Decision Making Under Deep Uncertainty (DMDU) represents a promising approach to help decision-makers confront such a wide range of unpredictable and variable future conditions. Unlike traditional frameworks that depend on accurate predictions and precise probabilities, DMDU accepts that the future is inherently unpredictable, especially in complex systems like human water systems, and emphasizes adaptive planning that evolves with new information on water supply, demand, and ecosystem health. This session aims to gather scientists to discuss and exchange knowledge of existing and emerging approaches for supporting the design and implementation of adaptive and robust water management strategies under deep uncertainty. We welcome contributions focused on recent methodological advances, including uncertainty and sensitivity analysis, scenario generation techniques, robust optimization, and experiences related to real-world applications.
PICO: Tue, 5 May, 16:15–18:00 | PICO spot 2
Traditional scenario-based approaches to water resources planning often miss consequential dynamics and diverse stakeholder vulnerabilities that emerge from complex interactions between climate, institutions, infrastructure, and human action. This presentation will demonstrate the use of bottom-up exploratory modeling frameworks in advancing our understanding of water scarcity in institutionally complex river basins by systematically exploring large ensembles of plausible futures. Working in the Upper Colorado River Basin—a system governed by prior appropriation water rights—we couple the State's water allocation model with exploratory modeling to simulate hundreds of thousands of plausible scenarios spanning diverse hydroclimatic conditions, demand changes, and other stressors. Paired with global sensitivity analysis and other diagnostics, we show how human institutions and system complexity fundamentally shape vulnerability: under identical scenarios, stakeholders experience vastly different impacts depending on their position within water rights and infrastructure networks. The analysis demonstrates several critical insights. First, dominant stressors controlling water shortages vary across users and across severity thresholds for individual users. Second, robustness assessments must account for multiple actors with distinct objectives, as no single metric captures all system responses to stress. Third, scenario storylines can be identified and used to describe consequential multi-actor dynamics and inform planning, despite these limitations. This framework is currently extended with new stochastic weather generation tools using multivariate copulas to explore deeply uncertain precipitation-temperature relationships and their compounding effects on water scarcity. This work demonstrates how exploratory modeling can transform traditional water resources planning from evaluating predetermined scenarios to systematically discovering consequential uncertainties and generating actionable storylines for decision-making under deep uncertainty.
How to cite: Hadjimichael, A.: Exploratory Modeling for Understanding Water Scarcity in Coupled Human-Natural Systems: From Vulnerability Assessment to Scenario Storyline Discovery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7830, https://doi.org/10.5194/egusphere-egu26-7830, 2026.
Decision-making under Deep Uncertainty (DMDU) offers approaches to support robust, adaptive strategies for complex water resources decision-making. However, practical uptake of DMDU remains limited, partly due to fragmented access to resources and a lack of an inventory of available tools. This study introduces a comprehensive catalogue of tools and resources. Through a structured survey and expert elicitation, we identify 28 resources and 16 tools that support DMDU research and practice and classify them using an established DMDU taxonomy. Our analysis reveals a focus on introductory guidance on the theory and methods of DMDU application. Technical, method-specific resources for implementing existing frameworks remain limited. Our results identify tools that support all core DMDU components, but they also highlight persistent scalability challenges. The resulting online catalogue provides a foundation for expanding the use of DMDU in practice and is intended as a living, community-driven platform.
How to cite: Gold, D., Schlumberger, J., Di Fant, V., Taner, U., Winter, G., and Kwakkel, J.: A review of tools and resources to support Decision-Making Under Deep Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11957, https://doi.org/10.5194/egusphere-egu26-11957, 2026.
Significant climate change and human activities have decreased the stability of water resource systems, leading to multiple uncertainties in streamflow prediction, reservoir operation optimization, decision-making, and adaptive adjustments for water resource scheduling. Understanding the impact of uncertainties on reginal streamflow is necessary and crucial to identifying reservoir operation strategies and decision-making responses. We proposed an integrated systematic “inflow predition”– “reservoir operation”– “optimization”– “decision-making risk analysis” chain considering the transmission of multiple uncertainties.The uncertainty of streamflow prediction is disclosed based on error analysis and reservoir inflow process is simulated by stochastic scenario model. Then, the modified stochastic multi-criteria decision-making model were applied to identify the effects of inflow prediction on reservoir multi-objective operation and decision-making. Moreover, risk quantification indices were used to determine the uncertainty propagation and potential risks accumulated in the chain. We applied this framework to reservoir system in typical basins. The results indicate that the uncertainty of inflow predictions leads to stochastic process of reservoir operation and decision-making. The reservoir decision-making error risk is quantified and enhanced with deep uncertainty. We identified the preferred solutions for reservoir operation under different uncertainty levels with risk information to enhance the robustness of reservoir operation and decision-making.
How to cite: Yang, Z., Wang, Y., and Song, S.: Integrated Process Chain for Reservoir Inflow Prediction-Multi-objective Joint Optimal Scheduling-Risk-Informed Decision-making Considering Multiple Uncertainties Transmission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5536, https://doi.org/10.5194/egusphere-egu26-5536, 2026.
Changes to the relationship between precipitation and temperature due to climate change can exacerbate water scarcity by increasing evapotranspiration and reducing runoff and soil moisture. These changes are especially significant for the agricultural sector, where complex interactions between precipitation, temperature, and growing season dynamics produce deep uncertainties in agricultural water demands. While watershed managers have traditionally relied on “top-down” planning scenarios, these typically do not provide insights into the system’s internal variability, nor do they capture the range of plausible, yet deeply uncertain, changes in the regional hydroclimate. To address this shortcoming, we develop a multivariate, multisite, copula-based stochastic weather generator for bottom-up exploratory modeling analysis of agricultural water resources systems. Paired with a regional consumptive use model, this generator allows us to investigate differential impacts of climate change on diverse agricultural producers and crops. We demonstrate this framework in the Upper Colorado River Basin within the state of Colorado. The explored hydroclimatology shows precipitation and temperature as highly variable and elevation-dependent relative to their historical annual averages, spanning -95% and +600% and –10°C and +19°C at the extrema, respectively. As a result, we observe substantial changes in irrigation water requirements for agricultural parcels across the basin between –100% and +250% relative to historical averages; all producers see irrigation requirements increase higher than their historical averages in more than 50% of our sampled realizations, with producers at lower elevations seeing this increase in more than 75% of them. Global sensitivity analysis reveals that adequate access to water impacts producers' effective growing season lengths and thereby which climate variables most control crop water requirement: producers with adequate water are most sensitive to changes in temperature mean and variance while producers without adequate water are most sensitive to changes in precipitation variance—and not mean—with temperature contributions halving. These findings demonstrate how differential vulnerability drivers underscore the need for stakeholder-specific assessments that account for spatial heterogeneity and decision-relevant uncertainties in agricultural water demand.
How to cite: Thames, A., Hadjimichael, A., and Quinn, J.: Climate Sensitivity of Agricultural Water Demand Depends on Control Over Growing Season, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8065, https://doi.org/10.5194/egusphere-egu26-8065, 2026.
Hydropower is the largest source of renewable electricity and a central component of the water–energy nexus and the net-zero transition. Aging infrastructure, combined with climate variability and evolving grid demands, is reducing the efficiency and operational flexibility of Hydropower Plants (HP) designed for past climate and grid conditions. Meeting future energy needs requires modernizing the existing fleet, for example through turbine replacement which occurs every few decades.
In practice, turbines are often replaced with identical units that replicate legacy configurations optimized for past conditions, but replacements constitute an opportunity to redesign turbine head and discharge capacity to match evolving hydrology, reservoir operations, and grid needs. In drought-prone systems, for example, installing units optimized for lower head can sustain generation at reduced reservoir levels, as demonstrated by recent upgrades at Hoover Dam on the Colorado River. What is missing is a rigorous method to determine when and how turbines should be upgraded to ensure efficient, reliable, and sustainable outcomes.
This study addresses this need for large-scale hydropower upgrades by using a newly developed framework, HyTUNE (Hydropower Turbine Upgrade and Next-generation Planning). HyTUNE is a dynamic decision-support tool that integrates basin hydrology, HP hydraulics, and adaptive optimization to inform turbine replacement timing and configuration. HyTUNE learns from evolving system states and identifies threshold conditions where adjustments to design head or discharge capacity improve hydropower performance.
Application to the Hoover Hydropower Plant in the Colorado River Basin shows that HyTUNE’s adaptive policies consistently outperform benchmark replacement strategies across diverse hydrologic futures. The approach increases economic returns, measured as net present value, and enhances plant capability through higher firm power, peak-period generation, and operational efficiency, with fewer turbine replacements. Climate variability still shapes outcomes, with the largest benefits of HyTUNE compared to benchmark expected under wetter conditions, and the strongest improvements in firm power and operational efficiency under drier conditions. HyTUNE offers a practical framework for hydropower systems facing the combined challenges of modernization, climate uncertainty, and growing demand.
How to cite: Zaniolo, M., Yildiz, V., and Voisin, N.: Modernizing Hydropower through Turbine Upgrades Improves Efficiency and Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15349, https://doi.org/10.5194/egusphere-egu26-15349, 2026.
Urban water utilities face the combined pressures stemming from evolving drought extremes, increasing demands, and financial constraints, prompting a growing interest in regional cooperative dynamic and adaptive infrastructure investment pathway strategies. Theoretically, these strategies promise improved resource efficiency by realizing economies of scale, adding flexibility for achieving improved supply reliability, and, ideally, limiting individual and collective financial risks. However, prior work has shown that implementation uncertainty in regional partners’ cooperative actions, characterized by modest deviations from a prescribed set of Pareto-approximate actions, can drive counterparty risks and potentially exacerbate collaborating actors’ vulnerabilities to deeply uncertain supply and financial challenges. To address this challenge, we contribute the Deeply Uncertain Pathways for Implementation Uncertainty (DU Pathways IU) framework, an evolutionary multi-objective reinforcement learning (eMORL) approach that accounts for human-driven implementation uncertainty when optimizing for regional cooperative water supply management and planning pathway strategies that remain robust to external socio-economic uncertainties and drought extremes.
In this work, we demonstrate that the DU Pathways IU approach yields a broader range of regional water supply pathway strategies that more fully utilize the full suite of cooperative management and planning actions available to regional actors. This broader set of highly cooperative pathway strategies exhibit more controlled supply and financial performance degradation when stress-tested under implementation uncertainties (i.e., perturbations to pathway strategies’ decision variables). In addition to remaining stable in the face of unexpected deviations from the recommended set of regional cooperative actions, these strategies achieve higher robustness across all regional actors. Further sensitivity analysis reveals that highly cooperative pathway strategies experience reduced sensitivity to perturbations to other actors’ actions. Consequently, cooperating utilities have more control over their individual performance and reduced uncertainty when assessing the needed timing and prioritization of future infrastructure investments. Overall, this work facilitates the discovery of highly cooperative regional water supply planning and management pathway strategies that remain stable under implementation uncertainty. It is broadly applicable to water utility managers who seek improved transparency into how modest perturbations in cooperative actions drive potential performance conflicts across multiple actors implementing both individual and cooperative actions in a regional system.
How to cite: Lau, L., Reed, P., and Gold, D.: Accounting for human implementation uncertainties in the discovery of robust water supply infrastructure pathways using evolutionary multi-objective reinforcement learning., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9084, https://doi.org/10.5194/egusphere-egu26-9084, 2026.
The scientific investigation of global climate change characterizes a complex geophysical system in transition. Broadly characterized, that system transformation includes a wider range of potential futures with emergent system qualities and tipping points that undermine the foundational planning assumptions for historical water supply planning. While the geophysical system encompasses the whole of the planet, the intersection with human-built systems and institutions for water supply management occupies a diverse range of delimited local situations. From the perspective of human-managed water systems, the geophysical system transformation is reflected in a social system also in transition. Like the geophysical system, social adaptation exhibits emergent social system qualities and tipping points. As this plays out in multiple centers of water governance across multiple adaptive institutional management scales, each locally adaptive strategy becomes yet another agent of change in the larger network of water supply management. At these multiple scales of management, decision makers are faced with long-term and long lead time adaptive investment decisions to secure water supply reliability without the usual certainty that any particular project will address evolving conditions. Urgent action is needed involving significant decisions without what was traditionally viewed as sufficient information.
Typically, the suite of Decision Making Under Deep Uncertainty (DMDU) approaches support decision-makers through participatory deliberation with analysis. With structured decision making, scenario development, scenario discovery, explicit inclusion of multiple worldviews, and frame reflection; there is a reasonable chance for mutual accommodation even if a common worldview proves elusive. But what if the most effective scale of management no longer matches the historical institution’s scale and governance? To be effective, these tools require a radical reframing of the scale of management. If existing scale of resource management reflects the historical dynamics of a hydrologic system; the built infrastructure, management scale, and institutional governance are unlikely to fit an evolving system.
The Metropolitan Water District of Southern California is the largest treated water supplier in the United States. Metropolitan supplies 19 million people with supplies imported hundreds of miles from the Colorado River and the California State Water Project through its 26 member agencies. Those imported water supplies are increasingly affected by global climate change.
Metropolitan has employed DMDU analytical approaches since 2010 while experiencing a series of unprecedented trends in water supply and demand. At its 2023 Board retreat, climate change adaptation became the central focus for Metropolitan. It is currently engaged in developing a climate adaptation master plan for water. It’s a messy process complicated by issues of scale and uncertainty, compounded by eroding supply reliability and fiscal challenges.
Based on over fifteen years of participating in Metropolitan’s water resource planning, this presentation provides a participant’s report on how institutional system transformation is proceeding in southern California. What has worked, and what hasn’t, reflects an institution grappling with a radical reframing of its value proposition to fit the changing scale of water supply management. For the scientific community, this offers an inside look at how decision makers struggle to adapt to change.
How to cite: Graumlich, H.: Finding the Fit: Reframing the Institutional Context for Adaptive Change in DMDU, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22147, https://doi.org/10.5194/egusphere-egu26-22147, 2026.
Agricultural systems are increasingly exposed to deep uncertainty as climate change amplifies the frequency, intensity, and unpredictability of hydroclimatic extremes. This uncertainty affects the behavior of farmers, whose individual responses have a significant impact on the resilience of regional land-use and water systems. However, the ways in which different types of uncertainty affect farmers’ adaptation decisions remain insufficiently understood, as scientific discourse has emphasized model-based uncertainty while giving less attention to behavioral dimensions.
This contribution presents a conceptual framework that integrates uncertainty research into the Model of Private Proactive Adaptation to Climate Change (MPPACC), which allows the analysis of the internal reasoning behind action or inaction in farmers’ drought and flood adaptation decisions. Drawing on survey data from 102 farmers in Lower Saxony, Germany, and using multiple linear regressions to examine the relationships specified in the conceptual framework, we analyze how various dimensions of uncertainty are perceived and how these perceptions influence responses to hydro-climatic extremes. The results show that uncertainty perceptions function as dynamic factors shaping behavior and highlight underlying mechanisms explaining why some farmers delay or avoid adaptation despite rising environmental risks, while others adopt proactive measures in response to uncertainty. Specifically, personal attitudes and uncertainty-related competences influence how uncertainty is interpreted, while reliance on forecast and past experiences can amplify perceived uncertainties, contrary to prevailing assumptions.
Thus, this work offers insights for designing strategies for policymakers that more accurately reflect the decision contexts of individuals under deep uncertainty, including strengthening farmers’ decision competence through capacity-building and improving science communication and uncertainty narratives.
How to cite: Heinzel, C. and Höllermann, B.: How Uncertainty Shapes Climate (In)action: Behavioral Dynamics of Farmers’ Adaptation Decisions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1577, https://doi.org/10.5194/egusphere-egu26-1577, 2026.
In real-world policymaking, decision-makers must act amid both deep and normative uncertainty. Deep uncertainty arises when system models, probabilities, and even the boundaries of the problem are contested or unknown, while normative uncertainty arises when stakeholders disagree about values, priorities, and how to evaluate trade-offs. Together, these uncertainties can make outcomes, likelihoods, and even the definition of “success” fundamentally ambiguous. Yet most model-based policy assessments have limited capacity to guide decisions under these conditions: a recommendation may be robust to uncertain futures yet ethically unjust, or ethically appealing but not robust under uncertainty. In practice, model-based assessments often embed a single ethical standpoint or conflate deep and normative uncertainty, rather than explicitly engaging with competing preferences and conceptions of justice.
In this study, we examine how deep and normative uncertainties in integrated assessment models (IAMs) affect climate mitigation policy recommendations. Although IAMs are widely used to derive “optimal” mitigation pathways, they are subject to both kinds of uncertainty. We therefore draw a clear conceptual distinction between deep and normative uncertainty and model them separately. We combine Decision Making under Deep Uncertainty (DMDU) methods with social choice theory and multi-agent, multi-objective optimization in a single modelling framework, JUSTICE. This separation matters in practice because it helps decision-makers diagnose the source of disagreement and identify who can help resolve it—for example, whether a deadlock calls for additional scientific evidence or for ethical deliberation about what ought to be prioritized. It also avoids collapsing multiple social objectives into a single welfare metric—or adopting a single conception of justice—which typically requires contentious weighting choices and implicit assumptions about which distributive justice lens should guide the analysis, all of which are inherently normative.
Our results show that ethical framing and robustness preferences under deep and normative uncertainty significantly influence both the pace and distribution of global mitigation efforts. In highly aggregated IAM-based policy optimization, normative uncertainty can outweigh deep uncertainty in socioeconomic projections. Explicitly disaggregating competing objectives and ethical perspectives is therefore essential for revealing distributional consequences and engaging questions of distributive justice. By keeping metrics disaggregated, using multi-objective analysis to expose trade-offs, and testing robustness across rival weightings and justice framings, our approach makes normative assumptions explicit rather than implicit. More broadly, the framework expands the decision space, reveals trade-offs, and represents diverse stakeholder values, thereby addressing tenets of procedural justice in model-based policymaking. When integrated into IAMs, it can support the design of fairer climate policies, strengthen legitimacy and stakeholder engagement, and facilitate climate negotiations and collective action.
How to cite: Zatarain Salazar, J., Biswas, P., and Kwakkel, J.: Decision-Making under Normative Uncertainty: Methods and an Application to Climate Mitigation , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2039, https://doi.org/10.5194/egusphere-egu26-2039, 2026.
Models are essential for representing the complex interactions that shape human–water systems, yet existing approaches often struggle to capture basin-specific feedbacks and to support systematic sustainability assessments at the river-basin scale. Here, we develop a generic and extensible Integrated Social–Ecohydrological System Dynamics (ISEHSD) model that explicitly couples social, ecological, and hydrological processes within a unified feedback structure. The framework represents dynamic interactions across eleven interconnected sectors and enables basin-scale sustainability assessment based on biophysical boundaries, capturing outcomes arising from both natural dynamics and human interventions. ISEHSD explicitly resolves the co-evolution of agri-food systems, water, and ecological impacts. The framework is demonstrated through a use-case implementation for the Yellow River Basin, an arid river basin subject to intensive anthropogenic pressures. Global sensitivity analysis and uncertainty quantification are employed to identify key nonlinear interactions and to evaluate alternative development and management strategies. Results indicate that severe water stress is not expected to be relieved before 2045 under the scenario analysis. Even under the most sustainability-oriented scenario, human water demand could exceed the severe water stress threshold by 22% (ranging from 6% to 40%) in 2100. Cross-system transformations, including enhanced water efficiency and sustainable agricultural practices, remain essential to reducing water stress in the basin. Beyond this application, ISEHSD provides a scalable and interpretable modelling framework that supports multi-scenario policy analysis, integrated visual analytics, and stakeholder-oriented dialogue for river-basin sustainability planning.
How to cite: Yu, L. and Wang, S.: ISEHSD: a feedback-based, integrated social-ecohydrological system model for studying basin-level transformation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11510, https://doi.org/10.5194/egusphere-egu26-11510, 2026.
Climate change is projected to increase the frequency and intensity of extreme climate events such as precipitation events and floods. Furthermore, compound events - multiple (extreme) events or hazards that occur simultaneously - are likely to become more prevalent on a warmer planet. Adaptation plans developed for single hazards may (unknowingly) ignore compound events, leading to an adaptation gap and potentially negative effects for other hazardous situations. However, uncertainties in changes of climate extremes, their concurrence, and the complexity of their interactions, render adaptation planning fundamentally more difficult for compound events than for individual events. This research explores adaptation strategies for uncertain compound events in the Netherlands, a low-lying urbanized deltaic system that faces the threat of compound events driven by extreme rainfall, storm surges, or high river discharges. We focus on compound events where extreme rainfall is the primary hazard driver (e.g., two rainfall events or a rainstorm and a storm surge) and aim to create a better understanding of the effects of past and future compound events in the Dutch water system. Our study provides a first insight into the effect of adaptation measures at a regional scale using archetype areas that schematically represent the Dutch delta system.
We create a compound event database to develop storylines narrating past events and illustrating how adaptation measures were used to protect the Netherlands against high water levels and inundation. The database identifies areas that are especially vulnerable to compound events. We then stylize these areas into archetypes in a hydrologic model, and use the model to explore different adaptation measures, such as drainage, pumps, and storage capacity, under various compound event scenarios. We utilize an exploratory modeling method for decision-scaling based stress testing using the hydrologic model specified for the different archetypal areas in the Netherlands to determine the nature of (future) compound events that may generate water system vulnerability.
Past events recorded in the database show that the east and west of the Netherlands may respond differently to similar compound events due to differences in hydrological setting, water management (free draining in the east and mainly man-made controlled systems in the west), and the dependency of drainage on downstream conditions, which in turn are affected by storm surges and sea level. Our first model results for the western archetype region show the importance of feedback between the different delta components in the development of flood risks, as e.g., high water levels downstream may affect adaptation requirements and limits upstream. They also confirm that the compounding effect of rainfall events may disproportionally amplify the problem, for example, while the system can deal with an individual rainfall event, a subsequent event can substantially increase inundation.
How to cite: Mennes, D., Dunn, F., Middelkoop, H., Gold, D., and Haasnoot, M.: Exploring compounding rainfall events and potential adaptation measures in complex delta systems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6391, https://doi.org/10.5194/egusphere-egu26-6391, 2026.
Physical infrastructure and institution safeguard farmers from flooding in coastal Bangladesh in a interconnected way. In this region, crops are protected from flooding by levees, a form of physical infrastructure. These levees are maintained by self-organized cooperations, an informal institution. We also test counterfactual dynamics of index insurance, a formal institution. With interconnections of multiple components, it is difficult to understand the complex landscape of sustainability for successful policymaking. To address the gap, we develop a spatially-explicit agent-based model with interconnected components in the coastal Bangladesh to capture how farmer's strategic decisions on insurance participation and cooperation evolve. In the model, we define "sustainable" cases when (i) most farmers participate in index insurance and cooperate for levee maintenance, (ii) levee is kept nearly at the targeted level, and (iii) insurance agencies do not fall into debts. Our model shows that the coupled system is sustainable in a restricted combination of target levee and insurance index levels. More interestingly, we find a diverse versions of cascading failures between index insurance, cooperation, and levees. We then use global sensitivity analysis modified for stochastic models to capture both deterministic and stochastic contributions of inputs on uncertainties of system being sustainable. Ultimately, this study establishes a novel framework of capturing and analyzing cascading failures to fully understand complex human-water interplays, supporting a better design of climate adaptation policies against climate change.
How to cite: Oh, W. S., Sung, K., Santos, F., Best, K., Levin, S., and Rubenstein, D.: Cascading failures of index insurance, cooperation, and levees in coastal Bangladesh: agent-based modeling and global sensitivity analysis for stochastic models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15917, https://doi.org/10.5194/egusphere-egu26-15917, 2026.
It is challenging for decision-makers (DM) to deal with uncertainties in multi-level agricultural water resource systems, where DMs independently make decisions but have different levels of power. In this paper, we model the multi-level agricultural water resources system under deep uncertainties as a Stackelberg game, use multi-level programming to solve equilibrium water allocation problems, and introduce robustness metrics into multi-level programming to balance solution feasibility and model optimality within uncertain environments. The approach is applied to a shallow groundwater system with three decision levels, pursuing, from the top level to the bottom one, high food production, fair water allocation, and increased economic benefit. The model generated a series of optimal equilibrium solutions with different robustness degrees. DMs can choose “rational” solutions according to their acceptable costs, oriented robustness degree, expected objective values, and advance risk assessment of uncertainties. Among these solutions, we capture a critical point with high objective values and strong robustness, where DMs can accomplish both objective optimality and solution robustness with a low cost. The proposed approach in this study provides a posterior decision support to consider solution robustness while designing policies in multi-level agricultural water resource systems under deep uncertainties.
How to cite: Zhang, X., Castelletti, A., Wang, X., and Guo, P.: Robust Stackelberg equilibrium water allocation patterns in shallow groundwater areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15048, https://doi.org/10.5194/egusphere-egu26-15048, 2026.
Oceanic islands often rely on groundwater for up to 95% of freshwater supply, yet groundwater recharge remains one of the most uncertain components of island water budgets due to steep climatic gradients, volcanic aquifer heterogeneity, sparse monitoring and strong interannual variability. This uncertainty is frequently underrepresented in planning and can propagate into groundwater allocation rules and infrastructure decisions, potentially fostering maladaptive trajectories.
We analyse how deep uncertainty in recharge estimation propagates through the groundwater-management chain, from recharge assessment and water-balance calculations to abstraction limits, coastal salinization risk and long-term infrastructure planning. We quantify an “uncertainty cascade” using multi-island examples from the Canary Islands (Macaronesia). In El Hierro, recharge estimates reported in the literature span ~30–114 mm yr⁻¹ (CV ≈ 44%), while recent physically-based modelling constrained by evapotranspiration yields ~58 mm yr⁻¹ (≈15.6 hm³ yr⁻¹), implying a ~50% downward revision compared to commonly adopted values. For Gran Canaria, estimated renewable groundwater resources range from ~140 hm³ yr⁻¹ (1970s) to ~80 hm³ yr⁻¹ in current plans, while our assessment suggests substantially lower values (~40–63 hm³ yr⁻¹ depending on assumptions). In La Palma, our calibrated estimate indicates ~50.8 hm³ yr⁻¹ (2000–2020), yet desalination planning is already being advanced for agricultural supply reliability in highly stressed areas.
Within the GENESIS project framework, we further evaluate climate change as an additional pressure capable of inducing a dramatic reduction in recharge across island aquifers, aggravating overexploitation, accelerating seawater intrusion thresholds and reinforcing desalination dependence (lock-in). We discuss adaptation pathways aligned with the European Climate Adaptation Strategy, with a focus on ultra-peripheral (outermost) regions, where water systems are among Europe’s most climate-sensitive and likely to experience early impacts. We highlight how Nature-based Solutions (NbS) and reclaimed water management can reduce demand on groundwater, increase system robustness, and protect critical island water infrastructures. Ultimately, uncertainty-aware governance is essential for equitable adaptation and to ensure that no communities are left behind.
How to cite: García-Gil, A., Sariago, R., Martínez-León, J., Jimenez, J., Meixueiro Ríos, G., and Baquedano, C.: From recharge deep uncertainty to desalination lock-in: propagation of groundwater recharge uncertainty into water-resources management and adaptation pathways in Macaronesian oceanic islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18255, https://doi.org/10.5194/egusphere-egu26-18255, 2026.
Sustainable water management entails meeting current water demand without compromising future availability, which requires balancing technical, environmental, economic, and safety factors. Achieving this balance requires management schemes capable of recognizing and adapting to changing conditions, particularly under uncertainty scenarios arising from climate variability, aquifer behavior, and temporal fluctuations in demand.
Water resource systems are inherently complex due to the coexistence of uncertainties in both subsurface conditions, such as spatial heterogeneity in hydraulic conductivity fields and transient flow dynamics, and demand conditions, characterized by nonlinear, time-dependent variations. These uncertainties directly influence the reliability of the supply, the efficiency of pumping strategies, and the long-term stability of groundwater systems.
Historically, the concept of safe yield has guided groundwater management by defining the sustainable extraction limit without deteriorating the aquifer system. However, under increasing uncertainty, it becomes necessary to evolve toward the concept of safe supply, which simultaneously considers the physical stability of the aquifer and its adaptive capacity to respond to future variations in demand and hydrogeological conditions.
Within this framework, machine learning techniques provide powerful tools to address different sources of uncertainty. On the one hand, Gaussian Processes (GPs) enable the modeling of uncertainty in water demand time series, offering probabilistic predictions that explicitly capture expected temporal variability in consumption. On the other hand, unsupervised learning methods, applied to ensembles of geological realizations, allow identifying representative subsets of hydraulic conductivity fields that approximate subsurface uncertainty at a significantly reduced computational cost. This approach captures relevant spatial variability without relying on exhaustive Monte Carlo simulations, facilitating multi-objective analysis and optimization under uncertainty.
Thus, integrating hydrogeological simulation with machine learning algorithms enables the development of adaptive groundwater management, where uncertainty, both temporal and geological, is treated as an explicit component of the decision-making process, strengthening water security and ensuring the long-term sustainability of the resource.
How to cite: Rodriguez Pretelin, A. and Morales Casique, E.: COCO, a cost-optimal combined operationframework for the management of WellheadProtection Areas under transient flow, geologicaluncertainty, and unknown groundwater demand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2617, https://doi.org/10.5194/egusphere-egu26-2617, 2026.
