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The impact of spatial resolution on hourly flood modeling in large watersheds.

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Title: The impact of spatial resolution on hourly flood modeling in large watersheds.
Authors: Ye, Lei1 (AUTHOR), Li, Xiaoyang1 (AUTHOR) lixiaoyang1998@mail.dlut.edu.cn, Li, Jilie1 (AUTHOR), Zhang, Chi1 (AUTHOR), Zhou, Huicheng1 (AUTHOR)
Source: Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 10, p2995-3018. 24p.
Subject Terms: *Spatial resolution, *Watersheds, *Machine learning, *Flood forecasting, *Hydrologic models, *Rainfall
Geographic Terms: China
Abstract: The spatial resolution of hydrological modeling is a critical factor affecting flood simulation accuracy, especially in large watersheds characterized by complex watershed characteristics. However, its influence on the accuracy of hourly flood simulations at both watershed outlets and internal locations remains insufficiently understood, hindering rational spatial-resolution selection for large-scale flood forecasting. This study evaluates hourly flood simulations across five spatial resolutions (1, 3, 5, 10 km, and sub-watershed) at the watershed outlet and multiple internal stations in the Jialing River Basin, China (157 041 km2). An XGBoost-based model is employed to identify flood characteristics sensitive to spatial resolution and to quantify their nonlinear effects on simulation accuracy. Based on these relationships, spatial-resolution recommendations are derived for different flood-characteristic categories, and the effectiveness of spatial refinement under coarse rainfall inputs is examined. Results show that spatial refinement markedly improves simulation accuracy at internal locations but yields only marginal gains at the watershed outlet. Watershed area is identified as the dominant factor governing resolution sensitivity, while rainfall characteristics and underlying-surface properties exert strong nonlinear influences. Fine grids (1–3 km) are most effective under flood conditions with strong nonlinearity, but their advantages diminish rapidly as rainfall inputs become coarser, indicating that increased spatial resolution cannot compensate for insufficient rainfall information. Overall, these findings advance current understanding of spatial-resolution effects on hourly flood simulations and provide practical guidance for spatial-resolution selection in large-watershed modeling. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194401719
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: The impact of spatial resolution on hourly flood modeling in large watersheds.
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  Data: <searchLink fieldCode="AR" term="%22Ye%2C+Lei%22">Ye, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xiaoyang%22">Li, Xiaoyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lixiaoyang1998@mail.dlut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jilie%22">Li, Jilie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chi%22">Zhang, Chi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Huicheng%22">Zhou, Huicheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. 2026, Vol. 30 Issue 10, p2995-3018. 24p.
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  Data: *<searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Flood+forecasting%22">Flood forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The spatial resolution of hydrological modeling is a critical factor affecting flood simulation accuracy, especially in large watersheds characterized by complex watershed characteristics. However, its influence on the accuracy of hourly flood simulations at both watershed outlets and internal locations remains insufficiently understood, hindering rational spatial-resolution selection for large-scale flood forecasting. This study evaluates hourly flood simulations across five spatial resolutions (1, 3, 5, 10 km, and sub-watershed) at the watershed outlet and multiple internal stations in the Jialing River Basin, China (157 041 km2). An XGBoost-based model is employed to identify flood characteristics sensitive to spatial resolution and to quantify their nonlinear effects on simulation accuracy. Based on these relationships, spatial-resolution recommendations are derived for different flood-characteristic categories, and the effectiveness of spatial refinement under coarse rainfall inputs is examined. Results show that spatial refinement markedly improves simulation accuracy at internal locations but yields only marginal gains at the watershed outlet. Watershed area is identified as the dominant factor governing resolution sensitivity, while rainfall characteristics and underlying-surface properties exert strong nonlinear influences. Fine grids (1–3 km) are most effective under flood conditions with strong nonlinearity, but their advantages diminish rapidly as rainfall inputs become coarser, indicating that increased spatial resolution cannot compensate for insufficient rainfall information. Overall, these findings advance current understanding of spatial-resolution effects on hourly flood simulations and provide practical guidance for spatial-resolution selection in large-watershed modeling. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.5194/hess-30-2995-2026
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 2995
    Subjects:
      – SubjectFull: Spatial resolution
        Type: general
      – SubjectFull: Watersheds
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Flood forecasting
        Type: general
      – SubjectFull: Hydrologic models
        Type: general
      – SubjectFull: Rainfall
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: The impact of spatial resolution on hourly flood modeling in large watersheds.
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            NameFull: Ye, Lei
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            NameFull: Li, Xiaoyang
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            NameFull: Li, Jilie
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            NameFull: Zhang, Chi
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            NameFull: Zhou, Huicheng
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            – D: 15
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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              Value: 30
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            – TitleFull: Hydrology & Earth System Sciences
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