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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
Research Data Collections
local : UQ:289097
- Local : RDM ID: 27aaea40-fdd4-11ed-b17b-5ba0b2c0e1cb
- DOI : 10.48610/8246441