The behavior of individuals, businesses, and government entities before, during, and immediately after a disaster can dramatically affect the impact and recovery time. However, existing risk-assessment methods rarely include this critical factor. In this Perspective, we show why this is a concern, and demonstrate that although initial efforts have inevitably represented human behavior in limited terms, innovations in flood-risk assessment that integrate societal behavior and behavioral adaptation dynamics into such quantifications may lead to more accurate characterization of risks and improved assessment of the effectiveness of riskmanagement strategies and investments. Such multidisciplinary approaches can inform flood-risk management policy development.

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Raising interests in 'nature-based solutions' (NBS) inspired attempts to organise their principles and qualities within comprehensive and internally consistent evaluation frameworks, so as to demonstrate the superior performance of 'working with nature'. However, the proposed frameworks stop short of taking into account the changing conditions in which NBS are set to operate. Climate change, in particular, can alter ecosystems and their services, and may undermine the performance of green solutions that rely on these. We present here a 'dynamic' assessment framework explicitly accounting for the impact of climate change on the effectiveness of the proposed NBS. The framework is based on an innovative approach integrating system analysis and backcasting. Although it has not yet applied within the NBS context, backcasting is well-suited to seize the transformational character of NBS, as it encourages 'breakthrough' leaps rather than incremental improvements. Our framework factors in NBS' multifunctional character and is designed to capture associated direct benefits/costs and co-benefits/costs. It is meant to be applied ex ante to ideally support the choice between innovative NBS and traditional options, in an effort to respond to the societal challenges identified by the EU Research & Innovation agenda on the environment.

Abstract. Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.

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Flood damage assessments are often based on stage-damage curve (SDC) models that estimate economic damage as a function of flood characteristics (typically flood depths) and land use. SDCs are developed through a site-specific analysis, but are rarely adjusted to economic circumstances in areas to which they are applied. In Italy, assessments confide in SDC models developed elsewhere, even if empirical damage reports are collected after every major flood event. In this paper, we have tested, adapted and extended an up-to-date SDC model using flood records from Northern Italy. The model calibration is underpinned by empirical data from compensation records. Our analysis takes into account both damage to physical assets and losses due to foregone production, the latter being measured amidst the spatially distributed gross added value.

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In this paper we developed and tested an integrated methodology for assessing direct and indirect economic impacts of flooding. The methodology combines a spatial analysis of damage to physical stocks with a general economic equilibrium approach using a regionally-calibrated (to Italy) version of a Computable General Equilibrium (CGE) global model. We applied the model to the 2000 Po river flood. To account for the uncertainty in the induced effects on regional economies, we explored three disruption and two recovery scenarios. The results prove that: i) indirect losses are a significant share of direct losses, and ii) the model is able to capture both positive and negative economic effects of a disaster in different areas of the same country. The assessment of indirect impacts is essential for a full understanding of the economic outcomes of natural disasters.