in this lake could be responsible for organic pollution, likely driven by anthropogenic inputs. This study constitutes a preliminary assessment of the ecosystem, and further investigations are needed to better understand its biogeochemical functioning and the nature of the disturbances affecting it.
In centenary celebration of IAHS,
The backwater effect can be caused by a number of factors, including the bridge's pier width and deck level. In lowland regions, the backwater effect can be particularly problematic, as it can lead to flooding and inundation of farmlands and houses. This study examines the impact of bridge structure on the backwater effect in the lowland region of Shreekhandapur, Kavre, Nepal. The study area is characterized by a hill slope and inner river valley, with a gentle river profile that results in low flow velocity and increased deposition. The study compares the backwater effect of an old demolished bridge with four piers of 1.2 m width and abutments on both sides to a new bridge with a single pier of 0.4 m width and a deck level that is 1 m higher. HEC-RAS software was used to analyse the steady flow and create a flood hazard map. The results show that the new bridge with a reduced pier width and increased deck level significantly reduces the backwater effect. The flood hazard map shows that the new bridge along with floodwalls reduces the potential hazard-prone areas by up to 50 %. The findings of this study can be applied to disaster mitigation and bridge design in lowland regions. By reducing pier width and increasing deck level, bridges can be designed to minimize the backwater effect and reduce the risk of flooding.