IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i7d10.1007_s11269-021-02836-0.html
   My bibliography  Save this article

Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps

Author

Listed:
  • Jiun-Huei Jang

    (National Cheng Kung University)

  • Petr Vohnicky

    (National Cheng Kung University)

  • Yen-Lien Kuo

    (National Cheng Kung University)

Abstract

Flood risk maps are important references for disaster prevention and emergency response. The accuracy of risk maps is greatly affected by the resolutions of hazard and vulnerability maps. To determine the impact of map resolutions on flood risk analysis, a total of 12 risk maps were generated for Shanhua District, Taiwan, via the integration of hazard and vulnerability maps under two resolutions and three return periods. The hazard, vulnerability, and risk maps were classified into five levels according to flood depth, socio-economic indicators, and their products, respectively. The results show that the downscaling of hazard maps greatly increases the hit rate by 28% and decreases the false alarm rate by 53% in the flood risk analyses of households. In contrast, the downscaling of vulnerability maps only slightly increases the hit rate without an obvious decrease in the false alarm rate. To improve flood risk analysis under time and budget limitations, numerical downscaling of hazard maps should be given higher priority because it reduces the structural errors in hydraulic simulations that cannot be compensated for by the statistical downscaling of vulnerability maps.

Suggested Citation

  • Jiun-Huei Jang & Petr Vohnicky & Yen-Lien Kuo, 2021. "Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2215-2230, May.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:7:d:10.1007_s11269-021-02836-0
    DOI: 10.1007/s11269-021-02836-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02836-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-021-02836-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sofia Melo Vasconcellos & Masato Kobiyama & Fernanda Stachowski Dagostin & Claudia Weber Corseuil & Vinicius Santana Castiglio, 2021. "Flood Hazard Mapping in Alluvial Fans with Computational Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1463-1478, March.
    2. Paul Hudson, 2018. "A comparison of definitions of affordability for flood risk adaption measures: a case study of current and future risk-based flood insurance premiums in Europe," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(7), pages 1019-1038, October.
    3. Jin-Cheng Fu & Hsiao-Yun Huang & Jiun-Huei Jang & Pei-Hsun Huang, 2019. "River Stage Forecasting Using Multiple Additive Regression Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4491-4507, October.
    4. Boyu Feng & Jinfei Wang & Ying Zhang & Brent Hall & Chuiqing Zeng, 2020. "Urban flood hazard mapping using a hydraulic–GIS combined model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(3), pages 1089-1104, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ebrahim Karimi Sangchini & Amin Salehpour Jam & Jamal Mosaffaie, 2022. "Flood risk management in Khorramabad watershed using the DPSIR framework," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3101-3121, December.
    2. Karim Solaimani & Fatemeh Shokrian & Shadman Darvishi, 2023. "An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 403-425, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ana C. Cebrián & Ricardo Salillas, 2021. "Forecasting High-Frequency River Level Series Using Double Switching Regression with ARMA Errors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 299-313, January.
    2. Karim Solaimani & Fatemeh Shokrian & Shadman Darvishi, 2023. "An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 403-425, January.
    3. Saeed Azimi & Mehdi Azhdary Moghaddam, 2020. "Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1369-1405, March.
    4. Jiun-Huei Jang & Kun-Fang Lee & Jin-Cheng Fu, 2022. "Improving River-Stage Forecasting Using Hybrid Models Based on the Combination of Multiple Additive Regression Trees and Runge–Kutta Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1123-1140, February.
    5. Lloyd Ling & Zulkifli Yusop & Joan Lucille Ling, 2021. "Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia," Mathematics, MDPI, vol. 9(8), pages 1-24, April.
    6. Siva R Venna & Satya Katragadda & Vijay Raghavan & Raju Gottumukkala, 2021. "River Stage Forecasting using Enhanced Partial Correlation Graph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4111-4126, September.
    7. M. Karamouz & F. Fooladi Mahani, 2021. "DEM Uncertainty Based Coastal Flood Inundation Modeling Considering Water Quality Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3083-3103, August.
    8. Minh Pham Quang & Krti Tallam, 2022. "Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    9. Bikram Manandhar & Shenghui Cui & Lihong Wang & Sabita Shrestha, 2023. "Urban Flood Hazard Assessment and Management Practices in South Asia: A Review," Land, MDPI, vol. 12(3), pages 1-29, March.
    10. Max Tesselaar & W. J. Wouter Botzen & Toon Haer & Paul Hudson & Timothy Tiggeloven & Jeroen C. J. H. Aerts, 2020. "Regional Inequalities in Flood Insurance Affordability and Uptake under Climate Change," Sustainability, MDPI, vol. 12(20), pages 1-30, October.
    11. Devendra Kumar Jain & Asif Chida & R. D. Pathak & Raghbendra Jha & Stephanie Russell, 2022. "Climate risk insurance in Pacific Small Island Developing States: possibilities, challenges and vulnerabilities—a comprehensive review," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(3), pages 1-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:35:y:2021:i:7:d:10.1007_s11269-021-02836-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.