http://197.159.135.214/jspui/handle/123456789/980 2026-06-25T09:08:00Z 2026-06-25T09:08:00Z Oussou, Enagnon Francis http://197.159.135.214/jspui/handle/123456789/1253 2026-06-25T09:02:05Z 2025-08-01T00:00:00Z

Groundwater-atmosphere Interactions as Environmental Drivers of Water Cycle, Energy, and Greenhouse Gas Fluxes in West Africa Oussou, Enagnon Francis Reducing discrepancies in the simulation of water and energy fluxes remains a key challenge in accurately representing surface water flux processes, particularly in regions with limited observational data. This study evaluates the sensitivity of the WRF-Hydro model to three parameterization schemes - Free Drainage (FD), TOPMODEL, and MMF - over West Africa. The results show that MMF outperforms the other schemes in representing surface water flux variables, especially in topographic convergence zones, where soil moisture and evapotranspiration in riverbeds increase by 20% with respect to FD. At the catchment scale, soil moisture, evapotranspiration, and groundwater storage are well simulated, with correlation coefficients reaching 0.9. In addition, model calibration for the Donga River gives reliable performance with KGE value up to 0.74. The shrubland, bare soil, and grassland (SBG) in MODIS-IGBP land cover is substituted by the Evergreen Broadleaf Forest (EBF), Savanna (SAV), and Woody Savanna (WS) to mimic the the Great Green Wall (GGW) initiative. At basin scale, the seasonal cycle and inter-annual variability are well captured as there is a strong linear relationship between the observed and simulated values with correlation coefficients from 0.9 to 0.97. The KGE values reaches respectively 0.72, 0.71, and 0.72 in Oueme, Sissili, and Faga catchments. Compared to the current land use (REF) scenario, EBF-VC and WS-VC experiments decrease the mean soil moisture (SM) by 0.2 and 0.1 mm, while the SAV-VC increases it by 0.8 mm in drier conditions in Faga. However, the scenarios reveal a decrease of mean SM by 0.5, 0.6, and 0.1 mm for EBF-VC, SAV-VC, and WS-VC in higher precipitation areas (Oueme). Remarkably, the average ET is increased whatever the climatic condition except for SAV-VC in Sissili where a negative effect is recorded. For instance, EBF-VC, SAV-VC, and WS-VC increases the average ET by 0.25, 0.08, 0.07 mm d-1 in Faga. EBF-VC, SAV-VC and WS-VC experiments reduce streamflow respectively by 24%, 18%, and 21% in Donga and 31%, 26%, and 28% in Oueme. The change in the surface fluxes (e.g., LH, SH, GH, RN, ET) and subsurface dynamics (e.g., water table depth) in response to the variation of the lineaments permeability (K) is evaluated with three experiments namely High, Moderate, and Low K (see section 3.4.2.5). Remarkably, the most significant change in the diurnal cycle of the energy fluxes occurred around noon. Compared to the reference simulation (without lineament), High and Moderate K experiments decrease the outgoing longwave (LW) by -2% and -1% in the dry season. Higher permeability in the fractures results in a decrease of the outgoing longwave radiation. An increase of 1.1 and 1.5 W m-2 of sensible heat is associated with Moderate K and Low K experiments from March to May. The energy balance closure increases significantly by 36.9 and 25.4% for Moderate K and High K experiments from September to November (SON). The average groundwater storage (GWS) of the basin increases with High K and Moderate K experiments by 355.8 and 326.8 million m3. The climate projections of five Global Circulation Models (GCMs) namely GFDL-ESM4, HadGEM3-GC31-LL, IPSL-CM6A-LR, MIROC6, and NorESM2-MM under two different Shared Socioeconomic Pathways (SSP1-2.6, SSP5-8.5) are used to assess the subsurface dynamics’ sensitivity to extreme warming scenarios. Under SSP1-2.6, 3 out the 5 GCMs show an increase of groundwater storage (GWS) by 0.45 to 30.39 million m3 in Donga basin. All the GCMs indicate a decrease of mean surface water storage (SWS) under SSP5-8.5 projection except GFDL-ESM4. According to NorESM2-MM, IPSL-CM6A-LR, MIROC6, and HadGEM3-GC31-LL projections, a decrease of surface water storage (SWS) will occur whatever the warming level by the end of the century. Changes in land use and land management significantly affect the global emissions budget, influencing the climate through biogeochemical processes. This study provides the assessment of soil greenhouse gas GHG emissions in the Sudanian savanna region of West Africa using a chamber-based experimental setup. Our results reveal significant variation in methane (CH₄) fluxes across the sites. However, nitrous oxide (N₂O) fluxes did not vary significantly, likely due to uniformly low nitrogen input across all systems. The highest seasonal CH₄ emissions were recorded in the rainfed rice field (0.69 ± 0.17 and 0.82 ± 0.22 kg C ha-1 season-1, on average), while the forest reserve acted as a net CH₄ sink (−0.019 ± 0.20 and −0.42 ± 0.13 kg C ha-1 season- 1). In contrast, soils across all sites, both managed and natural, were sources of N₂O, with fluxes ranging from 0.01 kg N ha⁻¹ season⁻¹ in the forest reserve to 0.16 kg N ha⁻¹ season⁻¹ in the rice field. This study also analyzed the environmental drivers of GHG fluxes and found that CH₄ variability was significantly influenced by soil water content and soil temperature (partial R² between 0.21 and 0.42). No significant relationship was observed between these variables and N₂O emissions. These results highlight that changes in land cover and land management in the Sudanian can substantially increase CH₄ emissions, while their impact on N₂O fluxes is marginal. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-08-01T00:00:00Z Diouf, Diarra http://197.159.135.214/jspui/handle/123456789/1252 2026-06-25T08:54:56Z 2025-06-01T00:00:00Z

Climate Impact on Bacterial Meningitis Occurrence over West Africa Diouf, Diarra Bacterial meningitis remains a significant public health concern in West Africa, where seasonal outbreaks are closely linked to environmental factors such as low humidity, high temperatures and exposure to Saharan dust. While the influence of local climatic conditions on meningitis is well documented, the role of large-scale climate variability, particularly that of the El Niño–Southern Oscillation (ENSO), influencing global climates through complex atmospheric and oceanic mechanisms is still not fully understood. These mechanisms may affect Saharan dust activities and local climate conditions which are associated with meningitis outbreaks during the dry season. This study provides a comprehensive assessment of the relationship between climate and meningitis in West Africa by exploring the interactions between ENSO variability, Saharan dust dynamics and bacterial meningitis incidence in two climatically distinct regions: the Sahel (SAH) and the Gulf of Guinea (GG). This thesis addressed four specific objectives such as: 1) to characterise the seasonal meteorological and dust-related conditions that shape the meningitis cycle; 2) to examine the interannual and spatial variability of these environmental drivers; 3) to identify the large-scale atmospheric and oceanic mechanisms influencing dust variability; and 4) to develop machine learning models for estimating meningitis incidence, using environmental variables and vaccination coverage as inputs. Weekly environmental and meningitis cases data from 2006 to 2020 were analyzed at seasonal and interannual scale and predict meningitis incidence. At large-scale, monthly Saharan dust, Sea Surface temperature, Sea level pressure and winds components from 1980 to 2020 were employed. Empirical orthogonal function (EOF), lag Spearman correlation, regression map and composite analyses were applied to reveal a local and large-scale relationship between Saharan dust patterns influencing meningitis dynamics and its ENSO impact. At seasonal and interannual scale, high concentration of particulate matter (PM10) and aerosol optical depth (AOD) in January reliably precede meningitis outbreaks. The persistence and intensity of the leading mode of dust variability (70.5% in the SAH with peaks in March–April and 70.8% in the GG during January–March) associated with relative humidity (RH) below 20% in the SAH and 45% in the GG, increase the number of cases. At large-scale, SST anomalies in the equatorial Pacific (warm/cool) coincide with elevated dust variability (AOD-PC1) during the JFM–MAM in the GG and FMA–MAM over the SAH. Models like XGBoost in Nigeria (R² = 0.638), CatBoost in Burkina Faso (R² = 0.619), Gradient Boosting in Niger (R² = 0.487) and Random Forest in Mali (R² = 0.355) identified as the best. However,vaccination status, RH and meridional wind consistently emerged as the most influential predictors across all countries. These findings advance our understanding of how large-scale climate variability and dust dynamics influence meningitis outbreaks in West Africa. They also demonstrate the utility of machine learning (ML) approaches for forecasting disease risk and support the integration of climate-informed early warning systems, particularly in settings where data is scarce. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-06-01T00:00:00Z Sitta, Aissatou http://197.159.135.214/jspui/handle/123456789/1251 2026-06-24T15:19:35Z 2025-06-01T00:00:00Z

Operational Seasonal Climate Forecast Methods and Skills over the Sahelian Region of West Africa Sitta, Aissatou In the context of increasing climate change and variability, reliable climate seasonal forecasts play critical role in agricultural planning. Recurring droughts, erratic rainfall, and limited adaptive capacity in the Sahel have highlighted the need for timely and usable climate information to enhance resilience among smallholder farmers. The West African Regional Climate Outlook Forums (WARCOF or PRESASS in French) forecasting system, predicts annually the rainy season key parameters (season cumulative rainfall, onset date, cessation date and length of dry spells). The WARCOF often shows significant percentage of forecast mismatches with observation. Thus, it urges to evaluate the performance and practical relevance of the WARCOF/PRESASS approach that has been used since 2011. The research aimed to assess the technical skill of the forecasting system, identify pathways to improve prediction accuracy, and examine how farmers access and utilize forecast information. Specifically, it was achieved through: first by the evaluation of the forecast skills for onset, cessation, and dry spells; then by the research on alternative predictors and statistical methods for forecast enhancement; and finally by the investigation on the dissemination and adoption of forecasts information among smallholders farmers. The guiding research question was: how can seasonal climate forecasts be improved and effectively communicated to strengthen smallholders’ adaptive strategies in West Africa? A mixed-methods approach was adopted. Quantitative forecast verification techniques (such as reliability diagrams, ROC curves, and Brier scores) were used to analyze historical forecasts against observed data from 1991 to 2023. The Climate Predictability Tool (CPT) was applied for statistical modelling using sea surface temperature (SST) anomalies, precipitation hindcasts, and newly tested predictors like April-May-June (AMJ) cumulativerainfall and wet-day frequencies. Additionally, qualitative data obtained from a baseline survey of 619 farmers and quantitative data from two-year demonstration trials in four municipalities of southwest Niger were used. Key findings indicate that in the WARCOF/PRESASS approach, onset forecasts demonstrated higher skill and reliability compared to cessation and dry spells, particularly when using precipitation hindcasts (e.g., NASA and CFSv2) as predictors. Incorporating wet-days and AMJ rainfall totals as alternative predictors significantly improved forecast skill. However, dry spell forecasts exhibited low discrimination ability. The field-level study revealed that only 42.3% of farmers had access to forecasts, and those who fully applied the information achieved measurable yield gains (up to 234 kg/ha) compared to traditional practices, validating the forecast's real-world utility. The positive impact of the forecast information on yield was more noticeable in drought prone areas (trials sites in Sahelian and Sudano-Sahelian zones) compared to wetter areas in the Sudanian zone; showing the relevance of climate information dissemination in the most vulnerable area. The study concludes that enhancing the PRESASS forecasting system requires scientific recalibration using high-performing predictors and user-oriented communication strategies. Co-production of forecasts, capacity building, and integration with local agricultural calendars are essential to maximize uptake and effectiveness. The findings contribute to climate resilience strategies and support SDGs 1 (No Poverty), 2 (Zero Hunger), and 13 (Climate Action), as well as WASCAL's priorities on sustainable agriculture and climate risk reduction. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-06-01T00:00:00Z