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On 15 October 2015, a storm-induced flood hit the central sector of Benevento Province (southern Italy) causing two deaths and severe damage to infrastructure, buildings and local agriculture. This area has a long history of similar events and since 1924 its major river segments have been monitored with several hydrometric stations. We used data from two of these stations and a LiDAR derived high-resolution topography to develop a flood hazard map. For map computation, we first derived a flood inundation map from topography. Subsequently we estimated the probability of exceedance of each specific fluvial stage from the combination of a Generalized Extreme Value and a Gamma fits of available hydrometric data. As boundary condition, we considered a reference scenario corresponding to an estimated 500 year flood. The hazard maps provide an overview of the flood hazard in the central sector of Benevento Province and floodplains zonation in flood perspective. [ABSTRACT FROM AUTHOR]

Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time. [ABSTRACT FROM AUTHOR]

In October 2015, two intense rainfall events hit the central and southern regions of Italy and triggered a combination of different and widespread effects, including floods, landslides, and soil erosion. These outcomes devastated about 68 municipalities of the Benevento province (Campania region), killed two people, and caused millions of euros worth of damage to structures, infrastructures, and agriculture. The town of Benevento was one of the sectors most affected by overflow. Extensive areas characterized by flyschoid outcrops experienced widespread occurrences of soil erosion and landslides, and destructive, high-velocity debris flows (about 50) afflicted areas that had experienced heavy rainfall of higher intensity (total rainfall of 415.6 mm). In this study, the characteristics of these rainfall events and related geomorphological processes were determined by (i) analyzing the available rainfall data to identify the spatial pattern, distribution, and statistical characteristics of the two storms and (ii) mapping the storm effects, such as flooded areas, landslide types, and soil erosion. These effects were then related to the spatial distribution of the storms and the local geological and geomorphologic settings that drove their initiation and development. [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]