Abstract. Floods are one of the most frequent and damaging natural threats worldwide. Whereas the assessment of direct impacts is well advanced, the evaluation of indirect impacts is less frequently achieved. Indirect impacts are not due to the physical contact with flood water but result, for example, from the reduced performance of infrastructures. Linear critical infrastructures (such as roads and pipes) have an interconnected nature that may lead to failure propagation, so that impacts extend far beyond the inundated areas and/or period. This work presents the risk analysis of two linear infrastructure systems, i.e. the water distribution system (WSS) and the road network system. The evaluation of indirect flood impacts on the two networks is carried out for four flooding scenarios, obtained by a coupled 1D–quasi-2D hydraulic model. Two methods are used for assessing the impacts on the WSS and on the road network: a pressure-driven demand network model and a transport network disruption model respectively. The analysis is focused on the identification of (i) common impact metrics, (ii) vulnerable elements exposed to the flood, (iii) similarities and differences of the methodological aspects for the two networks, and (iv) risks due to systemic interdependency. The study presents an application to the metropolitan area of Florence (Italy). When interdependencies are accounted for, results showed that the risk to the WSS in terms of population equivalent (PE/year) can be reduced by 71.5 % and 41.8 %, if timely repairs to the WSS stations are accomplished by 60 and 120 min respectively; the risk to WSS in terms of pipe length (km yr−1) reduces by 53.1 % and 15.6 %. The study highlights that resilience is enhanced by systemic risk-informed planning, which ensures timely interventions on critical infrastructures; however, for indirect impacts and cascade effects, temporal and spatial scales are difficult to define. Perspective research could further improve this work by applying a system-risk analysis to multiple urban infrastructures.
Network Attack Path Selection and Evaluation Based on Q-Learning