IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i20p11250-d654528.html
   My bibliography  Save this article

Development of Integrated Flooding Early Warning and Rainfall Runoff Management Platform for Downtown Area of Shanghai

Author

Listed:
  • Zhenbao Shi

    (State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092, China
    Shanghai Bibo Water Design and Research Center, Shanghai 200233, China)

  • Qingran Shen

    (Shanghai Bibo Water Design and Research Center, Shanghai 200233, China)

  • Qiong Tan

    (Shanghai Water Planning and Design Research Institute, Shanghai 200233, China)

  • Tian Li

    (State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092, China)

Abstract

To enhance the capacity of Shanghai’s drainage network to guard against flooding, this study used data obtained from an urban drainage network and spatial geological information to conduct precise analysis on an area of approximately 31 km 2 with various land uses in downtown Shanghai and to establish a two-dimensional model. Based on the two-dimensional model, an integrated urban flooding early warning and rainfall runoff management platform was developed through combining meteorological data and real-time remote sensing data of the drainage network operation. Through precise simulation of the rainstorm runoff process, projection of the scope and magnitude of urban surface runoff hazard impact, issuance of flooding forecasts, and provision of hazard early warning and decision-making support, the developed platform is capable of providing risk assessment of the drainage system and early warning of flooding risk.

Suggested Citation

  • Zhenbao Shi & Qingran Shen & Qiong Tan & Tian Li, 2021. "Development of Integrated Flooding Early Warning and Rainfall Runoff Management Platform for Downtown Area of Shanghai," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11250-:d:654528
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/20/11250/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/20/11250/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Huan Xu & Ying Wang & Xiaoran Fu & Dong Wang & Qinghua Luan, 2023. "Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China," IJERPH, MDPI, vol. 20(5), pages 1-23, March.
    2. Joško Trošelj & Han Soo Lee & Lena Hobohm, 2023. "Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations," Sustainability, MDPI, vol. 15(18), pages 1-33, September.

    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:gam:jsusta:v:13:y:2021:i:20:p:11250-:d:654528. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.