Evaluating the effect of data-richness and model complexity in the prediction of coastal sediment loading in Solomon Islands Article Swipe
Nicholas Hutley
,
Mandus Boselalu
,
Amelia Wenger
,
Alistair Grinham
,
Badin Gibbes
,
Simon Albert
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1748-9326/abc8ba
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1748-9326/abc8ba
Global biophysical data are increasingly accessible due to improvements in remote sensing and open datasets. These datasets can be of particular value in remote and data-poor environments to enable estimates of water quality impacts from catchment land clearing. Given the resources required to collect field observations and calibrate detailed process-based models, global datasets are often the only sources available to parameterise simple models however the comparative use of these data sources in process-based models is relatively unexplored. This study compares the widely applied models of Integrated Valuation of Ecosystem Services and Trade-offs and Soil and Water Assessment Tool (SWAT) to a tropical catchment in the Solomon Islands using globally available data. These uncalibrated models are contrasted with a SWAT model calibrated with measured streamflow and turbidity in the catchment and meteorologically forced by data from a nearby weather station. These catchment models were coupled with models of sedimentation to examine deposition rates in the coastal lagoon adjacent to the catchment. Model validation using measured coastal sedimentation rates demonstrated that simpler modelling approaches (one-dimensional basin sedimentation and two-dimensional sediment extent modelling) were marginally better than more complex approaches (three-dimensional Delft3D) in data-poor conditions. However, investment in local catchment observations significantly improved the accuracy of simulation outputs. This insight can guide decisions about model complexity, data-richness and investment in local environmental monitoring in these challenging environments.
Related Topics
Concepts
Environmental science
Drainage basin
Sedimentation
Hydrology (agriculture)
Soil and Water Assessment Tool
SWAT model
Streamflow
Sediment
Geography
Geology
Cartography
Geotechnical engineering
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1748-9326/abc8ba
- OA Status
- gold
- Cited By
- 7
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3117551874
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3117551874Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1748-9326/abc8baDigital Object Identifier
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Evaluating the effect of data-richness and model complexity in the prediction of coastal sediment loading in Solomon IslandsWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2020Year of publication
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2020-11-09Full publication date if available
- Authors
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Nicholas Hutley, Mandus Boselalu, Amelia Wenger, Alistair Grinham, Badin Gibbes, Simon AlbertList of authors in order
- Landing page
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https://doi.org/10.1088/1748-9326/abc8baPublisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1088/1748-9326/abc8baDirect OA link when available
- Concepts
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Environmental science, Drainage basin, Sedimentation, Hydrology (agriculture), Soil and Water Assessment Tool, SWAT model, Streamflow, Sediment, Geography, Geology, Cartography, Geotechnical engineering, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2024: 2, 2023: 3, 2021: 2Per-year citation counts (last 5 years)
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80Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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