Volume 11 Issue 6
Dec.  2021
Turn off MathJax
Article Contents
Kensuke Takenouchi, Katsuya Yamori. Synergistic Integration of Detailed Meteorological and Community Information for Evacuation from Weather-Related Disasters: Proposal of a “Disaster Response Switch”[J]. International Journal of Disaster Risk Science, 2020, 11(6): 762-775. doi: 10.1007/s13753-020-00317-3
Citation: Kensuke Takenouchi, Katsuya Yamori. Synergistic Integration of Detailed Meteorological and Community Information for Evacuation from Weather-Related Disasters: Proposal of a “Disaster Response Switch”[J]. International Journal of Disaster Risk Science, 2020, 11(6): 762-775. doi: 10.1007/s13753-020-00317-3

Synergistic Integration of Detailed Meteorological and Community Information for Evacuation from Weather-Related Disasters: Proposal of a “Disaster Response Switch”

doi: 10.1007/s13753-020-00317-3
  • Available Online: 2021-12-25
  • Publish Date: 2021-12-25
  • Meteorological information used for disaster prevention has developed rapidly in terms of both type and specificity. The latest forecasting models can predict weather with very high resolutions that can characterize disaster risk at the local level. However, this development can lead to an overdependency on the information and a wait-and-see attitude by the public. At the same time, residents share and use various types of information for disaster response, such as local conditions, in addition to official disaster information. Our research in Japan verified the practicality and efficiency of synergistically integrating these types of information by examining actual evacuation cases. The current numerical forecasting models sufficiently identify locality from the viewpoint of various administrative scales such as prefectures, municipalities, and school districts, but the improvements to these models have failed to improve residents' judgment in successful evacuation cases. We therefore analyzed the relationship between meteorological information and residents' disaster response and confirmed that they were strongly correlated and were contributing factors in preventing disasters. We revealed differences between a community's disaster prevention culture and the disaster information provided. This led us to propose a new concept in community disaster prevention that we call the “disaster response switch,” which can serve as a data-driven risk management tool for communities when used in combination with advanced meteorological disaster information.
  • loading
  • Alcántara-Ayala, I., and A.R. Moreno. 2016. Landslide risk perception and communication for disaster risk management in mountain areas of developing countries: A Mexican foretaste. Journal of Mountain Science 13(12): 2079–2093.
    Asakura, Y., M. Hangyo, and M. Komachi. 2016. Disaster analysis using user-generated weather report. In Proceedings of the 2nd Workshop on Noisy User-generated Text, 11–16 December 2016, Osaka, Japan, 24–32.
    Aulov, O., and M. Halem. 2012. Human sensor networks for improved modeling of natural disasters. Proceedings of the IEEE 100(10): 2812–2823.
    Buckland, J., and M. Rahman. 1999. Community-based disaster management during the1997 Red River flood in Canada. Disasters 23(2): 174–191.
    Cabinet Office. 2005. Cases of countermeasures against heavy rainfall and storm using community cooperation. http://www.bousai.go.jp/fusuigai/sonota/index.html. Accessed 29 Mar 2019 (in Japanese).
    Castro, C., J. Sarmiento, R. Edwards, G. Hoberman, and K. Wyndham. 2017. Disaster risk perception in urban contexts and for people with disabilities: Case study on the city of Iquique (Chile). Natural Hazards 86(1): 411–436.
    Chen, L., Y. Liu, and K. Chan. 2006. Integrated community-based disaster management program in Taiwan: A case study of Shang-An village. Natural Hazards 37(1–2): 209–223.
    Chou, J., and J. Wu. 2014. Success factors of enhanced disaster resilience in urban community. Natural Hazards 74(2): 661–686.
    Dudhia, J. 2014. A history of mesoscale model development. Asia-Pacific Journal of Atmospheric Sciences 50(1): 121–131.
    Engel, K., G. Frerks, L. Velotti, J. Warner, and B. Weijs. 2014. Flood disaster subcultures in The Netherlands: The parishes of Borgharen and Itteren. Natural Hazards 73(2): 859–882.
    Fang, S., L. Xu, Y. Zhu, and Y. Liu. 2015. An integrated information system for snowmelt flood early-warning based on internet of things. Information Systems Frontiers 17(2): 321–335.
    Fukunaga, H., M. Masaki, and K. Kono. 2014. The first “emergency warning” issued for heavy rain caused by typhoon: How emergency information was delivered. The NHK Monthly Report on Broadcast Research 64(1): 2–29 (in Japanese).
    GDPFS (Global Data-processing and Forecasting System). 2015. 2015 GDPFS/NWP reports. WMO technical progress report on the global data-processing and forecasting system and numerical weather prediction research. http://www.wmo.int/pages/prog/www/DPFS/ProgressReports/2015/GDPFS-NWP-2015.html. Accessed 29 Mar 2019.
    Geospatial Information Authority of Japan. 2014. FY2014 area survey of prefectures and municipalities in Japan. Tsukuba, Japan: Geospatial Information Authority of Japan (in Japanese).
    Geospatial Information Authority of Japan. 2018. GSI tiles (seamless photo maps) with zoom level 12. https://cyberjapandata.gsi.go.jp/xyz/seamlessphoto/{z}/{x}/{y}.jpg. Accessed 29 Mar 2019 (in Japanese).
    Gierlach, E., B.E. Belsher, and L.E. Beutler. 2010. Cross-cultural differences in risk perceptions of disasters. Risk Analysis 30(10): 1539–1549.
    Gojmerac, I., A. Preinerstorfer, C. Ruggenthaler, C. Schuster, A. Almer, R. Stocker, and V. Heussler. 2016. Public warning and alert system for Austria. In Proceedings of the 2016 3rd International Conference on Information and Communication Technologies for Disaster Management, 13–15 December 2016, Vienna, Austria, 1–7.
    Ho, M., D. Shaw, S. Lin, and Y. Chiu. 2008. How do disaster characteristics influence risk perception? Risk Analysis 28(3): 635–643.
    IPCC (Intergovernmental Panel on Climate Change). 2012. Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and Ⅱ of the Intergovernmental Panel on Climate Change, ed. CB. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, et al. Cambridge and New York: Cambridge University Press.
    Isouchi, C. 2018. The heavy rainfall in west Japan in 2018 and community disaster prevention power—Case of Takahama district in Matsuyama, Ehime. Presented at the 2018 symposium of “The heavy rainfall in west Japan in 2018, community disaster prevention power and disaster recovery support”, jointly hosted by the Japan Society of Community Disaster Management Plan and the College of Risk Management at Nihon University (in Japanese).
    Japan Meteorological Agency. 2017. Joint WMO technical progress report on the global data processing and forecasting system and numerical weather prediction research activities for 2017. http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/report/2017_Japan.pdf. Accessed 29 Mar 2019.
    Japan Meteorological Agency. 2018a. The brochure of the Japan Meteorological Agency. https://www.jma.go.jp/jma/en/Activities/brochure201803.pdf. Accessed 29 Mar 2019 (in Japanese).
    Japan Meteorological Agency. 2018b. Survey report on the utility of meteorological information. http://www.jma.go.jp/jma/kishou/hyouka/manzokudo/29manzokudo/29manzokudo_data.pdf. Accessed 29 Mar 2019 (in Japanese).
    Japan Meteorological Agency. 2018c. Meteorological reports on disasters: The heavy rainfall in northern Kyushu in July, 2017 and the continued rainfall from stationary seasonal rain front from 7 June to 27 July in 2017. http://www.jma.go.jp/jma/kishou/books/saigaiji/saigaiji_2017/saigaiji_201801.pdf. Accessed 29 Mar 2019 (in Japanese).
    Japan Meteorological Agency. 2019. Meteorological reports on disasters: The heavy rainfall in July 2018 and the continued rainfall from stationary seasonal rain front from 20 May to 10 July in 2018. http://www.jma.go.jp/jma/kishou/books/saigaiji/saigaiji_2018/saigaiji_201902.pdf. Accessed 29 Mar 2019 (in Japanese).
    Japan Meteorological Agency. 2020. 60 year history of numerical weather prediction. https://www.jma.go.jp/jma/kishou/know/whitep/doc_1-3-2-1/all.pdf. Accessed 29 Mar 2019 (in Japanese).
    Kakimoto, R., and T. Fujimi. 2013. Questionnaire survey of residents in the disaster area. Journal of Natural Disaster Science 32(1): 37–42 (in Japanese).
    Katada, T., and M. Kanai. 2010. Design of communication to establish independent evaluation rules for slope disasters by residents. Journal of the Japan Society of Civil Engineers F5–1(1): 106–121 (in Japanese).
    Katada, T., Y. Oikawa, and M. Kodama. 2005. Study on the determination process of human behavior in flood disasters. Journal of the Japan Society of Civil Engineers 786/IV–67: 77–88 (in Japanese).
    Katada, T., Y. Oikawa, and Y. Shimizu. 1998. A study on the decision making process of the evacuation activities during a flood. Advances in River Engineering 4(1): 291–296 (in Japanese).
    Kayano, T. 2001. Cyclone shelters and Mizuya: Toward a third culture of disaster management. Hokusei Review of the School of Economics at Hokusei Gakuen University 39: 39–52 (in Japanese).
    Kurokawa, M., and H. Seiwa. 1986. Effects of residents’ place identity on their anxiety about a flood and coping strategies. The Japanese Journal of Psychology 57(2): 91–94 (in Japanese).
    Kaziya, A., K. Akaishi, T. Yokota, F. Kusano, N. Sekiya, and Y. Takahashi. 2018. Reduction of evacuation rate after the Izu Oshima sediment disaster in 2013 and examination of its cause and measures based on a questionnaire survey. Disaster Information 16(1): 37–47 (in Japanese).
    Kobayashi H., and A. Tanaka. 2017. Effect of disaster knowledge structure on residents’ behavioral intention against disaster – Case of Kanto Tohoku heavy rainfall disaster 2015. Disaster Information 15(2): 137–147 (in Japanese).
    Kodama, M., M. Kanai, T. Katada, and M. Hatano. 2014. Study on evacuation characteristics based on a survey of residents’ decision-making with a disaster scenario role-playing system. Disaster Information 12(1): 64–75 (in Japanese).
    Kusano F., T. Yokota, K. Akaishi, I. Matsuo, and A. Nimoto. 2015. Survey of evacuation intentions during a heavy rainfall disaster in Kiho, Mie prefecture. Disaster Information 13(1): 96–100 (in Japanese).
    McLennan, B.J. 2018. Conditions for effective coproduction in community-led disaster risk management. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations 31(2): 316–332.
    Ministry of Education, Culture, Sports, Science and Technology. 2015. FY2015 school basic survey. Tokyo: Ministry of Education, Culture, Sports, Science and Technology (in Japanese).
    Ministry of Internal Affairs and Communications. 2013. FY2013 survey of administrative affairs on community associations. Tokyo: Ministry of Internal Affairs and Communications (in Japanese).
    Ministry of Land, Infrastructure, Transport and Tourism. 2018. Summary of river project 2018. http://www.mlit.go.jp/river/pamphlet_jirei/kasen/gaiyou/panf/pdf/c1.pdf. Accessed 29 Mar 2019 (in Japanese).
    NHK (Japan Broadcasting Corporation). 2018. Morning close-up (Kesa no kurōzuappu), broadcast on 1 August 2018. https://www.nhk.or.jp/ohayou/digest/2018/08/0801.html. Accessed 29 Mar 2019 (in Japanese).
    Oikawa, Y., N. Kodama, and T. Katada. 2005. Study on the determination process of human behavior in flood disasters. Journal of the Japan Society of Civil Engineers 786/IV–67: 89–101 (in Japanese).
    Oikawa, Y., and T. Katada. 2016. Effects of a repetitive false evacuation advisory on residents’ behavior. Disaster Information 14(1): 93–104 (in Japanese).
    Pearce, L. 2003. Disaster management and community planning, and public participation: How to achieve sustainable hazard mitigation. Natural Hazards 28(2): 211–228.
    Qadir, J., A. Ali, R. Rasool, A. Zwitter, A. Sathiaseelan, and J. Crowcroft. 2016. Crisis analytics: Big data-driven crisis response. Journal of International Humanitarian Action 1(1): 1–21.
    Saito, K., J. Ishida, K. Aranami, T. Hara, T. Segawa, M. Narita, and Y. Honda. 2007. Nonhydrostatic atmospheric models and operational development at JMA. Journal of the Meteorological Society of Japan 85B: 271–304.
    Sayo-cho Tropical Storm Etau Disaster Investigation Committee. 2010. Investigation report of the tropical storm Etau disaster in 2009. Sayo, Japan: Sayo-cho Tropical Storm Etau Disaster Investigation Committee (in Japanese).
    Siebeneck, L.K., and T.J. Cova. 2012. Spatial and temporal variation in evacuee risk perception throughout the evacuation and return-entry process. Risk Analysis 32(9): 1468–1480.
    Su, Y., F. Zhao, and L. Tan. 2015. Whether a large disaster could change public concern and risk perception: A case study of the 7/21 extraordinary rainstorm disaster in Beijing in 2012. Natural Hazards 78(1): 555–567.
    Takahagi, K., T. Ishida, A. Sakuraba, K. Sugita, N. Uchida, and Y. Shibata. 2015. Proposal of the disaster information transmission common infrastructure system intended to rapid sharing of information in a time of mega disaster. In Proceedings of the 18th International Conference on Network-Based Information Systems, 2–4 September 2015, Taipei, China, 505–510.
    Takahashi, K. 2014. Disaster mitigation lore “distribution of manjū (bean cakes). In Disaster mitigation lores—Community wisdoms to save lives, ed. K. Takahashi, 83–110. Tokyo: Kokonshoin Press (in Japanese).
    Takenouchi, K., C. Nakanishi, K. Yamori, M. Sawada, K. Takeuchi, and H. Fujiwara. 2015. A trial of collaboration on local weather information at Nakajima school district in Ise. Journal of Natural Disaster Science 34(3): 243–258 (in Japanese).
    Takenouchi, K., Y. Kano, and K. Yamori. 2018. Role of community judgment standards in northern Kyushu heavy rainfall in 2017—Function of disaster response switch during disasters. Journal of the Japan Society of Civil Engineers F6–74(2): I_31–I_39 (in Japanese).
    Tanaka, K., K. Umeno, M. Ikeda, and M. Hori. 2015. Psychological investigation on the perceived danger of unsafe evacuation behavior in knowledge-to-action gap. Cognitive Studies 22(3): 356–367 (in Japanese).
    Usuda, Y., M. Hanashima, R. Sato, and H. Sano. 2017. Effects and issues of information sharing system for disaster response. Journal of Disaster Research 12(5): 1002–1014.
    Ushiyama, M. 2014. An analysis of residents’ understanding of heavy rainfall emergency warnings in a flood inundation area. Special Issue of Journal of Natural Disaster Science 33(1): 75–85 (in Japanese).
    Wei, J., F. Wang, and M.K. Lindell. 2016. The evolution of stakeholders’ perceptions of disaster: A model of information flow. Journal of the Association for Information Science and Technology 67(2): 441–453.
    Working group on evacuations from water-related disasters, landslides, and debris flows caused by the heavy rainfall in July 2018. 2018. Issues and realities of the heavy rainfall in July 2018. The third reference document in “Report on evacuations from water-related disasters, landslides, and debris flows caused by the heavy rainfall in July 2018″. http://www.bousai.go.jp/fusuigai/suigai_dosyaworking/pdf/sankosiryo3.pdf. Accessed 29 Mar 2019 (in Japanese).
    Xiao, Y., Q. Huang, and K. Wu. 2015. Understanding social media data for disaster management. Natural Hazards 79(3): 1663–1679.
    Yamori, K. 2009. The double bind of disaster information. Disaster Information 7(1): 28–33 (in Japanese).
    Yasumoto, S., M. Ushiyama, and N. Sekiya. 2018. Analysis on evacuation behavior during the typhoon No.1610 disaster in Iwaizumi town. Journal of Natural Disaster Science 37(1): 33–45 (in Japanese).
    Zhang, X., L. Yi, and D. Zhao. 2013. Community-based disaster management: A review of progress in China. Natural Hazards 65(3): 2215–2239.
    Zhu, R., Y. Liu, H. Jiang, and Z. Yin. 2011. Visualization of weather-induced disaster warning information system using Google Earth API based on Mashup. In Proceedings of the 2011 International Conference on Multimedia Technology, 26–28 July 2011, Hangzhou, China, 3789–3793.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (498) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return