Operational numerical weather prediction in Sub-Saharan Africa remains fragile despite increasing availability of models and data. A key limiting factor is the limited operational and institutional capacity of National Meteorological Services to operate, assess, adapt, and sustain forecasting chains within routine operations for early warning systems. Short-term, tool-oriented training initiatives often fail to address this gap, as learning remains weakly embedded in daily service delivery and rarely translates into sustained operational competence.This paper presents a qualitative case study examining how co-production principles can be operationalized to support the development of operational competence for flood early warning. The study draws on a long-term capacity-development experience involving the National Meteorological Services of Niger and Burkina Faso and the Tuscany Regional Meteorological Service in Italy. The approach combined long-term embedding of trainees within an operational forecasting team, training-of-trainers, and peer-to-peer institutional collaboration, linking learning to real operational workflows.Results are analysed across methodological, operational, and institutional dimensions. Methodological findings show how co-production principles can structure competence-oriented training processes by integrating instructional design, operational practice, and iterative evaluation. Operational results highlight the importance of sustained practice and routine verification, while institutional results point to the role of training of trainers and public institutional collaboration supporting sustainability of competence beyond individual skills and knowledge.By reframing competence as a foundational component of climate services, the study offers transferable insights for capacity development in resource-constrained and transboundary contexts.
Most flood risk assessments in rural areas of Low- and Middle-Income Countries rely on outdated, coarse-resolution geoinformation. Participation from exposed communities remains rare, and risk reduction measures are seldom identified. Our purpose is to address these shortcomings in a densely populated, frequently flooded rural area along the Niger River. The novelty of the study lies in the use of fine-grained spatial information and its integration with local knowledge, which led to the selection of appropriate risk-reduction measures. Risk was quantified monetarily as the product of hazard and expected damage. Riverine flooding and flash floods from tributaries are identified as the primary threats. Flood-prone areas for three return periods were identified using a 4-m resolution surface elevation model and BASEMENT’s two-dimensional hydraulic modelling. Assets were first identified using Google Earth imagery and subsequently verified on-site. Risk reduction measures were initially selected through a participatory SWOT analysis and then prioritised using eight criteria. The study found that the areas under cultivation have remained stable over the last 10 years, but settlements significantly expanded in flood-prone areas. A centennial flood during the dry season could cause €5.4 million in damage, with the municipality of Kourteye accounting for €3.4 million. Risk was primarily determined by damage to irrigated crops rather than buildings. Priority measures included extending and reinforcing levees in rice-growing areas, providing an Early Warning System, and improving sanitation and hygiene. However, the benefits of reducing the risk outweigh the costs only in the municipalities of Karma and Namaro.
Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km2 that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes information on (i) areas exposed to the two flood types that characterise the river's hydrological regime and flash floods from the wadi, (ii) flood-prone crops, buildings and (iii) measures for risk treatment. Discharge data, a 4 m horizontal-resolution digital elevation model, and two-dimensional hydraulic modelling with BASEMENT were used to identify flood-prone areas. Visual interpretation of high-resolution satellite imagery in Google Earth, together with field inspections, enabled the identification of exposed assets. The Information System on Rural Markets of Niger and house compensation values recognised during resettlement-related works enabled asset valuation. Risk was expressed in monetary terms as the product of flood probability and expected damage. Risk-reduction measures were identified with stakeholders through a SWOT analysis and prioritised using eight criteria. The dataset can support emergency plans, flood early warning systems, rescue and recovery operations and flood risk management. Dataset: https://data.mendeley.com/datasets/9v349h653n/6 (accessed on 1 June 2026).Dataset License: CC-BY, 4.0
In order to study the behavior of the solar photovoltaic 7MW Malbaza depending on the irradiation and temperature, modeling of the various components of the system is essential. It is in this context that we have done modeling of a PV module comprising the solar field of the said plant. In this article, we initially presented the basic mathematical model of the photovoltaic cell, reported the equations models with two diodes and a diode, and later considered the case of the model with a diode to perform modeling PV of the central module. Secondly, we implemented the model developed in MATLAB Simulink to study the current-voltage characteristics for different irradiations considering the constant temperature and then for different temperature keeping constant light. The results are similar to the current-voltage characteristics provided by the manufacturer. This allowed us to conclude that the model developed is reliable. Finally, we will study the influence of radiation and temperature on the production of the module.
In this article, a new configuration of MOX gas sensor was designed, fabricated, and characterized, with a particular design including a micro-heater and a sensing electrode on the same plane using uncomplicated and low-cost steps. A spiral form of the micro-heater and sensing electrode were fabricated in 5 mm × 5 mm surface on a Si/SiO2/Al-0.5Cu. Owing to its high sensitivity in gas sensing, the SnO2 layer was deposited on the top of the sensor. The performances of the fabricated sensor were performed by electrical characterization and sensing behavior under ethanol, LPG, and natural gas. The thermal characterization gave a TCR of 3.2 × 10−3 °C−1 for SnO2/Al-0.5Cu micro-heater. The evolution under gases of the typical device at different temperatures ranging from 218 to 280 °C and relative humidity from 40% to more than 80% showed high response, stability, and reproducibility. The selectivity of the fabricated sensor towards ethanol makes it a perfect candidate for ethanol sensing.
