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Understanding what makes people vulnerable to flooding is key in informing the risk management process. Non-structural measures, such as risk communication, can reduce vulnerability by improving flood risk awareness, but they require a deep understanding of which factors influence risk awareness, and how. We analysed and untangled the role of experience with, and knowledge of, floods by conducting a survey in a municipality in North-eastern Italy that was hit by a flash flood in 2018. The results show that previous experience with floods influences risk awareness not only directly, but also indirectly through the knowledge that was gained from that experience. In addition, specific (as opposed to generic) definitions of experience have been found to be better suited for exploring their effects on risk awareness. Based on the literature and on our results, we propose an experience-knowledge typology to help unravel the complex role that these two variables play in shaping flood risk awareness. [ABSTRACT FROM AUTHOR]

Over the last few years, several socio-hydrological studies have investigated the risk dynamics generated by the complex interactions between floods and societies, with a focus on either changing reservoir operation rules or raising levees. In this study, we propose a new socio-hydrological model of human–flood interactions that represents both changes in the reservoir management strategies and updating of the levee system. Our model is applied to simulate three prototypes of floodplain management strategies to cope with flood risk: green systems, in which societies resettle outside the flood-prone area; technological systems, in which societies implement structural measures, such as levees; and green-to-techno systems, in which societies shift from green to technological approaches. Floodplain dynamics are explored simulating possible future scenarios in the city of Brisbane, Australia. Results show that flood risk is strongly influenced by changes in flood and drought memory of reservoir operators, while risk-awareness levels shape the urbanisation of floodplains. Furthermore, scenarios of more frequent and higher magnitude events prove to enhance social flood memory in green systems, while technological systems experience much higher losses. Interestingly, green-to-techno systems may also evolve toward green floodplain management systems in response to large losses and technical/economical unfeasibility of larger structural measures. [ABSTRACT FROM AUTHOR]

Priorities and Interactions of Sustainable Development Goals (SDGs) with Focus on Wetlands. The authors wish to make the following correction to this paper [[1]]: The author name "Zahra Kalantary" should be "Zahra Kalantari". 1 Jaramillo F., Desormeaux A., Hedlund J., Jawitz J.W., Clerici N., Piemontese L., Rodríguez-Rodriguez J.A., Anaya J.A., Blanco-Libreros J.F., Borja S. Priorities and interactions of Sustainable Development Goals (SDGs) with focus on wetlands. [Extracted from the article]

The design of flood defence structures requires the estimation of flood water levels corresponding to a given probability of exceedance, or return period. In river flood management, this estimation is often done by statistically analysing the frequency of flood discharge peaks. This typically requires three main steps. First, direct measurements of annual maximum water levels at a river cross-section are converted into annual maximum flows by using a rating curve. Second, a probability distribution function is fitted to these annual maximum flows to derive the design peak flow corresponding to a given return period. Third, the design peak flow is used as input to a hydraulic model to derive the corresponding design flood level. Each of these three steps is associated with significant uncertainty that affects the accuracy of estimated design flood levels. Here, we propose a simulation framework to compare this common approach (based on the frequency analysis of annual maximum flows) with an alternative approach based on the frequency analysis of annual maximum water levels. The rationale behind this study is that high water levels are directly measured, and they often come along with less uncertainty than river flows. While this alternative approach is common for storm surge and coastal flooding, the potential of this approach in the context of river flooding has not been sufficiently explored. Our framework is based on the generation of synthetic data to perform a numerical experiment and compare the accuracy and precision of estimated design flood levels based on either annual maximum river flows (common approach) or annual maximum water levels (alternative approach). [ABSTRACT FROM AUTHOR]