View in EDS HTML Full Text PDF Full Text

Insights into uncertainties in future drought analysis using hydrological simulation model.

Saved in:
Bibliographic Details
Title: Insights into uncertainties in future drought analysis using hydrological simulation model.
Authors: Kim, Jin Hyuck1, Chung, Eun-Sung2 eschung@seoultech.ac.kr
Source: Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 1, p227-247. 21p.
Subject Terms: *Drought forecasting, *Runoff analysis, *Measurement uncertainty (Statistics), *Hydrogeological modeling, *Calibration, *Meteorological databases
Abstract: Hydrological analysis utilizing a hydrological model requires a parameter calibration process, which is largely influenced by the length of calibration data period and prevailing hydrological conditions. This study aimed to quantify these uncertainties in future runoff projection and hydrological drought based on future climate data and the calibration data of the hydrological model. Future climate data were sourced from three Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) of 20 general circulation models (GCMs). The Soil and Water Assessment Tool (SWAT) was employed as the hydrological model, and hydrological conditions were determined using the Streamflow Drought Index (SDI), with calibration data lengths ranging from 1 to 20 years considered. Subsequently, the uncertainty was quantified using Analysis of Variance (ANOVA). After calibrating SWAT parameters, the validation performance was found to be influenced by the hydrological conditions of the calibration data. Hydrological model parameters calibrated using a dry period simulated runoff with 11.4 % higher performance in dry conditions and 6.1 % higher performance in normal conditions, while hydrological model parameters calibrated using a wet period simulated runoff with 5.1 % higher performance in wet conditions. While the ANOVA results confirmed that GCMs are the dominant source of total uncertainty, the uncertainty contribution from the hydrological model calibration in estimating future runoff was analyzed to be 3.9 %–9.8 %, particularly significant in the low runoff period. The uncertainty contribution in future hydrological drought analysis resulting from the calibration of hydrological model parameters was analyzed to be 2.7 % on average, which is lower than that of future runoff projection. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 191076192
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Insights into uncertainties in future drought analysis using hydrological simulation model.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kim%2C+Jin Hyuck%22">Kim, Jin Hyuck</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chung%2C+Eun-Sung%22">Chung, Eun-Sung</searchLink><relatesTo>2</relatesTo><i> eschung@seoultech.ac.kr</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. 2026, Vol. 30 Issue 1, p227-247. 21p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Drought+forecasting%22">Drought forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Runoff+analysis%22">Runoff analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Measurement+uncertainty+%28Statistics%29%22">Measurement uncertainty (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrogeological+modeling%22">Hydrogeological modeling</searchLink><br />*<searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br />*<searchLink fieldCode="DE" term="%22Meteorological+databases%22">Meteorological databases</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Hydrological analysis utilizing a hydrological model requires a parameter calibration process, which is largely influenced by the length of calibration data period and prevailing hydrological conditions. This study aimed to quantify these uncertainties in future runoff projection and hydrological drought based on future climate data and the calibration data of the hydrological model. Future climate data were sourced from three Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) of 20 general circulation models (GCMs). The Soil and Water Assessment Tool (SWAT) was employed as the hydrological model, and hydrological conditions were determined using the Streamflow Drought Index (SDI), with calibration data lengths ranging from 1 to 20 years considered. Subsequently, the uncertainty was quantified using Analysis of Variance (ANOVA). After calibrating SWAT parameters, the validation performance was found to be influenced by the hydrological conditions of the calibration data. Hydrological model parameters calibrated using a dry period simulated runoff with 11.4 % higher performance in dry conditions and 6.1 % higher performance in normal conditions, while hydrological model parameters calibrated using a wet period simulated runoff with 5.1 % higher performance in wet conditions. While the ANOVA results confirmed that GCMs are the dominant source of total uncertainty, the uncertainty contribution from the hydrological model calibration in estimating future runoff was analyzed to be 3.9 %–9.8 %, particularly significant in the low runoff period. The uncertainty contribution in future hydrological drought analysis resulting from the calibration of hydrological model parameters was analyzed to be 2.7 % on average, which is lower than that of future runoff projection. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191076192
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.5194/hess-30-227-2026
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 227
    Subjects:
      – SubjectFull: Drought forecasting
        Type: general
      – SubjectFull: Runoff analysis
        Type: general
      – SubjectFull: Measurement uncertainty (Statistics)
        Type: general
      – SubjectFull: Hydrogeological modeling
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Meteorological databases
        Type: general
    Titles:
      – TitleFull: Insights into uncertainties in future drought analysis using hydrological simulation model.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kim, Jin Hyuck
      – PersonEntity:
          Name:
            NameFull: Chung, Eun-Sung
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 10275606
          Numbering:
            – Type: volume
              Value: 30
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Hydrology & Earth System Sciences
              Type: main
ResultId 1