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Flood Forecasting via a Combination of Stochastic ARIMA Approach and Deterministic HEC-RAS Modeling

Fang, Yanhui
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449142353

Abstract Details

2015, Doctor of Philosophy (PhD), Ohio University, Civil Engineering (Engineering and Technology).
Most flood forecasting methods at present time are focused on real-time or short-range forecasting using precipitation data and rainfall-runoff modeling approach. While these methods can be beneficial in flood warning and emergency response, they are not suitable for long-term planning in flood risk management. In order to tackle this issue, a new method of flood forecasting was developed and tested in this study. The proposed method consists of two major components. Firstly, the magnitude of annual flood peaks is forecasted and their corresponding water surface levels are estimated. This is implemented by coupling the stochastic ARIMA approach, employed for annual peak flow forecasting, and the deterministic HEC-RAS modeling method for computing water surface profiles. Secondly, the month in which the annual flood peaks would occur is also predicted using the ARIMA approach based on the monthly maximum of daily mean flows. This technique was inspired by the observation that the month when annual peak flow occurs nearly always coincides with the month when the maximum of daily mean flow is the greatest. The proposed methodology was tested using the historical streamflow data of the channelized reach of the Hocking River. The reach was surveyed to obtain the geometric data as well as sediment data necessary to build the HEC-RAS model. The forecast result from the study proved that the ARIMA approach was capable of forecasting both the magnitude and the month of annual flood peaks, and the calibrated HEC-RAS model was validated by existing data. Overall the proposed methodology demonstrated its strength in long-term annual flood forecasting. Outcomes of this study are significant in that it is the first attempt to forecast annual floods in terms of both the magnitude and the month of occurrence. A new technique was also developed and tested to forecast the month of occurrence by constructing a time series of date instead of the conventional flow data. The proposed methodology could potentially benefit long-term planning and flood risk management.
Tiao Chang (Advisor)
Water Resource Management
flood forecasting; long-term; ARIMA; HEC-RAS

Recommended Citations

Citations

  • Fang, Y. (2015). Flood Forecasting via a Combination of Stochastic ARIMA Approach and Deterministic HEC-RAS Modeling [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449142353

    APA Style (7th edition)

  • Fang, Yanhui. Flood Forecasting via a Combination of Stochastic ARIMA Approach and Deterministic HEC-RAS Modeling. 2015. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449142353.

    MLA Style (8th edition)

  • Fang, Yanhui. "Flood Forecasting via a Combination of Stochastic ARIMA Approach and Deterministic HEC-RAS Modeling." Doctoral dissertation, Ohio University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449142353

    Chicago Manual of Style (17th edition)

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© 2015, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.