North Africa is entering a new climate era defined by chronic drought, accelerating warming, and unprecedented pressure on water systems that were never designed for this level of stress. Over the last decade, the region has experienced a succession of dry years, but recent analyses from the Copernicus Global Drought Observatory show that since late 2023 the drought signal in northern Africa has been both multi-annual and structurally deeper than past cycles, with pronounced precipitation deficits, rising evapotranspiration, and abnormal land-surface temperatures. These dynamics have led to measurable impacts on groundwater recharge, agricultural productivity, and reservoir inflows, creating a complex context for long-term water management [1-2].
In parallel to this vulnerability, the rapid maturation of artificial intelligence for Earth-system monitoring now offers a unique opportunity for North African countries to transform how they anticipate and manage climate stress. Machine learning models, especially hybrid methods that integrate physical hydrological equations with AI-driven pattern recognition are capable of fusing disparate climate and hydrological data sources into powerful prediction systems. Peer-reviewed studies published over the last two years show that ensemble learning, LSTM networks, and physics-guided deep learning models can deliver high-resolution soil-moisture projections, seasonal drought forecasts, and anomaly detection across semi-arid regions with increasing accuracy. Satellite inputs such as MODIS and Sentinel vegetation indices, GPM precipitation, GRACE terrestrial water storage, and land-surface temperature products enhance predictive skill even where in-situ monitoring networks are sparse, an important advantage for North African countries. At the same time, recent literature on extreme-event prediction emphasizes the importance of trustworthy AI, interpretability, and uncertainty quantification, ensuring that these systems support effective decision-making [3-5].
Within this evolving landscape, Algeria stands out as the most strategically positioned country to host a regional hub for predictive drought and water modeling. Its geography spans nearly every climatic zone of North Africa, from the Mediterranean coast to the High Plateaus and deep into the Sahara, offering an unparalleled natural laboratory to train and validate models under highly heterogeneous conditions. Algeria also benefits from full coverage of global satellite missions and the Copernicus program, whose open-access data provide high-quality drought indicators for the region. These assets reduce traditional barriers faced by developing countries, making it technically feasible to deploy advanced AI systems without prohibitive investments in new observational infrastructure. Moreover, Algerian universities, national research centres, and emerging AI programs have demonstrated growing scientific capacity, enabling the country to lead collaborative initiatives with neighbouring states and international partners.
Establishing a regional hub in Algeria would involve building an integrated data backbone that merges satellite products, national meteorological and hydrological station networks, reservoir records, land-use datasets, and soil-moisture measurements, following standards already used by global drought observatories. Hybrid AI–hydrology models could then generate drought indices, reservoir inflow forecasts, agricultural water-demand projections, and early warning signals for emerging climate anomalies [6].
These systems should eventually feed into operational decision-support platforms designed for water authorities, civil-protection agencies, farmers, and urban planners, making climate information directly actionable. Capacity-building efforts would be essential, enabling Algerian institutions to develop, maintain, and improve the predictive models while positioning the country as a service provider for neighbouring regions. Such cooperation aligns closely with the principles and priorities repeatedly outlined in UNFCCC regional climate initiatives.
The potential benefits are substantial. Predictive drought modeling can support more efficient reservoir operations, optimize irrigation practices, reduce economic losses in climate-sensitive sectors, and assist utilities in anticipating water demand under changing seasonal conditions. It can also enable earlier and more targeted adaptation actions, helping authorities plan strategic responses to hydrological stress before it becomes critical [7]. Regionally, a specialized Algerian hub could act as a shared climate-adaptation knowledge base, allowing Maghreb and Sahel countries to benefit from standardized drought-monitoring methods, harmonized early-warning systems, and common data protocols.
Any deployment of advanced AI systems must be accompanied by strong governance and methodological safeguards. Data gaps in remote areas, uneven gauge coverage, and the risk of model bias necessitate hybridization with physical models and continuous validation. Transparency in model design, open documentation, and user-centred interfaces are essential for ensuring trust and widespread adoption. Clear governance arrangements for data sharing, ethical use, and inter-agency coordination will help ensure that advanced forecasting strengthens institutional capacity across the region [8].
Given the combined pressures of climate change and the accelerating development of AI capabilities, the moment is ideal for Algeria to assume a leadership role in regional drought and water modeling. International bodies such as the UNFCCC, the Adaptation Fund, the Green Climate Fund, WMO, and the Copernicus program have strong incentives to support such an initiative: it aligns with global adaptation priorities, delivers tangible benefits, and enhances climate resilience across a strategically important region. If properly supported, Algeria could emerge as a central node in a North African climate-intelligence network—a place where satellite observation, machine learning, hydrological science, and policy converge to secure the region’s water future.
References
[1] Adeyeri, O.E. Hydrology and Climate Change in Africa: Contemporary Challenges, and Future Resilience Pathways. Water 2025, 17, 2247. https://doi.org/ 10.3390/w17152247
[2] Vecchia P. Ravinandrasana, Christian L. E. Franzke. The first emergence of unprecedented global water scarcity in the Anthropocene. Nature Communications volume 16, Article number: 8281 (2025)
[3] Duan, Y.; Bo, Y.; Yao, X.; Chen, G.; Liu, K.; Wang, S.; Yang, B.; Li, X. A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China. Remote Sens. 2025, 17, 1000. https://doi.org/10.3390/ rs17061000
[4] Liu,J.; Liu, T.; Huang, L.; Ren, Y.; He, P. Next-Generation Drought Forecasting: Hybrid AI Models for Climate Resilience. Remote Sens. 2025, 17, 3402. https://doi.org/ 10.3390/rs17203402
[5] Geng Q, Yan S, Li Q and Zhang C (2024) Enhancing data-driven soil moisture modeling with physically-guided LSTM networks. Front. For. Glob. Change 7:1353011. doi: 10.3389/ffgc.2024.1353011
[6] Bounab,R.; Boutaghane,H.; Boulmaiz, T.; Tramblay, Y. Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria. Atmosphere 2025, 16, 213. https:// doi.org/10.3390/atmos16020213
[7] Liu,J.; Li, M.; Li, R.; Shalamzari, M.J.; Ren, Y.; Silakhori, E. Comprehensive Assessment of Drought Susceptibility Using Predictive Modeling, Climate Change Projections, and Land Use Dynamics for Sustainable Management. Land 2025, 14, 337. https://doi.org/ 10.3390/land14020337
[8] Nastoska, A.; Jancheska, B.; Rizinski, M.; Trajanov, D. Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries. Electronics 2025, 14, 2717. https:// doi.org/10.3390/electronics14132717
