Data
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/8246441&rft.title=Great Barrier Reef non-cyclonic and on-reef wave model predictions&rft.identifier=RDM ID: 27aaea40-fdd4-11ed-b17b-5ba0b2c0e1cb&rft.publisher=The University of Queensland&rft.description=Data is contained in NetCDF format (see https://www.unidata.ucar.edu/software/netcdf/) and readable by a range of software (QGIS, python, gfortran). The Great Barrier Reef is broken into four regions (Far Northern Queensland, Cairns to Cooktown, Townsville to Whitsunday and Mackay and Capricorn), and includes a range of wave parameters including significant wave height, peak wave period, wave energy density, wave energy flux, still water and near bed characteristic velocity amplitude, each at mean, maximum and several percentiles (90, 95 & 99) in between. Much more information is contained in See Roelfsema, C. M., Kovacs, E. M., Ortiz, J. C., Callaghan, D. P., Hock, K., Mongin, M., Johansen, K., Mumby, P. J., Wettle, M., Ronan, M., Lundgren, P., Kennedy, E. V. and Phinn, S. R., 2020. Habitat maps to enhance monitoring and management of the great barrier reef. Coral Reefs. for this particular dataset. See Callaghan, D. P., Leon, J. X. and Saunders, M. I., 2015. Wave modelling as a proxy for seagrass ecological modelling: Comparing fetch and process-based predictions for a bay and reef lagoon. Estuarine, Coastal and Shelf Science, 153: 108-120. on the methods used.&rft.creator=Associate Professor David Callaghan&rft.creator=Associate Professor David Callaghan&rft.creator=Dr David Callaghan&rft.date=2023&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Ocean engineering&rft_subject=Maritime engineering&rft_subject=ENGINEERING&rft_subject=Environmental engineering&rft_subject=Civil engineering not elsewhere classified&rft_subject=Civil engineering&rft.type=dataset&rft.language=English Access the data

Contact Information

dave.callaghan@uq.edu.au
School of Civil Engineering

Full description

Data is contained in NetCDF format (see https://www.unidata.ucar.edu/software/netcdf/) and readable by a range of software (QGIS, python, gfortran). The Great Barrier Reef is broken into four regions (Far Northern Queensland, Cairns to Cooktown, Townsville to Whitsunday and Mackay and Capricorn), and includes a range of wave parameters including significant wave height, peak wave period, wave energy density, wave energy flux, still water and near bed characteristic velocity amplitude, each at mean, maximum and several percentiles (90, 95 & 99) in between. Much more information is contained in See Roelfsema, C. M., Kovacs, E. M., Ortiz, J. C., Callaghan, D. P., Hock, K., Mongin, M., Johansen, K., Mumby, P. J., Wettle, M., Ronan, M., Lundgren, P., Kennedy, E. V. and Phinn, S. R., 2020. Habitat maps to enhance monitoring and management of the great barrier reef. Coral Reefs. for this particular dataset. See Callaghan, D. P., Leon, J. X. and Saunders, M. I., 2015. Wave modelling as a proxy for seagrass ecological modelling: Comparing fetch and process-based predictions for a bay and reef lagoon. Estuarine, Coastal and Shelf Science, 153: 108-120. on the methods used.

Issued: 2023

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Habitat maps to enhance monitoring and management of the Great Barrier Reef

local : UQ:2852e2f

Roelfsema, Chris M., Kovacs, Eva M., Ortiz, Juan Carlos, Callaghan, David P., Hock, Karlo, Mongin, Mathieu, Johansen, Kasper, Mumby, Peter J., Wettle, Magnus, Ronan, Mike, Lundgren, Petra, Kennedy, Emma V. and Phinn, Stuart R. (2020). Habitat maps to enhance monitoring and management of the Great Barrier Reef. Coral Reefs, 39 (4), 1039-1054. doi: 10.1007/s00338-020-01929-3

Wave modelling as a proxy for seagrass ecological modelling: comparing fetch and process-based predictions for a bay and reef lagoon

local : UQ:348933

Callaghan, David P., Leon, Javier X. and Saunders, Megan I. (2015). Wave modelling as a proxy for seagrass ecological modelling: comparing fetch and process-based predictions for a bay and reef lagoon. Estuarine, Coastal and Shelf Science, 153, 108-120. doi: 10.1016/j.ecss.2014.12.016

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local : UQ:289097

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