Downloads: 205
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
su11041090.pdf | 5.14 MB | Adobe PDF | View/Open |
Title: | Simulation-Based Exceedance Probability Curves to Assess the Economic Impact of Storm Surge Inundations due to Climate Change: A Case Study in Ise Bay, Japan |
Authors: | Jiang, Xinyu Mori, Nobuhito https://orcid.org/0000-0001-9082-3235 (unconfirmed) Tatano, Hirokazu https://orcid.org/0000-0001-7209-4358 (unconfirmed) Yang, Lijiao |
Author's alias: | 森, 信人 多々納, 裕一 |
Keywords: | exceedance probability curves storm surge inundation climate change economic impact Ise Bay |
Issue Date: | Feb-2019 |
Publisher: | MDPI AG |
Journal title: | Sustainability |
Volume: | 11 |
Issue: | 4 |
Thesis number: | 1090 |
Abstract: | Understanding storm surge inundation risk is essential for developing countermeasures and adaptation strategies for tackling climate change. Consistent assessment of storm surge inundation risk that links probability of hazard occurrence to distribution of economic consequence are scarce due to the lack of historical data and uncertainty of climate change, especially at local scales. This paper proposes a simulation-based method to construct exceedance probability (EP) curves for representing storm surge risk and identifying the economic impact of climate change in the coastal areas of Ise Bay, Japan. The region-specific exceedance probability curves show that risk could be different among different districts. The industry-specific exceedance probability curves show that manufacturing, transport and postal activities, electricity, gas, heat supply and water, and wholesale and retail trade are the most affected sectors in terms of property damage. Services also need to be of concern in terms of business interruption loss. Exceedance probability curves provide complete risk information and our simulation-based approach can contribute to a better understanding of storm surge risk, improve the quantitative assessment of the climate change-driven impacts on coastal areas, and identify vulnerable regions and industrial sectors in detail. |
Rights: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
URI: | http://hdl.handle.net/2433/245215 |
DOI(Published Version): | 10.3390/su11041090 |
Appears in Collections: | Journal Articles |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.