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.