PICO
In 2019, scientists from African countries called for more research on drought and better drought forecasting and management (Padma, 2019). Between 2020 and 2023, the Horn of Africa had experienced the worst drought in 40 years, with severe consequences related to reduced agricultural productivity and high food prices (Okoth, 2024). In this presentation, we will showcase the drought research done within the DOWN2EARTH project with a case study in Kenya.
Agro-pastoral livelihoods in the Horn of Africa (HoA) are acutely exposed to climate variability due to the predominance of rain-fed systems. Yet drought risk emerges from more than rainfall deficits—it reflects interacting biophysical processes, socio-economic vulnerability, and institutional response capacity. We advance an integrated, impact-based and adaptation-informed framework by combining statistical risk modelling across Kenya’s arid and semi-arid lands (ASALs) with a coupled socio-hydrological and agent-based simulation of human–water interactions.
First, using Spearman correlations and Random Forest regression, we link drought hazards to observed societal impacts and identify distinct timescale sensitivities: short (2–6 months) precipitation deficits align with increased household water trekking distances, while medium-to-long drought indices (5–24 months) better explain declines in milk production and increases in malnutrition. Clustering counties by vulnerability profiles improves predictive skill. Socio-economic clustering best captures water access outcomes, whereas environmental clustering better explains agricultural and nutrition impacts. Extending to probabilistic risk via Random Forest hindcasts (1984–2014) yields Average Annual Loss (AAL) and Probable Maximum Loss (PML) estimates, highlighting spatial heterogeneity: high water-access risk in northwestern Kenya and elevated livestock, milk, and malnutrition risk in eastern and southeastern counties. Priority adaptation pathways include sanitation and safe water access, poverty reduction, and small-scale water infrastructure.
Second, the ADOPT‑AP framework couples the DRYP hydrological model with a behavioural agent model to simulate bounded-rational adaptation and policy scenarios. Sensitivity analysis identifies irrigation abstraction as the dominant driver of both drought hazard and adaptation uptake. Replacing upstream commercial farms with communities or forests increases downstream streamflow and groundwater, modestly improving water access and production in drought years. During the 2020–2023 drought, doubling extension access marginally boosts low-cost measure adoption but not capital-intensive options, underscoring finance constraints; scaling water harvesting improves milk and reduces water trekking but has mixed crop effects and downstream hydrological trade-offs.
Together, these results demonstrate how vulnerability-informed, spatially targeted interventions and dynamic adaptation modelling can be used to strengthen early warning, guide equitable water governance, and build long-term resilience. However, improving drought management requires more than research. Early warnings are for example often not acted upon because of cultural values or limited resources. We therefore advocate for more transdisciplinary research, co-creation of drought adaptation solutions, and strengthening connections between communities and formal governance actors.
References
Padma, T. V. (2019). African nations push UN to improve drought research. Nature, 573(7774).
Okoth, D. (2024). The cost of African drought. Nature Africa, doi.org/10.1038/d44148-024-00075-0.
How to cite: Odongo, R. A., Streefkerk, I., Van Loon, A. F., Wasonga, O., De Moel, H., Wens, M., De Bruijn, J., and Aerts, J.: Advancing drought risk analysis and management: a case study of Kenya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19778, https://doi.org/10.5194/egusphere-egu26-19778, 2026.
Mediterranean savanna ecosystems exhibit a hydrological regime with marked wet and dry seasons. This variability makes droughts a recurrent hazard that impacts water supply, food security, wildlife, and economy. Consequently, a thorough drought definition and monitoring are essential to foresee drought impact, to support decision-making processes, and to design mitigation and adaptation strategies. However, drought definition is not easy, specifically, in data-scarce areas, where ground observations are sparse or unavailable. In these cases, satellite remote sensing and geospatial data are a valuable alternative to in situ information.
This study evaluates the usefulness of satellite-based indicators to characterize drought dynamics in these data scarce environments. Special emphasis was put in two aspects: (i) exploring lagged relationships and cascading effects among different drought types and (ii) the capacity of different remote-sensing-based indexes to capture agricultural drought in several land cover of these environments. The Kruger National Park in South Africa (KNP) over the period 2000 – 2023 was selected as pilot case area due to the characteristic recurrence of droughts.
Therefore, meteorological, agricultural, and hydrological droughts were computed over four different land cover classes: savanna, forest, grassland, and cropland. Each of these areas were identified using ESACCI Land Cover (1992 – 2015). Meteorological drought was defined through the Standardized Precipitation Index (SPI) computed using the ERA5-Land precipitation data (25km). Agricultural drought was analyzed using three different methods, with different levels of complexity. First, the 16-day MOD13Q1 NDVI product. Second, the daily Evaporative Stress Index (ESI), defined as the ratio of actual to reference evapotranspiration (ET), as a proxy for ecosystem water stress. Actual ET was estimated using a Two Source Energy Balance (TSEB) model driven by MOD11A1 Land Surface Temperature (LST). Third, a MODIS-based Composite Drought Index (CDI) derived from air temperature, precipitation, and NDVI was also considered. Finally, hydrological drought was assessed through the Standardized Streamflow Index (SSI) derived from Global Flood Awareness System (GloFAS) v4 river discharge data. Drought events were identified using standard thresholds for SPI, SSI, and CDI, while ESI and NDVI thresholds were defined by land cover and month to account for phenology.
Regarding the connection between different droughts, the preliminary results show for all the classes analyzed that only the more severe meteorological droughts, that is those occurring during 2003-04 and 2015-16, have a direct impact on agricultural drought. The effect on hydrological drought is lumped in comparison. When comparing the different agricultural drought methods, we found that NDVI is the index more sensitive to changes, particularly in non-forested areas which are more dependent on precipitation, while ESI is better representing abrupt fluctuation in forests and savannas. On the contrary, CDI poses a more homogenous value, what makes it overestimate the presence of droughts.
These initial results show the potential of coupling different spatial data sources, geospatial and remote-sensing-based to define different droughts and their relations in data scare regions. In addition, they allow providing some initial recommendations about their different responses depending on the land cover analyzed.
Acknowledgments: This work is part of the grant RYC2022-035320-I, funded by MCIN/AEI/10.13039/501100011033 and FSE+.
How to cite: Gabella, D., Pimentel, R., Nieto, H., Burchard-Levine, V., Dube, T., and Andreu, A.: How to assess drought in data scarcity areas? A case study in Kruger National Park (South Africa) , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12394, https://doi.org/10.5194/egusphere-egu26-12394, 2026.
Southern Africa is increasingly vulnerable to long-term drought, particularly in arid to semi-arid regions. However, insufficient monitoring makes the timing and extent of groundwater response to droughts difficult to quantify. Here, we combine evidence of past drought in the satellite record with water table modeling to evaluate the spatio-temporal relationship between agricultural and hydrological drought across Southern Africa from 1981-2025. We use the normalized difference vegetation index (NDVI) from the STFLNDVI dataset as a proxy for agricultural drought. We simulate monthly water table depth (WTD) at 30 arcsecond resolution using the Water Table Model (WTM), which dynamically couples surface and subsurface hydrologic processes.
By defining standardized anomalies derived from NDVI and simulated WTD, we aim to examine spatio-temporal drought propagation characteristics between agricultural and hydrological drought. Drought periods are compared against documented droughts to evaluate whether the human experience of drought is reflected in simulated groundwater changes. The goal of this study is to provide a regional assessment of drought propagation into groundwater across Southern Africa and to identify where groundwater systems may be vulnerable to drought.
How to cite: Quigley, L. and Callaghan, K.: A multi-proxy approach to evaluating Drought in Southern Africa: climate, vegetation, and water table, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15327, https://doi.org/10.5194/egusphere-egu26-15327, 2026.
Near-surface rainfall estimates from Commercial Microwave Links (CMLs) are a viable source of rainfall information in data scarce regions, notably Low and Middle-Income countries in the tropics. CMLs are point-to-point radio links commonly used in cellular telecommunication networks. When it rains, the radio signal between two cell phone towers is (partially) attenuated, and this rain-induced attenuation can be used to infer the average rainfall intensity along the path. Typically, every 15 minutes the minimum and maximum received signal levels are stored in network management systems by mobile network operators for quality monitoring purposes. Based on these signal levels it is possible to estimate path-averaged rainfall intensities, which can be interpolated to produce high-resolution rainfall maps.
In this study we investigate the use of several thousands of CMLs, predominantly located in heavily urbanized areas, during one rainy season in Nigeria. We use 32 hourly rain gauges (12 from Nigeria’s Meteorological Agency, and 20 from the Trans-African Hydro-Meteorological Observatory) as a reference to compare with the path-averaged rainfall intensities from CMLs within 5 km of a gauge. To quantify the uncertainties in CML rainfall estimates we compare the performance of these links with different frequency and polarization across the same path. We make a similar comparison by comparing interpolated rainfall maps from CMLs to available gridded (satellite) rainfall products on a seasonal basis. As such, this study aims to highlight the added value of using CMLs as an opportunistic source of rainfall estimation in a region where reference rainfall information from dedicated ground-based sensors is very limited. It offers a balanced outlook for the use of these near-surface rainfall estimates with their associated uncertainties as input for hydrometeorological applications at the kilometer scale, ranging from numerical weather prediction to calibration of satellite precipitation products, and hydrological modelling.
How to cite: Walraven, B., Droste, A., Overeem, A., Coenders, M., Hut, R., and Uijlenhoet, R.: Opportunities for opportunistic sensing of rainfall with Commercial Microwave Links in Nigeria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6809, https://doi.org/10.5194/egusphere-egu26-6809, 2026.
The African monsoon is governed by the complex interplay of large-scale atmospheric and oceanic processes. Understanding how these drivers influence rainfall variability can improve the prediction of hydro-meteorological extremes (e.g. heavy rainfall, droughts) for this vulnerable region. This study examines the role of dominant climate modes, such as the El Niño–Southern Oscillation (ENSO) and the Atlantic Niño, in modulating rainfall variability and extremes over West Africa. Unlike previous studies, this investigation is conducted across seven objectively defined rainfall zones in West Africa, describing typical rainfall regimes (e.g. Sahelian) for the region. Moreover, the analysis builds on an advanced quality-controlled station-based rainfall dataset, namely the West African Historical Precipitation Database, compiled over the past decade to improve the coverage and quality of data from rain gauges in this region. To describe the state of ENSO, the Atlantic Niño and other climate modes, various state-of-the art indices (e.g. MEIv2, ATL3, DMI, AMM, SOI) and indices specifically established for West African Monsoon (e.g. the African Southwesterly Index ASWI) are used. The statistical relationships between climate modes and rainfall variability are assessed for the seasonal rainfall amount and other rainfall statistics (e.g. onset, rainfall probability, mean-wet day amount) for the main monsoon phases over a period of 50 years (e.g. 1960 to 2010). Preliminary results show that JAS-rainfall for the Sahelian and Sudan savanna region is controlled by both, ENSO and Atlantic Niño, and low-level wind dynamics. The ASWI alone can explain up to 50% of the rainfall variability in this region compared to 20% for MEIv2 and ALT3. Moving southwards to the coastal regions with two monsoon peaks (MJJ and SON), ENSO becomes the dominant driver for MJJ-rainfall with lagged impacts between 1 to 3 months. Notably, ATL3 displays regime-dependent sign reversals, highlighting the contrasting impacts of Atlantic Niño across the region. Our findings indicate that WAM is strongly influenced by various drivers whose dependence structure with monsoonal rainfall varies in space and time. This reflects shifts in teleconnection dynamics and emphasizes the development of modelling approaches that can capture this non-stationarity in a suitable way.
How to cite: Bliefernicht, J., Kloos, L., Sawadogo, W., Sy, S., Ndiaye, A., Jagdhuber, T., and Kunstmann, H.: The role of ENSO and Atlantic Niño on rainfall variability and extremes in West Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15382, https://doi.org/10.5194/egusphere-egu26-15382, 2026.
African countries' economic growth and sustainable development is being constrained by their capability to adapt to climate change, in particular regarding water availability and distribution. Hydrological processes exhibit a high degree of temporal and spatial variability, and their modelling is affected by issues of nonlinearity of physical processes, conflicting spatial and temporal scales, and uncertainty in parameter estimates. In addition, conventional hydrometeorological data has long suffered from data breaks due to changes in reporting methods and from gaps (missing information), especially in Africa.
This study focuses on modelling the impact of climate change and variability on the long-term distribution of water-balance components in East Africa through the evaluation of historical patterns and future projections.
The methodology uses an integrated Soil and Water Assessment Tool (SWAT) with a machine learning model to examine historical data, to capture nonlinear hydrological patterns, and to generate accurate projections. The modelling analysis is partitioned into two phases: (1) a land-phase module, where SWAT simulates processes from the event of raindrops onto the land surface to the stream, and (2) a climate-phase module, where Regional Climate Models (RCMs) will be used to produce time series for a set of climatic variables under different scenarios. Machine learning algorithms that closely align with RCM will be used to impact assessments on water resources. Based on this, we aim to predict the impact of drought related to the water resources for sustainable agricultural production potential across the region.
At the EGU General Assembly, we will present the spatio-temporal variability of seasonal water balance in East Africa, and the modelling framework for climate change projection and drought prediction.
Acknowledgments: This research is supported by Eni S.p.A. through the Eni Award “Debut in Research: Young Talents from Africa”. We thank our company tutors Alessandro Nardella and Alessandra Bertoli for their invaluable guidance and technical expertise.
How to cite: Kebede, T. A., Laio, F., and Viglione, A.: Hydrological Response to Climate Change and Variability in East Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-531, https://doi.org/10.5194/egusphere-egu26-531, 2026.
In recent years, artificial reservoirs have attracted increasing attention, not only for their essential role in mitigating hydrological and meteorological extremes but also for their vulnerability to climate variability in region like West Africa. This growing interest has been supported by advances in remote sensing, which now allow near-real-time monitoring of reservoir surface extent (RSE) dynamics. However, regional-scale research quantifying and communicating trends and variability in reservoir surface dynamics remains limited. Additionally, while the effects of large-scale climate indices on hydrological processes have been largely investigated, any study has specifically examined how RSE respond to these climate indices across West Africa. At the same time, regression-based machine learning approaches, frequently used to assess multiple teleconnections while addressing multicollinearity issues, often lack systematic evaluations of their robustness and reliability. These gaps constitute important challenges for both local and regional efforts to monitor hydroclimatic shift impacts and anticipate water resource stress under ongoing global warming. In this study, we addressed these challenges by assessing the spatiotemporal variability and trends in RSE dynamics from 1985 to 2022 for 482 reservoirs across West Africa in relation to ten Sea Surface Temperature Anomaly (SSTA) indices. We further evaluated the performance of three supervised machine learning methods, Ridge Regression, Elastic Net, and Partial Least Squares (PLS) to identify the most suitable for modeling and predicting the effects of SSTA indices on RSE. Finally, we identified the dominant oscillations influencing RSE dynamics and highlighted the regional response patterns of West African reservoirs to the ten SSTA indices. We found strong interannual and decadal variability in both the SSTA indices and RSE, underscoring a dynamic coupling between oceanic conditions and terrestrial hydrology in West Africa. Among the ten indices, the Western Mediterranean Index (WMED) shows the strongest and statistically significant upward trend (p < 0.05). At the reservoir level, 43.26% of the 485 reservoirs exhibit significant long-term trends, with 31.07% showing declines. Of the three algorithms tested, PLS delivers the best generalizability and the most stable out-of-sample predictive performance, but only when using PCA-filtered and low-collinearity predictors. WMED emerges as the most influential driver, with moderate contributions from the Atlantic modes (AMO and AMM). Finally, SSTA regression coefficients vary widely across reservoirs, with minimal spatial clustering, indicating uneven reservoir sensitivity to oceanic oscillations likely shaped by local factors.
How to cite: Kouassi, V. B. S., Chun, K. P., Yao, B. A. F., Soro, G. E., Agbenorhevi, A. E., Goula, A. B. T., Kelome-Ahouangnivo, N. C., and Klaus, J.: Influence of Large-Scale Climate Indices on Reservoir Surface Extent Variability in West Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-667, https://doi.org/10.5194/egusphere-egu26-667, 2026.
Our work has aimed to deal with the long-term groundwater response to understand the natural and human roles at the continental scale, with a time window ranging from 2003 to 2024. A comprehensive mass balance of the terrestrial water storage (TWS) approach was used for the determination of monthly groundwater storage change to attribute its variability to both climatic and human-induced drivers. Our findings show a long-term upward pattern of groundwater storage anomaly (GWSA), demonstrating a dual reality of groundwater response to precipitation, where the flux of the groundwater system (recharge/discharge) is quickly responsive to inter-annual extremes of precipitation, with a response time of 0 months. However, the state (total storage) is slow and integrated with a multi-year response time peaking approximately 29 months after a precipitation event. There is a significant hydro-climatic regime shift that developed after 2018, where groundwater was being replenished to levels never seen before. This surge occurred because of positive precipitation anomalies caused by a prolonged multi-year La Niña phase, and there is a shift within the regime of the hydrogeological system of the continent from negative human contribution at the beginning (2003) to positive human intervention toward the end of the study time range. This is primarily driven by sustained decreasing groundwater withdrawal, mainly for agricultural activities, at a slower rate than the continent's total annual groundwater renewal. Although the continent has recently benefited from a period of intense, climate-driven recharge (post-2019), this has occurred in defiance of a massive and growing human-induced withdrawal signal. This positive continental trend also masks severe concurrent regional droughts, such as the catastrophic 2020-2022 La Niña-induced drought in the Horn of Africa. These findings present the first complete picture of the African groundwater system, underscoring its susceptibility to significant climatic phenomena such as the El Niño-Southern Oscillation (ENSO). It also highlights the critical need to incorporate regional variations and diverse climatic factors in all future water security assessments carried out on water security. Moreover, continental groundwater storage anomaly is a robust proxy for the integrated impact of major climate teleconnections like ENSO. This work measures these opposing forces and finds that the current positive balance of groundwater storage is vulnerable and relies on the proper maintenance of the positive wet climate. This indicates the need for sustainable policy interventions in the groundwater sector based on long-term agricultural water productivity to maintain the value of the most significant African source of freshwater.
How to cite: Brhane, G. K., Ni, C.-F., and Liu, C.-Y.: Continental-Scale Groundwater Response Dynamics to Climate Variability in Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-193, https://doi.org/10.5194/egusphere-egu26-193, 2026.
In climate-vulnerable and data-scarce countries like Ethiopia, developing an understanding of groundwater–surface water (GW–SW) interactions is essential to understanding hydrological and hydrogeological functioning, ecological resilience, water security, and sustainable water resource management. Water resources in Ethiopia support rain-fed and irrigated agriculture, domestic supply, urban centers, hydropower, and water-dependent ecosystems. Current understanding of GW–SW processes in Ethiopia is fragmented, with most existing groundwater and surface water studies conducted independent of one another. The result is substantial knowledge gaps pertaining to effects of widespread irrigation, land-use change, and climate change on connected water systems, capacity of aquifers during droughts, and seasonal GW–SW exchange fluxes and GW contribution to stream flow. A literature analysis of GW–SW interactions in Ethiopia highlights methodological and scientific limitations, and indicates opportunity to improve GW–SW understanding through fully-integrated GW–SW modeling. GW–SW interaction investigations in Ethiopia are primarily local and clustered geographically. The dynamic feedbacks between surface water, unsaturated zones, and aquifers are still not well quantified, with most studies relying on point-scale hydrogeochemical indicators, baseflow separation methods, or groundwater potential mapping. Predictive understanding hydroclimatic datasets and hydrogeological processes is limited by lack of long-term monitoring data, inconsistent conceptual models, and minimal representation of land-use change and unknown human water abstraction. There are no fully-integrated GW–SW modeling studies at a basin/watershed scale in Ethiopia, although loosely coupled models have been used. As part of a Natural Resources Canada Technical Assistance Program with Wollo University, a HydroGeoSphere fully-integrated GW–SW model for the Borkena watershed, located on the northeastern margin of the Rift Valley, has been created for educational and training purposes. The development of the HGS model followed the framework laid out in the Canada1Water initiative for national scale water resources assessment. As part of the model construction process, requisite data has been assembled from globally extensive sources, including geology, hydrology, soil, landcover and vegetation, and climatology; thus demonstrating that state-of-the-art models can be deployed in perceived data sparse regions. Following construction, model application can be demonstrated towards long-term water security planning, climate resilience, and sustainable water management in Ethiopia. The framework developed in this project is broadly applicable to other Sub-Saharan African nations, where process-based understanding of GW–SW interactions is indispensable but still lacking.
How to cite: Adamu, A. Y., Nigussie, A. B., Frey, S. K., Ejigu, A. A., and Russell, H. A. J.: Groundwater–Surface Water Interactions in Ethiopia: A Review of Knowledge Gaps, Emerging Opportunities, and the Role of Integrated HydroGeoSphere Modeling under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21954, https://doi.org/10.5194/egusphere-egu26-21954, 2026.
Coastal megacities like Lagos, Nigeria, face major water security challenges. Rapid population growth and urbanization are placing enormous pressure on water resources. Lagos domestic water supply is almost entirely sourced from its underlying multi-layered aquifer. Sustainable groundwater management requires a robust conceptual model of that Lagos coastal aquifer system that resolves subsurface heterogeneity, hydrostratigraphy, hydraulic connectivity, degree of confinement, saltwater intrusion, and anthropogenic contamination. Such model is currently hampered by fragmented subsurface data and prevalence of undifferentiated lithologies. The coastal aquifer system has been typically described as comprising three main aquifers units separated by aquitards of variable thickness and discontinuous lateral extent, and underlain by a thick clay aquitard found between ~150-250+ m depth below surface, with a general dipping toward the ocean. This study develops a data-driven framework using novel integration of borehole geophysical datasets with machine learning (ML) techniques to improve Lagos aquifer system conceptualisation and quantify uncertainty.
We compiled and synthesized an extensive database, including over 100 borehole gamma-ray and resistivity logs. The well-logs were processed using unsupervised ML clustering to objectively delineate the aquifer lithology and hydrostratigraphy. Gamma-ray log responses revealed pronounced vertical and lateral heterogeneity, with distinct clay-rich and sand-dominated horizons that allow clearer differentiation of previously undifferentiated aquifer/aquitard units across the aquifer system. Resistivity patterns further delineated the saline water occurrence in southern Lagos, revealing the clearer saline intrusion extent.
Building upon the lithological and hydrological delineations, we further constructed a high-resolution 3D aquifer model using a novel machine learning (ML) technique. Unlike conventional geostatistical methods like Ordinary Kriging, which can under-perform for the non-stationary processes inherent to complex coastal sedimentary geology, we developed a specialized artificial neural network (ANN) scheme. This architecture used a series of distance-based basis functions as covariates to directly predict spatial interpolation weights. Critically, the training process incorporates ridge regression and an entropy-based regularization, enabling the model to capture both smooth regional trends and abrupt lithological variations observed in boreholes. Furthermore, the framework provides robust uncertainty quantification by differentiating between data noise (aleatoric uncertainty) and model uncertainty (epistemic uncertainty addressed using ensemble methods and Monte Carlo dropout). The ML technique was validated against synthetic benchmarks and applied to generate a probabilistically constrained 3D model by transforming discrete, irregular borehole observations into a continuous, uncertainty-aware volumetric representation.
The 3D model offers an unprecedented view of aquitard continuity and aquifers hydraulic connectivity, potential recharge pathways, and areas vulnerable to over-abstraction or saline intrusion, creating a robust framework for groundwater assessment and sustainable management in Lagos. This technique offers a transferable framework for hydrogeological studies in data-limited coastal megacities across Africa.
How to cite: Olabode, O., Giannakis, I., and Comte, J.-C.: Data-driven conceptualisation of the complex multilayered coastal aquifer system underlying Lagos megacity, Nigeria: Integrating borehole geophysics and machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18298, https://doi.org/10.5194/egusphere-egu26-18298, 2026.
Understanding groundwater recharge processes in dryland and seasonally dry subhumid environments is central to improving water‑resource resilience under increasing climatic variability and land‑use change. This study integrates multi‑year groundwater level observations from crystalline basement aquifers in an unpumped agricultural setting about 30 km north of Harare, Zimbabwe (2018-2025) with recent groundwater‑level data from eight boreholes monitored in the pumped urban area of the city (2022–2025). Together, these high-resolution (30-min frequency) datasets provide insight into both natural and urbanised dryland systems, where recharge is highly sensitive to rainfall variability and human pressures.
Recharge in the agricultural sites was characterised using water‑table fluctuation (WTF) methods, chloride mass balance (CMB), water‑stable isotopes, and dissolved gas residence time tracers. The effect of variation in land use—such as tilled land, land under conservation agriculture, and woodland—on the responses to cumulative rainfall and rainfall events of varying magnitude was studied by integrating daily rainfall data collected at the research site. Recharge was observed for most years across all sites and was controlled by hydrogeological settings, rainfall totals and antecedent conditions, i.e. the groundwater level at the end of the preceding dry season. No measurable recharge occurred at most of the agricultural sites during a year of poor rainfall (380 mm total), highlighting the strong climatic dependency of basement‑aquifer recharge in subhumid drylands. Annual groundwater level variations were mostly limited to 2 to 3 m.
In contrast, the urban groundwater dataset from Harare revealed markedly different recharge behaviour. Groundwater‑level fluctuations were strongly influenced by nearby pumping, producing hydrographs that diverged from the smoother, rainfall‑controlled signals seen in natural settings and showing much stronger annual variation of groundwater levels up to about 15 m. However, pumping did not mask the overall annual recharge pattern, though at some sites, groundwater capture was markedly increased. A subset of the boreholes had similar hydrographs to those in the unpumped, agricultural setting. The variability in drawdown and recovery responses across the eight urban boreholes underscores the need for well‑designed, spatially distributed groundwater‑monitoring networks to evaluate the sustainability of abstraction and to detect changes in recharge availability under increasing urban water demand. In addition, capturing and integrating existing datasets—such as drilling logs, pump tests, and historical abstraction records—can provide valuable baseline information to support groundwater management in Harare.
Taken together, these findings advance understanding of recharge processes in both natural and urban dryland basement aquifers, emphasise the sensitivity of recharge to climatic variability, and highlight the implications for sustainable groundwater management under changing land use and rainfall regimes.
How to cite: Brauns, B., Kativhu, O. T., Lapworth, D. J., MacDonald, A. M., Mudimbu, D., Owen, R. J. S., Shumba, S., and Souta, M.: Recharge in natural and urbanised subhumid dryland basement aquifers in Sub-Saharan Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18605, https://doi.org/10.5194/egusphere-egu26-18605, 2026.
Parameter equifinality remains a central challenge in hydrological modeling, limiting the reliability of process-based tools such as the Soil and Water Assessment Tool (SWAT). This study evaluates how multi-variable calibration strategies that combine in-situ streamflow with five remote sensing global actual evapotranspiration (RSAET) products (GLEAM v3.6, GLEAM v4.2, ETMonitor, PML, and SSEBop) can reduce equifinality, using the Essaouira watershed (Morocco) as a study case. A total of 10,000 Monte Carlo simulations were performed, from which the 100 best-performing parameter sets were selected for posterior uncertainty assessment. A Composite Identifiability Score (CIS) was developed by integrating normalized metrics of standard deviation, entropy, peak-to-width ratio, and Kullback-Leibler divergence to quantify parameter identifiability.
Results show that streamflow-only calibration (S0) yields the highest CIS, confirming the strong constraining power of discharge on routing and runoff parameters. However, multi-variable calibration further reduces equifinality for several soil–plant–atmosphere parameters, with the Streamflow + GLEAM v3.6 configuration achieving the highest multi-source CIS, followed by SSEBop and GLEAM v4.2. In terms of performance, streamflow-only scenarios achieve the highest NSE and lowest PBIAS, while hybrid streamflow–AET calibrations maintain strong predictive skill and improve the physical consistency of ET-related processes. In contrast, AET-only calibrations exhibit poor runoff-volume accuracy and large water-balance inconsistencies.
Overall, integrating complementary AET datasets with discharge observations enhances parameter identifiability, constrains key hydrological processes, and mitigates equifinality. This demonstrates a practical pathway to strengthen SWAT model robustness in data-scarce regions, as is the case for many African basins. These results are preliminary, and ongoing work aims to consolidate them by extending the calibration and identifiability framework to include soil-moisture remote-sensing products, with the goal of further constraining soil-water dynamics and reducing remaining model uncertainties.
How to cite: Ouarani, M., Alitane, A., Manyari, Y., Mulla, D., and Ait Brahim, Y.: Reducing Hydrological Uncertainty: A Multi-Variable SWAT Calibration Using Global AET Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1029, https://doi.org/10.5194/egusphere-egu26-1029, 2026.
Coastal erosion is a persistent and growing challenge along West African shorelines, posing serious risks to livelihoods, infrastructure, and coastal ecosystems. In Ghana, erosion along the eastern coastline is driven by strong wave action, sea-level rise associated with climate change, unregulated coastal development, and sediment retention resulting from dam construction. In response, a range of hard engineering interventions, such as sea walls, breakwaters, armour rock revetments, and groyne systems were implemented in 2017 to reduce shoreline retreat. However, systematic and cost-effective monitoring is required to assess the effectiveness and long-term impacts of these interventions. This study documents the development of a digital coastal erosion monitoring tool using the Digital Earth Africa (DE Africa) platform, focusing on Blekusu, a highly erosion-prone coastal community in eastern Ghana, where major sea-defense structures have been constructed. Relevant national stakeholders, including the Ghana Hydrological Authority, the Ghana Meteorological Agency (GMet), and the Water Research Institute (WRI), were identified through an IWMI–DE Africa stakeholder training workshop and engaged to co-create the coastal erosion use case. An ecosystem of DE Africa tools was used, including DE Africa Explorer, DE Africa Maps, and DE Africa Sandbox. The Explorer served as a unified interface to query and retrieve analysis‑ready coastline datasets and metadata (2010–2024), spanning both pre‑ and post‑intervention periods, derived from 30 m Landsat imagery and tidal modeling. The DE Africa Sandbox served as an integrated development environment for Python-based analysis, enabling users to access, process, and visualize coastline dynamics without relying on multiple external tools. Existing coastal erosion notebooks within the Sandbox were adapted, significantly reducing the learning curve and development time. Code sharing among team members facilitated collaborative development and aligned with open-access data-sharing principles. IWMI and DE Africa jointly reviewed the use case and developed online training materials that allow users to enroll, build capacity, and earn certification. Shoreline extraction and change-detection analyses revealed persistent accretion exceeding 30 m in several sections following the intervention, although localized stabilization was observed near engineered structures. These results demonstrate the reliability of DE Africa as a scalable, accessible, and cost-effective platform for coastal monitoring and support its integration into national coastal management and planning strategies. Continued investment in DE Africa and its integration into university curricula would further expand its impact across Africa.
How to cite: Mortey, E., Agyekum, J., Yiadom, A., Kumah, M., Tilahun, S., Haile, A. T., and Seid, A.: Digital Earth Africa as a Platform for Coastal Erosion Monitoring along Ghana’s Eastern Coastline, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13916, https://doi.org/10.5194/egusphere-egu26-13916, 2026.
Water stress and groundwater overexploitation constitute critical challenges to water sustainability in semi-arid regions. In Morocco, the Saïss Basin represents a strategic agricultural area facing increasing pressure on groundwater resources due to intensive irrigation, fragmented governance, and limited coordination among water stakeholders. This study assesses the effectiveness of stakeholder’s integration within the Integrated Water Resources Management (IWRM) framework, with a focus on governance, participation, and decision-making processes. A mixed-methods approach was adopted, combining stakeholder surveys, institutional analysis, and a SWOT-based evaluation to address both technical and socio-institutional dimensions of water management. Key stakeholders’ groups, including public authorities, water agencies, agricultural users, and local organizations, were analyzed in terms of their roles, interactions, and influence on groundwater management. The results reveal a persistent gap between IWRM principles and their practical implementation, characterized by limited stakeholder coordination, uneven participation, and sectoral fragmentation. While institutional frameworks for IWRM exist, their operationalization remains constrained by power asymmetries, insufficient data sharing, and weak integration of local actors in decision-making. The SWOT analysis highlights opportunities for improving stakeholder engagement through participatory platforms, capacity building, and the integration of scientific knowledge into policy processes.
This study provides evidence-based insights into the governance barriers hindering effective IWRM implementation in groundwater-dependent regions. The findings contribute to ongoing debates on adaptive water governance and offer practical recommendations to strengthen stakeholder integration as a pathway toward sustainable groundwater management and the achievement of SDG 6 in arid and semi-arid regions.
How to cite: Ijlil, S., Essahlaoui, A., Hssaisoune, M., Essahlaoui, N., Van Rompaey, A., Mili, E. M., Ait Lahssaine, I., Faouzi, E., and Bouchaou, L.: Stakeholders engagement in Integrated Water Resources Management in the Saïss Basin, Morocco: Challenges and Responses under climate change conditions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19169, https://doi.org/10.5194/egusphere-egu26-19169, 2026.
