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In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale risk estimation methods are increasingly applied. In this context, an implemented methodology for microscale flood risk evaluation is presented, which considers direct and tangible damage as a function of hydrometric height and allows for quick estimates of the damage level caused by alluvial events. The method has been applied and tested on businesses and residential buildings of the town of Benevento (southern Italy), which has been hit by destructive floods several times in the past; the most recent flooding occurred in October 2015. The simplified methodology tries to overcome the limitation of the original method—the huge amounts of input data—by applying a simplified procedure in defining the data of the physical features of buildings (e.g., the number of floors, typology, and presence of a basement). Data collection for each building feature was initially carried out through careful field surveys (FAM, field analysis method) and subsequently obtained through generalization of data (DGM, data generalization method). The basic method (FAM) allows for estimating in great detail the potential losses for representative building categories in an urban context and involves a higher degree of resolution, but it is time-consuming; the simplified method (DGM) produces a damage value in a shorter time. By comparison, the two criteria show very similar results and minimal differences, making generalized data acquisition most efficient. [ABSTRACT FROM AUTHOR]

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The change of the Earth's climate and the increasing human action (e.g., increasing impervious areas) are influencing the recurrence and magnitude of flooding events and consequently the exposure of urban and rural communities. Under these conditions, flood hazard analysis needs to account for this change through the adoption of nonstationary approaches. Such methods, showing how flood hazard evolves over time, are able to support a long-term plan of adaptation in hazard changing perspective, reducing expected annual damage in flood prone areas. On this basis, in this paper a reevaluation of flood hazard in the Benevento province of southern Italy, is presented, providing a reduced complexity methodological framework for near future flood hazard prediction under nonstationary conditions. The proposed procedure uses multiple nonstationary probability models and a LiDAR-derived high-resolution inundation model to provide present and future flood scenarios in the form of hazard maps. Such maps are derived using a spatialization routine of stage probability across the inundation model that is able to work at different scales. The analysis indicates that, overall, (i) flood hazard is going to decrease in the next 30 years over the Benevento province and (ii) many areas of the Calore river floodplain are going to be subject to higher return level events. Consequently, many areas would require new guidelines of use as the hazard level decreases. Limitations of the analysis are related to the choice of the probability model and the parameter estimation approach. A further limit is that, currently, this method is not able to account for the presence of mitigation measurements. However, result validation indicates a very high accuracy of the proposed procedure with a matching degree, with a recently observed 225-years flood, estimated in 98%. On this basis, the proposed framework can be considered a very important approach in flood hazard estimation able to predict near future evolution of flood hazard as modulated by the ongoing climate change. [ABSTRACT FROM AUTHOR]

• We propose a new practice for flood hazard mapping in presence of multiple gauging stations. • The method uses an original interpolation/assignation coded algorithm for multiple probability models fusion. • The use of LiDAR data and multiple probability models increases the reliability of the hazard map. • The method has the potential to support flood hazard mapping over large areas. • Comparison with 2D hydraulic simulation confirm the high performance of the proposed approach. Hazard mapping is essential for risk assessment and mitigation measurement design in flood prone areas. In Europe, long-term fluvial stage data, acquired since the 18th century, represent a resource of fundamental importance in this perspective, especially where rivers monitoring is completed by multiple stations distributed along the course. In these conditions, a major challenge is represented by the possibility of incorporating multiple probability models, representative of river dynamics at different distance from the mouth, in flood hazard estimation over so large areas. In this paper, we propose a new procedure of hazard estimation based on LiDAR derived flood inundation model and multiple hydrometric time series that, using a specifically developed algorithm/code of interpolation/assignation of multiple probability models, has the potential to work at local to national scale providing reliable estimation also in presence of urban areas. We applied the developed procedure and associated algorithm/code to a selected study area in southern Italy, recently hit by a destructive flood event, and quantitatively evaluate model performance. Confidence interval computation provides an overview of uncertainty related to flood magnitude estimation by extreme value analysis, indicating a substantial uncertainty related to 500 years flood magnitude estimation. Sensitivity analysis indicates a high degree of robustness of the developed procedure. Result validation through comparison against the observed 2015 flood event indicates that the method has the potential to support flood hazard analysis at regional to national scale. Limits of method application are related to the basic assumption of stationarity of hydrologic time series that might be considered too "simplicistic" in a changing climate also related to the limited length of some time series that only in few cases have no discontinuities. The absence of propagation modelling as part of the estimation procedure might be considered as an additional limit since in complex topographic and hydrological conditions it might provide a better evaluation of flood hazard. However, comparison of the 500 years flood derived from our procedure and 500 years flood scenarios derived by 2D hydraulic simulations indicate the capabilities of our procedure in identifying area floodable by specific events with only local overestimation that generally increase safety in human life protection perspective. This confirms the potential of considering multiple probability models distributed along the river course in flood hazard estimation perspective and indicate that our procedure can be a valid alternative to simulation based flood hazard estimation procedures. [ABSTRACT FROM AUTHOR]