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A real-time flood forecasting system with dual updating of the NWP rainfall and the river flow

Author

Listed:
  • Jia Liu
  • Jianhua Wang
  • Shibing Pan
  • Kewang Tang
  • Chuanzhe Li
  • Dawei Han

Abstract

Numerical weather prediction (NWP) models are gaining more and more attention in providing high-resolution rainfall forecasts for real-time flood forecasting. In this study, the weather research and forecasting (WRF) model is integrated with the probability distribution model (PDM) to make real-time flow forecasts in a small catchment located in Southwest England. In order to improve the accuracy of the NWP rainfall and flow forecasts, dual real-time updating is carried out in the forecasting system through data assimilation. The three-dimensional variational data assimilation technique is coupled with the WRF model to assimilate radar reflectivity and traditional meteorological data; meanwhile, the autoregressive moving average model works with the rainfall–runoff model PDM to assimilate real-time flow observations. Four 24-h storm events with different characteristics of rainfall–runoff responses are selected from the study catchment to test the performance of the constructed forecasting system. The flood forecasting accuracy is found to be largely improved by incorporating the NWP forecasted rainfall when the lead time is beyond the catchment concentration time. The assimilation of radar and meteorological data also shows great advantage in improving the NWP rainfall forecasts. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Jia Liu & Jianhua Wang & Shibing Pan & Kewang Tang & Chuanzhe Li & Dawei Han, 2015. "A real-time flood forecasting system with dual updating of the NWP rainfall and the river flow," 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. 77(2), pages 1161-1182, June.
    • Handle: RePEc:spr:nathaz:v:77:y:2015:i:2:p:1161-1182
    DOI: 10.1007/s11069-015-1643-8
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    References listed on IDEAS

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    1. Dooge, James C. I., 1973. "Linear Theory of Hydrologic Systems," Technical Bulletins 160041, United States Department of Agriculture, Economic Research Service.
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    Cited by:

    1. Y. Umer & V. Jetten & J. Ettema & L. Lombardo, 2022. "Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment," 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. 111(2), pages 1813-1844, March.
    2. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.
    3. P. Shirisha & K. Venkata Reddy & Deva Pratap, 2019. "Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4799-4820, November.
    4. Qinge Peng & Xingnian Liu & Er Huang & Kejun Yang, 2019. "Experimental study on the influence of vegetation on the slope flow concentration time," 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. 98(2), pages 751-763, September.

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