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.
Flood hazard mapping incorporating multiple probability models
Guerriero L.;Ruzza G.;Guadagno F. M.;Revellino P.
2020-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
