Urbanization and climate change are exacerbating stress on aging urban critical infrastructure systems, including water, energy, mobility, and telecommunication networks. Simulation tools and scenario analyses able to capture the interdependencies among these different infrastructure systems are crucial to support decision making and realize sustainable and resilient development. Yet, existing simulation tools are mostly developed within the boundaries of individual application sectors and information often remains siloed, despite the increasing data and computational opportunities offered by the digital transformation of many infrastructure sectors. In this work, we present how the ide3a project (international alliance for digital e-learning, e-mobility and e-research in academia – https://ide3a.net) addresses this research gap. ide3a is building a digital campus to support digital learning, research, and mobility in collaboration within a network of six European partner universities. Several senior and early career researchers with multidisciplinary backgrounds in water management, IT systems, mobility, energy, urban planning, sustainability, and psychology, work together to integrate state-of-the-art research on critical infrastructure and digitalization into traditional higher education curricula. As part of the ide3a portfolio of digital tools for learning and research, we present a prototype of “ConnectiCity”, an open-source simulation-based serious game that integrates multi-sectoral models to perform simulations of interconnected critical infrastructure systems and quantify cascading effects under various climate, social, and technical scenarios. Along with other ide3a activities, it is used to train early career researchers and students alike to enrich their transdisciplinary knowledge, foster critical system thinking, drive research on urban critical infrastructure dynamics, and ultimately working across disciplines to tackle contemporary urban challenges.

Because climate change is both a physical and social phenomenon, personal experience has been considered the first step to entail how individuals perceive climate change risk and which actions can be promoted to reduce their vulnerability. Considering that agriculture is affected by climate change in several ways, farmers can provide first-hand observations of climate change impacts and suggest better adaptation options. However, modeling farmers’ behavior is a non-trivial task: personal experience is well recognized as a complex non-linear, multi-variate process due to the high heterogeneity and uncertainties in human cognition and decision-making processes. Furthermore, individual understandings of climate change are always contextualized within broader considerations, meaning that farmers are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Despite the burgeoning of research on climate change, information about farmers’ awareness and risk perception is not geographically homogenized and varies substantially among countries and regions. For example, studies from Global North regions are scarce and emphasize how farmers characterize themselves rather than how they perceive and react to climate change. Drawing on farmers’ surveys in the Lombardy region (Italy), we provide an empirical study to pre-test the triple-loop analysis of farmers’ behavior regarding climate change: awareness, perceived impacts, and adaptation measures and barriers. Applying descriptive statistics and considering socio-economic data and farm characteristics, we address two main research questions: 1) What are farmers’ perceptions of climatic impacts and which responses do they promote? 2) How do personal experience and attitude change is conditioning farmers’ adaptation capacity? Obtained results from accurate bottom-up knowledge on farmers’ behavior may increase policy-makers’ and managers’ understanding of climate change and re-think local policies, which is essential to address agricultural risks in climate change hotspots.

Climate change is both a physical and social phenomenon in which individual understandings are contextualized within broader considerations: individuals are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Personal experience, local knowledge, and social-learning influence climate risk perception and vary substantially among countries and regions. Likewise, they differently affect individuals and social groups at the regional and local scale, among whom exposures, attitudes, and capacities to manage risks vary greatly. A climate storyline approach is hence well-suited to study human observations, compound climate risks, and inform and conceptualize human–water systems interactions. Narrative storylines are used as input drivers to climate models, to represent different development pathways, which are usually characterized and applied at national and sub-national scales. Storylines aim to provide new social scenarios that address local human cognition uncertainties and improve human behavior modelling and robustness when addressing decision-making processes. Climate risks and hazards understanding can be communicated by presenting the experiences or a sequence of events, facts, and observations that are plausible and potentially critical for the system under study. Methods guiding storytelling are usually focused on conducting interviews with stakeholders, carrying out collective workshops, developing appropriate focal questions, and iterating between model results and key stakeholders. Therefore, can other data collection tools be used to reduce uncertainty in physical aspects of climate change from individuals’ local experience and perception? This contribution presents a triple-loop survey to detail the core elements of farmers’ perception and behavior when addressing climate change risk. We collect first-hand observations from northern Italian farmers about how climate change affects their activity and how extreme events are conditioning their adaptation capacity. Emphasis is placed on understanding the driving factors (risk awareness, perceived impacts, and adaptation measures and barriers) involved in the physically self-consistent past events and the plausibility of those factors. Moreover, we want to test if these factors can provide relevant implications for appropriately modelling storylines in decision-making processes. Tentative results can be useful to discuss the methodological framework of storylines building and narratives modelling, and at which point surveys can be an alternative and complementary way of dealing with deep uncertainty within climate risk management and social scenarios modelling.

Climate change is arguably the most severe and complex challenge facing today’s society, a cross-cutting issue affecting many sectors and connected to other global challenges, such as ensuring sustainable water management and food security. Agricultural systems are adversely influenced by climate change through increased water stress, change in run-off patterns, seasonality fluctuation, and temperature variations. Farmers are, hence, a valuable source of first-hand observations of climate change as they may provide a deeper understanding of their manifestation, relevance, and effects. Social and behavioural sciences have investigated the influence of farmers’ experiences in increasing climate change adaptation capability and improving decision-making processes at the system level. The conclusion is that local perceptions provide sufficient baseline information for understanding individual and collective exposure to climate risks, an essential element for effective policy formulation and implementation. Traditional management approaches based on simple, linear growth optimization strategies, overseen by command-and-control policies, have proven inadequate for effective adaptation to climate change. Conversely, accurate bottom-up approaches focused on social learning can complement the system transformation by building collaborative problem solving among individuals, stakeholders, and decision-makers. In this context, deepening social perception becomes fundamental for two main reasons: i) it is a key component of the socio-political context, and ii) it is an essential step for behaviour transformation and attitude change. In this line, associative processing methods, such as interviews and surveys, have been discussed for their ability to monitor the nature, extent, significance, and influence of personal experience on climate change adaptation. Also, modelling techniques have been recognized in social sciences as effective mechanisms to simulate the social influence in decision-making processes. System dynamics (e.g., causal loop diagrams, CLD) and Agent-Based Models (ABM) can include feedback between social and physical environments, define individuals’ and stakeholders’ narratives, and map the social network with agents’ interactions. This proposal aims at testing how qualitative data can enable policy-makers and managers to understand and re-think water management and climate change policies at the local level, which is essential to address agricultural risks. From a system dynamics approach, we examine how ABMs can most effectively integrate behavioural data collected from semi-structured interviews and surveys to increase robustness in decision-making processes while attending to farmers’ behaviour on climate change adaptation. We surveyed 460 farmers and semi-structured interviews with 13 irrigation consortiums from northern Italy to deepen a triple loop analysis on climate change awareness, perceived impacts, and adaptive capacity.

A drought is a slowly developing natural phenomenon that can occur in all climatic zones and can be defined as a temporary but significant decrease in water availability. Over the past three decades, the cost of droughts in Europe has amounted to over 100 billion euros and it is expected to considerably increase as future droughts are projected to be more severe and long-lasting. Although drought monitoring and management are largely studied in the literature, they often fail at yielding precise information on critical events occurrence and associated impacts such as reduction of electricity production or crop failures. This is due to the difficulty of capturing the complexity of drought dynamics, which evolve over diverse temporal (and spatial) scales, including short-term meteorological droughts, medium-term agricultural droughts, and long-term hydrological droughts, as well as the non-physical aspects related to droughts (water management, irrigation, etc.). In this work, we contribute a Machine Learning based framework named FRIDA (FRamework for Index-based Drought Analysis) for the identification of impact-based, site-specific drought indexes. FRIDA is a fully automated data-driven approach that relies on advanced feature extraction algorithms to identify relevant drought drivers from a pool of candidate hydro-meteorological variables. Selected predictors are combined into an index representing a surrogate of the drought conditions in the considered area, including either observed or simulated water deficits or remotely sensed information about the state of the crops. FRIDA is portable across different contexts for supporting the formulation of basin-specific indexes to better inform drought management strategies. Several real-world examples will be used to provide a synthesis of recent applications of FRIDA in case studies featuring diverse hydroclimatic conditions and variable levels of data availability.

Demand-side management strategies based on customized feedback have proved their worth in supporting water conservation efforts and behavior change programs. Several studies in both the water and energy sectors report of observed short-term savings deriving from feedback-based programs and awareness campaigns, often based on smart metered data and high levels of customization in presenting information on resource usage to users in the form of past consumption, real-time information, peer comparison, analogies, and resource saving tips. Yet, feedback-based programs are often run as part of experimental trials with a limited duration, and their effectiveness is therefore only evaluated for a short time span, potentially overlooking rebound effects. Assessing the long-term effect of feedback information on behavior change is still an open research question. In this work, we analyze the long-term impacts of a smart-meter fed gamified ICT platform providing customized feedback to water users, which was deployed starting in 2014 in a long experiment trial with over 200 users of the Global Omnium utility in Valencia (Spain). The platform core is a data-driven demand management pipeline that enables water utilities to foster consumer engagement and promote water conservation via customized feedbacks. It includes customized water saving tips, peer-comparison of water usage, and a reward program based on gamification tools and mechanisms. After three years of development and testing from 2014 to 2017, the platform has proven to be very effective in the short-term, when a user is engaged. A 5.7% volumetric water use reduction among Global Omnium users was achieved after the first year of full implementation, along with a 20% approximate water consumption difference with respect to non-platform users. Here, we analyze the smart meter data of the platform users, respectively after one and two years from the end of the funded platform trial period, to assess long-term behavior changes and rebound effects on different groups of platform adopters.

Balancing socio-ecological systems among competing water demands is a difficult and complex task. Traditional approaches based on limited, linear growth optimization strategies overseen by command/control have partially failed to account for the inherent unpredictability and irreducible uncertainty affecting most water systems due to climate change. Governments and managers are increasingly faced with understanding driving-factors of major change processes affecting multifunctional systems. In the last decades, the shift to address the integrated management of water resources from a technocratic “top-down” to a more integrated “bottom-up” and participatory approach was motivated by the awareness that water challenges require integrated solutions and a socially legitimate planning process. Assuming water flows as physical, social, political, and symbolic matters, it is necessary to entwining these domains in specific configurations, in which key stakeholders and decision-makers could directly interact through social-learning. The literature on integrated water resources management highlights two important factors to achieve this goal: to deepen stakeholders’ perception and to ensure their participation as a mechanism of co-production of knowledge. Stakeholder Analysis and Governance Modelling approaches are providing useful knowledge about how to integrate social-learning in water management, making the invisible, visible. The first one aims to identify and categorize stakeholders according to competing water demands, while the second one determines interactions, synergies, overlapping discourses, expectations, and influences between stakeholders, including power-relationships. The HydroSocial Cycle (HSC) analysis combines both approaches as a framework to reinforce integrated water management by focusing on stakeholder analysis and collaborative governance. This method considers that water and society are (re)making each other so the nature and competing objectives of stakeholders involved in complex water systems may affect its sustainability and management. Using data collected from a qualitative questionnaire and applying descriptive statistics and matrices, the HSC deepens on interests, expectations, and power-influence relationships between stakeholders by addressing six main issues affecting decision-making processes: relevance, representativeness, recognition, performance, knowledge, and collaboration. The aim of this contribution is to outline this method from both theory and practice perspective by highlighting the benefits of including social sciences approaches in transdisciplinary research collaborations when testing water management strategies affecting competing and dynamic water systems.

Climate change tends to be addressed by accurate statistics and modelling, but it is generally perceived abstractly, being considered a distant psychological risk in which impacts and effects are spatially and temporally differentiated. In other words, people’s attitude towards climate change is that it will impact other individuals and communities that are geographically, temporally, and even generationally removed from themselves. However, due to the hybrid nature of climate change as both a physical and social phenomenon, individuals are not ‘blank slates’ receiving information and facing climate change. Many have argued that deepening personal experience could be the first step for reducing individual and community psychological distance of climate change while increasing the potential for behavior change. Considering that agriculture affects and is affected by climate change in several ways, farmers can provide first-hand observations of climate change impacts and testing different adaptation options. This contribution provides an overview of the intellectual structure of farmers’ behavior on climate change awareness, perceived risks, and adaptation capacity. A portfolio of 108 survey studies published in the last decade was selected for a comprehensive analysis. Exploratory variables such as farmers’ socio-demographic characteristics, level of climate change awareness, major perceived impacts, and adaptation measures, parameters, and barriers have been reported. In addition to the bibliographic analysis, the first results from a survey conducted in different irrigation systems in northern Italy will be tested to identify(dis)similar trends in farmers’ behavior. The identification of not only farmers’ behavior gaps but also their causing reasons will contribute to focus attention on most concerning issues and provide more accurate bottom-up knowledge to managers and decision-makers.

The value of streamflow forecasts to inform water infrastructure operations has been extensively studied. Yet, their value in informing infrastructure design is still unexplored. In this work, we investigate how dam design is shaped by information feedbacks. We demonstrate how flexible operating policies informed by streamflow forecasts enable the design of less costly reservoir relative to alternatives that do not rely on forecast information. Our approach initially establishes information bounds by selecting the most informative lead times of perfect streamflow forecasts to be included in the infrastructure design. We then analyze the design and operational sensitivities relative to realistic imperfect streamflow forecasts synthetically modeled to explicitly represent different biases. We demonstrate our approach through an ex-post analysis of the Kariba dam in the Zambezi river basin. Results show that informing dam design with perfect forecasts enable attaining the same hydropower production of the existing dam, while reducing infrastructure size and associated capital costs by 20%. The use of forecasts with lower skill reduces this gain to approximately 15%. Finally, the adoption of forecast information in the operation of the existing system facilitate an annual average increase of 60 GWh in hydropower production. This finding, extrapolated to the new planned dams in the basin, suggests that consideration of forecast informed policies could yield power production benefits equal to 75% of the current annual electricity consumption of the Zambian agricultural sector. Forecast information feedbacks have a strong potential to become a valuable asset for the ongoing hydropower expansion in the basin.