This paper presents a long-term environmental observation dataset from six sites in Burkina Faso and Ghana, providing measurements of exchanges of carbon, water, and energy between ecosystems and the atmosphere. The data were collected across different land-use types and processed using a consistent workflow to ensure quality and comparability. The openly available dataset supports climate research, environmental monitoring, and improved understanding of environmental change in West Africa.

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.

This study explored how droughts have evolved over time in northern Benin, a region that relies heavily on rainfall for farming. By analyzing rainfall and river flow patterns, the research found that while conditions improved after major droughts in the 1970s and 1980s, recent years have seen longer dry spells and worsening water shortages. These results can inform better planning for farming and water use in a changing climate.

The objective is to assess the causes of exceptional floods in the Ouémé basin using the HEC-RAS model. The results of the calculation made it possible to characterize: the losses and damage due to human settlement on the banks and agricultural production in the flood zone, the flooded extent and the height of submersion depend on the return period, most of the Flood waters converge towards the west of the basin (low risk) and towards the east around the Damè-Wogon depression (high risk).

Knowledge of rainfall statistical behaviour is a prerequisite to designing rainwater drainage facilities. In West Africa, most practitioners use the Gumbel distribution regardless of rainfall statistical behaviour. This study quantifies biases induced by the use of the Gumbel distribution. It was found that the use of the Gumbel distribution instead of appropriate distributions leads to an underestimation (−45.9 %) of annual daily rainfall maxima and thus to an uncertain design of facilities.