A novel construct for scaling groundwater–river interactions based on machine-guided hydromorphic classification Article Swipe
Zhangshuan Hou
,
Huiying Ren
,
Chris Murray
,
Xuehang Song
,
Yilin Fang
,
Evan Arntzen
,
Xingyuan Chen
,
James Stegen
,
Maoyi Huang
,
J. D. Gomez‐Velez
,
Zhuoran Duan
,
William Perkins
,
Marshall C. Richmond
,
Tim Scheibe
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1748-9326/ac24ce
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1748-9326/ac24ce
Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. Predictive numerical models are needed to understand responses of river corridors to environmental change and to support sustainable watershed management. We posit that systematic hydromorphic classification provides a scaling construct that facilitates extrapolation of outputs from local-scale mechanistic models to reduced-order models applicable at reach and watershed scales. This in turn offers the potential to improve large-scale predictions of river corridor hydrobiogeochemical processes. Here we present a new machine-guided hydromorphic classification methodology that addresses the key requirements of this objective, and we demonstrate its application to a segment of the Columbia River in the northwestern United States. The resulting hydromorphic classes form spatially coherent and physically interpretable hydromorphic units that exhibit distinct behaviors in terms of distributions of subsurface transit times (a primary control on critical biogeochemical reactions). This approach forms the basis of ongoing research that is evaluating the formulation of reduced-order models and transferability of results to other river reaches and larger scales.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1748-9326/ac24ce
- OA Status
- gold
- Cited By
- 6
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198389112
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3198389112Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1748-9326/ac24ceDigital Object Identifier
- Title
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A novel construct for scaling groundwater–river interactions based on machine-guided hydromorphic classificationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-08Full publication date if available
- Authors
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Zhangshuan Hou, Huiying Ren, Chris Murray, Xuehang Song, Yilin Fang, Evan Arntzen, Xingyuan Chen, James Stegen, Maoyi Huang, J. D. Gomez‐Velez, Zhuoran Duan, William Perkins, Marshall C. Richmond, Tim ScheibeList of authors in order
- Landing page
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https://doi.org/10.1088/1748-9326/ac24cePublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1088/1748-9326/ac24ceDirect OA link when available
- Concepts
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Watershed, Extrapolation, Scale (ratio), Computer science, Environmental science, Scaling, Hydrology (agriculture), Biogeochemical cycle, Machine learning, Statistics, Ecology, Geology, Geography, Cartography, Mathematics, Biology, Geometry, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2021: 1Per-year citation counts (last 5 years)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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