Information-theoretic Portfolio Decision Model for Optimal Flood Management

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Authors

Matteo Convertino , Antonio Annis, Fernando Nardi 

Abstract

The increasing impact of flooding urges more effective flood management strategies to guarantee sustainable ecosystem development. Recent catastrophes underline the importance of avoiding local flood management, but characterizing large scale basin wide approaches for systemic flood risk management.
Here we introduce an information-theoretic Portfolio Decision Model (iPDM) for the optimization of a systemic ecosystem value at the basin scale by evaluating all potential flood risk mitigation plans. iPDM calculates the ecosystem value predicted by all feasible combinations of flood control structures (FCS) considering environmental, social and economical asset criteria. A multi-criteria decision analytical model evaluates the benefits of all FCS portfolios at the basin scale weighted by stakeholder preferences for assets criteria as ecosystem services. The risk model is based on a maximum entropy model (MaxEnt) that predicts the flood susceptibility, the risk of floods based on the exceedance probability distribution, and its most important drivers. Information theoretic global sensitivity and uncertainty analysis is used to select the simplest and most accurate model based on a flood return period. A stochastic optimization algorithm optimizes the ecosystem value constrained to the budget available and provides Pareto frontiers of optimal FCS plans for any budget level. Pareto optimal solutions maximize FCS diversity and minimize the criticality of floods manifested by the scaling exponent of the Pareto distribution of flood size that links management and hydrogeomorphological patterns. The proposed model is tested on the 17,000 $km^2$ Tiber river basin in Italy.
iPDM allows stakeholders to identify optimal FCS plans in river basins for a comprehensive evaluation of flood effects under future ecosystem trajectories.

DOI

https://doi.org/10.31223/osf.io/k5aut

Subjects

Civil and Environmental Engineering, Civil Engineering, Computational Engineering, Earth Sciences, Engineering, Environmental Engineering, Environmental Health and Protection, Environmental Sciences, Geomorphology, Hydraulic Engineering, Hydrology, Life Sciences, Natural Resources and Conservation, Natural Resources Management and Policy, Operations Research, Systems Engineering and Industrial Engineering, Other Civil and Environmental Engineering, Other Engineering, Other Environmental Sciences, Other Physical Sciences and Mathematics, Physical Sciences and Mathematics, Probability, Risk Analysis, Statistics and Probability, Sustainability, Systems Engineering, Water Resource Management

Keywords

river basin management; floods; systemic risk; MaxEnt; portfolio decision model; MCDA

Dates

Published: 2019-06-27 06:08

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License

GNU Lesser General Public License (LGPL) 2.1

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