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A Principal Component Regression Method for Estimating Low Flow Index

Author

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  • Saeid Eslamian
  • Mehdi Ghasemizadeh
  • Monireh Biabanaki
  • Mansoor Talebizadeh

Abstract

Low flow indices are very important for water resources planning, pollution control, conservation and even recreational use. Determining these indices depends on having access to daily flow discharges. However, in some cases, such data are either insufficient or are not available at all. Hence, in these cases, estimation of the indices requires the use of data in catchments for which streamflow data have been collected. In this paper, it was attempted to estimate the low flow index (7Q10), the 7-day, 10-year lowflow, using principal component regression (PCR) based on physiographic and hydrologic variables. To do so, a two-step procedure was followed. In the first step, ranking method was applied to determine the best fitted distributions on yearly minimum discharges in each gauging station according to distribution suitability for fitting on extremes; the better the distribution fits the data, the higher number is given as ranking. Adding the ranking numbers dedicated to each gauging station, it was revealed that Gamma distribution with two parameters got the highest value and therefore was chosen as the representative distribution in the region. Using Gamma distribution in gauging stations, 7Q10 was estimated in all gauging stations in the basin. In the second step, a PCR was developed due to existence of high-correlated independent variables. To choose the influential components for use in PCR, eigenvector analysis and factor analysis were performed. The results show that the components chosen through the two approaches correspond to each other well. To evaluate the efficiency of the developed PCR in modeling 7Q10, calibration and verification were pursued. The results approve the efficiency of model in predicting 7Q10 in the region under study. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Saeid Eslamian & Mehdi Ghasemizadeh & Monireh Biabanaki & Mansoor Talebizadeh, 2010. "A Principal Component Regression Method for Estimating Low Flow Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2553-2566, September.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:11:p:2553-2566
    DOI: 10.1007/s11269-009-9567-2
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    References listed on IDEAS

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    1. E. R. Mansfield & J. T. Webster & R. F. Gunst, 1977. "An Analytic Variable Selection Technique for Principal Component Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(1), pages 34-40, March.
    2. K. Engeland & H. Hisdal, 2009. "A Comparison of Low Flow Estimates in Ungauged Catchments Using Regional Regression and the HBV-Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2567-2586, September.
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    Cited by:

    1. Samane Saadat & Davar Khalili & Ali Kamgar-Haghighi & Shahrokh Zand-Parsa, 2013. "Investigation of spatio-temporal patterns of seasonal streamflow droughts in a semi-arid region," 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. 69(3), pages 1697-1720, December.
    2. Arash Modaresi Rad & Davar Khalili & Ali Akbar Kamgar-Haghighi & Shahrokh Zand-Parsa & Seyed Adib Banimahd, 2016. "Assessment of seasonal characteristics of streamflow droughts under semiarid conditions," 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. 82(3), pages 1541-1564, July.
    3. Maryam Azizabadi Farahani & Davar Khalili, 2013. "Seasonality Characteristics and Spatio-temporal Trends of 7-day Low Flows in a Large, Semi-arid Watershed," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4897-4911, November.
    4. Gokmen Tayfur & Vijay Singh, 2011. "Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1253-1267, March.
    5. Konstantina Risva & Dionysios Nikolopoulos & Andreas Efstratiadis & Ioannis Nalbantis, 2018. "A Framework for Dry Period Low Flow Forecasting in Mediterranean Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4911-4932, December.
    6. Wenrui Huang & Xiaohai Liu & Xinjian Chen & Michael Flannery, 2011. "Critical Flow for Water Management in a Shallow Tidal River Based on Estuarine Residence Time," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2367-2385, August.
    7. Selen Orta & Hafzullah Aksoy, 2022. "Development of Low Flow Duration-Frequency Curves by Hybrid Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1521-1534, March.

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