Unravelling the Impacts of Climate Variability on Surface Runoff in the Mouhoun River Catchment (West Africa)

This study assesses the impacts of climate variability on surface runoff generation in the Mouhoun River Catchment (MRC) in Burkina Faso, in the West African Sahel. The study uses a combination of observed and reanalysis data over the period 1983–2018 to develop a SWAT model (KGE = 0.77/0.89 in calibration/validation) further used to reconstitute the complete time series for surface runoff. Results show that annual rainfall and surface runoff follow a significant upward trend (rainfall: 4.98 mm·year−1, p-value = 0.029; runoff: 0.45 m3·s−1·year−1, p-value = 0.013). Also, rainfall appears to be the dominant driver of surface runoff (Spearman’s ρ = 0.732, p-value p-value = 0.386). The study highlights the added value of the coupling of hydrological modeling and reanalysis datasets to analyze the rainfall–runoff relationship in data-scarce and poorly gauged environments and therefore raises pathways to improve knowledge and understanding of the impacts of climate variability in Sahelian hydrosystems

Validation of high-resolution satellite precipitation products over West Africa for rainfall monitoring and early warning

Satellite rainfall estimation products (SRPs) can help overcome the absence of rain gauge data to monitor rainfall-related risks and provide early warning. However, SRPs can be subject to several sources of errors and need to be validated before specific uses. In this study, a comprehensive validation of nine high spatial resolution SRPs (less than 10 km) was performed on monthly and dekadal time scales for the period 2001–2015 in West Africa. Both SRPs and reference data were remapped to a spatial resolution of 0.1 ° and the validation process was carried out on a grid scale, with 1,202 grids having at least one rain gauge throughout West Africa. Unconditional statistical metrics, such as mean absolute error, Pearson correlation, Kling-Gupta efficiency and relative bias, as well as the reproducibility of rainfall seasonality, were used to describe the agreement between SRPs and reference data. The PROMETHEE II multi-criteria decision analysis (MCDA) method was employed to rank SRPs by considering criteria encompassing both their intrinsic characteristics and performance metrics. Overall, IMERGv6-Final, MSWEPv2.2, RFE2, ARC2, and TAMSATv3.1, performed reasonably well, regardless of West African climate zones and rainy season period. Given the performances displayed by each of these SRPs, RFE2, ARC2, and MSWEPv2.2 would be suitable for drought monitoring. TAMSATv3.1, IMERGv6-Final, RFE2, ARC2, and MSWEPv2.2 are recommended for comprehensive basin water resources assessments. TAMSATv3.1 and MSWEPv2.2 would be of interest for the characterization of variability and long-term changes in precipitation. Finally, TAMSATv3.1, ARC2, and MSWEPv2.2, could be good alternatives to observed data as predictants in West African Regional Climate Outlook Forum (RCOF) process

West Africa regional training on ENACTS-related capacity for National Meteorological Services and the Regional Climate Centre

The AICCRA West Africa cluster in collaboration with AGRHYMET and IRI organized a regional capacity training on ENACTS tools. The Agence Nationale de la Météorologie (ANAM) of Burkina Faso hosted National Meteorological Services as well as the Regional Climate Center (AGRHYMET) experts from two AICCRA and three non-AICCRA countries from West Africa to build their capacity to produce more tailored and real-time climate information and services for decision making. Capacity building focused on the following topics: • Use of Climate Data Tool (CDT) for data organization, quality control, merging of different datasets, data analysis, and visualization; • Introduction to IRI’s Automatic Weather Station Data Tool (ADT); and • Use of IRI’s Next Generation (NextGen) seasonal forecast system for seasonal rainfall forecast

Scaling the Next Generation of Seasonal Climate Forecasts through the West African Regional Climate Outlook Forum

Ninety participants, including 15 women, from the 17 National Meteorological and Hydrological Services (NMHSs) in West and Central Africa were successfully trained on the NextGen/PyCPT tool through AICCRA side during 2022 PRESASS. NMHS staff have gained a good understanding of the benefits of the NextGen/PyCPT tool as a vital element in the development of better seasonal forecasts at national and regional levels, and how to use PyCPT. About half of the trainees planned to share the knowledge, skills and resources acquired during the training with their colleagues at institutional level. Follow up training was organized to selected AICCRA anchored and non-AICCRA countries in West Africa to strengthen the pool of expertise in the use of the NextGen tools. This needs to be strengthened in the future

Towards a new approach for Seasonal Climate Forecasting in West Africa

West African Regional Climate Outlook Forums (RCOF) help end-users minimize climate-related risks and maximize benefits in different sectors. However, the current RCOF process or approach for generating climate information is subject to some shortcomings. To improve seasonal forecasting in West Africa, it is suggested that advances in computer technology, improved climate models, and the availability of products from several global climate centers be leveraged to develop an objective integrated seasonal forecasting process that will serve as a reference for the West African RCOF

Prevalence and Molecular Characterization of Mycobacterium bovis in Slaughtered Cattle Carcasses in Burkina Faso; West Africa

This cross-sectional study was conducted at the slaughterhouses/slabs of Oudalan and Ouagadougou in Burkina Faso, between August and September 2013. It aimed at determining the prevalence of bovine tuberculosis (bTB) suggestive lesions in slaughtered cattle carcasses and to identify and characterize the mycobacteria isolated from these lesions. A thorough postmortem examination was conducted on carcasses of a total of 2165 randomly selected cattle. The overall prevalence of bTB suggestive lesions was 2.7% (58/2165; 95% CI 2.1–3.5%). Due to the low number of positive samples, data were descriptively presented. The lesions were either observed localized in one or a few organs or generalized (i.e., miliary bTB) in 96.6% (n = 57) and 3.4% (n = 2), respectively. The identified mycobacteria were M. bovis (44.4%, n = 20), M. fortuitum (8.9%, n = 4), M. elephantis (6.7%, n = 3), M. brumae (4.4%, n = 2), M. avium (2.2%, n = 1), M. asiaticum (2.2%, n = 1), M. terrae (2.2%, n = 1), and unknown non-tuberculous mycobacteria (NTM) (11.1%, n = 5). Moreover, eight mixed cultures with more than one Mycobacterium species growing were also observed, of which three were M. bovis and M. fortuitum and three were M. bovis and M. elephantis. In conclusion, M. bovis is the predominant causative agent of mycobacterial infections in the study area. Our study has identified a base to broaden the epidemiological knowledge on zoonotic transmission of mycobacteria in Burkina Faso by future studies investigating further samples from humans and animals, including wild animals employing molecular techniques

Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning : HYPE Models in the Sirba River

In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem inWest 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 effciency NSE = 0.58) thanWWH(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” forNH(Heidke skill score HSS = 0.53) and “good” forWWH(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

Strengthening flood and drought risk management tools for the Lake Chad Basin

Lake Chad is extremely sensitive to climate variability because it is a shallow inland lake, and about 97.5% of its water supply depends on the Chari-Logone River System and other tributaries. Any increase or decrease in lake volume inflow means a substantial increase or decrease in lake area. Droughts in the Sahel Region and within the basin after the 1970s had great impact on discharges of different tributaries, which led to a drastic decrease of water inflow in the lake, as well as significant seasonal and inter-annual variation of the lake area over the last 50 years. Information gaps about the water system and uncertainties about climate variability and change remain a challenge. Hydrological extremes, both floods and droughts, present a threat to agriculture and water resource management within the Lake Chad Basin. Drought and flood monitoring over the basin is difficult because of the shortage of observational data, both historic and in real time. Satellite remote sensing and hydrological modelling are techniques used to compensate for the data collection shortcomings of the region. The Africa Flood and Drought Monitoring (AFDM) provides drought and flood monitoring, and short-term and seasonal forecasting that combine climate prediction, hydrological modelling and remote sensing data in the sub-Saharan African continent. For the Lake Chad Region, the system was adapted with higher resolution to provide near-real-time water levels, as well as short-term forecast of flood risks, as well as medium-term forecasts of drought hazards and long-term projections of climate change impacts. Preliminary results are very encouraging; the system will continue to be updated, tested and validated to enable its operational use by decision-makers at all levels.</p