Human-water feedbacks have been increasingly studied in the last decades, motivating the foundation of new disciplines such as socio-hydrology and, in general, enhanced interest toward conceptualization and modelling of the spatial and temporal dynamics of human-water systems. With anthropogenic activities being widely recognized as a major driver of global change and the human population being increasingly exposed to hydroclimatic extreme events, human systems are now at the forefront of the water cycle. Yet, human preferences, behaviors, and decisions in relation to water systems - including water usage dynamics, adoption of precautionary measures against climate extremes, and adaptation of urban landscapes - are often modelled based on behavioral or economic theories, or derived from small-scale samples. This often leads to heterogeneous results, which are often case-specific, or lack validation against real-world observations.

The availability of increasingly fine-resolution data from distributed sensors and databases (e.g., water consumption data from intelligent meters, flood insurance adoption records at the household level, and socio-demographic data) and earth observations (e.g., aerial and satellite imagery) provides us with an empirical basis to model heterogeneous individual and societal behavioral patterns, along with their determinants.

In my research, I strive to develop multi-disciplinary data-driven behavioral modelling approaches that bridge hydrologic/hydraulic sciences, informatics, economics, and systems engineering and harness information from multi-scale human data and earth observations and the power of data analytics and machine learning to better understand, model, and characterize human behaviors in coupled human-water systems. In this talk, I will first provide an overview of recent advances in descriptive behavioral modelling in human-water systems, with a focus on household-to-continental scale modelling of residential water consumption patterns and adoption of household flood insurance. Second, I will elaborate on modelling challenges that are motivating ongoing research related to machine learning-based behavioral models, including model explainability, data and computational requirements, generalization and scalability, and the influence of data resolution in time and space. Finally, I will discuss how developing descriptive models that learn human behaviors retrospectively can be used to inform forecasting tasks and formulate policy-relevant recommendations to shape future societal adaptation to climate change. Implications span from informing the design of feedback-based digital user engagement in pursuit of water conservation, to fostering proactive climate adaptation, addressing societal inequalities and heterogeneous water access and affordability conditions, or evaluating incentive programs and policies for sustainable urban development.

How to cite: Cominola, A.: Learning from the past to shape the future. Harnessing multi-scale human data and earth observations to foster sustainable water usage and societal adaptation to climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19445, https://doi.org/10.5194/egusphere-egu24-19445, 2024.

Integrated Water Resources Management (IWRM) stands as a globally advocated approach heralded for its promise to orchestrate equitable and coordinated water allocation, usage, and governance. However, its implementation varies significantly across different river basins and countries. Transboundary water resources management in the Aral Sea basin presents a critical environmental and political challenge in the region, bringing concerns not only to basin countries, including Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan but also to external actors.  The European Union (EU) has emerged as a pivotal donor in developing sustainable water resource management since the early 1990s. The EU's involvement stems from recognizing water challenges as potential threats to regional security and stability and fosters diverse regional and bilateral water programs and projects. This study delved into the evolution of the EU policies concerning environment and water management in the Aral Sea basin, focusing on promoting IWRM, raising environmental awareness, and building capacities. Methodologically, the research employed semi-structured interviews conducted with national, regional, and international experts engaged in EU initiatives, along with a synthesis of academic publications, EU official documents, and recent reports. 

Research reveals the changes in EU water policy in Central Asia since the 2000s, including shifts in objectives, the scale of cooperation, and the interplay between EU policies and the perceptions, responses, and shaping by regional actors. The EU has successfully promoted certain norms of ‘good water governance,’ and Central Asian countries have, to a certain degree, adopted them in their policies and legal frameworks. However, The EU's reliance on soft tools and multi-stakeholder dialogues, limited financial commitments, coordination challenges, and local political constraints have constrained its impact on the ground. This situation has created a palpable sense of 'dialogue fatigue' among national stakeholders. The contextual disparities, divergent interests, and issues at stake between the EU and Central Asian countries pose significant obstacles to transferring EU experiences and practices. Central Asian actors' responses to EU water initiatives, amid the influence of the external and internal political environment, bear implications for sustainable water management. These implications are particularly pressing given the region's vulnerability to climate variability, the geopolitical landscape, and the countries' capacity to navigate multiple crises.

How to cite: Assubayeva, A. and Sehring, J.: EU engagement for sustainable water management in the Aral Sea basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14942, https://doi.org/10.5194/egusphere-egu24-14942, 2024.

Inter-basin water diversion has emerged as a critical measure for achieving a balanced distribution of water resources across basins. This process requires careful planning and implementation of an effective water supply operation scheme. However, an observed decrease in prediction accuracy with extended time periods reduces the effectiveness of long-term water resource management, presenting a dilemma between the risk of water scarcity due to insufficient water diversion and water wastage resulting from excessive diversion. Moreover, stringent regulations on water resources management are critical to achieving intensive water use. Setting limits on the dynamic spatial-temporal allocation of water resources poses a persistent scientific issue requiring immediate attention. Therefore, this study proposed a multi-objective risk-based optimization model for the inter-basin water diversion system under multiple uncertainties and water-use constraints. Backed by probabilistic predictions of local streamflow and water demand via scenario tree method, the water shortages and wastage risks along with costs associated with water diversion were identified. Simultaneously, a dynamic decomposition method for the total water-use constraints, considering changes in various streamflow scenarios, was proposed. Real-time water supply operations under severe constraints and heightened uncertainty were investigated in this study, while the eastern route of the South-to-North Water Diversion Project in Jiangsu Province, China was selected as the case study. The principal findings were as follows: (a) Conflicting relationships exists in this complex system, with the loss of water shortage and the cost of water diversion being the main contradictions. By utilizing high-prediction information, the water diversion was reduced by 41.9%, spilled water by 72.0%, and the water deficit by 10.6%, contributing to achieving an equilibrium in terms of cost, loss, and risk of water diversion and provision; (b) The total water-use constraint effectively controlled inter-basin water diversion and spillage, thus promoting the optimal exploitation of local water supply potential. The core of the water-use constraint is to promote the utilization of water resources through the compression of inter-basin water diversion; (c) By applying the dynamic decomposition of total water-use constraint, the water supply and consumption in a typical dry year increased by 15.46 × 10⁸ m³, theoretically reducing the water deficit by 18.0% compared to the rigid constraint condition, meeting essential agricultural needs during severe drought conditions; (d) The incorporation of chance-constraint functions allowed for a more aggressive water diversion strategy (increasing additional water diversion by 0.574 × 10⁸ m³) while mitigating risks associated with operational decision-making, thereby enhancing reliable water resources management.

How to cite: Mo, R. and Xu, B.: Risk-based multi-objective optimization model for the inter-basin water diversion system under multiple uncertainties and water-use constraints, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2524, https://doi.org/10.5194/egusphere-egu24-2524, 2024.

Arid regions confront significant challenges due to the scarcity of natural water resources, leading to the depletion of non-renewable reserves and an escalating reliance on unconventional water sources. The intricacies of water resource management within such regions are compounded by the dynamic interplay of influential factors, including rapid urbanization and population expansion.

This research employs an innovative System Dynamics (SD) methodology to construct a comprehensive model aimed at understanding the complex dynamics inherent in water resource management within Qatar, characterized by an arid climate. A Decision Support System (DSS), functioning as a simulated environment, was developed to project the behavioral patterns of Qatar's water resource system from 2021 to 2070. This projection encompasses nine distinct scenarios, categorically addressing changes in physical, environmental, and socio-economic dynamics. These scenarios were further evaluated against Water Sustainability and Reliability Indexes to provide a comprehensive assessment. The outcomes of this study underscore that the conventional “business-as-usual” approach to water resource management can ensure a sustainable balance between water supply and demand for a limited span of 32 years, with the most optimistic scenario extending this sustainability horizon to 50 years. Furthermore, groundwater conservation strategies were integrated and simulated, accentuating the imperative to preserve groundwater resources as an indispensable "backstop" for the nation.

The developed model not only addresses Qatar's specific challenges but also offers insights applicable to other Gulf Cooperation Council (GCC) countries and similar arid regions. This research contributes a robust decision-making tool for policymakers and stakeholders to assess the long-term implications of various management scenarios. It contributes to the development of sustainable and resilient water policies for the years to come, particularly in navigating the uncertainties inherent in arid region water resource management.

How to cite: Elomri, A., Aloui, S., Zghibi, A., and Mazzoni, A.: System Dynamics Modeling for Resilient Water Resource Management in Arid Regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5064, https://doi.org/10.5194/egusphere-egu24-5064, 2024.

Growing societal water demands and decreasing water supplies are straining water available for ecosystems and communities in many basins. Increasingly, the only viable option to meet growing urban water demands is to reallocate water from rural agricultural water uses when water supplies have already been fully allocated and it is no longer possible to develop new water supplies. Despite the growing importance of rural-to-urban water transfers, the implications of these transfers on rural prosperity and inequalities are poorly understood. Here, we couple an agent-based model (ABM) with an input-output model to capture the behavior of individual irrigators and how their water transfer decisions propagate through the broader rural economy and shape social dynamics. In this presentation, we will detail our unique modeling framework and share initial results testing multiple hypotheses evaluating how rural-urban water transfers are shaped by social, hydrologic, regulatory, and economic context. This research brings new insights that can be used to evaluate the direct and indirect socioeconomic impacts of water transfers and it can help shape policy to minimize potential negative externalities associated with water transfers.

How to cite: Marston, L., Amaya, M., and Lin, C.-Y.: From Fields to Faucets: Modelling the Dynamics of Rural-Urban Water Transfers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2718, https://doi.org/10.5194/egusphere-egu24-2718, 2024.

Disaster Risk Management (DRM) is increasingly complex due to interacting climate risks from concurrent hazards. The Dynamic Adaptive Policy Pathways for Multi-Risk (DAPP-MR) framework has been introduced to assess DRM policies' effectiveness under deep uncertainties - such as future climate change - and to develop integrated adaptive strategies considering interactions across hazards, sectors, and time. So far, no use cases were available that provide evidence regarding the utility of DAPP-MR.

In this presentation we examine DAPP-MR through a synthetic multi-risk modelling case study, focusing on DRM pathways for managing flood and drought risks across different sectors for a period of 100 years. The case study, inspired by a Dutch river delta, accounts for multi-hazard interaction effects such as including co-occurring or preceding droughts amplifying flood risk, and consecutive flood events as well as multi-sector dynamics. While providing insights into the model development process, the result analysis and conclusions, we also discuss the challenges and benefits of combining multi-risk thinking and climate change adaptation decision-making approaches and the implications of multi-risk dynamics on trade-offs and synergies of different risk management strategies.

How to cite: Schlumberger, J., Haasnoot, M., Aerts, J., Bril, V., van der Weide, L., and de Ruiter, M.: Unravelling the complexity of multi-risk systems and adaptation pathways, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4002, https://doi.org/10.5194/egusphere-egu24-4002, 2024.

Scientists and decision-makers globally confront systemic challenges posed by the intertwining issues of water scarcity and climate change. These challenges give rise to cascading impacts across ecological and socioeconomic systems, often exacerbated by feedback loops and unforeseeable consequences (UNDRR, 2021). As non-linear changes loom, the reliance on consolidative modeling becomes dangerous, risking the activation of disastrous tipping points with severe implications for both nature and humans (Kreibich et al., 2022). The costs associated with neglecting uncertainties in modeling and policy spans diverse domains, including ecosystems, income, employment, capital value, insurance, etc. (UNDRR, 2022; Parrado et al., 2019; Adamson and Loch, 2021). This aligns with the concept of Knightian or deep uncertainty, where the external context, system dynamics, and conflicting outcomes are not fully known or agreed upon (Knight, 1921; Marchau et al., 2019; Lempert et al., 2006). A growing scientific and policy consensus emphasizes the need to move beyond traditional notions of optimality and deterministic prediction in conditions of deep uncertainty. Resilience and robustness emerge as crucial concepts, requiring the development of socio-ecological system (SES) models that explicitly quantify uncertainties (Adamson and Loch, 2021; Di Baldassarre et al., 2016; IPCC, 2021; UNDRR, 2021).

Recent research in SES, including coupled human and natural systems and socio-hydrology science, offers innovative modeling techniques integrating human and natural components. These techniques account for feedbacks and heterogeneity between systems, improving insight and the ability to predict tipping points (Gain et al., 2021). Recent studies demonstrate the potential of linking coupled models of human-water systems with sensitivity analysis and multi-system ensembles for robust water management policies (Basheer et al., 2023; Smith et al., 2021).

This review initially identified 2160 papers, filtering them to 198 studies that account quantitatively modelling in, both human and water systems. The geographical focus spans the USA, Europe, Australia, the Middle East, South America, China, and East Africa. The models range from piecewise equations to full-fledged representations. However, structural uncertainties are seldom explored, with only 3.5% of studies conducting multi-model ensemble experiments. This highlights a significant oversight in recognizing biases from simplifications. Conversely, parameter uncertainties are more frequently addressed (20.2%), focusing on hydrology, groundwater, behavioral, infrastructure, climatic, economic, and agronomic variables. Input uncertainties, notably contemporary (discharge data) and future (climate change) inputs, are extensively studied (148 out of 198), employing methods like expert judgment and Monte Carlo simulations. Despite this, the review highlights a limited exploration of structural uncertainties and the potential inadequacy of linear piecewise equations, emphasizing the need for more nuanced and robust approaches to enhance the accuracy and reliability of socio-environmental systems modeling.

Based on this study, we make the following recommendations to mainstream uncertainty quantification into SES modeling: (i) Quantify parameter and structural uncertainties within systems, (ii) Quantify structural uncertainties between models, (iii) Input uncertainties must be more thoroughly assessed and model assumptions systematically revised, (iv) Deliver actionable science that mainstreams uncertainty quantification into decision making, (v) Establish balanced stakeholder engagement and clear and transparent science-policy engagement rules, (vi) Balance complexity and usefulness to keep the model relevant.

How to cite: González-López, H., Foster, T., Gil-García, L., Di Baldassarre, G., Pulido-Velazquez, M., Mysiak, J., and Pérez-Blanco, C. D.: Socio-ecological systems modeling on water resources management under uncertainty: A literature review. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5414, https://doi.org/10.5194/egusphere-egu24-5414, 2024.

The alteration in hydrological patterns due to climate change is causing a rise in the occurrence and severity of hydrological extremes like droughts and floods. This, in turn, is leading to conflicts over water resources and increasing financial risk for several economic sectors, such as hydropower and agriculture.
Parametric insurance is a tool for hedging financial risks that is already used for making water-related sectors more resilient. With respect to traditional loss-based insurance, parametric schemes are more flexible, simple and cost-effective, and they also minimize moral hazard and promote further adaptation efforts.
Parametric schemes rely on the definition of an index, correlated with revenue losses, and on predetermined thresholds to trigger payouts compensating for losses. Different conditions can be proposed to contract buyers, varying the payout structure, contract price (i.e., premium) and duration. For example, while in standard index-based insurance, a fixed premium is paid on an annual basis, in ‘collar’ contracts a premium is only paid in years when the index exceeds a pay-off threshold, i.e., when revenues are high. Indexes can also be designed in a variety of ways, but they should be highly correlated with losses, reliable and transparent. While simple indexes based on a hydrological variable are often preferred, multivariate indexes can, in some cases, be more suitable due to the multiple factors influencing revenue losses. This is the case for the hydropower sector, which is highly dependent on both hydrological and market variability. Different parametric insurance contract types have been proposed in the past for various sectors, but alternative schemes are seldom compared on the same system. 

In this study, we investigate the mitigation of economic impacts deriving from droughts and energy market variability on hydropower companies by comparing alternative parametric insurance schemes. Such alternatives are built considering a variety of payout structures, including standard and collar schemes, and both univariate and multivariate indexes based on hydro-meteorological and market variables. We test our methodology on three hydropower operators in the Lake Como system, in the Italian Alps, an area where global warming has caused local temperatures to increase more than double the global average and stakeholders are increasingly exposed to drought risk.
Results show that the use of a multivariate index explicitly considering electricity prices greatly increases performance compared to a simpler univariate hydrological index. In addition, while both standard and collar contracts can effectively reduce revenue variability and improve revenue floor, the collar contract can provide better performance at a lower price. However, such more complex contracts are more sensitive to parameter values and their calibration should be performed carefully.

How to cite: Scarpellini, L., Ficchì, A., Giuliani, M., and Castelletti, A.: Parametric insurance for hydropower: Comparing alternative schemes combining hydrologic and market data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6125, https://doi.org/10.5194/egusphere-egu24-6125, 2024.

The increasing population and new urban developments have posed challenges to urban water management, such as domestic water scarcity and deteriorated water quality.  Meanwhile, the existing development planning frameworks fail to facilitate an effective approach to enhance an efficient and sustainable urban water design. They tend to isolate groups and evidence by relying on different independent models. The state-of-the-art Water Systems Integrated Modelling framework (WSIMOD), which simulates the terrestrial water cycle integrally including physical and human processes, has been developed to provide holistic and integrated evidence to help with decision-making processes. The WSIMOD model has been previously implemented at the river sub-catchment resolution, while a complete decision-making process usually involves different groups, such as city authorities, water companies and environmental regulators, with multiple objectives at multiple spatial resolutions. In the current work, we propose the novel use of the model for multi-resolution simulations (local, borough and river sub-catchment), and aim to help multi-stakeholders and decision-makers understand potential challenges to achieving multi-objectives in a coordinated way. We will also explore the effectiveness of measures to offset urban water issues induced by new developments in the current and future scenarios. Our work can provide insight into efficient and sustainable urban water management strategies for multi-stakeholder planning and future adaptation under uncertainty.

How to cite: Zhang, Z., Rico Carranza, E., and Mijic, A.: Novel Use of Integrated Water System Model for Decision-Making Processes at Different Scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6421, https://doi.org/10.5194/egusphere-egu24-6421, 2024.

Water, a vital resource, plays a crucial role in supporting human health, ensuring food security, enabling energy production, and sustaining ecosystem services. However, water deficit is the main concern for developing countries caused by a number of factors including finite water supplies, increase in population, and climate change. A sustainable approach to manage the water resources is the optimal distribution of available resources, which recognizes the complex relationships between water systems and the effects they have on the environment, society and economy. In this study, the optimal allocation of land and water resources is carried out across five different sectors such as domestic, agriculture, livestock, industrial, and ecological in an annual time step. Munneru basin is selected as a study area which comes under lower region of the Krishna River Basin, India. Soil and Water Assessment Tool (SWAT) is used to calculate the water availability. Furthermore, for each administrative unit within the basin, the irrigation water requirements for crops are calculated using the CROPWAT tool. Study incorporates objective functions that take into account both social and economic factors. The multi-objective optimisation function maximizes the usage of water and land resources and optimize benefits from the agricultural sector. To achieve these goals, the Non-dominated Sorted Genetic Algorithm (NSGA-II) is used. Additionally, multi-criteria decision-making technique, such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify better solutions among the Pareto optimal solutions generated by the NSGA-II. Through the application of advanced optimisation algorithms and decision-making techniques, this study aims to contribute valuable insights to the field of water resource management in India. As this approach represents a crucial tool for sustainable development at the basin level, it provides a solid foundation for further extension to other basins across the globe. Furthermore, this work offers the potential for future research into the impacts of climate change and land use/land cover changes on water allocation over various timeframes, without compromising benefits from agriculture sector.

How to cite: Buri, E. S., Keesara, V. R., and Kotapati Narayanaswamy, L.: Sustainable Planning of Water and Land Resources in the Munneru River Basin, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7221, https://doi.org/10.5194/egusphere-egu24-7221, 2024.

Small hydropower plants (SHPs) present an eco-friendly and economically viable alternative to conventional dam-based plants. With only 36% of their worldwide capacity currently tapped, there is potential for substantial global expansion including in industrialised nations. Most SHPs follow run-of-the-river (RoR) scheme, depending on the fluctuating flow of rivers because of their negligible storage capacity. They are deployed in a world characterised by a changing hydro-climate and unpredictable socio-economic evolutions. Due to their inability to regulate  discharge fluctuations as well as their dependency on selling energy at higher rates, these plants are significantly vulnerable to these changes. Design alternatives  are generated through traditional approaches relying on cost-benefit analysis that use past hydroclimatic conditions and disregard operational considerations, without assessing investment robustness in the face of changes. What is more, optimization and robustness analysis of these systems typically require a significant amount of computing time and resources necessitating high performance computing.

We introduce a new framework for robust hydropower system design to address these issues. This framework uses and extends HYPER, a state-of-the-art toolbox that computes technical performance, energy production, maintenance and operational costs of a design.  It combines HYPER with many-objective robust decision making (MORDM) to define robust alternatives.   Our implementation involves a systematic four-step process: (1) Introducing a two-objective formulation to identify design parameters balancing cost and revenue. (2) Creating alternative futures by sampling deeply uncertain factors, encompassing socio-economic (electricity prices, interest rate,  cost overrun) and hydroclimatic factors (median, coefficient of variation, the 1st percentile of flows). These streamflow statistics are then transformed into flow duration curves using an innovative approach. (3) Robustness quantification of alternative designs using two newly introduced financial robustness metrics based on the probability of making the plant financially viable. (4) Identify the most critical parameters influencing robustness through sensitivity analysis and scenario discovery.  We then employ a computationally efficient approximation approach to streamline resource-intensive steps  in optimisation and robustness analysis.

Results indicate that employing the MORDM approach in the design of RoR hydropower plants offers valuable insights into the trade-offs between cost and revenue, while supporting design with a range of viable alternatives aiding in the determination of the most robust and reliable design. Maximising the benefit cost ratio yields more robust and financially viable solutions than maximising NPV, as it leads to less costly designs that generate slightly less revenue on average but tend to better exploit low flows. Traditional design approaches employing identical turbine configurations and focusing on NPV maximisation, have proven to be less effective when compared to designs incorporating non-identical turbines. Moreover, such designs have demonstrated greater vulnerability to climate change, primarily attributable to their less flexible configuration. 

Combined optimization and robustness analysis of a RoR design, initially taking 120 hours, is also made computationally inexpensive through a novel method involving strategic data input reduction. This innovation resulted in a significant 95% reduction in processing time, while maintaining nearly identical outcomes in both steps. An open-source Python version of this methodology is scheduled to be available by July 2024.

How to cite: Yildiz, V., Brown, S., and Rougé, C.: Advancing Small Hydropower Design: A Novel Framework for Robust and Sustainable Solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9445, https://doi.org/10.5194/egusphere-egu24-9445, 2024.

In recent years, climate change has significantly intensified the frequency and severity of drought events. Rising temperatures, altered precipitation patterns, and changing weather dynamics have led to more prolonged and intense droughts altering water availability and exacerbating tradeoffs, especially in complex multi-sector water systems.

An archetypal example of this situation is the Lake Como water system, in the north of Italy. Lake Como is operated to provide water downstream to the agricultural sector, control floods on the lake shores, and contrast low water levels that would negatively impact navigation and aquatic ecosystems. The conflict between the interests of these sectors is expected to exacerbate in the years to come due to the evolving hydroclimatic regimes. Among different adaptation options considered by the regional authority, we investigate the potential expansion of the lake's active storage capacity enabled by the recent construction of flood mobile dykes.

Here, we contribute a framework for evaluating the impact of droughts on multiple water users. Specifically, we adopt a synthetic weather generator to create multiple streamflow ensembles (scenarios) controlling the drought’s frequency, duration, and intensity. Drought features are then linked to impacts (e.g., agricultural deficit) using a simulation model of the lake. Failure thresholds are defined for each impact indicator to set the minimum level of performance acceptable to each sector. Finally, a logistic classifier is used to identify the combination of drought features leading to a system failure.

Our results show that system failures can be accurately estimated using a linear combination of drought frequency, duration, and intensity. The combined effect of these three characteristics, rather than the extreme values of one of them, is responsible for system failure. Our analysis also proves that storage expansion is fundamental to reduce the downstream deficit, as well as to prevent most of the flood events.

How to cite: Sangiorgio, M., Zaniolo, M., Giuliani, M., and Castelletti, A.: Assessing the Effect of Droughts on Complex Multi-sector Water Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10180, https://doi.org/10.5194/egusphere-egu24-10180, 2024.

The future uncertainties, spanning both climatic (e.g., precipitation) and non-climatic (e.g., population growth) factors, will significantly impact urban water demands and supplies. To enhance future water security in urban areas, the Integrated Urban Water Management (IUWM) approach has gained popularity globally. The IUWM approach emphasizes a varied water supply source, addresses multiple sustainability objectives, and ensures the provision of fit-for-purpose water. This study uses an Integrated Urban Water Balance Model (IUWBM), developed in eWater Source platform version 5.10.0.11841, which utilizes different types of water (i.e., river water, groundwater, harvested stormwater, rooftop rainwater, and recycled wastewater) to meet future water needs. However, depending on how much water each source supplies, combining different water sources to meet the demand may result in trade-offs with the integrated urban water system's total cost and energy consumption. Given the future uncertainties, it is crucial to develop robust optimal water mix solutions that are minimal in total costs and total energy consumption across various future scenarios. This study proposes to address this challenge, focusing on Bengaluru (i.e., a city in India) as a case study due to its relevance to the identified issues. The IUWBM is linked to an optimization tool (i.e., Insight Version 5.10.0.11841, which uses the NSGA-II algorithm). Three robustness metrics—Laplace Principle of Insufficient Reasons, Hurwicz Optimistic-Pessimistic Rule, and Signal-to-Noise Ratio—are added to a multi-scenario, multi-objective optimization problem to make the decision-making more robust. All optimal solutions generated adhere to constraints, maintaining an average volumetric and time reliability of water supply above 99.50% for the study area. The findings reveal that low-cost optimal water mix solutions tend to exhibit higher energy consumption as they prioritize savings in the capital costs of building the new water supply infrastructure. Capital costs, therefore, significantly impact the total costs, while operating energy plays a crucial role in total energy consumption in Bengaluru urban water supplies. The present research also found that harvested stormwater and recycled wastewater emerge as potentially low-cost, low-energy, and reliable sources for potable and non-potable water, respectively, under future uncertainties. Additionally, recycled wastewater is preferable for non-potable uses as it offers the added benefit of mitigating adverse environmental impacts on Bengaluru's valleys and lakes.

How to cite: Mohapatra, S. S., Arora, M., Wu, W., and Tiwari, M. K.: Managing Urban Water Supplies under Future Uncertainties: A Case Study of Bengaluru, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12035, https://doi.org/10.5194/egusphere-egu24-12035, 2024.

Providing reservoirs with accurate forecasts is crucial for effective real-time flood control. This research focuses on the key role of forecasts in real-time flood management for reservoirs. A new approach was developed in this study, integrating a forecast-driven methodology to handle uncertainty in reservoir flood control operations. This involves a novel hybrid of two post-processing techniques: the Cloud model and error-based copula functions, together termed as the stochastic errors-based Cloud (SE-Cloud). Additionally, a multi-objective robust optimization model (MRO) was proposed, encompassing risk, resilience, and vulnerability, to address flood control challenges using ensemble forecasts. For comparative purposes, a two-objective stochastic optimization model (TSO) was also created, aiming to reduce both the highest expected reservoir level and peak discharge. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. The main findings of this study are: (1) The SE-Cloud model was proved to be more efficient in predicting peak flow events and in representing uncertainties in forecasts, with an improvement in hypervolume values ranging from 13.14% to 39.65% over the Cloud model. (2) The MRO strategy resulted in a higher inflow release compared to the TSO, leading to a 0.05m reduction in the anticipated highest water level and a 4.29% increase in peak discharge. (3) With the resilience value downstream remaining constant, it was suggested that increasing upstream vulnerability by using the MRO strategy would not lead to a decrease in resilience. The findings highlight the potential of AI-based ensemble forecasts in augmenting flood control robustness.

How to cite: Guo, Y., Xu, Y.-P., Yu, X., Liu, L., and Gu, H.: AI-based ensemble flood forecasts and its implementation in multi-objective robust optimization operation for reservoir flood control, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17021, https://doi.org/10.5194/egusphere-egu24-17021, 2024.

Pywr is an open-source water resource system simulation model. It was created almost 10 years ago. Since that time it has been widely adopted in the UK water resources planning community and also used by several researchers around the world. The original design goals were to create a fast, free and extendable library that could handle running large datasets on complex real-world problems. Pywr’s speed has made it popular with researchers and practitioners simulating large water systems under uncertainty (where many future scenarios must be considered).

Here we present an extension, a new simulation approach that exploits modern CPU hardware and instructions. The new method simulates multiple scenarios in parallel using “single instruction, multiple data” (SIMD) techniques. We apply SIMD to a simple interior point method that is capable of solving multiple similar linear programs in parallel. We compare our method against a conventional non-SIMD linear program solver (CLP) and demonstrate that it can provide significant speed-ups for some water resource simulations. We benchmark this method using a GPU using 100s of thousands of scenarios. Our results demonstrate that by exploiting modern CPU features it is possible to achieve further speed-ups for water resource simulations. More efficient (faster) simulation allow practitioners to explore more scenarios, find more robust solutions and/or use more complex models. Some existing and on-going applications will be briefly introduced.

Finally, we reflect on the adoption and evolution of Pywr over the last 10 years, and look at its current usage in UK water resources planning. We explain how the advances above will help planners and developers alike, hopefully setting the foundation of Pywr for the next 10 years.

How to cite: Tomlinson, J. and Harou, J.: Latest advances and reflections on 10 years of open-source development and applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19631, https://doi.org/10.5194/egusphere-egu24-19631, 2024.

Significant water supply-demand gaps are projected in many regions of India under current scenarios. The government is considering and implementing different measures to support a sustainable water resources management in irrigated agriculture, e.g. incentives to reduce water abstractions, decreasing energy subsidies, metering, effective water pricing or community-based water management. Also, informal water markets are widespread in India; however, their impacts are largely unknown and under-researched due to a paucity in related data. We analyse the development and determinants of farmers’ water purchasing behaviour and related expenditure using a large representative household survey for India over two years. In addition, we merged district level average statistics for precipitation, temperature and groundwater storage with the survey data. Modelling results show that, after accounting for several control variables, irrigation water purchases were more likely where groundwater levels were already low, farmers have a diversified access to water sources but no access to public piped drinking water supply. Particularly in groundwater irrigation areas, purchases were also more likely where: a) conflicts are prevalent within the village; b) families solve (water supply) conflicts individually; c) farmers are not members in a cooperative; and d) farmers have low confidence in State or village governments. Increased expenditure (INR/acre) for irrigation water was associated when purchasing mainly from private as compared to government tubewell owners. There is a need for future research to examine this dataset at local spatial scales and per different irrigation types. This is reflected in the different results for the state-specific models. Overall, results highlight the severity of the state of India’s groundwater resources, local community cohesion issues and the need for better regulation and monitoring in water management, e.g. with regards to informal water markets and agricultural subsidies, to better serve local farming communities and the environment. At the same time, different water-related policies need to take into account the effects of multiple implemented measures as well as the existence of informal water markets.

How to cite: Haensch, J., Mehta, Y., and Brümmer, B.: Impact and drivers of informal water markets in irrigation regions in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20819, https://doi.org/10.5194/egusphere-egu24-20819, 2024.

The UN disaster report highlights that 90% of disasters from 1995 to 2015 were a consequence of hydrometeorological changes. An analysis by the World Meteorological Organization revealed that water-related disasters, including droughts, heatwaves, typhoons, and floods, which are partly driven by climate change, caused extensive global property damage and loss of life. Despite concerted global efforts for net-zero carbon emissions, there is a noticeable lack of integration of water in decarbonization strategies.

Traditionally, the water sector's response to the climate crisis has primarily centered on establishing policies and systems for water management to adapt to vulnerability, rather than actively participating in greenhouse gas reduction (mitigation). This strategic preference is largely attributed to the absence of a distinct organizational classification for the water sector in the national greenhouse gas inventory, leading to an unclear identification of the sector's own emissions profile.

In light of these challenges, this study addresses the critical need for assessing greenhouse gas emissions within the water sector and advocates for the establishment and implementation of carbon-neutral policies tailored specifically to the sector's unique characteristics. By delving into these imperatives, the study seeks to bridge the gap between global climate efforts and the water sector, fostering a more comprehensive and sustainable approach to climate resilience and mitigation."

Within the study's scope, the organizational boundary of the water sector was meticulously delineated. It encompasses water supply, wastewater, and livestock manure treatment services, as well as water resources facilities such as dams, reservoirs, and river spaces. Comprehensive assessments were conducted to calculate greenhouse gas emissions and absorption within these boundaries. This nuanced approach aims to provide a detailed understanding of the carbon emissions associated with key components of the water sector.

Moreover, the study identifies and assesses potential trajectories for attaining carbon neutrality in the water sector through the development and examination of three distinct scenarios. The demand-led scenario prioritizes water efficiency, leakage management, adoption of a vegetarian diet, and achieving energy self-sufficiency. The technology-led scenario emphasizes innovative technologies and substantial financial investments, while the combined scenario integrates elements from both the demand-led and technology-led pathways, offering a nuanced and balanced approach.

In conclusion, this case study illuminates a promising trajectory toward achieving carbon neutrality in the water sector by 2050, particularly when adopting a mixed scenario that combines elements from the three outlined scenarios. The comprehensive insights garnered from this study contribute to a more sustainable, resilient, and low-carbon future, highlighting the integral role of the water sector in global climate objectives.

Additionally, the study concludes that efforts towards carbon neutrality in the water sector represent a policy direction that enhances the public benefits of adaptation and reduction strategies. By actively engaging in mitigation measures, the water sector not only contributes to climate goals but also enhances its adaptive capacity, creating a synergistic approach that maximizes positive outcomes for both the environment and society. This integrated strategy highlights the potential for carbon neutrality initiatives in the water sector to serve as a model for broader climate action policies, emphasizing the interconnected benefits of sustainable practices.

How to cite: Han, H., Kim, J. S., Jung, K., Sung, J., Kang, S., Chae, Y., and Hyun, Y.: Achieving Carbon Neutrality in the Water Sector: Unlocking Co-Benefits Between Climate Mitigation and Adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21887, https://doi.org/10.5194/egusphere-egu24-21887, 2024.