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Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts

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  • Salman Sharifazari
  • Shahab Araghinejad

Abstract

One of the water resources modeling requirements is sufficient knowledge of long-term series of meteorological and hydrological parameters especially normal discharge in structural position and specific points of the basin. In this study, based on the nearest neighbor resampling method, a non-parametric model has been developed for a contemporaneous and correlated series simulation. To evaluate the performance of the developed model, hydrological basin time series of the Sirwan River located in west of Iran were simulated. The results obtained from the developed model were compared with the results of parametric models, PARMA and MPAR. Assessment of the compared results showed that the developed model had a better efficiency and performance in simulating time series than the parametric models. Also, the simulated time series statistical characteristics had a better conformity with the observed time series. Moreover, simulated time series with the concern of climate scenarios, e.g., increase or decrease annual discharge, showed that the developed model had a good efficiency in considering these changes and effects in time series simulation. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:14:p:5309-5322
    DOI: 10.1007/s11269-015-1119-3
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    References listed on IDEAS

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    1. Mohammed Sharif & Donald Burn & Karen Hofbauer, 2013. "Generation of Daily and Hourly Weather Variables for use in Climate Change Vulnerability Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1533-1550, March.
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    6. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
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    Cited by:

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    2. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
    3. Ali Ahani & Mojtaba Shourian & Peiman Rahimi Rad, 2018. "Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 383-399, January.
    4. Hakan Tongal & Martijn J. Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.

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