Abstract
Managing coastal flood risks involves selecting a portfolio of different strategies. Analyzing this choice typically requires a model. State-of-the-art coastal risk models provide detailed regional information, but they can be difficult to implement, computationally challenging, and potentially inaccessible. Simple economic damage models are more accessible but may not incorporate important features and thus fail to model risks and trade offs with enough fidelity to support decision making. Here, we develop a new framework to analyze coastal flood risk management. The framework is computationally inexpensive yet incorporates common features of many coastal cities. We apply this framework to an idealized coastal city and assess and optimize two objectives using combinations of risk mitigation strategies against a wide range of future states of the world. We find that optimization using combinations of strategies allows for identification of Pareto optimal strategy combinations that outperform individual strategy options.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 341-353 |
| Number of pages | 13 |
| Journal | Environmental Modelling and Software |
| Volume | 119 |
| DOIs | |
| State | Published - Sep 2019 |
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All Science Journal Classification (ASJC) codes
- Software
- Environmental Engineering
- Ecological Modeling
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Optimization of multiple storm surge risk mitigation strategies for an island City On a Wedge. / Ceres, Robert L.; Forest, Chris E.; Keller, K.
In: Environmental Modelling and Software, Vol. 119, 09.2019, p. 341-353.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimization of multiple storm surge risk mitigation strategies for an island City On a Wedge
AU - Ceres, Robert L.
AU - Forest, Chris E.
AU - Keller, K.
PY - 2019/9
Y1 - 2019/9
N2 - Managing coastal flood risks involves selecting a portfolio of different strategies. Analyzing this choice typically requires a model. State-of-the-art coastal risk models provide detailed regional information, but they can be difficult to implement, computationally challenging, and potentially inaccessible. Simple economic damage models are more accessible but may not incorporate important features and thus fail to model risks and trade offs with enough fidelity to support decision making. Here, we develop a new framework to analyze coastal flood risk management. The framework is computationally inexpensive yet incorporates common features of many coastal cities. We apply this framework to an idealized coastal city and assess and optimize two objectives using combinations of risk mitigation strategies against a wide range of future states of the world. We find that optimization using combinations of strategies allows for identification of Pareto optimal strategy combinations that outperform individual strategy options.
AB - Managing coastal flood risks involves selecting a portfolio of different strategies. Analyzing this choice typically requires a model. State-of-the-art coastal risk models provide detailed regional information, but they can be difficult to implement, computationally challenging, and potentially inaccessible. Simple economic damage models are more accessible but may not incorporate important features and thus fail to model risks and trade offs with enough fidelity to support decision making. Here, we develop a new framework to analyze coastal flood risk management. The framework is computationally inexpensive yet incorporates common features of many coastal cities. We apply this framework to an idealized coastal city and assess and optimize two objectives using combinations of risk mitigation strategies against a wide range of future states of the world. We find that optimization using combinations of strategies allows for identification of Pareto optimal strategy combinations that outperform individual strategy options.
UR - http://www.scopus.com/inward/record.url?scp=85068475009&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068475009&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2019.06.011
DO - 10.1016/j.envsoft.2019.06.011
M3 - Article
AN - SCOPUS:85068475009
VL - 119
SP - 341
EP - 353
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
SN - 1364-8152
ER -