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A Novel Statistical Weighting Approach for Multiple GCMs Ensembles to Enhance Drought Projections.

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Title: A Novel Statistical Weighting Approach for Multiple GCMs Ensembles to Enhance Drought Projections.
Authors: Ali, Farman1,2,3 (AUTHOR), Han, Jing-Cheng1,2,3 (AUTHOR) hanjc.2014@tsinghua.org.cn, Su, Dong2,3 (AUTHOR), Yousaf, Mahrukh4 (AUTHOR), Ali, Zulfiqar4 (AUTHOR), Rahman, Shafeeq Ur1 (AUTHOR), Huang, Yuefei5,6 (AUTHOR)
Source: Water Resources Management. Jul2026, Vol. 40 Issue 9, p1-21. 21p.
Subject Terms: *Statistical weighting, *Drought forecasting, *Climate change models, *Droughts, *Precipitation forecasting
Geographic Terms: Tibet (China)
Abstract: Ensembling multiple Global Climate Models (GCMs) reduces biases of individual models, leading to more accurate drought forecasts. However, traditional Multi-Modeling Ensembling (MME) can't account for volatility and non-linear relationships in GCMs, resulting in inaccurate precipitation estimates and unreliable drought assessments. This study introduces the hybrid GARCH-SVR weighting scheme for MME, which enables the simultaneous treatment of volatility and nonlinear predictive errors. This novel MME approach combines two models: Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Regression (SVR). The GARCH captures variance and irregularity in time series through assigning weights to models based on their GARCH variances. Meanwhile, SVR models the complex nonlinear relationship and assigns weights to models based on their prediction error. As a result, a novel drought index, the Hybrid Weighted GARCH-SVR Drought Index (HWGSDI) is derived. HWGSDI improves drought assessment by addressing variance fluctuations and non-linearity in the precipitation time series from GCMs. The projection of this index is based on the relationship between observed and ensembled data using linear regression. Different comparative measures were used to evaluate the performance of the proposed weighted scheme, with results demonstrating that the Hybrid GARCH-SVR weighting scheme outperforms the Simple Model Average (SMA), Weighted Ensemble (WE) Scheme, GARCH, and SVR. Further, the Steady-State Probabilities of drought categories were spatially computed to understand the long-term behavior of drought in the Tibetan Plateau. The results showed that the probability of experiencing 'No Drought' remained relatively high, while the persistent probabilities of 'Extreme Drought' highlight a warning for the impacts of climate change. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
FullText Text:
  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 194358795
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A Novel Statistical Weighting Approach for Multiple GCMs Ensembles to Enhance Drought Projections.
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  Data: <searchLink fieldCode="AR" term="%22Ali%2C+Farman%22">Ali, Farman</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Jing-Cheng%22">Han, Jing-Cheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> hanjc.2014@tsinghua.org.cn</i><br /><searchLink fieldCode="AR" term="%22Su%2C+Dong%22">Su, Dong</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yousaf%2C+Mahrukh%22">Yousaf, Mahrukh</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ali%2C+Zulfiqar%22">Ali, Zulfiqar</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rahman%2C+Shafeeq+Ur%22">Rahman, Shafeeq Ur</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Yuefei%22">Huang, Yuefei</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Water+Resources+Management%22">Water Resources Management</searchLink>. Jul2026, Vol. 40 Issue 9, p1-21. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Statistical+weighting%22">Statistical weighting</searchLink><br />*<searchLink fieldCode="DE" term="%22Drought+forecasting%22">Drought forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change+models%22">Climate change models</searchLink><br />*<searchLink fieldCode="DE" term="%22Droughts%22">Droughts</searchLink><br />*<searchLink fieldCode="DE" term="%22Precipitation+forecasting%22">Precipitation forecasting</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Tibet+%28China%29%22">Tibet (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Ensembling multiple Global Climate Models (GCMs) reduces biases of individual models, leading to more accurate drought forecasts. However, traditional Multi-Modeling Ensembling (MME) can't account for volatility and non-linear relationships in GCMs, resulting in inaccurate precipitation estimates and unreliable drought assessments. This study introduces the hybrid GARCH-SVR weighting scheme for MME, which enables the simultaneous treatment of volatility and nonlinear predictive errors. This novel MME approach combines two models: Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Regression (SVR). The GARCH captures variance and irregularity in time series through assigning weights to models based on their GARCH variances. Meanwhile, SVR models the complex nonlinear relationship and assigns weights to models based on their prediction error. As a result, a novel drought index, the Hybrid Weighted GARCH-SVR Drought Index (HWGSDI) is derived. HWGSDI improves drought assessment by addressing variance fluctuations and non-linearity in the precipitation time series from GCMs. The projection of this index is based on the relationship between observed and ensembled data using linear regression. Different comparative measures were used to evaluate the performance of the proposed weighted scheme, with results demonstrating that the Hybrid GARCH-SVR weighting scheme outperforms the Simple Model Average (SMA), Weighted Ensemble (WE) Scheme, GARCH, and SVR. Further, the Steady-State Probabilities of drought categories were spatially computed to understand the long-term behavior of drought in the Tibetan Plateau. The results showed that the probability of experiencing 'No Drought' remained relatively high, while the persistent probabilities of 'Extreme Drought' highlight a warning for the impacts of climate change. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194358795
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s11269-026-04516-3
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 21
        StartPage: 1
    Subjects:
      – SubjectFull: Statistical weighting
        Type: general
      – SubjectFull: Drought forecasting
        Type: general
      – SubjectFull: Climate change models
        Type: general
      – SubjectFull: Droughts
        Type: general
      – SubjectFull: Precipitation forecasting
        Type: general
      – SubjectFull: Tibet (China)
        Type: general
    Titles:
      – TitleFull: A Novel Statistical Weighting Approach for Multiple GCMs Ensembles to Enhance Drought Projections.
        Type: main
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            NameFull: Ali, Farman
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            NameFull: Han, Jing-Cheng
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            NameFull: Su, Dong
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            NameFull: Yousaf, Mahrukh
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            NameFull: Ali, Zulfiqar
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            NameFull: Rahman, Shafeeq Ur
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            NameFull: Huang, Yuefei
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 09204741
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              Value: 40
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Water Resources Management
              Type: main
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