Abstract:Rising of the sea level and/or heavy rainfall intensification significantly enhance the risk of flooding in low-lying coastal reclamation areas. Therefore, there is a necessity to assess whether channel hydraulic networks and pumping systems are still efficient and reliable in managing risks of flooding in such areas in the future. This study addresses these issues for the pumping system of the Mazzocchio area, which is the most depressed area within the Pontina plain, a large reclamation region in the south of Lazio (Italy). For this area, in order to assess climate change impact, a novel methodological approach is proposed, based on the development of a simulation-optimization model, which combines a multiobjective evolutionary algorithm and a hydraulic model. For assigned extreme rainfall events and sea levels, the model calculates sets of Pareto optimal solutions which are obtained by defining two optimality criteria: (a) to minimize the flooding surface in the considered area; (b) to minimize the pumping power necessary to mitigate the flooding. The application shows that the carrying capacity of the hydraulic network downstream of the pumping system is insufficient to cope with future sea level rise and intensification of rainfall.

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

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