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Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations.

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Title: Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations.
Authors: Chandran P., Bincy1 (AUTHOR), Arunkumar, R.1 (AUTHOR) arunkr@nitc.ac.in
Source: ISH Journal of Hydraulic Engineering. Nov2024, Vol. 30 Issue 5, p705-719. 15p.
Subjects: Artificial neural networks, North Atlantic oscillation, El Niño, Southern oscillation, Atmospheric circulation
Abstract: In this study, the relationship between large-scale climatic drivers and streamflow of the Chalakudy River Basin, Kerala, was analysed using methods such as bivariate wavelet coherence (BWC), multiple wavelet coherence (MWC), and partial wavelet coherence (PWC) analysis. The four prominent global climate indices chosen are the Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO), along with other local climate drivers. The BWC analysis showed that streamflow and rainfall had a very strong in-phase relationship, whereas the maximum temperature and average temperature showed an anti-phase relationship. In the case of global climate drivers, ENSO has a significant impact on the streamflow of the Chalakudy River Basin. The average wavelet coherence (AWC), which quantifies the teleconnections, confirms the observation with a high coherency of 0.75 between streamflow and rainfall and 0.51 for streamflow and ENSO. Streamflow prediction models were developed using random forest (RF) and artificial neural network (ANN) techniques by considering the influence of significant global and local climatic drivers. It was observed that the RF model performed slightly better than the ANN model, with R = 0.875, NSE = 0.766, RMSE = 23.468, and RSR = 0.524. [ABSTRACT FROM AUTHOR]
Copyright of ISH Journal of Hydraulic Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 180430225
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  Label: Title
  Group: Ti
  Data: Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations.
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Chandran+P%2E%2C+Bincy%22">Chandran P., Bincy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Arunkumar%2C+R%2E%22">Arunkumar, R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> arunkr@nitc.ac.in</i>
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  Data: <searchLink fieldCode="JN" term="%22ISH+Journal+of+Hydraulic+Engineering%22">ISH Journal of Hydraulic Engineering</searchLink>. Nov2024, Vol. 30 Issue 5, p705-719. 15p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22North+Atlantic+oscillation%22">North Atlantic oscillation</searchLink><br /><searchLink fieldCode="DE" term="%22El+Niño%22">El Niño</searchLink><br /><searchLink fieldCode="DE" term="%22Southern+oscillation%22">Southern oscillation</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+circulation%22">Atmospheric circulation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this study, the relationship between large-scale climatic drivers and streamflow of the Chalakudy River Basin, Kerala, was analysed using methods such as bivariate wavelet coherence (BWC), multiple wavelet coherence (MWC), and partial wavelet coherence (PWC) analysis. The four prominent global climate indices chosen are the Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO), along with other local climate drivers. The BWC analysis showed that streamflow and rainfall had a very strong in-phase relationship, whereas the maximum temperature and average temperature showed an anti-phase relationship. In the case of global climate drivers, ENSO has a significant impact on the streamflow of the Chalakudy River Basin. The average wavelet coherence (AWC), which quantifies the teleconnections, confirms the observation with a high coherency of 0.75 between streamflow and rainfall and 0.51 for streamflow and ENSO. Streamflow prediction models were developed using random forest (RF) and artificial neural network (ANN) techniques by considering the influence of significant global and local climatic drivers. It was observed that the RF model performed slightly better than the ANN model, with R = 0.875, NSE = 0.766, RMSE = 23.468, and RSR = 0.524. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ISH Journal of Hydraulic Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/09715010.2024.2394802
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 705
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: North Atlantic oscillation
        Type: general
      – SubjectFull: El Niño
        Type: general
      – SubjectFull: Southern oscillation
        Type: general
      – SubjectFull: Atmospheric circulation
        Type: general
    Titles:
      – TitleFull: Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations.
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      – PersonEntity:
          Name:
            NameFull: Chandran P., Bincy
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          Name:
            NameFull: Arunkumar, R.
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          Dates:
            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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              Value: 30
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              Value: 5
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            – TitleFull: ISH Journal of Hydraulic Engineering
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