A conceptual framework to help choose appropriate blue nature-based solutions.

Biodiversity loss and climate change have severely impacted ecosystems and livelihoods worldwide, compromising access to food and water, increasing disaster risk, and affecting human health globally. Nature-based Solutions (NbS) have gained interest in addressing these global societal challenges. Although much effort has been directed to NbS in urban and terrestrial environments, the implementation of NbS in marine and coastal environments (blue NbS) lags. The lack of a framework to guide decision-makers and practitioners through the initial planning stages appears to be one of the main obstacles to the slow implementation of blue NbS. To address this, we propose an integrated conceptual framework, built from expert knowledge, to inform the selection of the most appropriate blue NbS based on desired intervention objectives and social-ecological context. Our conceptual framework follows a four incremental steps structure: Step 1 aims to identify the societal challenge(s) to address; Step 2 highlights ecosystem services and the underlying biodiversity and ecological functions that could contribute to confronting the societal challenge(s); Step 3 identify the specific environmental context the intervention needs to be set within (e.g. the spatial scale the intervention will operate within, the ecosystem's vulnerability to stressors, and its ecological condition); and Step 4 provides a selection of potential blue NbS interventions that would help address the targeted societal challenge(s) considering the context defined through Step 3. Designed to maintain, enhance, recover, rehabilitate, or create ecosystem services by supporting biodiversity, the blue NbS intervention portfolio includes marine protection (i.e., fully, highly, lightly, and minimally protected areas), restorative activities (i.e., active, passive, and partial restoration; rehabilitation of ecological function and ecosystem creation), and other management measures (i.e., implementation and enforcement of regulation). Ultimately, our conceptual framework guides decision-makers toward a versatile portfolio of interventions that cater to the specific needs of each ecosystem rather than imposing a rigid, one-size-fits-all model. In the future, this framework needs to integrate socio-economic considerations more comprehensively and be kept up-to-date by including the latest scientific information.

Pluvial flood risk assessment for 2021-2050 under climate change scenarios in the Metropolitan City of Venice.

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021-2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995-2020. The methodology developed implements spatio-temporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995-2020 based on historical data, and risk maps for each quarter of the period 2021-2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline.