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Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition

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  • Babak Vaheddoost

    (Bursa Technical University)

  • Hafzullah Aksoy

    (Istanbul Technical University)

Abstract

Frequency domain analysis using an additive decomposition method is proposed to reconstruct the missing hydrometeorological data of selected sites in Lake Urmia basin in Iran. Precipitation, evaporation, streamflow and groundwater time series are used for this aim. Trends, within- and multi-year cycles, and randomness are taken into account to reconstruct each of the time series for which models are developed, calibrated and validated separately. Statistical similarity between the observed and reconstructed time series is checked. Statistical characteristics including the average, standard deviation, skewness, and the first-order autocorrelation coefficient are well preserved at the reconstructed time series. A conceptual water budget model is also established to check for the consistency between the reconstructed and the observed datasets. The water budget model is taken as a quantitative way to confirm that the frequency domain analysis using the additive decomposition is an effective method for the reconstruction of the missing hydrometeorological data based on the case study performed for the Lake Urmia basin in Iran.

Suggested Citation

  • Babak Vaheddoost & Hafzullah Aksoy, 2019. "Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3899-3911, September.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:11:d:10.1007_s11269-019-02335-3
    DOI: 10.1007/s11269-019-02335-3
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    References listed on IDEAS

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    1. Babak Vaheddoost & Hafzullah Aksoy & Hirad Abghari, 2016. "Prediction of Water Level using Monthly Lagged Data in Lake Urmia, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4951-4967, October.
    2. Mehmetcik Bayazit & Hafzullah Aksoy, 2001. "Using wavelets for data generation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 157-166.
    3. Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Muhammad Sultan & Fiaz Ahmad & Tahir Sultan & Zakir Hussain Dahri & Irfan Ali, 2019. "Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 955-973, February.
    4. Vahid Nourani & Amir Molajou & Ali Davanlou Tajbakhsh & Hessam Najafi, 2019. "A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1769-1784, March.
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    Cited by:

    1. Ahmadzadeh, Hojat & Mansouri, Bahareh & Fathian, Farshad & Vaheddoost, Babak, 2022. "Assessment of water demand reliability using SWAT and RIBASIM models with respect to climate change and operational water projects," Agricultural Water Management, Elsevier, vol. 261(C).

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