3,593 research outputs found

    Rebuild, Retreat, or Resilience: Can Taipei Plan for Resilience?

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    Taiwan is ranked as the country most exposed to multiple hazards (The World Bank 2005). Taipei City is the capital city as well as the economic and political center of Taiwan. The United Nations report World Urbanization Prospects: The 2011 Revision places Taipei City third on the list of the worldรข??s top 10 urban areas exposed to three or more natural hazards, with the highest risk of cyclones, floods, and landslides. In order to gauge the vulnerabilities and damages of Taiwan and Taipei City, this research creates a natural disaster density indicator (NDDI) to conduct a comparative study of Taiwan, Japan, China, U.S.A., U.K., France, and the Netherlands over the past three decades. The results indicate that Taiwan has both the highest disaster occurrence and highest death toll among these seven countries. The Taipei case study, a chronology of policies implemented to prevent flooding, explains that costly engineering structures, rebuilding, and fortification against floods eventually created a false sense of security, which has encouraged more intensive residential and commercial developments in flood-prone areas, and led to a higher level of vulnerability. This research further simulates and evaluates the vulnerabilities of population, land value, properties, GDP, and critical facilities in three scenarios: heavy rainfall, typhoon conditions, and extreme weather rainfall, through the technology of Geographic Information System (GIS) by using ArcMap 10.2.2 software. The results indicate 40% of Taipei City is located in flood risk areas in an extreme weather scenario. This percentage is higher than other global cities such as Londonรข??s 15%, Tokyoรข??s 10%, and New York Cityรข??s 25%. Based on the 10% of total flooding areas above 0.5 meter, the vulnerable population is estimated at 200,000 people, or 7% of the total population. The GDP impact will be more than 28billion.Morethan28 billion. More than 67 billion of land value is vulnerable. A least one million subway passengers will be impacted each day. There is little evidence that the urban poor are particularly vulnerable to floods. On the contrary, some neighborhoods with high income households face a higher risk of floods. Very few medical centers, oil and gas stations, and electrical power substations are located in flood-prone areas, but, a large number of public schools, administrative buildings, and major subway stations are susceptible. Additionally, the likelihood analysis of flooding in an extreme weather rainfall scenario concludes that the possibility will be five times that of the existing assumption with a flood in every 200 years. Thus, Taipei Cityรข??s infrequent once-in-two-century floods are likely to occur more frequently. Further, the 1% of Taipei metropolitan region flooding above 1 meter will possibly cost up to $ 1.5 billion in damages. Therefore, in the future, rather than strengthening and rebuilding costly structures, Taipei should focus on land-use and environmental planning for resilience. Urban policies should include environmentally responsible development in the face of continued population and economic growth, and being resilient regarding natural disasters. Most important is the need of a strong political commitment and leadership to initiate and implement urban policies toward resilience. In doing so, resilience can be achieved in Taipei

    Density and disasters: economics of urban hazard risk

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    Today, 370 million people live in cities in earthquake prone areas and 310 million in cities with high probability of tropical cyclones. By 2050, these numbers are likely to more than double. Mortality risk therefore is highly concentrated in many of the worldโ€™s cities and economic risk even more so. This paper discusses what sets hazard risk in urban areas apart, provides estimates of valuation of hazard risk, and discusses implications for individual mitigation and public policy. The main conclusions are that urban agglomeration economies change the cost-benefit calculation of hazard mitigation, that good hazard management is first and foremost good general urban management, and that the public sector must perform better in generating and disseminating credible information on hazard risk in cities.Banks&Banking Reform,Environmental Economics&Policies,Hazard Risk Management,Urban Housing,Labor Policies

    ๋„์‹œํ™์ˆ˜ ์ €๊ฐ์„ ์œ„ํ•œ ๊ทผ๊ฑฐ๊ธฐ๋ฐ˜ ๊ณ„ํš : ์„œ์šธ์‹œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2021. 2. ๊ฐ•์ค€์„.์ตœ๊ทผ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•ด ๋ฐœ์ƒ๋˜๊ณ  ์žˆ๋Š” ์‚ฌํšŒ/๊ฒฝ์ œ์  ํ”ผํ•ด๋Š” ๊ธ‰์†ํžˆ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ 2์ฐจ์  ํ”ผํ•ด๋กœ๋Š” ํญ์—ผ, ํ™์ˆ˜ ๋“ฑ์ด ์žˆ๋‹ค. ๊ทธ์ค‘์—์„œ ๊ทนํ•œ ๊ฐ•์šฐ๋Š” ๋„์‹ฌ์ง€์—ญ์— ํฐ ํ”ผํ•ด๋ฅผ ๋ฐœ์ƒ์‹œํ‚ค๊ณ  ์žˆ๋‹ค. ๋Œ€ํ‘œ์ ์œผ๋กœ๋Š” 2011๋…„ ๋ฐœ์ƒํ•œ ์ง‘์ค‘ํ˜ธ์šฐ ๋“ฑ์ด ์žˆ๋Š”๋ฐ, ๋‹น์‹œ์—๋Š” ์ตœ๋Œ€ 110.5 mm/hr์˜ ๊ธฐ๋ก์ ์ธ ๊ฐ•์ˆ˜๊ฐ€ ๋‚ด๋ ธ๋‹ค. ํ˜„๋Œ€ ๋„์‹œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํ™์ˆ˜์˜ ๋Œ€๋ถ€๋ถ„ ์›์ธ์€ ๋ถˆํˆฌ์ˆ˜์„ฑ ํฌ์žฅ๋ฉด์˜ ๊ธ‰๊ฒฉํ•œ ์ฆ๊ฐ€์™€ ๋‚ด์ˆ˜๋ฐฐ์ œ ๋ถˆ๋Šฅ, ๋ฌผ์ˆœํ™˜ ์‹œ์„ค ๋ถ€์žฌ ๋“ฑ์˜ ์›์ธ์ด ์žˆ๋‹ค. ๊ธฐ์ƒ์ฒญ์—์„œ ์ œ๊ณตํ•˜๋Š” ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅด๋ฉด, ํ–ฅํ›„ 100๋…„๊ฐ„ ๋„์‹œ์˜ ํ‰๊ท  ๊ฐ•์ˆ˜๋Ÿ‰์€ ์ค„์–ด๋“ค ๊ฒƒ์œผ๋กœ ํŒŒ์•…๋œ๋‹ค. ํ•˜์ง€๋งŒ, ์ผ์‹œ์— ํญ์šฐ๊ฐ€ ๋‚ด๋ฆฌ๋Š” ๋นˆ๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ๊ตญ์ง€์„ฑ ํ”ผํ•ด๊ฐ€ ๋šœ๋ ทํ•˜๊ฒŒ ๋ฐœ์ƒํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ํ˜„์žฌ ์ƒํƒœ์˜ ๊ธฐ๋ฐ˜์‹œ์„ค๋“ค์— ๋ณด์ˆ˜๋‚˜ ๋ฐฉ์–ด๊ธฐ์ˆ ์ด ์ˆ˜๋ฆฝ๋˜์ง€ ์•Š์œผ๋ฉด, ๊ทธ ํ”ผํ•ด๋Š” ์ƒ๋‹นํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด ์„ธ ๊ฐ€์ง€ ์—ฐ๊ตฌ ๋ชฉํ‘œ๋ฅผ ์ˆ˜๋ฆฝํ•˜์—ฌ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ, ๊ธฐ์ƒ์ฒญ์—์„œ ์ œ๊ณตํ•˜๋Š” ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค(RCP 4.5/RCP 8.5)๋กœ ์ธํ•ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ํ–ฅํ›„ 80๋…„(2020๋…„-2100๋…„)์˜ ๋„์‹œ ํ™์ˆ˜ ํ”ผํ•ด๋Ÿ‰์„ ์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ, ์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„๋œ ํ”ผํ•ด๋Ÿ‰์— ๊ธฐ๋ฐ˜ํ•œ ์žฌํ•ด ์ €๊ฐ ์‹œ์„ค์„ ์„ ์ •ํ•˜๊ณ , ์žฌํ•ด์˜ ์ €๊ฐ๋Ÿ‰์„ ๋ถ„์„ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ทผ๊ฑฐ ๊ธฐ๋ฐ˜(Evidence-Based Planning)์˜ ์‹œ์„ค๋ฐฐ์น˜ ๋ฐ ์„ค๊ณ„ํ•œ๋‹ค. ์žฌํ•ด ์ €๊ฐ ์‹œ์„ค์€ ๋ฏธ๋ž˜ ์„ธ๋Œ€๊ฐ€ ์ง€์†์ ์œผ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์นœํ™˜๊ฒฝ(Eco-Friendly) ์‹œ์„ค๋ฌผ์„ ์„ ์ •ํ•˜์˜€๋‹ค. ์„ธ ๋ฒˆ์งธ, HCFD (Hazard Capacity Factor Design) ๋ชจ๋ธ์˜ ๊ฐœ๋ฐœ์„ ํ†ตํ•ด, ํ–ฅํ›„ ๋ณ€ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์„ค๋ฌผ๋“ค์˜ ์šฉ๋Ÿ‰๊ณผ ์„ฑ๋Šฅ์— ๋Œ€ํ•ด ์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„ํ•œ๋‹ค. HCFD ๋ชจ๋ธ์€ ์ €๊ฐ ๊ธฐ์ˆ ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๊ณ ๋ คํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋ฐฉ์–ด ๊ธฐ์ˆ ๋กœ ์ด ์„ธ ๊ฐ€์ง€๋ฅผ ๋„์ž…ํ•˜์˜€๋‹ค. ์ €๋ฅ˜์กฐ, ํˆฌ์ˆ˜์„ฑ ํฌ์žฅ ๊ทธ๋ฆฌ๊ณ  ์ƒํƒœ์ˆ˜๋กœ๊ฐ€ ์ด์— ํ•ด๋‹นํ•œ๋‹ค. ์ €๋ฅ˜์กฐ์˜ ๊ฒฝ์šฐ, ํ™˜๊ฒฝ๋ถ€์—์„œ ์ง€์ •ํ•˜๊ณ  ์žˆ๋Š” ๋ฒ•๋ น์„ ์ฐธ๊ณ ํ•˜์—ฌ ๋„์ž… ๊ฐ€๋Šฅํ•œ ์šฉ๋Ÿ‰์„ ํŒŒ์•…ํ•˜์˜€๋‹ค. ํˆฌ์ˆ˜์„ฑํฌ์žฅ๊ณผ ์ƒํƒœ์ˆ˜๋กœ๋Š” ๋ฒ•๋ น์œผ๋กœ ๋ช…ํ™•ํžˆ ๊ทœ์ •ํ•˜๋Š” ์„ค๊ณ„ ์ง€์นจ์ด ์—†๊ธฐ์—, ํƒ€ ์—ฐ๊ตฌ ๋ณด๊ณ ์„œ๋ฅผ ์ฐธ๊ณ ํ•˜์˜€๋‹ค. ๊ฐ ๊ธฐ์ˆ ๋“ค์˜ ๋„์ž… ๊ทœ๋ชจ๋ฅผ ์‚ฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ Arc-GIS ArcHydro Plug in์„ ์‚ฌ์šฉํ•˜์˜€๊ณ  Watershed๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. Watershed์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ฒ”์œ„๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ธฐํ›„๋ณ€ํ™”์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ์ œ๊ณตํ•˜๋Š” ๊ฐ•์ˆ˜๋Ÿ‰์„ ์‹œ๊ฐ„ ๋‹จ์œ„๋กœ ๋ถ„์„ํ•˜์˜€๊ณ , ์ด๋ฅผ ์œ„ํ•ด Huff Curve ๊ณต์‹์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์œ„์—์„œ ์–ธ๊ธ‰๋œ ์„ธ ๊ฐ€์ง€ ๊ธฐ์ˆ ์€ ๋น—๋ฌผ์˜ ์ €์žฅ ์šฉ๋Ÿ‰์„ ์ฆ๊ฐ€์‹œ์ผœ ํ™์ˆ˜ ์™„ํ™”์— ๊ธฐ์—ฌํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์„ธ ๊ฐ€์ง€ ๊ธฐ์ˆ ์„ ๋ชจ๋‘ ๋„์ž…ํ•˜์˜€์„ ๋•Œ 2050๋…„๊ณผ 2060๋…„์—๋Š” RCP 8.5 ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๋ชจ๋“  ํ™์ˆ˜ํ”ผํ•ด๋ฅผ ์ €๊ฐํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. 2070๋…„ ์ดํ›„์—๋Š” ์œ ์ถœ์ด ๋ฐœ์ƒํ•  ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์ง€๋งŒ, ์ ์‘ ๊ธฐ์ˆ ์„ ํ†ตํ•ด ํ™์ˆ˜๋ฅผ ํฌ๊ฒŒ ์ค„์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๋…ผ๋ฌธ์—์„œ๋Š” 10๋…„ ๋‹จ์œ„์˜ ํ™์ˆ˜์™€ ์ ์‘๋Ÿ‰์„ ์‚ฐ์ •ํ•˜์˜€์ง€๋งŒ, ์ถ”ํ›„ ํ›„์† ์—ฐ๊ตฌ์—์„œ๋Š” 1๋…„ ๋‹จ์œ„์˜ ๋ถ„์„์ด ์‹ค์‹œ๋˜์–ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋˜ํ•œ ์ €๋ฅ˜์กฐ ๋‚ด๋ถ€์— ํ‡ด์ ๋˜๋Š” ๋น„์ ์˜ค์—ผ์›์˜ ์ฒญ์†Œ ์‹œ๊ธฐ๊ฐ€ ์‚ฐ์ •๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์ €๋ฅ˜์กฐ์˜ ๊ฒฝ์šฐ MOUSE ํšŒ๊ท€ ๋ถ„์„์„ ํ†ตํ•ด ๋‚ด๋ถ€์— ์ถ•์ ๋œ ๋น„์ ์˜ค์—ผ์› ์ œ๊ฑฐ ์‹œ๊ธฐ๋ฅผ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ๋น—๋ฌผ ์ €๋ฅ˜์กฐ ๋‚ด๋ถ€ ๊ด€๋ฆฌ๋Š” ํฌ๊ฒŒ ์ฃผ์˜๋‹จ๊ณ„, ์ผ๋ฐ˜๋‹จ๊ณ„, ์•ˆ์ „๋‹จ๊ณ„๋กœ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด๋ฅผ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ์ฃผ์˜๋‹จ๊ณ„์— ํ•ด๋‹นํ•˜๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด๋Š” 9๊ฐœ, ์ผ๋ฐ˜๋‹จ๊ณ„์— ํ•ด๋‹นํ•˜๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด๋Š” 10๊ฐœ, ์•ˆ์ „๋‹จ๊ณ„์— ํ•ด๋‹นํ•˜๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ๊ณ„๋Š” 5๊ฐœ๊ฐ€ ํ•ด๋‹นํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋„์ถœ๋œ ๊ฒฐ๋ก  ๋ฐ ์˜์˜๋Š” ์„ธ ๊ฐ€์ง€๋กœ ์š”์•ฝ๋œ๋‹ค. ์ฒซ์งธ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ผ ๋ฐœ์ƒํ•  ์ˆ˜์žˆ๋Š” ํ™์ˆ˜ ๊ฐ€๋Šฅ์„ฑ์„ 10๋…„ ์ฃผ๊ธฐ๋กœ ๋ถ„์„ํ–ˆ๋‹ค.RCP 8.5 ์‹œ๋‚˜๋ฆฌ์˜ค์™€ RCP 4.5 ์‹œ๋‚˜๋ฆฌ์˜ค ๋ชจ๋‘ 2070๋…„ ์ดํ›„์— ๋นˆ๋ฒˆํ•œ ํ™์ˆ˜์˜ ์ถ”์ด๋ฅผ ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. RCP 8.5 ์‹œ๋‚˜๋ฆฌ์˜ค์˜ 2090๋…„์— ๊ฐ•์ˆ˜๋Ÿ‰์ด ๊ฐ€์žฅ ๋งŽ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. RCP 4.5 ์‹œ๋‚˜๋ฆฌ์˜ค 2100๋…„์˜ ๊ฒฝ์šฐ, ์ตœ๋Œ€ 690 mm, ์‹œ๊ฐ„๋‹น ๊ฐ•์ˆ˜๋Ÿ‰์€ 238 mm๊นŒ์ง€ ๋‚ด๋ฆด ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋‘ ๋ฒˆ์งธ, ๋ณธ ์—ฐ๊ตฌ ๋…ผ๋ฌธ์€ ๊ฐ ๊ธฐ์ˆ ์˜ ์šฉ๋Ÿ‰์„ ์ž์น˜๊ตฌ๋ณ„๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐ€์ •ํ•œ ์„ค์น˜ ๊ทœ์ •์— ๋”ฐ๋ฅด๋ฉด ์„œ์šธ์‹œ ์ „์—ญ์— ์„ค์น˜ํ•  ์ˆ˜ ์žˆ๋Š” ๋น—๋ฌผ ์ €๋ฅ˜์กฐ์˜ ๋ถ€ํ”ผ๋Š” 776,588 mยณ, ํˆฌ์ˆ˜์„ฑ ํฌ์žฅ์€ 89,049 mยณ, ์ƒํƒœ์ˆ˜๋กœ๋Š” 81,986 mยณ์ด๋‹ค. ๊ฐ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด๊ฐ€ ๋‘ ๊ฐ€์ง€ ๊ธฐ์ˆ ๋งŒ์„ ์ ์šฉํ•˜์˜€์„ ๋•Œ ํšจ์œจ์ ์ธ ์กฐํ•ฉ์„ ์ œ์•ˆํ•œ ๊ฒƒ์€ ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ€์ง€๋Š” ์ค‘์š”ํ•œ ์˜์˜์ž…๋‹ˆ๋‹ค. ์…‹์งธ, ๊ฐ ์žฌํ•ด์ €๊ฐ ๊ธฐ์ˆ ๋กœ ์ €๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์ถœ๋Ÿ‰์„ ์ •๋Ÿ‰ํ™”ํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ง€์—ญ ์ฐจ์›์˜ ๋ถ„์‚ฐ์  ํ˜•ํƒœ์˜ ํ™์ˆ˜๊ฐ€ ๋” ์ž์ฃผ ๋ฐœ์ƒํ•˜๊ณ , ์žฌ๋‚œ ์ €๊ฐ ๊ธฐ์ˆ ์˜ ์ •๋Ÿ‰์  ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค๋Š”๋ฐ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋Š” ๋„ค ๋ถ€๋ถ„์œผ๋กœ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•œ๊ณ„๋Š” ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์ด๋‹ค. ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์ด๋‚˜ ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๋ณ€ํ™”๋Š” ๊ฐ•์ˆ˜๋Ÿ‰ ๊ฐ’์„ ํฌ๊ฒŒ ๋ณ€๊ฒฝํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์˜ค๋ฅ˜๊ฐ€ ์ ์€ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ํ–ฅํ›„ ์—ฐ๊ตฌ๊ฐ€ ๋” ์ค‘์š”ํ•œ ์—ฐ๊ตฌ๋กœ ๋ฐœ์ „ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์ตœ๊ทผ ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰ ์ค‘์ด๊ธฐ ๋•Œ๋ฌธ์—, ์ฒซ ๋ฒˆ์งธ ํ•œ๊ณ„์ ์„ ๋ณด์™„ํ•œ ํ›„์†์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋  ๊ฒƒ์ด๋ผ ํŒ๋‹จ๋œ๋‹ค. ๋‘ ๋ฒˆ์งธ ํ•œ๊ณ„๋Š” RCP 4.5 / RCP 8.5 ์‹œ๋‚˜๋ฆฌ์˜ค๊ฐ€ 10๋…„์˜ ๋นˆ๋„๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์„ธ ๋ฒˆ์งธ ํ•œ๊ณ„์ ์€ ์‚ฌํšŒ ๋ณ€ํ™” ์š”์ธ์ด ๋ฐ˜์˜๋˜์ง€ ์•Š์•˜๋‹ค๋Š” ๊ฒƒ ์ž…๋‹ˆ๋‹ค. ๋„ค ๋ฒˆ์งธ๋Š” ๊ฒ€์ฆ์˜ ํ•œ๊ณ„์ž…๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œ์šธ์‹œ์˜ ์œ ์ถœ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฐ์ˆ  ๋ฐฉ์ •์‹๊ณผ GIS Arc-hydro๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค.์ถ”ํ›„ SWMM ๋“ฑ์˜ ํ™์ˆ˜ ํ•ด์„ ํ”„๋กœ๊ทธ๋žจ์„ ํ™œ์šฉํ•˜์—ฌ ์ถ”๊ฐ€์ ์ธ ๊ฒ€์ฆ์ด ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ์œ„์˜ ๋„ค ๊ฐ€์ง€ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ํ›„์† ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์–ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ํŠนํžˆ ์ฒซ ๋ฒˆ์งธ ๋ฌธ์ œ์ ์ธ ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๋ถˆํ™•์‹ค์„ฑ ํ•œ๊ณ„์ ์€ ํ›„์†๋˜๋Š” ์„ธ ๊ฐ€์ง€ ํ•œ๊ณ„์ ์„ ๋ฐœ์ƒ์‹œํ‚ค๊ธฐ์—, ํ•„์ˆ˜์ ์œผ๋กœ ํ•ด๊ฒฐ๋˜์–ด์•ผ ํ•œ๋‹ค.The social and economic damage caused by climate change has increased rapidly over the last several decades, with increasing instances of heat waves, floods, and extreme rainfall. Of these, the damage caused by extreme rainfall is still ongoing, and more extreme rainfall is expected in Korean Peninsula in the future. There was up to 110.5 mm/hr of rainfall in Seoul, which caused 69 casualties and approximately USD 27.6 million in economic damage. Most of the causes of flooding in modern cities include a sharp increase in non-permeable packaging surfaces and a lack of water circulation facilities. According to climate change scenarios provided by the Korea Meteorological Administration, the average rainfall in cities over the next 100 years is expected to decrease. However, it is predicted that future instances of heavy rain will occur in the future, causing large amounts of local damage. If the current state of infrastructure is not equipped with repair or mitigating technologies, the damage will be significant. This study was conducted based on the following three objectives. First, to quantitatively analyze urban flood damage over the next 80 years (2020-2100) that could be caused by the climate change scenario provided by the Korea Meteorological Administration. Second, this study was selected disaster mitigation facilities and analyzed their impact on disaster mitigation. It also arranges and designs facilities based on an evidence-based planning. Sustainable facilities were selected by introducing eco-friendly facilities for future generations as mitigate technologies. Third, through the development of the HCFD (Hazard Capacity Factor Design) model, the capacity and performance of the facilities that may change in the future were analyzed. HCFD model was used to consider ways to maintain mitigating technologies. In order to achieve these goals, a total of three mitigating technologies have been installed. This includes water tanks, permeable pavement, and ecological waterways. In the case of water tanks, the capacity was calculated by referring to the statutes designated by the Ministry of Environment. Also, an Arc-GIS ArcHydro Plug-in was used to calculate the scale of each technology and watershed was analyzed. The precipitation provided by the climate change scenario was analyzed on an hourly basis to determine the extent to which watershed affects it, and the Huff dimensionless curve was used for this purpose. These three mitigating technologies can contribute to flooding by increasing the storage capacity of rainwater. This study suggests that all floods can be reduced by RCP8.5 in 2050, 2060. Although there will be run-off after 2070, it is analyzed that technology will significantly reduce the volume of the flood. It is deemed that a one-year analysis should be conducted in consideration of the maintenance aspects in the future. Furthermore, removal timing of the non-point source pollutant was calculated. In the case of water tanks, the amount of non-point source pollutant accumulated inside and the removal timing were calculated through MOUSE regression analysis. Internal management of water tank is classified into caution stage, general stage and safe stage. There were nine local governments that corresponded to the caution stage, ten local governments of general stage and five local governments of safe stage. There are three main conclusions drawn from the results of this study. First is that the possibility of flooding that could occur according to climate change scenarios was analyzed at a 10-year frequency. Both the RCP 8.5 scenario and RCP 4.5 scenario showed frequent flooding after 2070. For the RCP 8.5 scenario, it is predicted that the year 2090 has the highest amount of precipitation. However, for RCP 4.5 scenario 2100, the maximum daily rainfall is approximately 690 mm, with hourly precipitation of 238 mm. The second is that capacity of each technology was analyzed. According to the installation rules assumed in this study, the volume of water tanks that can be installed throughout the Seoul Metropolitan Government is 776,588 mยณ, permeable pavement is 89,049 mยณ, ecological waterway is 81,986 mยณ. It is siginificant that each local government has suggested an efficient combination of two technologies. Third, the amount of runoff that can be reduced by each mitigating technology was quantified. This study has identified that flooding at the local level will be more frequent and is meaningful in analyzing the quantitative effects of disaster mitigation technologies. Besides, when each local government installed flood mitigation technology in the future, quantification data would be provided to ensure optimized decision making for each situation. The limitations of this study can be diagnosed by dividing them into four parts. The first limitation is uncertainty about climate change scenarios. Since changes in carbon emissions or scenarios can significantly change precipitation values, it is believed that future studies will develop into a more significant study if a scenario with fewer errors is used. The second limitation is that the study was conducted at a frequency of 10 years, as both RCP4.5 / RCP8.5 scenarios were analyzed daily. Third, social change factors are not reflected. Fourth is the limitation of verification. In this study, an arithmetic equation and GIS Arc-hydro were used to calculate the run-off in the Seoul Metropolitan Government. The most ideal method to verification is to compare the results with other software. The reliability of this study can be improved by comparing the amount of runoff before applying technologies using programs such as SWMM, STORM, and MUSIC. Future studies, therefore, should be carried out to overcome the above four limitations. In particular, uncertainty problem of the climate change scenario should be solved.Chapter 1. Introduction 1 1.1 Background 1 1.2 Objectives 5 1.3 Scope 6 1.4 Definition of Floods 8 1.5 Vulnerability 11 Chapter 2. Literature Review 14 2.1 Overview 14 2.2 Policy Review 19 2.3 Types of Defense Technologies 21 2.4 Types of Analysis Programs 33 2.5 Target Site 37 2.6 Climate Change Scenarios 38 Chapter 3. Methodology 41 3.1 Hydrologic Analysis 41 3.2 Application of Mitigation Technology and Estimation of flood damage 46 3.3 Calculation of Current Rainfall Capacity and Run-off 48 3.4 Estimation of Hourly Precipitation in Climate Change Scenarios (RCP 8.5/RCP 4.5) using the Huff curve 51 3.5 The Concept of HCFD (Hazard Capacity Factor Design) Model for observing Future Ability Changes of Facilities 53 Chapter 4. Results 56 4.1 Site Analysis 56 4.2 The 10-year frequency flood damage analysis 65 4.3 Variation of the flooded area after application of disaster mitigating technology 71 4.4 Amount of non-point pollutant deposits in the water tank and maintenance time using the MOUSE regression equation 86 Chapter 5. Summary and Conclusions 94Maste

    Losses Associated with Secondary Effects in Earthquakes

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    The number of earthquakes with high damage and high losses has been limited to around 100 events since 1900. Looking at historical losses from 1900 onward, we see that around 100 key earthquakes (or around 1% of damaging earthquakes) have caused around 93% of fatalities globally. What is indeed interesting about this statistic is that within these events, secondary effects have played a major role, causing around 40% of economic losses and fatalities as compared to shaking effects. Disaggregation of secondary effect economic losses and fatalities demonstrating the relative influence of historical losses from direct earthquake shaking in comparison to tsunami, fire, landslides, liquefaction, fault rupture, and other type losses is important if we are to understand the key causes post-earthquake. The trends and major event impacts of secondary effects are explored in terms of their historic impact as well as looking to improved ways to disaggregate them through two case studies of the Tohoku 2011 event for earthquake, tsunami, liquefaction, fire, and the nuclear impact; as well as the Chilean 1960 earthquake and tsunami event

    An Approach to Developing a Spatio-Temporal Composite Measure of Climate Change-Related Human Health Impacts in Urban Environments

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    Introduction: Rapid population growth along with an increase in the frequency and intensity of climate change-related impacts in costal urban environments emphasize the need for the development of new tools to help disaster planners and policy makers select and prioritize mitigation and adaptation measures. Using the concept of the resilience of a community, which is a measure of how rapidly the community can recover to its previous level of functionality following a disruptive event is still a relatively new concept for many engineers, planners and policy makers, but is becoming recognized as an increasingly important and some would argue, essential component for the development and subsequent assessment of adaptation plans being considered for communities at risk of climate change-related events. The holistic approach which is the cornerstone of resilience is designed to integrate physical, economic, health, social and organizational impacts of climate change in urban environments. This research presents a methodology for the development of a quantitative spatial and temporal composite measure for assessing climate change-related health impacts in urban environments. Methods: The proposed method is capable of considering spatial and temporal data from multiple inputs, relating to both physical and social parameters. This approach uses inputs such as the total population density and densities of various demographics, burden of diseases conditions, flood inundation mapping, and land use change for both historical and current conditions. The research has demonstrated that the methodology presented generates sufficiently accurate information to be useful for planning adaptive strategies. To assemble all inputs into a single measure of health impacts, a weighting system was assigned to apply various priorities to the spatio-temporal data sources. Weights may be varied to assess how they impact the final results. Finally, using spatio-temporal extrapolation methods the future behavior of the same key spatial variables can be projected. Although this method was developed for application to any coastal mega-city, this thesis demonstrates the results obtained for Metro Vancouver, British Columbia, Canada. The data was collected for the years 1981, 1986, 1991, 1996, 2001, 2006 and 2011, as information was readily available for these years. Fine resolution spatial data for these years was used in order to give a dynamic simulation of possible health impacts for future projections. Linear and auto-regressive spatio-temporal extrapolations were used for projecting a 2050โ€™s Metro Vancouver health impact map (HIM). Conclusion: Results of this work show that the approach provides a more fully integrated view of the resilience of the city which incorporates aspects of population health. The approach would be useful in the development of more targeted adaptation and risk reduction strategies at a local level. In addition, this methodology can be used to generate inputs for further resilience simulations. The overall value of this approach is that it allows for a more integrated assessment of the city vulnerability and could lead to more effective adaptive strategies
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