Dynamic and adaptive policy pathways frameworks are being increasingly applied to guide deeply uncertain water infrastructure investments and adaptation strategies in systems around the world. Evolutionary multi-objective reinforcement learning (eMORL) has direct value for advancing these frameworks by improving our ability to better represent complex state-actions dynamics across actors and timescales. eMORL frameworks offer the potential to better understand the dynamics of state-aware actions that are contextually appropriate to the specific states of the world being experienced by system actors. However, the implications of the tradeoffs represented across alternative adaptive water supply investment policies pose nontrivial communication challenges. Investment pathways performance tradeoffs are typically communicated using highly aggregated metrics distilled to a single, expected value across actors and time. Here, this work addresses two main challenges. First, aggregated summary metrics do not capture the time-varying impacts of deeply uncertain (DU) factors on individual and system-wide performance and robustness. Second, aggregated summary metrics do not convey transparently state-action interdependencies between actors and performance objectives across time.
Our results address these challenges using a six-utility cooperative water supply infrastructure investment pathways example for the Research Triangle region in North Carolina. In our results, we contribute by-world, by-actor investment pathway diagnostics that clarify the consequential external deep uncertainties and state information feedbacks over time that strongly shape individuals’ adaptive actions. First, time-varying SHAP analysis clarifies the dynamics of which DU factors explain significant robustness shifts over time and across actors. Second, Information Theoretic Sensitivity Analysis identifies the key state variables that drive actions for each utility during specific periods of stress. In summary, our results can help decision-makers better understand how to navigate evolving vulnerabilities in their investment pathways and improve monitoring strategies to track changes consequential deep uncertainties over time.
How to cite: Reed, P. and Lau, L.: Exploiting multi-objective reinforcement learning and explainable artificial intelligence to better navigate deep uncertainties in water supply infrastructure pathways, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2893, https://doi.org/10.5194/egusphere-egu25-2893, 2025.
The increasing variability of climate conditions introduces substantial uncertainty into water system planning, making it more challenging to enhance system resilience. Achieving cost-efficient planning, where actual pressures can be addressed with minimal investment, remains critical. While Decision-Making under Deep Uncertainty (DMDU) approaches show promise, their cost-efficiency is rarely evaluated, and lengthy monitoring requirements limit post-implementation assessments. This study proposes a novel benchmarking framework for pre-implementation evaluation of DMDU methods. The framework uses historical climate data to simulate planning outcomes under uncertain future climates. It compares these outcomes to theoretical cost-optimal scenarios, thus offering quantitative insights for refining DMDU strategies to improve cost-efficiency.
The framework is demonstrated through a fluvial flood resilience case study in Luton, UK, focusing on real options. The results reveal that the original real options approach underinvests in the early stages of planning, leading to notable resilience deficits. A refined real options strategy mitigates these deficits by increasing investments but at the expense of higher total costs throughout the planning period. Moving forward, refinements should emphasise improving climate projections and avoiding interventions that do not effectively enhance resilience. The benchmarking framework provides a valuable tool for researchers and planners to evaluate and strengthen resilience planning strategies in water systems.
How to cite: Mijic, A., Liu, L., and Pianosi, F.: A Benchmarking Framework for Refining DMDU Approaches Toward Cost-Efficient Water System Resilience Under Deep Uncertainty, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4967, https://doi.org/10.5194/egusphere-egu25-4967, 2025.
Title: A Dutch Perspective to Bridge Scientific DMDU Knowledge and Drinking Water Practice
author(s): Joeri Willet1, Peter van Thienen1
affiliation(s): 1KWR Water Research Institute, Nieuwegein, Netherlands
There is an emerging realization of the value of methods for decision making under deep uncertainty (DMDU) in the Dutch drinking water industry. However, we ascertain that there is a disconnect between science and practice which hinders effective adoption of DMDU methods, at least in the Dutch drinking water context. We identify that scientific research tends to focus on large scale/global sources of deep uncertainty (such as climate change and shifts human populations) and their potential impacts. Practitioners acknowledge the existence of these global uncertainties but tend to characterize small scale/local processes (such as stakeholder preferences) as deeply uncertainty. This disconnect between science and practice poses a challenge for effective adaptive planning, especially in a sector where historically the flexibility of solutions (infrastructure) has been low, and reveals the need for more efforts to disseminate DMDU as an approach for the uncertain future.
In the Netherlands DMDU approaches can be valuable to deal with the decreasing availability of water sources and reductions in water quality, both of which are subject to deep uncertainty. The interactions between multiple sectors and multiple sources of uncertainty should be considered, as confirmed by experts at drinking water companies. We therefore see DAPP-MR (Schlumberger et al., 2022) as a promising DMDU method in this context, which we are preparing to pilot. In addition we identify the need for effective collaboration processes which facilitate ‘joint fact finding’, ‘joint exploration’ and ‘joint decision making’ between stakeholders to move from traditional approaches towards DMDU approaches. In this contribution we will discuss the learning process we envision for this.
Schlumberger, J., Haasnoot, M., de Ruiter, M., & Aerts, J. C. J. H. (2022). Towards a Disaster Risk Management Pathways Framework for Complex and Dynamic Multi-Risk: DAPP-MR. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4164233
How to cite: Willet, J. and van Thienen, P.: A Dutch Perspective to Bridge Scientific DMDU Knowledge and Drinking Water Practice, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8056, https://doi.org/10.5194/egusphere-egu25-8056, 2025.
Water supply managers worldwide are challenged by climate change and population growth. To maintain reliable water supplies, many urban water systems require significant infrastructure investment. Deep uncertainty in future water demand growth, the nature and speed of climate impacts, and financial conditions challenge the development of sustainable infrastructure investment portfolios. If water managers under-invest or construct new infrastructure too slowly, they risk water supply shortfalls under challenging future conditions. However, if challenging conditions do not manifest, the cost of large, near-term investments raises the risk of financial instability and stranded assets. Recent work has shown that adaptive pathway approaches using stochastic multiobjective reinforcement learning (MORL) and state-aware risk-of-failure (ROF) based rule systems can develop robust infrastructure adaptation policies that balance supply reliability and financial stability. ROF-based infrastructure pathway policies tailor investment decisions to observed future conditions, generating unique infrastructure pathways for each future state of the world.
A core challenge with the adoption of ROF-based infrastructure pathways is the volume of information they produce, which can overwhelm water managers and lead to decision paralysis. Recent innovations in visual analytics (VA) and explainable AI (XAI) offer new tools for exploring large and complex data sets. These tools emphasize interactive visualizations to incorporate human expertise into the analysis and provide multiple perspectives for the data, the model, and their outcomes. In this work, we develop a new interactive VA system that allows water managers to explore dynamic adaptive infrastructure pathway policies interactively. Our framework centers on interactive Set Streams, a visual technique that represents pathways on a timeline of branching and merging streams to explore adaptive pathway alternatives. The system allows users to interact dynamically with pathway alternatives and apply preferences across performance objectives and infrastructure sequencing. We demonstrate our system on the Sedento Valley test case, an urban water supply benchmarking problem where three water utilities seek to develop cooperative and adaptive water supply pathway policies.
How to cite: Hochman, Z., Chatzimparmpas, A., and Gold, D.: An interactive visual-analytic system to support dynamic and adaptive infrastructure pathways for urban water supply planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17679, https://doi.org/10.5194/egusphere-egu25-17679, 2025.
Dynamic Adaptive Policy Pathway (DAPP) maps are used to plan management decisions in contexts of high uncertainty, such as those driven by environmental changes affecting critical assets. Recent discussions emphasize their relevance for addressing complex common-pool resource challenges, where diverse species, actors, and ecosystem services are intricately connected. However, designing DAPPs for such multifaceted social-ecological systems (SES) is challenging due to the extensive range of potential adaptation options.
This study presents a general method to address these challenges by leveraging Ostrom’s theoretical frameworks for the governance of common pool resources – the Institutional Analysis & Development framework (IADF), the Social-Ecological Systems framework (SESF), and the Coupled Infrastructure Systems framework (CISF). These frameworks were used to design nested DAPP maps that structure a large number of adaptation actions across multiple levels of institutional arrangement (operational, collective-choice, constitutional), and then develop a mathematical model to analyze the dynamic robustness of a SES across all potential pathways.
The method was applied to predict and understand DAPP maps for supporting the collective management of hedgerow networks delivering diverse ecosystem services. DAPP maps for two SES were compared – one rural and one peri-urban – in France’s agro-ecological landscapes of the Auvergne region. We further modeled the impact of climate change on hedgerows characterized by different size and species richness, revealing the sensitivity of these DAPP maps to transit between nine nested institutional arrangements.
We discuss the methodological and practical implications of this approach for managing SES characterized by greater diversities of interconnected species, actors, and ecosystem services, highlighting its strengths and challenges in guiding adaptation under deep uncertainty.
How to cite: Pichancourt, J.-B., Brias, A., and Bonis, A.: Integrating Adaptation Pathways and Ostrom's Framework for Sustainable Governance of Social-Ecological Systems in a Changing World, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2561, https://doi.org/10.5194/egusphere-egu25-2561, 2025.
Computational modelling provides a vital tool to evaluate risks and benefits of different investment or management options on a virtual system before they are implemented on real water resource systems. In England and Wales, models are used to inform a range of decisions across different spatial and temporal scales – from company-level operational decisions during individual drought events to strategic infrastructure investment decisions at the national scale. Model outputs though are conditional on a range of uncertain assumptions and input data, due to our incomplete or imperfect knowledge of the drivers and the properties of the system being modelled. When models are used for long-term planning, the uncertainty about the current properties and drivers of the system is compounded with deep (i.e. poorly characterised) uncertainty about how these will evolve in the future.
In this talk we will present results from the USARIS (Uncertainty quantification and Sensitivity Analysis for Resilient Infrastructure Systems) project [ST/Y003713/1], which aims at setting the foundations for integrating Uncertainty Quantification and Sensitivity Analysis (UQ&SA) functionalities in the UK DAFNI (Data and Analytics Facility for National Infrastructure) platform (https://www.dafni.ac.uk/). We will discuss the value of global Sensitivity Analysis to systematically analyse the impact of varying uncertain factors and decision levers on model predictions and hence improve both the model evaluation and its use for decision-making under deep uncertainty - and demonstrate it by application to Pywr-WREW, the Python-based national-scale water resources model for England and Wales.
We will focus on a complex, multi-reservoir system in the Northumbrian region, and analyse the relative influence of the model’s decision levers (changes to operational preferences and management decisions) and uncertain inputs and properties (future climate, demand and environmental flow requirements) on a range of performance metrics. At the model evaluation stage, the global SA helps us to sense-check the model (i.e. making sure that the “right” input controls the “right” output) and to ensure that model predictions are sufficiently controlled by decision levers relative to the impact of other uncertain factors (otherwise the model would not be suitable for decision-making). At the options appraisal stage, the same methodology can be used (under the assumption “system=model”) to determine the key drivers of the future system performance (e.g. supply-side vs demand-side) and begin to identify “robust” decisions that work sufficiently well across a range of uncertain futures. Finally, we will discuss the scalability of our proposed approach to more complex/larger-scale systems, and blockers and enablers for uptake by practitioners in water companies and environment agencies.
How to cite: Pianosi, F., Salwey, S., Coxon, G., Wendt, D., and Murgatroyd, A.: The value of sensitivity analysis for the evaluation and use of water resource models under deep uncertainty, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13694, https://doi.org/10.5194/egusphere-egu25-13694, 2025.
In catchments that are poorly monitored or in catchments that are not gauged, the degree of uncertainty in predicting flood risk is high. This is unfortunately a very common picture in Bulgaria. The presence of climate change and the uncertainty in the determination of key input parameters such as peak water discharge, Manning's roughness coefficient, etc. introduce a deep uncertainty in flood modelling. Under these conditions, in the search for adaptive and reliable flood risk management strategies, uncertainty is quantified using the Monte Carlo method to generate probabilistic results and by analyzing it using Information-gap decision theory, a non-probabilistic method that is a quantified theory of robustness.
How to cite: Mavrova-Guirguinova, M.: Uncertainty Analysis of Flood Forecasting in Poorly Gauged Catchments , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13454, https://doi.org/10.5194/egusphere-egu25-13454, 2025.
Effective transboundary river management requires systematic, collaborative efforts among nations sharing a river basin to ensure sustainable and equitable water resource allocation, support energy generation and food security, mitigate environmental impacts, and preserve the ecological integrity of river systems. This study introduces a novel framework for incorporating equity into the systemwide optimization of transboundary river operations, explicitly addressing the asymmetrical distribution of energy and water supply risks among riparian nations. Unlike traditional approaches based on game theory and hydro-economic modeling, which focus on static compensatory schemes for cooperative management, our framework leverages dynamic operational flexibility to achieve equitable and robust water resource allocation within the existing and planned infrastructure. The framework employs multi-objective robust optimization at the basin scale to generate adaptive operational strategies that incrementally integrate inequality aversion in hydropower benefits among nations, as quantified by the Atkinson inequality index. Through fully coordinated reservoir operations, the approach adaptively allocates water flow and storage in response to changing hydrological conditions, ensuring a fair distribution of benefits and trade-offs across riparian states. We demonstrate this methodology using the Zambezi River basin, where planned dams in the upper (Zambia and Zimbabwe) and lower reaches (Mozambique) are projected to double hydropower capacity. Results highlight that incorporating equity considerations yields hydrologically robust strategies that balance trade-offs between hydropower production, irrigation demands, and environmental flow requirements at the national level. These findings underscore the transformative potential of adopting dynamic, equity-focused approaches to transboundary water resource management in the face of escalating climate variability and uncertainty.
How to cite: Castelletti, A., Arnold, W., and Giuliani, M.: Enhancing Robustness and Addressing Inequities through Operational Flexibility in Cooperative River Basin Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10724, https://doi.org/10.5194/egusphere-egu25-10724, 2025.
The operation of water resources is inherently complex, necessitating the reconciliation of competing objectives, the cooperation of interconnected infrastructures, and adherence to various regulations and stakeholder perspectives. Multi-objective optimization methods have emerged as powerful tools for addressing these complexities by enabling decision-makers to balance conflicting goals. The effectiveness of these methods is contingent upon the input variables utilized in water resource operation models. However, decision-makers often find it challenging to understand how different input variables impact optimization performance regarding different objectives, thereby reducing the interpretability of these models and impeding their practical application in real-world contexts. In this regard, the careful selection of input variables can enhance both the efficacy and interpretability of multi-objective water resource operations. We proposed a feature selection approach to assess the significance of various input variables in water system operation models under differing decision-making preferences and extract the most relevant information to alleviate conflicts among competing objectives. The approach is demonstrated on a cascade reservoir system within the Nile river basin, where the primary functions are power generation and irrigation water supply. A systematic feature selection framework that integrates Recursive Feature Elimination with Evolutionary Multi-Objective Direct Policy Search is employed to analyze the importance of various input variables in cascade reservoir management and identify the optimal input variables for operational policies tailored to specific decision-making preferences.
The application of this input variable selection framework in the multi-objective optimization of Nile operation policies yielded several key insights: (1) input variables that reflect collaborative operations among different reservoirs consistently received higher importance rankings in the selection process; (2) the use of these selected input variables significantly alleviated conflicts between power generation and irrigation water supply, especially when minimizing irrigation deficits was a priority; and (3) customizing reservoir operation policies with input variables chosen based on decision-making preferences enhanced the performance of the respective objectives. particularly when a lower irrigation deficit is desired. Furthermore, customizing reservoir operation policies with input variables chosen based on decision-making preferences can enhance the performance of the relevant objectives. Our findings also highlight the value of incorporating the water level of neighboring reservoirs as an input variable, when there exist multiple reservoirs within the operation system. Additionally, the study revealed that reservoirs with different operational targets—such as hydropower generation or irrigation supply—require distinct input variables. Interestingly, some of the selected variables lie outside the conventional set of inputs, which suggests the potential benefits of incorporating unconventional or external information into water system operations. This proposed framework holds promise for improving the interpretability of machine learning-based operational policies for water resource systems and fostering a stronger connection between these complex systems and their human operators.
How to cite: Yang, G., Giuliani, M., Castelletti, A., and Dong, Z.: A Feature Selection Framework for Enhancing Interpretability and Performance in Multi-Objective Water Systems Operations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7555, https://doi.org/10.5194/egusphere-egu25-7555, 2025.
Global change impacts are likely to compromise agricultural benefits and the ecological status of rivers. The latter would be caused by modifications in fish population dynamics as fish species react in different ways against hydrological changes, and the establishment of alien and invasive fish species. To guarantee native fish sustainability, impact assessment studies should encompass habitat evaluations and competition assessment under future scenarios (i.e., including future hydrological scenarios and land use, and changes in agricultural demands). Moreover, their interplay with economic uses should also be considered, designing adaptation measures that take advantage of synergies and minimize trade-offs between them. Dam reoperation is a promising alternative to this end, given its direct and immediate impact on downstream streamflows and its absence of building costs. However, it requires consensus between water users, including native fish; thus, it should be carefully evaluated taking into account stakeholders’ views.
This contribution presents a framework to develop dam reoperation strategies that simultaneously address global change impacts on agricultural benefits, native fish habitat and competition with invasive fish species in a water resource system. The developed methodological framework has been tested in the Serpis River Basin (Spain). The global change scenarios combined CMIP6 climate change projections with three land use scenarios: current crop surface and technology (reference), drip irrigation implementation and drip irrigation with changes in crop types and areas. Hydrological discharges associated with climate change scenarios were derived using the Témez conceptual hydrological model. Future crop water needs were estimated, for each climate scenario, using the AQUACROP model. The changes in the agricultural benefits related to these scenarios were obtained with a hydroeconomic model developed with the GAMS software. The effects in the availability of suitable habitat for native fish species (Eastern Iberian chub, Squalius valentinus and European eel, Anguilla anguilla) and its competition with invasive species (Bleak, Alburnus alburnus and Pumpkinseed, Lepomis gibbosus) were assessed by combining a 2D hydraulic model with the corresponding fuzzy logic-based habitat suitability models by species. The Pareto-optimal strategies for dam reoperation were obtained with the BORG-MOEA algorithm implemented using the Platypus Python library. The goals were the maximisation of the agricultural benefits and of native fish habitat, and the minimisation of the competition between the two groups of species.
Our results suggest a trade-off between economic and ecological objectives and a positive relation between native fish habitat and native-invasive competition. They also indicate that economic and ecological sustainability could not be achieved by dam reoperation in the most pessimistic scenarios. However, dam reoperation shows a significant potential to contribute to climate change adaptation, entirely reverting its impacts in the most optimistic scenarios. It also shows synergies with land use scenarios, suggesting that dam reoperation could boost the positive impacts after the implementation of drip irrigation.
Acknowledgements: This study has received funding from the European Union’s Horizon 2020 research and innovation programme under the GoNEXUS project (grant agreement No 101003722); as well as from the SOS-WATER project, under the European Union’s Horizon Europe research and innovation programme (GA No. 101059264).
How to cite: Martinez-Capel, F., Macian-Sorribes, H., Muñoz-Mas, R., Peano, D., Oliva-Paterna, F. J., and Pulido-Velazquez, M.: Dam reoperation to reconcile agricultural sustainability with native fish habitat and competition with invasive fish species in semi-arid areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15775, https://doi.org/10.5194/egusphere-egu25-15775, 2025.
Water management in basins where natural flow regime is altered by the presence of reservoirs is a complex issue that often requires the development of modelling tools to support managers in their decision-making process. However, when it comes to large basins where it is necessary to satisfy the different demands of a vast and heterogeneous territory, in which the pressures on the basin water resources are increased due to the effects of climate change, it makes modelling a challenging matter. It is in these cases when it is possible to use long series of data on hydro-meteorological variables collected in the basin reservoirs and stations to analyse the behaviour of the basin and its management over the period for which data are available.
A series of 20-years of hydro-meteorological data has been used in more than 30 reservoirs in the Guadalquivir River basin, in southern Spain, in order to obtain patterns of inflow and outflow regimes in the reservoirs, and to relate them to the supply to the different water demands. To do this, a distinction has been made between dry, medium and wet years, based on the SPI-12 in the basin. In order to obtain the patterns related to the natural regime of the basin, the headwater reservoirs have been selected, which do not have the inflow regime altered by the action of another reservoir upstream.
The results of the analysis show 5 different types of inflow-outflow dynamics depending on their location in the basin, that is, depending on the influence of the Mediterranean-Alpine climate in the upper areas of the basin, or the Atlantic influence near the mouth of Guadalquivir River. In addition, changes in management patterns have been identified depending on the type of year. Our results will be the basis for the development of management tools in large basins for short and medium-term forecasting of resource availability and demand satisfaction.
How to cite: Gómez-Beas, R., Contreras, E., Andreu, A., Pimentel, R., Aguilar, C., and Polo, M. J.: Patterns of reservoir operation over a 20-years period in the Guadalquivir basin and implications for basin management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20087, https://doi.org/10.5194/egusphere-egu25-20087, 2025.
This study proposes a novel approach for integrating interactive multi-objective optimization into Many Objective Robust Decision Making (MORDM) to involve decision-makers during the solution process. Unlike the a posteriori methods, this involvement provides an intuitive learning phase for the decision-maker with complete control to search and uncover the problem characteristics, the feasibility of their preferences, how uncertainty may affect the outcomes of a decision, and explore various parts of the Pareto fronts, one at a time, significantly reducing cognitive load and computation resources. We further introduce a hypothetical water management problem as a new benchmark problem for robust decision-making with multiple objectives under deep uncertainty, which is most suited for properly showcasing the robustness optimality trade-offs. Utilizing this example, we illustrate the stages and interactions of the proposed approach as a proof of concept.
How to cite: Shavazipour, B., Kwakkel, J., and Miettinen, K.: Interactive multi-scenario multi-objective robust optimization for decision-making under deep uncertainty, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8685, https://doi.org/10.5194/egusphere-egu25-8685, 2025.
Climate models have grown increasingly complex as they aim to capture interactions between environmental, social, and economic systems. These models are now routinely used to generate large ensembles of scenarios, requiring robust and scalable methods to extract meaningful insights. Our research demonstrates the application of Outlier Set Two-step Identification (OSTI) to systematically extract, evaluate and interpret outlying ensembles of futures from Integrated Assessment Model (IAM) outputs. OSTI is a novel technique that combines Gaussian Mixture Models for probabilistic clustering with Inter-cluster Mahalanobis distance measurement and hypothesis testing to identify clusters of scenarios that deviate significantly from typical patterns.
Here, we analyze irrigation withdrawal patterns across 27 major river basins using outputs from the Global Change Analysis Model (GCAM). GCAM integrates climate, economic, and human systems to explore future pathways through 2100 at five-year intervals. We apply OSTI to 3,000 scenarios of agricultural water demands through 2100, generated by varying seven key GCAM parameters including socioeconomic development, agricultural practices, groundwater availability, reservoir storage capacity, climate trajectories, and carbon tax policies.
We then systematically extract these OSTI-identified outlying futures to identify distinct patterns that appear repeatedly across multiple basins, focusing on scenarios that share unique combinations of socioeconomic and agricultural parameters. The extraction process highlights outlier sets against their input parameters to understand what combinations of model inputs lead to outlying behavior. In these outlying sets, water supply parameters have minimal influence on outlying future determination, while demand-related parameters dominate. We speculate this reflects GCAM's recursive economic equilibrium mechanism, which interprets physical water scarcity constraints in terms of economic cost but does not make them binding. The spatiotemporal analysis shows distinct irrigation withdrawal patterns across two time periods (2015-2050 and 2050-2100). Most basins exhibit increasing irrigation withdrawals until mid-century, followed by significant declines or stabilization, particularly for winter crops like wheat. This pattern strongly correlates with groundwater dynamics, where peak extraction occurs around 2050, followed by declining usage due to increasing pumping costs and declining water tables. However, high-value crops like cotton maintain relatively stable withdrawal patterns throughout the century, while sugarcane shows continued growth in some scenarios, reflecting adaptation to changing water availability and economic priorities.
These results establish OSTI as a diagnostic tool for systematic identification of limitations and potential artifacts in complex models like IAMs. As IAMs like GCAM become increasingly pivotal in understanding multi-sectoral dynamics under deep uncertainty, OSTI offers a robust and scalable tool for scenario discovery. Beyond, our approach is applicable to the exploration of large scenario ensembles in other contexts. It provides a scalable way to identify and analyze potentially outlying scenarios requiring special attention in adaptation planning.
How to cite: Sarfraz, A., Rougé, C., Mihaylova, L., Lamontagne, J., Birnbaum, A., and Dolan, F.: Identifying and Analyzing Outlying Futures in Integrated Assessment Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15962, https://doi.org/10.5194/egusphere-egu25-15962, 2025.
Addressing climate change through collective action is hindered by the unequal distribution of burdens and responsibilities and deep uncertainties inherent in the Human-Earth system. As a result, policymakers must navigate both empirical uncertainties within socioeconomic and climate systems, as well as normative uncertainties stemming from stakeholders' diverse values. Addressing these value differences is critical, as perceptions of fairness in mitigation policies are essential for their acceptance and implementation. While classical decision-making under deep uncertainty (DMDU) techniques have not yet been applied to problems involving normative uncertainty, they can be adapted for climate policymaking where multiple stakeholders hold conflicting values and policy objectives.
This study integrates three principles of distributive justice—Limitarianism, Utilitarianism, and Prioritarianism—to allocate the remaining carbon budget necessary to limit global warming below 2°C. We apply Limitarianism using emergent constraints—an established climate modelling method that identifies a remaining carbon budget robust across diverse climate and socioeconomic uncertainties—to determine a carbon budget that achieves the 2°C target. Building upon this robust emission limit, we compare Utilitarian and Prioritarian frameworks to distribute the remaining carbon budget among different nations and generations. The JUSTICE Integrated Assessment Model (IAM) operationalizes these principles within a multi-objective framework to search for Pareto-optimal mitigation policies that balance environmental and economic objectives while evaluating policy options through various lenses of distributive justice.
We utilize two established decision-making frameworks to develop adaptive mitigation policies: Multi-Objective Robust Decision-Making (MORDM) and Evolutionary Multi-Objective Direct Policy Search (EMODPS). MORDM rigorously tests potential policies against deep uncertainties to identify robust, Pareto-optimal choices. Simultaneously, EMODPS fine-tunes policies to reconcile stakeholders' diverse objectives, ensuring policies are adaptive and robust across both empirical uncertainties and normative values. These adaptive policies utilize feedback mechanisms providing flexibility to accommodate diverse future scenarios. This flexibility also facilitates the management of trade-offs between conflicting goals and values.
Our findings demonstrate that normatively robust policies can bridge the gap among policymakers with diverse perspectives by maintaining robustness across deep uncertainties, conflicting ethical viewpoints, and multiple objectives. We highlight the pivotal role of normative clarity in facilitating stakeholder dialogue and ensuring that climate policies are scientifically sound and socially equitable.
How to cite: Biswas, P., Zatarain Salazar, J., and Kwakkel, J.: Normatively Robust Mitigation Policy to Equitably Distribute the Remaining Carbon Budget, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12371, https://doi.org/10.5194/egusphere-egu25-12371, 2025.
