Orals: Fri, 8 May, 08:30–10:15 | Room 2.44
Water plays a critical role in mitigating climate impacts and building resilience, yet its role has been persistently marginalised in global climate governance. This study traces the historical exclusion of water from core discussions at the United Nations Framework Convention on Climate Change (UNFCCC) Conferences of the Parties (COP), examining negotiations from COP20 (2014) to COP29 (2024). Using qualitative document analysis, the research identifies how water-related issues transitioned from peripheral attention to gaining formal recognition through the Baku Water Dialogue at COP29. The Dialogue marks a pivotal shift, positioning water as a foundation for climate adaptation, resilience, and sustainable development. Findings highlight that fragmented governance structures, limited cross-sectoral coordination, and financial disparities have historically constrained the integration of water into climate strategies. The study argues that embedding water management into Nationally Determined Contributions (NDCs), National Adaptation Plans (NAPs), and climate finance frameworks is essential for systemic resilience. It concludes that adaptive governance, transboundary cooperation, and nature-based solutions offer practical, cross-sectoral pathways to managing water under uncertainty. The integration of water into global climate frameworks is not only a scientific necessity but also a strategic imperative for sustainable futures.
Keywords: Water governance, Climate policy, Adaptive management, COP negotiations
How to cite: Hammond Antwi, S.: Repositioning Water in Global Climate Governance: Lessons from COP20 to COP29 for Adaptive and Integrated Water Management under Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-64, https://doi.org/10.5194/egusphere-egu26-64, 2026.
To achieve Net Zero ambitions in England, as with other countries, there is a need to ensure security of water supply for the decarbonisation technologies that are pivotal to such aims. This requires future scenarios of water resources, particularly river flow, but to date, a majority of projections in England have focused solely on the impacts of anthropogenic warming. Future assessments of socioeconomic demand have typically been constructed separately for different sectors and have largely been estimated regionally rather than the fine scales needed for planning purposes. Hence, England has lacked readily accessible projections that integrate all of these factors to provide spatially-resolved assessments of future water resources. Indeed, internationally, there are few examples of hydrological projections that are fit-for-purpose for the challenge of quantifying future water resources for energy infrastructure alongside public supply and other demands.
Here we describe how the CS-N0W programme (Climate Services for a Net Zero World) has delivered spatially distributed projections of future resources for England, to the 2080s, accounting for both climate change and future changes in human influences on river regimes (namely abstractions and discharges). The projections are based on the latest 12km, 12-ensemble member UKCP18 climate projections, run through the 1km Grid-to-Grid distributed hydrological model. Crucially, this version of Grid-to-Grid incorporates layers of contemporary abstractions and discharges that are perturbed into the future according to three newly co-designed demand scenarios. These demand scenarios were developed by integrating existing scenarios of future water use for the public water supply and energy sectors alongside other abstractors (e.g. industry, agriculture). We showcase the potential of the projections for analysis of future water resources, quantifying changes in drought and low flow indicators for >600 catchments as well as larger-scale water resources planning regions. Finally, we describe how the projections have been turned into accessible, actionable information for policy- and decision-makers through a mapping and visualisation portal, co-designed with a stakeholder group representing a wide range of actors involved in water management.
How to cite: Hannaford, J., Bell, V., Barker, L., Baron, H., Davies, H., Fry, M., Keller, V., Magee, E., Nash, G., Rameshwaran, P., Smith, R., Tanguy, M., Thorpe, C., Greswell, E., Allen, S., and Beverton, A.: Future water resource assessments to support Net Zero: hydrological projections for England incorporating both climate change and socioeconomic demands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17629, https://doi.org/10.5194/egusphere-egu26-17629, 2026.
Vegetation restoration in drylands can enhance carbon sequestration but also intensify the regional carbon-water trade-off (CWT) by increasing water consumption. Optimizing restoration strategies to mitigate this trade-off is important for the long-term sustainability of dryland ecosystems. Therefore, this study presents a coupled assessment framework integrating Gross Primary Productivity (GPP) and Water Availability (WA) to quantify CWT, using the Loess Plateau (LP) as a representative case of large-scale dryland restoration. By integrating remote sensing and multi-source datasets with trend analyses and time-series diagnostics, this study quantified the spatiotemporal dynamics of CWT and used Random Forest models to identify key drivers and their thresholds, ultimately proposing targeted adaptive management strategies. The results showed that areas with a significant increase in GPP accounted for 90.42% of the LP, whereas areas with a significant decrease in WA accounted for 42.56%. 8.65% of the region was classified as intensified CWT zones, indicating potential hotspots of ecological degradation. Furthermore, interpretable machine learning revealed that the dominant drivers of CWT shifted from water limitation in high trade-off areas to energy limitation in low trade-off areas. These results suggest that current rapid, high-density restoration may constrain the long-term sustainability of vegetation growth, highlighting that adjusting spatial configuration is crucial for optimizing regional carbon-water relationships. Our findings characterize the spatial patterns of CWT and identify targeted mitigation strategies, providing critical insights into reconciling carbon sequestration with water consumption, which underpins sustainable vegetation restoration in the LP and other dryland ecosystems.
How to cite: Tang, B. and Lü, Y.: Spatial configuration is critical to mitigating carbon-water trade-offs for sustainable vegetation restoration in drylands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3283, https://doi.org/10.5194/egusphere-egu26-3283, 2026.
Ensuring reliable supplies of clean water and energy to a growing global population and under changing climate and extremes is an increasing challenge. The demands for both resources and their systemic interdependencies are particularly strong during droughts and heatwaves. Although research on the water–energy nexus has expanded in recent years, we still lack a fundamental understanding of how water–energy system processes propagate across time and space and may results in cascading impacts during extreme weather events. In addition, limited understanding exists on the trade-offs between management strategies and solutions designed to improve the supply of clean water and energy resources.
Here we will show compounds risks and impacts of climate change and extremes (i.e. droughts, heatwaves and compound events) on clean water and energy systems globally and discuss implications of management strategies to alleviate these risks on water–energy trade-offs. To quantify water and energy system processes in time and space, we developed new open access datasets and built new model frameworks integrating high spatiotemporal resolution models of hydrology, water quality, water use and energy systems.
Our results show that water use in the energy sector is substantially impacted by these hydroclimatic extreme events, with the strongest responses observed during heatwaves and compound drought–heatwave events1. Future climate change is projected to reduce thermoelectric power plant usable capacity globally through rising surface water temperatures and increasing water scarcity2. We found that declines in river flow during droughts over the last decades have to led to a 11% reduction in hydropower generation globally, using a hybrid physically-based and machine-learning model applied to our GloHydroRes global hydropower plant and reservoir dataset3.
Clean water scarcity intensifies across all sectors during these hydroclimatic extremes, due to reduced water availability, rising sectoral water demands, and deteriorating water quality4. While desalination and wastewater treatment and reuse are often promoted as key management strategies towards water scarcity alleviation, they come with substantial energy consumption, brine disposal challenges, and high costs. For instance, we quantified that desalination, wastewater treatment, and conventional drinking water treatment together account for up to ~5% of global electricity consumption, with strong regional variation5. In the Middle East, for example, desalination plants alone contribute to nearly one-fifth of total electricity use, largely powered by fossil fuels, resulting in tradeoffs with climate mitigation goals. This work is part of the B-WEX ERC project and will focus in a next step on developing joint clean water and energy transition pathways that remain robust under changing climate with increasing droughts, heatwaves, and compound events.
References
1 Cárdenas Belleza, G.A., M.F.P. Bierkens, M.T.H. van Vliet (2023) Environ. Res. Lett. 18 104008, https://doi.org/10.1088/1748-9326/acf82e
2 Jones, E.R. et al. (2025) Environmental Research: Water, 1, 2, 025002, https://10.1088/3033-4942/addffa
3 Shah, J. J. Hu, O.Y. Edelenbosch, M.T.H. van Vliet (2025) Scientific Data 12, 646, https://doi.org/10.1038/s41597-025-04975-0
4 van Vliet, M.T.H. (2023) Nature Water 1, 902–904, https://doi.org/10.1038/s44221-023-00158-6
5 Magni, M., E.R. Jones, M.F.P. Bierkens, M.T.H. van Vliet (2025) Water Research 277, 123245, https://doi.org/10.1016/j.watres.2025.123245
How to cite: van Vliet, M., Bakhshianlamouki, E., Cardenas Belleza, G., Jones, E., Magni, M., and Shah, J.: Compound risks of droughts and heatwaves on water and energy systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5943, https://doi.org/10.5194/egusphere-egu26-5943, 2026.
Ensuring safe, reliable, and climate-resilient drinking-water supplies remains a global challenge, particularly in rural regions where waterworks face raw-water constraints, aging infrastructure, operational inconsistency, and limited institutional capacity. This study presents a field-validated, sustainability-oriented key performance index (KPI-5) developed via multiyear operational records, monitoring datasets, and field inspections across 31 rural waterworks in a state in India. The framework provides one of the first structured, evidence-based, and scalable performance assessment models for rural water supply governance. The KPI-5 integrates five sustainability domains essential for long-term drinking-water security: (1) Water-quality indicators assessing sample-testing frequency, chlorine and turbidity checks at waterworks and households, cleanliness and structural conditions of tanks (Sedimentation & Storage), aerator performance in high-level tanks, filter-bed cleaning cycles, V-notch chamber maintenance, and the integrity of raw- and clear-water storage structures; (2) operational-efficiency indicators evaluating filtration-capacity utilisation, pump performance, leakage monitoring, installation of pressure and flow metres at works and tail ends, logbook accuracy, data-logging reliability, pump-maintenance intervals, and treatment-plant campus hygiene; (3) human-resource indicators assessing staff adequacy, induction training, technical proficiency in water treatment operations, electrical systems, and pipeline networks, as well as documentation quality; (4) demand–supply vs. treatment-capacity indicators examining alignment between supply volume and filtration design, compliance with standard norms, seasonal-demand management, future-demand forecasting, climate-impact preparedness, and the presence of active leak-control programmes; and (5) social-impact indicators capturing user satisfaction regarding water quality and quantity, health-impact perception, adequacy of end-pressure at distribution points, water-borne disease signals, water-wastage reduction practices, and effectiveness of local community-level grievance resolution. Each sub-indicator is evaluated on a 0–5 scale and normalised to a 0–1 domain value. This process produces a cumulative 0–5 sustainability score categorised as Excellent, Good, Moderate, Poor, or Worst. Field findings revealed severe inequities in per capita supply, varying from 21.9 to 892.2 LPCD (liters per capita per day) across neighboring villages, highlighting the need for structured monitoring and transparent governance. The implementation of the KPI-5 enabled the systematic identification of operational bottlenecks, staff-capacity gaps, filtration inefficiencies, and governance weaknesses. A built-in performance recognition mechanism further promotes accountability and long-term operational excellence. The KPI-5 framework unifies engineering performance, institutional capacity, equity in service delivery, public health protection, and climate-aware management into a single sustainability model. Its field validation in an Indian state demonstrates strong global applicability and direct relevance to multiple Sustainable Development Goals, including SDG-6 (Clean Water and Sanitation), SDG-3 (Good Health and Well-Being), SDG-9 (Industry, Innovation, and Infrastructure), SDG-10 (Reduced Inequalities), SDG-11 (Sustainable Communities), and SDG-16 (Accountable Institutions). This framework has the potential to benefit millions of people and provides a sustainable tool for advancing integrated, evidence-driven water-governance systems.
How to cite: Jindal, M. K., Joshi, V., Singh, D., Tewari, P. K., and Anand, V.: A Field-Validated KPI-5 Framework for Sustainable and Equitable Performance Assessment of Rural Water-Supply Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-627, https://doi.org/10.5194/egusphere-egu26-627, 2026.
To address multi-objective conflicts and hydrological uncertainty in joint flood control operation of reservoir groups, this study develops an uncertainty-aware optimization framework. An improved Vine Copula method with variable selection and structural simplification (RDV-Copula) is first introduced to describe the spatiotemporal dependence of multi-site flood processes. By simplifying the dependence structure, the method alleviates the complexity of high-dimensional modeling and generates stochastic inflow scenarios for reservoir operation. On this basis, a two-layer hedging–robust optimization model (TL-HRO) is formulated, in which hedging strategies are combined with robust optimization to coordinate flood control and hydropower generation objectives across current and future operation stages. The framework is applied to the Shifengxi Basin in Zhejiang Province, China. The analysis shows pronounced spatial dependence among flood processes, with a four-site flood synchronization probability of 41.92% and an average pairwise synchronization frequency of 65.87%. Compared with conventional approaches, the RDV-Copula achieves improvements in simulation accuracy of approximately 15.0%–61.2% while reducing model complexity, providing reliable stochastic inflow inputs for reservoir operation. Using these stochastic scenarios, the TL-HRO model is evaluated against a conventional multi-objective robust optimization (MORO) model. The results indicate that TL-HRO performs better in terms of both flood risk control and hydropower generation, with an average reduction of 58.26% in the optimization reference value relative to MORO. These findings suggest that the proposed approach can improve the overall performance of reservoir group operation under flood-related uncertainty and support flood control decision-making.
How to cite: Guo, Y., Yu, X., Xu, Y.-P., and Gu, H.: Multi-Objective Joint Robust Optimization for Flood Control Operation of Reservoir Groups under Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2400, https://doi.org/10.5194/egusphere-egu26-2400, 2026.
Climate change is increasingly challenging water governance systems worldwide, particularly in countries with strong hydroclimatic gradients and complex institutional arrangements. In Chile, observed and projected changes in precipitation, temperature, snow accumulation, glacier mass balance and extreme events are already affecting water availability, ecosystem integrity, and the effectiveness of existing water management instruments. These changes occur in a context of deep uncertainty, where future hydroclimatic conditions cannot be reliably characterized using single deterministic projections, posing fundamental challenges for long-term planning and day-to-day water governance.
This study presents an integrated framework for adaptive and climate-resilient water management, developed in close collaboration with the Chilean Water Authority (Dirección General de Aguas, DGA). The framework combines advances in climate and hydrological science with institutional analysis and participatory processes, aiming to support decision-making across multiple spatial and temporal scales. First, we synthesize observed and projected climate change impacts on Chilean water resources at national and macro-regional scales, drawing on updated hydroclimatic datasets and distributed hydrological modelling based on the Variable Infiltration Capacity (VIC) model. This includes explicit consideration of surface water, groundwater, snow, and glacier contributions, as well as changes in drought and flood regimes.
Second, we conduct a comprehensive institutional diagnosis of water management functions and practices, identifying the key Functions and Tasks of Water Management (FLGH for its acronym in Spanish) performed by public agencies and other actors. Through interviews, workshops and macro-regional participatory processes, we assess how these functions depend on hydrological variables and how they are being affected by climate change. We then propose a classification of FLGH according to their climate sensitivity, decision horizon and flexibility, distinguishing between functions requiring methodological adaptation and those primarily affected through changes in frequency or operational intensity.
Third, building on this classification, we develop adaptive decision criteria and methodological proposals to explicitly incorporate future climate uncertainty into water management functions. The approach is inspired in adaptive planning approaches such as Adaptation Pathways to support climate scenario based decisions for long-term, high-commitment decisions, while strengthening monitoring, enforcement, and short-term operational capacities for highly climate-sensitive functions. The framework emphasizes the need to align institutional mandates associated with water security criteria, and territorial heterogeneity, recognizing that adaptive strategies must differ across Chile’s broad climatic zones.
The results provide a transferable approach for embedding climate change and uncertainty into water governance systems, highlighting the importance of linking hydroclimatic science, institutional analysis, and participatory processes. While developed for Chile, the proposed framework is relevant for other regions facing increasing water scarcity, institutional fragmentation, and deep climatic uncertainty.
How to cite: Bustos, E. and Vicuña, S. and the Academic-Public Team for Adaptive Water Management in Chile: Towards adaptive and climate-resilient water management under deep uncertainty: lessons from Chile, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20708, https://doi.org/10.5194/egusphere-egu26-20708, 2026.
Interdependencies between water, energy, and food systems motivate linking system simulations with design tools for planning and management. We use the open-source Python water resources simulator Pywr and a Pywr-based power system simulator Pyenr. These simulators are linked using Pynsim, a generalised model integration framework. The integrated simulator is coupled with multi-objective evolutionary optimisation and machine learning algorithms. This enables exploration of infrastructure and operational intervention strategies and evaluation of trade-offs between multiple performance objectives. We apply the method to infrastructure planning in Ghana and the Eastern Nile region. For Ghana, national-scale future water and energy systems are planned to enhance equity in providing electricity and water, reduce carbon emissions, and improve system performance. For the Eastern Nile region (Ethiopia, Sudan, Egypt), the method explores and optimises joint irrigation and electricity expansion and operation strategies to improve nutritional and caloric outcomes from irrigation, manage emissions, and deliver cross-sector benefits under climate and socioeconomic uncertainties. We discuss limitations and directions for future research.
How to cite: Harou, J. and Ashraf, A.: Water-energy system design under uncertainty – lessons and recent advances, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23070, https://doi.org/10.5194/egusphere-egu26-23070, 2026.
Boil water advisories (BWAs) are essential public health alerts issued when drinking water safety is compromised, yet the United States lacks a centralized database to track these events. Such a dataset would enable epidemiological studies, infrastructure resilience assessments, and policy analysis to better understand advisory causes, impacts, and regional disparities. This research introduces a scalable framework for building this database and a generalizable methodology for converting unstructured online information into machine-readable datasets. Our approach integrates automated web scraping with large language models (LLMs) to extract and standardize advisory attributes such as location, duration, and cause. Preliminary validation compares U.S. data against ground-truth datasets from Canada and Kentucky to assess coverage and accuracy, with early findings indicating substantial capture of advisories despite variability in reporting formats. Future work will refine search strategies to improve precision and extend this methodology to other domains lacking centralized data, such as water quality violations and emergency notifications. This study demonstrates the potential of combining web scraping and LLM-based text processing to address critical data gaps in environmental and public health monitoring.
How to cite: Cook, E., Marston, L., and Cohen, A.: Toward a National Database of Boil Water Advisories in the United States Using Web Scraping and Large Language Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7185, https://doi.org/10.5194/egusphere-egu26-7185, 2026.
Groundwater is a shared (common-pool) resource and, as such, is vulnerable to individual users making decisions that benefit themselves but degrade the aggregate welfare of all users. However, past theoretical and empirical studies have shown mixed results regarding whether competition among individuals actually does degrade aggregate welfare in the groundwater context. Here, we help to clarify this discord by illustrating the relationship between (1) the length of a competitive groundwater user’s foresight for the impact of their present decision on their own future costs and (2) the externalities of a competitive groundwater user’s decision on the costs of other users in other locations. Toward this end, and whereas prior work in this area often treats competitive behavior as exclusively myopic or analyzes steady state outcomes, we deploy a novel framework where user foresight is a tunable parameter and where user decisions and their environment are dynamic. In our framework, groundwater users are participants in a Receding Horizon Game where at each time step of simulation (1) a generalized game is solved to obtain the optimal (open loop) pumping sequence of all users during a given foresight horizon, (2) each user plays only their first pumping decision, and (3) the procedure is repeated from the next time step with updated state information. We compare outcomes from different foresight lengths to the maximum social welfare solution and illustrate how longer foresight for individual groundwater users decreases the negative externalities of their decisions. We also illustrate how foresight (a temporal dimension) and distance between wells (a spatial dimension) interact to shape externalities. Further, this study demonstrates the suitability of the Receding Horizon Game approach, an emerging tool in the optimization and control community, to model and optimize dynamic behavior of competitive agents in other water resources systems.
How to cite: Chanfreau, L., Hall, S., Wallington, K., Müller, M., and Lygeros, J.: Impacts of Foresight Among Competitive Groundwater Users: An investigation through Receding Horizon Games, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12690, https://doi.org/10.5194/egusphere-egu26-12690, 2026.
Posters on site: Fri, 8 May, 10:45–12:30 | Hall A
Water resources management is fundamentally concerned with ensuring reliable water supply while simultaneously protecting society from water-related hazards. In recent decades, water resources systems have faced increasing challenges due to growing human water demand and escalating hydrologic uncertainty driven by climate change and socio-economic development. Under these conditions, optimizing the operation of existing water resources system has become essential for achieving efficient and adaptive water allocation strategies capable of meeting both present and future demands. Traditional optimization approaches, including classical mathematical programming and evolutionary algorithms, have been widely applied in water resources system analysis. However, their convergence efficiency often be argued when confronted with high-dimensional, nonlinear, and strongly constrained real-world problems. Recent advances in artificial intelligence and machine learning have introduced the Learn-to-Optimize (L2O) paradigm, in which a meta-optimizer trains neural networks to learn optimization update rules rather than directly optimizing decision variables. This framework offers the potential to enhance optimization performance, particularly for complex systems. Accordingly, this study evaluates the effectiveness of three optimization frameworks: (1) a classical quasi-Newton solver, (2) a long short-term memory (LSTM)-based L2O optimizer, and (3) an L2O framework integrated with a reinforcement learning agent. Their performance is systematically compared in terms of convergence behavior, solution quality, and computational efficiency. To assess robustness across different levels of problem complexity, the three methods are tested on a simple Trid benchmark, the nonlinear Rosenbrock function, as well as a large-scale water-supply allocation problem representing the Hsinchu regional water resources system in northern Taiwan. Preliminary results indicate that classical algorithms remain highly efficient for smooth, low-dimensional benchmark functions, whereas meta-learning-based optimizers demonstrate promising advantages when addressing nonlinear and highly constrained water resources optimization problems. Ongoing experiments aim to further quantify these performance differences across problem classes in a more rigorous and systematic manner.
Keywords: Optimization; Artificial Intelligence; Machine Learning; Learn-to-Optimize; Long-Short-Term Memory; Reinforcement Learning
How to cite: Wu, C.-E. and You, J.-Y.: Evaluating Learn-to-Optimize Frameworks for Complex Water Resources Allocation: A Comparison Between Meta-Learning, Reinforcement Learning, and Classical Solvers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4408, https://doi.org/10.5194/egusphere-egu26-4408, 2026.
Greece, as a predominantly coastal country, faces increasing pressure on freshwater resources due to climate variability, population distribution, tourism, and agricultural demands. Desalination represents a viable adaptation strategy, yet its energy intensity—particularly for reverse osmosis and water conveyance—varies significantly with distance from the coastline. This study employs a GIS-based feasibility zone analysis around the Greek coastline at incremental distances (10 m, 50 m, 100 m, 1 km, 10 km, 50 km, and 100 km) to spatially quantify the daily energy requirements for desalination and conveyance, aggregated by population and land use zones. Calculations are based on representative specific energy consumption values for desalination (typically 3–5 kWh/m³) and conveyance (increasing with elevation and distance), combined with population distribution data. Results reveal a strong concentration of energy demand near the coast: within 10 km, approximately 60.21% of total national desalination energy demand is covered, corresponding to 66.25% of the population (≈7.24 million people). By 50 km, this rises to 89.50% of energy demand and 93.00% of the population. Beyond 100 km, only 1.27% of the total energy requirement remains, yet with disproportionately higher per-cubic-meter costs due to conveyance challenges. To contextualize feasibility, daily household and services electricity consumption is allocated proportionally to population per zone. Desalination energy as a percentage of zonal electricity demand remains moderate near the coast but increases inland, highlighting the trade-offs for full national coverage. These findings support that desalination is energetically and technically most viable within 50 km of the coastline, covering the vast majority of the population with relatively low conveyance losses. Inland regions would benefit more from alternative strategies (e.g., rainwater harvesting, wastewater reuse, or inter-basin transfers). The approach provides a first-order metric for prioritizing desalination infrastructure and informs integrated water-energy nexus planning in Mediterranean coastal countries.
How to cite: Angelidis, M. A., Arvanitidis, I., Ioannidis, R., and Sargentis, G.-F.: Spatial Analysis of Desalination Energy Demand in Greece: Feasibility Zones from the Coastline for Water-Energy Planning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8222, https://doi.org/10.5194/egusphere-egu26-8222, 2026.
Recent applications of Robust Decision Making (RDM) have demonstrated their value for exploring socio-hydrological vulnerabilities under deep uncertainty in water-scarce regions. A recent study in the semi-arid coastal Quilimarí basin (central Chile), used an RDM framework combining stakeholder engagement and an integrated WEAP-MODFLOW model to reveal critical trade-offs between agricultural production, drinking water security, groundwater depletion and saline intrusion under climate and development uncertainties. While this work provided valuable insights into system vulnerabilities and stressors, it remained focused on exploratory analysis rather than on the explicit design of adaptive strategies.
This study builds directly on the Quilimarí RDM case study and advances the framework toward adaptive policy design, introducing two methodological innovations. First, we extend the RDM approach by integrating the Direct Policy Search (DPS) framework to identify robust and flexible water management strategies. Instead of evaluating a small set of predefined interventions, policies are formulated as adaptive decision rules that dynamically link observable system states such as groundwater levels, salinity thresholds or unmet drinking water demand, to management actions including abstraction restrictions, activation of alternative supplies or demand reallocation. This allows the systematic identification of pathways that evolve over time and remain robust across a wide ensemble of plausible hydroclimatic and socio-economic futures.
Second, to enable the computational requirement of DPS in data and process intensive basins, we develop a surrogate model that emulates the behavior of the full integrated surface-groundwater system. The original WEAP-MODFLOW model for Quilimarí, which explicitly represents groundwater dynamics, agricultural water use, and seawater intrusion, is approximated using an LSTM (Long Short-Term Memory), a type of Recurrent Neural Networks (RNN), trained on large ensembles of simulation outputs. The surrogate model preserves key nonlinearities and memory effects inherent to groundwater systems while reducing computational costs by several orders of magnitude, making large-scale adaptive policy search feasible.
The combined framework is applied to the Quilimarí basin to identify adaptive pathways that balance drinking water reliability, agricultural viability, and long-term groundwater sustainability under deep uncertainties as climate and land use change and growing population. Results show that the DPS using the surrogate model outperform static strategies identified in the original RDM analysis, particularly under severe drought and demand-growth scenarios, by avoiding maladaptation and reducing regret across objectives.
By explicitly linking the vulnerability exploration with the design of adaptive strategies, this study shows how RDM can be operationalized into implementable and flexible water management policies. The approach is transferable to other semi-arid coastal basins that face strong groundwater dependence, institutional constraints, and profound climate and other uncertainties.
How to cite: Poblete, D., Vicuña, S., Melo, Ó., Leray, S., Fletcher, S., and Zhang, M.: From vulnerability exploration to adaptive policy design in semi-arid coastal basins: surrogate-assisted robust pathways building in the Quilimarí case, Chile., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14370, https://doi.org/10.5194/egusphere-egu26-14370, 2026.
Multi-purpose reservoirs are critical infrastructure for the management of water resources, and reservoir operators must often balance multiple competing water demands. Climate change is an important threat to the resiliency of water resources in these reservoirs, as it can limit the efficacy of historical operations strategies as well as water demands and availability. The Conowingo Reservoir, a large multi-purpose reservoir located in Pennsylvania and Maryland along the Susquehanna River, has been managed actively for decades, yet current operating policies fail to adequately account for internal system variability and emerging threats to regional water resource reliability. Havre de Grace, Maryland, a small community at the mouth of the Susquehanna River, sources drinking water from the river, and during periods of low riverine freshwater flow, saline waters from Chesapeake Bay can intrude upstream, degrading public water supply and corroding infrastructure. To address this emerging problem, we first develop a statistical model for saltwater intrusion at the Havre de Grace drinking water intake using historical discharge, tide, and wind data from 2007 – 2024. Salinity responses to wind and tidal forcing under low flow conditions are rare and nonlinear, highlighting the need for targeted reservoir releases prior to and during salinity events. Second, we incorporate this model into a simulation-optimization framework for the Conowingo, training adaptive, state-aware, and dynamic release policies that release water from the reservoir downstream to flush out intruding saltwater while continuing to satisfy pre-existing regional water demands. Updating the current operations strategies will expand the scope of water resource management to an emerging compound stressor of water reliability, providing finer control over rare intrusion events that currently, and will continue to, threaten public health and infrastructure.
How to cite: Heidtman, E. and Hadjimichael, A.: Combating compounding drought and riverine saltwater intrusion hazards using reservoir operations: a case study in the tidal Susquehanna River , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14972, https://doi.org/10.5194/egusphere-egu26-14972, 2026.
Equity is increasingly central to water resources management as cities expand and socioeconomic inequalities persist. As climate change intensifies, urban water managers are tasked with ensuring reliable water supplies while navigating the complex distribution of benefits and burdens across diverse communities. While equitable access to clean water is a core component of Sustainable Development Goal 6: Ensure availability and sustainable management of water and sanitation for all, quantitative water resources models often lack an explicit representation of equity, instead prioritizing aggregate metrics such as cost-efficiency or system-wide reliability. Without explicit equity representations, models are limited in their ability to evaluate distributional outcomes and may unintentionally reinforce existing inequalities. In this study, we operationalize distributive equity within a quantitative water resources modeling framework to assess how equity principles influence infrastructure and operational decisions. We explore these principles in the Federal District of Brazil, a region with a high drought risk, rapid urbanization, and large income inequality. Using the WaterPaths water supply model coupled with the Borg multi-objective evolutionary algorithm, we explore how alternative distributive principles influence drought risk and equity outcomes within optimized water supply portfolios. In WaterPaths, we compare three distinct principles of distributive equity, Rawlsian, Utilitarian, and Sufficietarian, by developing three rival multi-objective problem formulations. We optimize each formulation and explore how trade-offs between conflicting objectives and equity outcomes change across the formulations. Results indicate that the choice of a specific equity principle can fundamentally shift trade-offs and alter the perceived performance of candidate management strategies. Our findings contribute to more transparent and robust system planning approaches, offering a pathway to integrate social justice directly into water resources decision-making under deep uncertainty.
How to cite: Mohamed, A., F. P. Bierkens, M., Lira, Y., M. A. Alves, C., and F. Gold, D.: Operationalizing Equity in Quantitative Water Resources Systems Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18870, https://doi.org/10.5194/egusphere-egu26-18870, 2026.
Climate change and increasing extreme events challenge food production due to the growing volatility of agricultural revenues. In water-scarce regions, farmers are exposed to hydrological droughts due to their dependence on regulated water systems. We assess how drought-indexed insurance could reduce income shocks for irrigation communities affected by climate change, in particular in the largest citrus-growing area in the EU (Valencia province).
Our analysis revealed insurance configurations that could be attractive to both farmers and insurers across surface and mixed water demands. However, their suitability depends on the climate change scenario. Tailoring the insurance design to the official State Drought Index of the area (Jucar River Basin) aligns the probability of receiving an indemnity with drought severity and the premiums paid. We also demonstrate that public sector participation would be crucial to achieving robust, efficient insurance schemes, given issues of insurance affordability among farmers and uncertainties in insurers' revenues.
How to cite: Pulido-Velazquez, M. and Macian-Sorribes, H.: Assessing drought-indexed insurance for irrigation communities under climate uncertainty: A case study from Mediterranean agriculture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23202, https://doi.org/10.5194/egusphere-egu26-23202, 2026.
