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Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA.

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Title: Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA.
Authors: Thébault, Cyril1 (AUTHOR) cyril.thebault@ucalgary.ca, Knoben, Wouter J. M.1 (AUTHOR), Addor, Nans2,3 (AUTHOR), Newman, Andrew J.4 (AUTHOR), Spieler, Diana1 (AUTHOR), Vásquez, Nicolás A.1 (AUTHOR), Song, Yalan5 (AUTHOR), Gründemann, Gaby J.1 (AUTHOR), Carney, Shaun6 (AUTHOR), Kumar, Mukesh7 (AUTHOR), van Werkhoven, Katie6 (AUTHOR), Shen, Chaopeng5 (AUTHOR), Wood, Andrew W.8,9 (AUTHOR), Clark, Martyn P.1 (AUTHOR)
Source: Hydrology & Earth System Sciences. Jun2026, Vol. 30 Issue 12, p3945-3977. 33p.
Subject Terms: *Streamflow, *Hydrologic models, *Ensemble learning, *Calibration, *Watersheds, *Water management
Geographic Terms: United States
Abstract: The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, we assess the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on our selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA.
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  Data: <searchLink fieldCode="AR" term="%22Thébault%2C+Cyril%22">Thébault, Cyril</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cyril.thebault@ucalgary.ca</i><br /><searchLink fieldCode="AR" term="%22Knoben%2C+Wouter J%2E M%2E%22">Knoben, Wouter J. M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Addor%2C+Nans%22">Addor, Nans</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Newman%2C+Andrew J%2E%22">Newman, Andrew J.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Spieler%2C+Diana%22">Spieler, Diana</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vásquez%2C+Nicolás A%2E%22">Vásquez, Nicolás A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Yalan%22">Song, Yalan</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gründemann%2C+Gaby J%2E%22">Gründemann, Gaby J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Carney%2C+Shaun%22">Carney, Shaun</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+Mukesh%22">Kumar, Mukesh</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22van Werkhoven%2C+Katie%22">van Werkhoven, Katie</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Chaopeng%22">Shen, Chaopeng</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wood%2C+Andrew W%2E%22">Wood, Andrew W.</searchLink><relatesTo>8,9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clark%2C+Martyn P%2E%22">Clark, Martyn P.</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>. Jun2026, Vol. 30 Issue 12, p3945-3977. 33p.
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  Data: *<searchLink fieldCode="DE" term="%22Streamflow%22">Streamflow</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, we assess the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on our selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=195100194
RecordInfo BibRecord:
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        Value: 10.5194/hess-30-3945-2026
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      – Code: eng
        Text: English
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        PageCount: 33
        StartPage: 3945
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      – SubjectFull: Streamflow
        Type: general
      – SubjectFull: Hydrologic models
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Watersheds
        Type: general
      – SubjectFull: Water management
        Type: general
      – SubjectFull: United States
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      – TitleFull: Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA.
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              Text: Jun2026
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              Y: 2026
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