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Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model

Author

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  • P. Shirisha

    (NIT Warangal)

  • K. Venkata Reddy

    (NIT Warangal)

  • Deva Pratap

    (NIT Warangal)

Abstract

Watershed is the basic unit for studying different hydrologic processes. Flow forecasting in a watershed is dependent upon the rainfall. The effect of erroneous rainfall prediction is a source of uncertainty in flow forecasting. In this study, a model is proposed to improve the flow forecasting on real-time basis. The proposed model has three components (1) Adaptive Grey Rainfall Forecasting Model, (2) Rainfall-Runoff Model and (3) Fuzzy Updating Model. The proposed forecasting model is tested for lead periods of 1 to 3 h with hourly rainfall and discharge data. In this study, four different cases using combination of three models are discussed and the results are compared. The study has been carried out on three Indian watersheds namely Banha, Harsul and Khadakohol. The performance of the model is measured using Nash Sutcliffe Efficiency (E), Correlation Coefficient (r), Error of Peak Discharge (EQpeak) and Error of Time to Peak (ETpeak). It is observed that the case with integration of all three models performed good with a forecasting efficiency of E = 0.950, 0.861, 0.564; and r = 0.991, 0.972, 0.897 for lead-1, 2, 3 respectively for Banha watershed. For Harsul watershed, E = 0.898, 0.704, 0.367; and r = 0.985, 0.949, 0.834 for lead-1, 2, 3 respectively. For Khadakohol watershed, E = 0.968, 0.932, 0.787; and r = 0.994, 0.987, 0.951 for lead-1, 2, 3 respectively. EQpeak is less than 10% for lead-1 for most of the events and increased slightly for lead-2 and lead-3. ETpeak is 0 h for all lead periods of the three watersheds. The proposed model is useful for farmers in planning and monitoring of water resources for crop management and helps in taking necessary actions during heavy rains and floods.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:14:d:10.1007_s11269-019-02398-2
    DOI: 10.1007/s11269-019-02398-2
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    References listed on IDEAS

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    1. Junhong Zhang & Lu Chen & Vijay Singh & Hongwen Cao & Dangwei Wang, 2015. "Determination of the distribution of flood forecasting error," 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. 75(2), pages 1389-1402, January.
    2. Junhong Zhang & Lu Chen & Vijay Singh & Wenhong Cao & Dangwei Wang, 2015. "Erratum to: Determination of the distribution of flood forecasting error," 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. 75(2), pages 2065-2065, January.
    3. Muhammad Ajmal & Muhammad Waseem & Jae-Hyun Ahn & Tae-Woong Kim, 2015. "Improved Runoff Estimation Using Event-Based Rainfall-Runoff Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1995-2010, April.
    4. 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.
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    Cited by:

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    2. 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.

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