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Abstract Climate change and urbanization challenge utilities’ pursuit of water security worldwide. While water utilities are directly impacted by climate change, their operations also contribute to greenhouse gas emissions. Digital technologies have proven effective in improving utilities’ operations, leading to a more sustainable urban water cycle. However, the global progress of digital water transformation remains largely understudied. Here, we present the results of an online survey involving 64 utilities from 28 countries investigating the impacts of digital transformation on the water utility sector, its drivers, and key-enabling technologies. We found that the water distribution system is the entry point to further adoption of digital technologies in the whole urban water cycle. Furthermore, technology adoption is driven primarily by economic benefits, followed by government regulation and hydroclimatic factors. Starting from the survey results, we point out avenues for further research targeting a better understanding of the influence of regulation, corporate mindset, and consumer involvement for successful digital transformation.

Agriculture is one of the most sensitive and vulnerable activities to climate variations; climate change impacts crop yield, soil processes, water availability, and pest dynamic. Farmers are on the front lines of climate change-induced stressors, shifts, and shocks, having to provide solutions as planners, performers, and innovators working under challenging and uncertain risk conditions. Extensive research has been carried out to deepen farmers’ behavior on risks posed by climate change. However, less attention was paid to irrigation district managers, even though their essential role in guaranteeing hydraulic safety, ensuring water supply, even as on-site observers of farmers’ behavior. This paper seeks to uncover the attitudes and actions of irrigation districts managers towards climate change, and their ability to gather significant information on the climate experiences of farmers in the Lombardy region, Italy. Employing a mix of semi-structured interviews and questionnaires, the exploratory approach gathered insights from the managers of the 12 public irrigation districts tasked with governing irrigation development and water management in the region. The study reveals that managers are cognizant of the shifting climate caused by warmer temperatures and extreme weather events, implementing both hard (e.g. water infrastructure maintenance, water storage) and soft (climate and weather services) adaptation countermeasures. Through their own experiences, managers brought to light the driving factors behind farmers’ willingness to adopt water-saving techniques or adapt their crops, despite facing obstacles (e.g. financial investment, institutional support). In addition, the identification of tipping points related to water scarcity and security is coupled with managers and farmers’ trust in technological solutions to partially counteract risk normalization. In line with the findings, suggestions were put forth to enhance managers’ adaptive capacity.

Urban water demand (UWD) forecasting is essential for water supply network optimization and management, both in business-as-usual scenarios, as well as under external climate and socio-economic stressors. Different machine learning and deep learning (DL) models have shown promising forecasting skills in various areas of application. However, their potential to forecast multi-step ahead UWD has not been fully explored. Modelling uncertain UWD patterns and accounting for variations in water demand behaviors require techniques that can extract time-varying information and multi-scale changes. In this research, we comparatively investigate different state-of-the-art machine learning- and DL-based predictive models on 1 d- and 7 d-ahead UWD forecasting, using daily demand data from the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different machine learning and DL models on single- and multi-step daily UWD forecasting. These models include an artificial neural network, a support vector regression, a light gradient boosting machine (LightGBM), and long short-term memory networks with and without an attention mechanism (LSTM and AM-LSTM). We benchmark their prediction accuracy against autoregressive time series models. Second, we investigate the potential enhancement in predictive accuracy by incorporating the wavelet transform and feature selection performed by LightGBM into these models. Results show that, overall, wavelet-enhanced feature selection improves the model predictive performance. The hybrid model combining wavelet-enhanced feature selection via LightGBM with LSTM (WT-LightGBM-(AM)-LSTM) can achieve high levels of accuracy with Nash-Sutcliffe Efficiency larger than 0.95 and Kling–Gupta Efficiency higher than 0.93 for both 1 d- and 7 d-ahead UWD forecasts. Furthermore, performance is shown to be robust under the influence of external stressors causing sudden changes in UWD.

With its scientifically proven effects and widespread acceptance, climate change stands as one of the most pressing and intricate challenges for society and economies. Farmers are on the frontline of managing climate change. Therefore, how they perceive and respond to climate change shapes their risk assessment and structures future resilience and adaptive capacity. Employing a bottom-up approach, we conducted 460 surveys randomly among farmers throughout the Lombardy region in northern Italy. A triple-loop approach considering climate change awareness, perceived impacts, and adaptation measures and barriers was implemented to characterize the potential heterogeneity of farmer behaviour and explore whether risk attitudes and adaptation actions differ between farmer profiles. We then profiled farmers through a clustering analysis. Four groups emerged, highlighting farmers’ adaptation preferences and risk attitudes: (1) cropping-adapted and isolated farmers, (2) cooperation-adapted and insecure farmers, (3) risk insurance-adapted and confident farmers, and (4) climate services-adapted and aware farmers. Although the groups vary in terms of farmers’ characterization and farming activities (e.g. young and highly educated vs. old and highly experienced farmers, rainfed vs. irrigated farms), similar patterns were observed regarding climate change awareness and perceived impacts—however, the contrast increased in terms of adaptation measures and barriers. Gaining a more comprehensive understanding of the diverse ways in which farmers assess risks and adapt can promote the transferability of bottom-up findings and inform the co-design of tailored and flexible adaptation instruments, minimizing the risk of maladaptation or ineffective transformation in the face of climate change.

Study region: The Tagus-Segura aqueduct (TSA) is a large and strategic water transfer scheme in Spain that connects Entrepeñas and Buendía reservoirs in the Tagus river headwaters to the Segura river basin, a highly stressed Mediterranean area. Study focus: The operating rules of the TSA underwent several modifications over the years, and the debate about which are the optimal parameters to meet the interests of the parties involved is still open. We employed Evolutionary Multi-Objective Direct Policy Search to jointly optimize the re-operation of the headwaters dams and the water transfer policy with respect to four conflicting objectives: Tagus and Segura water demands, hydropower production and socioeconomic benefit of the population living on the shores of the headwaters reservoirs. We tested the optimization under the baseline and the 2027 scenario, which foresees an increased environmental flow (EF) in the Tagus river. New hydrological insights for the region: The proposed operating rule presents optimized control parameters, a higher degree of freedom and a transferred volume that cyclically varies according to the hydrological stage of the year. In the 2027 scenario, despite the increased EF, the deficit in the aqueduct shows a limited increase compared to the historical solution (+10%), while the storage deficit is strongly reduced (−73%). This benefits the population living on the reservoirs shores and also ensures more stability to the aqueduct functioning.

Abstract Intensifying climate change impacts can divert the economic resources away from emission reduction toward adaptation to reduce rising damages, jeopardizing temperature stabilization within safe levels. Indeed, the traditional static welfare‐maximizing climate policy design leads to a conflict between mitigation and adaptation, invalidating the recently established consistency of cost‐benefit analysis with the Paris Agreement's targets. Here, we show that this tension can be resolved by integrating multi‐objective optimization and feedback control in the Dynamic Integrated Climate Economy model to design self‐adaptive climate policies trading off welfare maximization with the Paris Agreement compliance. These policies allow adjusting against uncertainty as information on the socio‐climatic system accumulates, thus representing the policy‐making process more realistically. We show that, the costs being the same as in traditional methods, warming above 2°C and the probability of overshooting can be drastically reduced, emphasizing the need for integrating adaptation and mitigation strategies and the value of embracing a self‐adaptive, multi‐objective perspective.

Benefit-cost analyses of climate policies have generated conflicting assessments; as social welfare is affected by regional heterogeneity. Here the authors show that economically optimal pathways are consistent with climate stabilization but are characterized by persistent economic inequalities due to climate damages.

In this paper, we propose a methodological framework for assessing the influence of climate uncertainty and technological innovation on renewable investments in small off-grid islands.At the core of the framework, an energy system model calculates the system performance in terms of Present Value of Cost. Through reiterate simulation of the model under different system designs, including photovoltaic, wind turbines, and wave energy converters, and future scenarios, the framework allows to identify the key climate drivers and technological parameters of system performance, and the most robust investments. The framework is demonstrated in the case study of Ustica Island, Italy.Results highlight wind speed as the key climate driver affecting system performance. The effects of technological innovation are instead strictly dependent on the technology considered and the level of risk aversion of the decision maker.With respect to the technology competitiveness, photovoltaic is nowadays the most robust investment irrespective of the future uncertainty on natural resource availability and technological innovation. The competitiveness of wind and wave technologies is instead strictly affected by climate and technological uncertainty. Although wind technology is currently more competitive than wave, except for high-risk averse decision-makers, results show that the wave improvement estimated for 2030 and 2050 could make this technology an effective investment in the short/medium term. This suggests the importance of carefully deciding the timing of the investments reducing current investments in the wind for installing higher wave capacity in the future could in fact lead to more effective investments over the entire planning horizon.

Study region: Omo-Turkana Basin, trans-boundary basin between Ethiopia and Kenya (North eastern Africa). Study focus: Significant investments in large dams have been mobilized in the Omo-Turkana basin to expand hydropower and support extensive irrigation projects. Assessing the impacts of these infrastructures, particularly on local stakeholders, constitutes a crucial foundation for socially inclusive as well as environmentally and economically sustainable development. This study showcases the potential of a participatory decision-analytic framework in investigating the impacts of alternative development pathways on competing stakeholders’ interests in the Omo-Turkana basin to support strategic planning under both current and projected hydroclimatic and socio-economic conditions. The optimal operation of the planned system expansion, including the current and future dam cascade and the irrigation projects, is investigated to provide insights into multisectoral trade-offs. Five main sectors with competing interests are considered: hydropower production, environmental protection, indigenous recession agriculture, fish yield in Lake Turkana, and large-scale commercial irrigated agriculture. New hydrological insights for the region: Results show that the planned infrastructure can negatively impact local stakeholders, particularly in terms of fish yields in Lake Turkana. Still, a potential exists for negotiating operational compromises that are both efficient and socially inclusive. Moreover, even though the performance of the planned infrastructure is expected to decline in the future under changing climate and irrigation demands, this can be mitigated by timely implementing robust solutions triggered by the alterations of streamflows in the northern part of the basin.

Filling of dams, when coinciding with droughts, can lead to severe downstream hydrology and ecology problems. The authors hence here provide a multisectoral perspective, using a dam in Ethiopia as an example, to develop adaptive filling solutions that support decision making, favourable filling timing and an effective filling policy.

Abstract Consumption-based feedback has been demonstrated to encourage water conservation behaviors. Smart meters and digital solutions can support customized feedback and reinforce behavioral change. Yet, most of the studies documenting water conservation effects induced by feedback and smart meter data visualization evaluate them in short-term experimental trials only. Here we show that water conservation behaviors promoted by smart meter-based consumption feedback and digital user engagement interventions might persist in the long term. We developed an analysis of 334 households in Valencia, Spain. We find that approximately 47% of the households engaged in our water conservation program achieved a long-term 8% reduction of volumetric water consumption, compared with pre-treatment observations. Water conservation behaviors persisted more than two years after the beginning of the program, especially for the households receiving sub-daily smart meter information. Our results provide empirical evidence that smart meter-based water consumption feedback and digital user engagement can effectively promote durable conservation behaviors.

Tropical cyclone genesis indices are valuable tools for studying the relationship between large-scale environmental fields and the genesis of tropical cyclones, supporting the identification of future trends of cyclone genesis. However, their formulation is generally derived from simple statistical models (e.g., multiple linear regression) and are not optimised globally. In this paper, we present a simple framework for optimising genesis indexes given a user-specified trade-off between two performance metrics, which measure how well an index captures the spatial and interannual variability of tropical cyclone genesis. We apply the proposed framework to the popular Emanuel and Nolan Genesis Potential Index, yielding new, optimised formulas that correspond to different trade-offs between spatial and interannual variability. Result show that our refined indexes can improve the performance of the Emanuel and Nolan index up to 8% for spatial variability and 16%–22% for interannual variability; this improvement was found to be statistically significant (p < 0.01). Lastly, by analysing the formulas found, we give some insights into the role of the different inputs of the index in maximising one metric or the other.

Study region: Lake Como watershed, between Northern Italy and Switzerland (Europe). Study focus: We analyse the potential vulnerabilities of existing water management practice in sub-alpine regulated lakes. We comparatively explore a wide range of inflow scenarios using a bottom-up approach, relying on synthetic alteration of observed climate drivers, and a top-down procedure, working with both CMIP5 and CMIP6 projections. We then run an ANOVA to backtrack components of variability in the projected lake inflows to key uncertainty sources. New hydrological insights for the region: Results show a general worsening of lake management performance under the top-down projections with respect to the equivalent bottom-up scenarios, with the CMIP6 ensemble yielding worst conditions than CMIP5. Moreover, the top-down, physically-based scenarios highlight non-linear patterns of climatic/hydrological variability that challenge historical management, while bottom-up projections overlook these trends. The results of the ANOVA demonstrate that at mid-century the choice of the general circulation model is the dominant factor influencing the variability, while climate patterns become more relevant toward the end of the century. Overall, our study provides numerical evidence of the ongoing and projected impact of climate change on sub-alpine regions and outlines how different inflow scenarios generation approaches result in different inflow variability and associated risk assessment.

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production.

Cost-benefit integrated assessment models generate welfare-maximizing mitigation pathways under a set of assumptions to deal with deep uncertainty in future scenarios. These assumptions include socio-economic projections, the magnitude and dynamics of climate impacts on the economy, and physical climate response. As models explore the uncertainty space within the boundaries of their objective functions, they risk providing scenarios which are too narrow and not sufficiently robust. Here, we apply robust and multi-objective decision-making methods to extract relevant information from a large ensemble of optimal emissions-reduction pathways generated by a regionalized cost-benefit integrated assessment model under deterministic welfare optimization. We show that shifting the focus from optimal to robust solutions reduces the uncertainty in mitigation strategies and aligns them toward the Paris goals. Moreover, we analyze the trade-offs between climatic (temperature), social (inequality) and economic (welfare) objectives and illustrate four robust pathways under various decision-making criteria. We show that robust mitigation strategies can lead to regional emission-reduction strategies which are fair. Our results show how to extract more comprehensive climate strategies from available scenario ensembles and that the highest discrepancies at the local level policies are found in the developing and most-impacted regions.

Farmers’ perception of climate change is crucial in adaptation intention and process. However, farmers’ perceptions may not be timely, accurate and systematically consistent with the direction and significance of observational records. Although some research compared farmers’ perceptions and climate data, little attention has been paid to comprehensibly analyse both data sources discrepancies based on empirical studies results. By combining bibliometrics and a systematic review approach, we identify which approaches are used to compare perceived and observed data, how both patterns have been mutually evolved, which factors determine their (in)consistency, and if their accordance and robustness affect farmers’ adaptive capacity. We analyse a portfolio of 147 papers collected from the Scopus library catalogue since 2000. The bibliometric analysis was coupled with an exploratory analysis of 98 papers selected from the original portfolio. The literature is extensive, fast-growing, and spans several disciplines. We identify four consolidated research lines: (a) perceived risk and farmers’ adaptive capacity nexus, (b) crop vulnerability due to temperature increase and erratic rainfall patterns, (c) forecasting use and influence in farmers’ decisions, and (d) climate change awareness conditioning farmers’ profiles. Nonetheless, we observe some research gaps: (a) a conceptual mismatch in ‘normal pattern’ or ‘drought’ meaning, (b) poor or limited data from meteorological stations, (c) overlook or oversimplification of local knowledge in describing perception, (d) farmers’ memory weaknesses to keep track of climate alterations, and (e) a geographical dissonance in favour of Global South regions. Our science-metric study also reveals some research questions to be consolidated: Can the perception of extreme events increase climate change awareness? Can greater awareness reduce discrepancy with observed data? How do heuristics and socio-psychological filters influence farmers’ awareness and interpretation of climate data? We suggest putting major efforts into reinforcing these research lines as part of a novel domain-dependent trend to reduce the discrepancy.

EU Member States are progressively decarbonizing their electricity systems by replacing fossil fuels with renewable sources to achieve rapid greenhouse gases emissions reductions. While the planned decarbonized system will be more resilient to hydroclimatic change than existing water-dependent portfolios, water availability and temperature are still influential factors during this transition to a carbon neutral electricity system, with potential negative impacts on the economy and the environment. Here, we conduct a model-based analysis to assess the impacts of hydroclimatic change on EU decarbonization strategies in two regions, the Iberian Peninsula (IP) and the Danube river basin, characterized by a high share of water-dependent energy sources and expected to be highly affected by climate change. We find that, under the reference electricity system scenario for 2040 aligned with the EU climate and energy strategies, generation from fossil fuels increases, in particular from combined cycle gas turbine plants, to balance the reduction of hydro generation consistently observed in the hydroclimatic scenarios examined. This reduction, in conjunction with increased thermal plants shutdown events due to high water temperature especially in the IP, produces load cuts undermining the reliability of the electricity system. Moreover, increased fossil fuel use results in higher generation costs and carbon intensity, jeopardizing emissions reduction targets and ultimately slowing down the decarbonization process.

The rapid shrinkage of Lake Urmia, one of the world’s largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia’s contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin’s water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998–2002). The collapse was caused by a marked overshoot of the basin’s hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr ^−1 ) during dry conditions to 5400 mcm yr ^−1 during wet periods with the average being 4100 mcm yr ^−1 . Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia.

In a changing climate and society, large storage systems can play a key role for securing water, energy, and food, and rebalancing their cross-dependencies. In this letter, we study the role of large storage operations as flexible means of adaptation to climate change. In particular, we explore the impacts of different climate projections for different future time horizons on the multi-purpose operations of the existing system of large dams in the Red River basin (China–Laos–Vietnam). We identify the main vulnerabilities of current system operations, understand the risk of failure across sectors by exploring the evolution of the system tradeoffs, quantify how the uncertainty associated to climate scenarios is expanded by the storage operations, and assess the expected costs if no adaptation is implemented. Results show that, depending on the climate scenario and the time horizon considered, the existing operations are predicted to change on average from -7 to +5% in hydropower production, +35 to +520% in flood damages, and +15 to +160% in water supply deficit. These negative impacts can be partially mitigated by adapting the existing operations to future climate, reducing the loss of hydropower to 5%, potentially saving around 34.4 million US$ year ^-1 at the national scale. Since the Red River is paradigmatic of many river basins across south east Asia, where new large dams are under construction or are planned to support fast growing economies, our results can support policy makers in prioritizing responses and adaptation strategies to the changing climate.