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The Inner Niger Delta (IND), one of the largest floodplain systems in Africa, sustains the livelihoods of more than three million people and is a driver of the rural economy of Mali as far as agriculture, fish production, and livestock are concerned. Because the IND ecosystem and economy are flood-dependent, it is important to monitor seasonal flooding variations. Many attempts to accomplish this task have relied on detailed datasets, such as daily discharge, daily rainfall, and evapotranspiration, which are not easily accessible for data-sparse areas. Additionally, because the area is large, this remains a challenging task. In this study, the interannual variability of seasonal inundation in the IND was investigated by leveraging the computing power of the Google Earth Engine and its large catalogue of open datasets. The main objective was to analyse the temporal and spatial distributions of the inundation extent during the last 13 years. A collection of Landsat 5, 7, 8, and 9 images were composited and different bands were used with various water and vegetation indices in a pixel-based supervised classification to detect the flood extent between 2010 and 2022. A significant improvement in classification accuracy was observed thanks to the different indices. The results suggest a general increasing trend in the maximum annual inundation extent. Throughout the study period, the maximum inundated area varied between 15,209 km2 in autumn 2011 and 21,536 km2 in autumn 2022. The upstream water intake led to a decrease of about 6–10% of the inundated area. Similar fluctuations in the inundated area, precipitation, and river discharge were observed. The proposed approach demonstrates a great potential for monitoring annual inundation, especially for large areas such as the IND, where in situ measurements are sparse. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess groundwater recharge in the hard-rock central region of Benin so as to compare it with the water needs of the local population. To reach this objective, we applied the Water Table Fluctuation (WTF) method, which requires long-term monitoring of groundwater level fluctuations. Groundwater level time series were used in combination with other data (including time series of surface water discharge and rainfall) to estimate groundwater recharge but also to shed further light on the relationship between surface water and groundwater. The results demonstrated that the minimum inter-annual groundwater recharge amount is about 1.09 × 109 m3, which is enough to cover the basic water needs of the local population. It should be highlighted that in sub-regions where the density of the population is high, water shortage can still occur with the above estimated groundwater recharge amount. This study has also illustrated that when applying the WTF method, sites with a highly uncertain specific yield can be detected. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dans la région soudanienne du Burkina Faso riche en bas-fonds, ces derniers représentent une facette du paysage inondable et fertile, ayant une fonction contre-aléatoire à travers des productions diversifiées en milieu humide en saison des pluies, et en contre-saison. Les activités de bas-fond permettent de réduire la vulnérabilité des groupes les plus exposés, comme les petites exploitations, les femmes et les jeunes, mais les arrangements fonciers restent précaires. C’est aussi un paysage particulièrement changeant avec des actions d’aménagement collectif hydraulique, rizicole et maraîcher. Les changements climatiques en cours atténuent l’aléa de la sécheresse sauf en début de saison, et un risque accru d’inondations est apparu. Ces tendances renforcent l’aléa du régime de l’eau, limitant les rendements, ce qui requiert des innovations dans les domaines technique et organisationnel, qui devront prendre en compte explicitement la dimension sociale et environnementale des bas-fonds.

Freshwater supply remains limited in West Africa due to lack of operational governance frameworks. In this study, the Water flow and balance Simulation Model (WaSiM) and the Soil and Water Assessment Tool (SWAT) were applied in the Ouriyori catchment (14.5 km2, Benin) to assess hydrological ecosystem services (HES) in terms of service flow and service capacity using the ecosystem accounting framework. The modelling exercises indicated satisfactory goodness-of-fit coefficients greater than 75% with an absolute bias of less than 25%. The HES capacity was in general higher than the HES flow for crop and household (surface/groundwater) water supplies, indicating that the catchment can potentially supply more water under optimal storage and management conditions. Positive and negative shifts in service capacities of crop water and household supplies were observed over the simulation period. These significant results can support sustainable interventions in securing water and food productions through increasing HES flow and capacity. [ABSTRACT FROM AUTHOR]

The need for a detailed investigation of the Vea catchment water balance components cannot be overemphasized due to its accelerated land-cover dynamics and the associated impacts on the hydrological processes. This study assessed the possible consequences of land-use change scenarios (i.e. business as usual, BAU, and afforestation for the year 2025) compared to the 2016 baseline on the Vea catchment's water balance components using the Soil and Water Assessment Tool (SWAT) model. The data used include daily climate and discharge, soil and land use/land cover maps. The results indicate that the mean annual water yield may increase by 9.1% under the BAU scenario but decrease by 2.7% under the afforestation scenario; actual evapotranspiration would decrease under BAU but increase under afforestation; and groundwater recharge may increase under both scenarios but would be more pronounced under the afforestation scenario. These outcomes highlight the significance of land-cover dynamics in water resource management and planning at the catchment. [ABSTRACT FROM AUTHOR]

Flood recession farming practiced in flood-prone areas and on the banks of rivers and lakes in arid or semi-arid environments essentially depends on the soil water stock after the flood has receded. During these last few decades, this coveted agriculture is increasingly challenged by severe water constraints, due to increased hydrological hazards and development projects aimed at controlling floods. These challenges are difficult to anticipate, and are the subject of a great deal of uncertainty regarding the sustainability of development projects in the concerned areas. In this study, recent hydraulic data of the Senegal River were analyzed to understand the constraints related to the river management in flood-prone areas. Satellite imagery analysis techniques were used to estimate flooded areas and establish relationships with the river regime. Agricultural practices implemented by farmers were also analyzed to evaluate the resilience of this cropping system to the risk of water stress. The results confirmed many constraints of different importance related to the objectives assigned to the management of dams under multiple water use context. It clearly came out that the water resource management rules relegate flood-recession agriculture to the lowest priorities. In addition, there are safety issues related to unexpected effects of flooding on the water structures and in the nearby inhabited localities of flood-prone areas. Knowing some characteristics of the flooding and of the river's levels and their relationships can be useful within the framework of an organized climate service that would help farmers and communities to better anticipate constraints. [ABSTRACT FROM AUTHOR]

High rainfall events and flash flooding are becoming more frequent, leading to severe damage to crop production and water infrastructure in Burkina Faso, Western Africa. Special attention must therefore be given to the design of water control structures to ensure their flexibility and sustainability in discharging floods, while avoiding overdrainage during dry spells. This study assesses the hydroclimatic risks and implications of floodplain climate-smart rice production in southwestern Burkina Faso in order to make informed decisions regarding floodplain development. Statistical methods (Mann-Kendall test, Sen's slope estimator, and frequency analysis) combined with rainfall-–runoff modeling (HBV model) were used to analyze the hydroclimatic conditions of the study area. Moreover, the spatial and temporal water availability for crop growth was assessed for an innovative and participatory water management technique. From 1970 to 2013, an increasing delay in the onset of the rainy season (with a decreasing pre-humid season duration) occurred, causing difficulties in predicting the onset due to the high temporal variability of rainfall in the studied region. As a result, a warming trend was observed for the past 40 years, raising questions about its negative impact on very intensive rice cultivation packages. Farmers have both positive and negative consensual perceptions of climatic hazards. The analysis of the hydrological condition of the basin through the successfully calibrated and validated hydrological HBV model indicated no significant increase in water discharge. The sowing of rice from the 10th to 30th June has been identified as optimal in order to benefit from higher surface water flows, which can be used to irrigate and meet crop water requirements during the critical flowering and grain filling phases of rice growth. Furthermore, the installation of cofferdams to increase water levels would be potentially beneficial, subject to them not hindering channel drainage during peak flow. [ABSTRACT FROM AUTHOR]

Although the evaluation of soil organic carbon (SOC) stocks across different types of land use and major reference soil groups is essential for mitigating climate change, there remains, to date, limited comprehensive understanding of whole tropical soil profiles. Therefore, this study aimed to explain the amount of SOC stocks in different land-use systems and across various soil groups, as well as its spatial pattern in the topsoil (0–30 cm) and subsoil (30–100 cm) within the savannah zone of Burkina Faso. Roughly 70 soil profiles were considered along with additional auger sampling to account for spatial variation in both cropland (CR) and savannah (SA). The machine learning technique random forest regression (RFR) and multiple linear regression (MLR) were used for modeling the surface and subsurface SOC stocks. For model calibration, covariates including land use, topographic, texture, and climatic data were considered as surrogate for soil forming factors. The prediction maps produced by the calibrated models were validated by an independent dataset. The results indicated that about 53% of the SOC stock over 1 m depth was held in the upper 30 cm. Only a marginal difference was recorded between the topsoil SOC stock in SA (41.4 t C ha −1 ) and CR (39.1 t C ha −1 ) soils. For the subsoil, a significant difference (p < 0.05) was observed between the SOC stock of CR soils recording about 40.2 t C ha −1 and SA soils with 26.3 t C ha −1 . Among the reference soil groups, the Gleysols located at lower elevation positions revealed the highest SOC stocks over 0–30 cm (44 t C ha −1 ) and 100 cm depth (86.6 t C ha −1 ). The Stagnosols (45.2 t C ha −1 ) followed by the Gleysols (42.7 t C ha −1 ) recorded the highest SOC stocks over 30–100 cm. The variability of SOC stock in the topsoil was primarily related to site-specific elements, such as particle-size fraction and wetness index, while its distribution in the subsoil was mainly associated with the topographical orientation represented by the slope aspect. Compared to the MLR, RFR estimated mean top- and subsoil SOC stocks of the catchment fairly well, along with lower statistical error metrics, though extreme values were not covered. Nevertheless, the findings on SOC stocks reinforce the view that the semi-arid ecosystems of West Africa still offer a significant opportunity for carbon sequestration for both topsoil and subsoil, and these results represent a baseline for future modeling of SOC dynamics in the region. [ABSTRACT FROM AUTHOR]