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Evaluating the effect of data-richness and model complexity in the prediction of coastal sediment loading in Solomon Islands

Bibliographic Data

ID15544178
AuthorsNicholas Hutley (0000-0003-0132-9054, The University of Queensland, corresponding author), Mandus Boselalu, Amelia S Wenger (0000-0002-0433-6164, Wildlife Conservation Society), Alistair Grinham (0000-0001-8313-2276, The University of Queensland), Badin Gibbes (0000-0002-6336-7446, The University of Queensland), Simon Albert (0000-0002-5947-7909, The University of Queensland)
Year2020
Volume15
Issue12
Pages124044-124044
Publication date2020-11-09
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/abc8ba
OpenAlexW3117551874
LanguageEN
References cited71

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

Cartography · Drainage basin · Geography · Hydrology (agriculture · Sediment · Sedimentation · Soil and Water Assessment Tool · Streamflow · SWAT model · Coastal wetland ecosystem dynamics · Environmental Science · Flood Risk Assessment and Management · Hydrology and Watershed Management Studies · Geology

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