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In recent decades, floods have become a major global concern. In Niamey (Niger) in West Africa, flooding is primarily caused by the significant increase in surface runoff resulting from heavy rainfall occuring between July and September in the upstream river basins of the three major tributaries of the Middle Niger River (Sirba, Gorouol ad Dargol catchments). While the Sirba is empirically considered as the largest driver to flooding in Niamey, its contribution have not been precisely established. This study analyzes the influence of these tributaries on the Niger River discharges at Niamey during the rainy season, with a particular focus on the Sirba River basin. Daily annual maximum discharge (AMAX) data from 1990 to 2022 timeseries are used as inputs to various statistical analyses, including trend analyses, change point detection, concordance analysis and flood dependency assessment. The results reveal a significant change point in 2009 and increasing trends between the Sirba and Niger River stations. The flood propagation time delay varies from 1 to 4 days between the upstream river basins tributaries and Niamey station, with a strong concordance in peak discharges, particularly dominant with the Sirba River. The Dynamic Time Warping (DTW) and the Gumbel copula analyses highlighted the significant control of the Sirba River basin on flooding in Niamey, while also highlighting the important roles played by other tributaries. These findings are crucial for improving flood prevention and further refine urban flood management strategies in Niamey and other cities globally, affected by fluvial floods.

This study focuses on the Sirba River Basin (SRB), a transboundary West African catchment of 38,950 km2 shared by Burkina Faso and Niger, which contributes to flooding downstream in Niamey (Niger). The study uses the HEC-HMS hydrological model to explore the dynamics of the daily rainfall–runoff relationship over the period 2006–2020. The model is calibrated using observed rainfall at 13 meteorological stations within the river basin and observed discharges at the Garbey Kourou hydrometric station outlet. Two types of simulation are compared: (i) a continuous simulation (CS) over the period 2006–2020 and (ii) an event-based simulation (ES) using selected major flood events in 2010, 2012, 2013, 2015 and 2020. The results showed satisfactory model performance under both modeling schemes (R2 = 0.84–0.87 for CS and R2 = 0.94–0.98 for ES), with a superior performance of ES over CS. Also, significant differences in the distribution of calibrated model parameters for the percent impervious and the attenuation flood wave factor were observed. A sensitivity analysis revealed that the curve number, initial abstraction, lag time and routing time factors were influential on the model outputs. The study therefore underscores the model’s robustness and contributes crucial insights for flood control management and infrastructure planning in the SRB.

The Sahelian regions are affected by an increasing number of catastrophic floods in recent years as a consequence of climate and land use/land cover changes. River flow data is key to understanding river behavior and develop flood mitigation and prevention strategies. The present study provides a revision and an update of the existing discharge dataset of the Sirba River with the aim of enhancing the reliability of these data. The revision also includes the recalibration of the Garbey Kourou rating curves. The analysis of the revised discharge time series strengthens the previous findings, evidencing a positive trend in flood frequency and intensity over the entire analyzed period of 1956–2018. This positive trend is more pronounced for the last 40 years due to a significant underestimation of the rating curves used. A relevant finding is a new changepoint in the time series, detected for 2008, which represents the beginning of the period in which the highest flood magnitudes were registered. The effect of land use/land cover changes and climate changes on the water resource is depicted using flow duration curves. This research produces a revised and more reliable discharge time series that will be a new starting point for future hydrological analyses.

In Sahelian countries, a vast number of people are still affected every year by flood despite the efforts to prevent or mitigate these catastrophic events. This phenomenon is exacerbated by the incessant population growth and the increase of extreme natural events. Hence, the development of flood management strategies such as flood hazard mapping and Early Warning Systems has become a crucial objective for the affected nations. This study presents a comprehensive hazard assessment of the Nigerien reach of the Sirba River, the main tributary Middle Niger River. Hazard thresholds were defined both on hydrological analysis and field effects, according to national guidelines. Non-stationary analyses were carried out to consider changes in the hydrological behavior of the Sirba basin over time. Data from topographical land surveys and discharge gauges collected during the 2018 dry and wet seasons were used to implement the hydraulic numerical model of the analyzed reach. The use of the proposed hydraulic model allowed the delineation of flood hazard maps as well the calculation of the flood propagation time from the upstream hydrometric station and the validation of the rating curves of the two gauging sites. These significative outcomes will allow the implementation of the Early Warning System for the river flood hazard and risk reduction plans preparation for each settlement.

Emerging hydrological services provide stakeholders and political authorities with useful and reliable information to support the decision-making process and develop flood risk management strategies. Most of these services adopt the paradigm of open data and standard web services, paving the way to increase distributed hydrometeorological services’ interoperability. Moreover, sharing of data, models, information, and the use of open-source software, greatly contributes to expanding the knowledge on flood risk and to increasing flood preparedness. Nevertheless, services’ interoperability and open data are not common in local systems implemented in developing countries. This paper presents the web platform and related services developed for the Local Flood Early Warning System of the Sirba River in Niger (SLAPIS) to tailor hydroclimatic information to the user’s needs, both in content and format. Building upon open-source software components and interoperable web services, we created a software framework covering data capture and storage, data flow management procedures from several data providers, real-time web publication, and service-based information dissemination. The geospatial infrastructure and web services respond to the actual and local decision-making context to improve the usability and usefulness of information derived from hydrometeorological forecasts, hydraulic models, and real-time observations. This paper presents also the results of the three years of operational campaigns for flood early warning on the Sirba River in Niger. Semiautomatic flood warnings tailored and provided to end users bridge the gap between available technology and local users’ needs for adaptation, mitigation, and flood risk management, and make progress toward the sustainable development goals.

In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models—Niger HYPE (NH) and World-Wide HYPE (WWH)—in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash–Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to “very good” for NH (Heidke skill score HSS = 0.53) and “good” for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10–20% of forecasts were unfortunately not produced in 2019, impacting operational availability.