In response to the impact of climate change and to the uncertainties associated with the various dimensions of hydrologic variability, water systems’ adaptation has risen to the top of global agendas. In accordance, identifying the additional science needed to improve our understanding of climate change and its impacts, including the scientific advances needed to improve the effectiveness of actions taken to adapt water systems, is of the utmost importance. To this aim, this research draws on a systematic bibliometric study of data, generated from the Web of Science research engine between 1990 and 2019, combined with a statistical analysis, to explore academic publication trends, and identify the strategic gaps and opportunities in global scientific research. The analysis shows the consistent level of national and international collaboration among authors, institutions, and countries, and highlights the substantial contribution of the USA and the UK to this research field. The statistical examination shows that the adaptation-informed literature on water systems remains fragmented, and predominantly centred on the framing of water resource planning and management, in addition to water engineering and infrastructure. The analysis also revealed a relatively skewed understanding of various important dimensions, such as governance, integrated water resources management, and stakeholder engagement, which are crucial for planning and implementing an efficient adaptation process. Observations reflect on the need to build water-related adaptive approaches based on a thorough understanding of potential climate uncertainties, rather than to generically address all the uncertainties in one scenario analysis. These approaches are required to combine short and longterm actions rather than considering only current and short-term measures, and to similarly associate policy and engineering, and equally consider the robustness, flexibility, reliability, and vulnerability during the planning phase.

The increasingly adverse impacts of climate change (e.g., rainfall patterns, droughts, and floods), coupled with the ever-increasing water demands, are often translated into a contingent liability for water users’ communities. Additional complexities arise due to competing priorities, water rights, and transboundary water sources. Therefore, conventional water management practices should shift toward more comprehensive and responsive integrative approaches, even for systems with limited data. Furthermore, water managers must prioritize dynamic and interactive management techniques for existing systems. One such management technique is water banking, which is the focus of this study. Herein, a dynamic interactive water allocation model, which encompasses the water managers and heterogeneous parties with competing demands, is developed. The voluntary sales of water shares between parties are illustrated through the specific case of the Medjerda River in Tunisia, an excellent example of a transboundary basin with limited hydrologic data and conflicting water use requirements between its upstream and downstream sectors. A set of scenarios is developed for the first analysis with this model: two management scenarios that include the no-water trade and the water banking option; three demand scenarios that include a combination of steady-, low-, and high-water demand conditions; and two hydrologic scenarios that include dry and wet conditions. Based on an economic model, the economic impacts of water banking are calculated using estimates of the costs of water shortages brought to users that illustrate the magnitude. The results show that the water banking technique can improve water resource availability by optimizing the management, operation, and conservation of natural and artificial water storage systems and water distribution infrastructure. Specifically, water banking can offset users’ profit losses during severe conditions (i.e., drought), even with limited hydrologic data. This water management technique would allow the Tunisian government to minimize the economic impacts on farmers from drought and to plan for future uncertainties by optimizing the water storage potential in years of abundant rainfall.

Addressing the interconnected challenges of food security, climate change, and population growth requires innovative and adaptive approaches to sustainable agriculture. Agricultural best management practices (BMPs) provide a promising framework for enhancing resilience, improving resource efficiency, and promoting biodiversity. However, the effectiveness of BMPs’ implementation largely depends on their alignment with local environmental, social, and economic conditions. This study presents a novel methodology for selecting and implementing BMPs based on stakeholder preferences, ensuring solutions are contextually relevant and widely accepted. Developed within the European Commission-funded WATDEV project, this methodology integrates a bottom-up and top-down decision-making framework, incorporating the perspectives of farmers, policymakers, and experts. The approach has been tested in four East African countries: Kenya, Ethiopia, Sudan, and Egypt, demonstrating its adaptability across diverse agroecological settings. Through a structured assessment involving stakeholder engagement, data-driven BMP selection, and participatory decision support tools, the study identifies and prioritizes BMPs that optimize water use, soil conservation, and climate resilience. Findings highlight that community-driven BMP selection enhances adoption rates and ensures solutions are technically feasible, economically viable, and environmentally sustainable. The methodology provides a scalable blueprint for integrating stakeholder preferences into agricultural planning, offering valuable insights for policymakers, researchers, and practitioners working toward sustainable food systems in East Africa and beyond.

This study investigated land degradation sensitivity in Southern Tunisia’s Jeffara region and examined the effectiveness of water harvesting techniques (WHTs) as countermeasures. Land Degradation Sensitivity Index was calculated using a modified MEDALUS framework, in which thematic quality indices were derived from normalized indicators (climate, soil, vegetation, and management) and combined through a geometric mean within a GIS environment. The model is validated with field observations. The research found that almost the entire study area (≈99%) was classified as critically sensitive under the baseline scenario. Contributing factors include extreme aridity, limited vegetation cover, significant soil erosion, and human pressures. The most severely degraded areas are found in mountainous zones, desert plains, and mining areas, whereas regions dominated by olive orchards showed moderate sensitivity levels. This lower sensitivity is associated with the drought tolerance and deep root systems of olive trees, which enhance resistance to prolonged dry periods. This study modeled the impact of implementing traditional WHTs, notably Jessour and Tabias. Under this scenario, a clear qualitative improvement was observed, with the proportion of land classified as critical decreasing from 99% to 77.3%, indicating a measurable reduction in land degradation sensitivity associated with the implementation of WHTs. Despite their environmental benefits, such as enhancing soil moisture and stabilizing agricultural yields, the spatial expansion of WHTs remains limited.