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Discharge Rating Curve Extension – A New Approach

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  • Chandrasekaran Sivapragasam
  • Nitin Muttil

Abstract

It is often necessary to have stage discharge curve extended (extrapolated) beyond the highest (and sometimes lowest) measured discharges, for river forecasting, flood control and water supply for agricultural/industrial uses. During the floods or high stages, the river may become inaccessible for discharge measurement. Rating curves are usually extended using log–log axes, which are reported to have a number of problems. This paper suggests the use of Support Vector Machine (SVM) in the extrapolation of rating curves, which works on the principle of linear regression on a higher dimensional feature space. SVM is applied to extend the rating curves developed at three gauging stations in Washington, namely Chehalis River at Dryad and Morse Creek at Four Seasons Ranch (for extension of high stages) and Bear Branch near Naselle (for extension of low stages). The results obtained are significantly better as compared with widely used logarithmic method and higher order polynomial fitting method. A comparison of SVM results with ANN (Artificial Neural Network) indicates that SVM is better suited for extrapolation. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Chandrasekaran Sivapragasam & Nitin Muttil, 2005. "Discharge Rating Curve Extension – A New Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(5), pages 505-520, October.
    • Handle: RePEc:spr:waterr:v:19:y:2005:i:5:p:505-520
    DOI: 10.1007/s11269-005-6811-2
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    Citations

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    Cited by:

    1. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.
    2. Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
    3. Jordan Clayton & Jason Kean, 2010. "Establishing a Multi-scale Stream Gaging Network in the Whitewater River Basin, Kansas, USA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3641-3664, October.
    4. Mohamad Basel Al Sawaf & Kiyosi Kawanisi & Cong Xiao, 2020. "Measuring Low Flowrates of a Shallow Mountainous River Within Restricted Site Conditions and the Characteristics of Acoustic Arrival Times Within Low Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3059-3078, August.
    5. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    6. Yen-Chang Chen & Yung-Chia Hsu & Kuang-Ting Kuo, 2013. "Uncertainties in the Methods of Flood Discharge Measurement," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 153-167, January.
    7. Ozgur Kisi, 2015. "Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5109-5127, November.
    8. Saritha Padiyedath Gopalan & Akira Kawamura & Hideo Amaguchi & Gubash Azhikodan, 2020. "A Generalized Storage Function Model for the Water Level Estimation Using Rating Curve Relationship," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2603-2619, June.

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