Data

Integrated coral rubble vulnerability map of the Great Barrier Reef (RRAP Rubble Stabilisation, QUT)

eAtlas
Salcedo-Castro, Julio, Dr ; Bryan, Scott, Dr. ; Callaghan, David, Dr. ; Kim, Catherine, Dr.
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://eatlas.org.au/data/uuid/4992ccca-2aa0-429a-9573-58d36c88a23f&rft.title=Integrated coral rubble vulnerability map of the Great Barrier Reef (RRAP Rubble Stabilisation, QUT)&rft.identifier=https://eatlas.org.au/data/uuid/4992ccca-2aa0-429a-9573-58d36c88a23f&rft.publisher=Australian Institute of Marine Science (AIMS)&rft.description=This vulnerability map results from integrating the spatial pattern of the following stressors: cyclonic waves, currents, cyclone tracks, heatwaves (DHW), crown of thorns starfish, and tsunamis. After summarizing historical data and reclassifying each variable into a common scale, they were integrated into a single vulnerability score whose spatial distribution is represented in this map. Methods The coral rubble vulnerability map was calculated by combining GBR wide datasets on currents (2015-2019), cyclone wave statistics (synthetic cyclones), Degree Heating Weeks (DHW) (1986-2021), crown of thorns starfish outbreaks (1985-2017), cyclone tracks (1950-2022), and tsunamis hazard (Probabilistic Tsunami Hazard Assessment, PTHA18). Each of these datasets were converted onto a common 0.005° raster grid (matching the final vulnerability map) using inverse distance weighting (IDW) interpolation for point format datasets (cyclone track points, COTs, tsunamis) and resampling (currents) for raster datasets. For wave and DHW the data was reprojected and resampled on to the common grid. These grids were then clipped to the GBRMPA GBR features reef boundaries. In order to obtain the vulnerability score, each variable was reclassified in ArcGIS Pro, using five equally spaced categories (quantising the data into discrete levels between the minimum and maximum values). As result, all variables ranged from 1 to 5. Finally, all six layers were added up, ignoring no-data pixels. In the case of currents, cyclonic waves and DHW, multiple measures were reviewed and considered. In the index maximum surface current, maximum significant wave height from cyclones and mean DHW were used. Further details of the input data can be found in the ‘Supplementary information - Maps of input data for integrated coral rubble vulnerability map of the Great Barrier Reef’. Limitations This first vulnerability map assumes equal weight for all variables included. There is an ongoing study aiming to test and identify which variables are more correlated with reef damage. This model does not consider the likely accumulation of rubble based on the topology of the reefs. It does not consider the production rate of coral that can result in coral rubble or that some of the inshore reefs particularly in Broad Sound are not coral reefs. Format The spatial model is 1988x2707 pixels with a spatial reference of WGS84 (EPSG:2346). The original dataset is stored in ESRI GRID format (6322 KB), which was converted to a GeoTiff for use in the eAtlas (22 MB). Data dictionary Pixel values in the raster correspond to low vulnerability (7) through to high vulnerability (23)Maintenance and Update Frequency: asNeeded&rft.creator=Salcedo-Castro, Julio, Dr &rft.creator=Bryan, Scott, Dr. &rft.creator=Callaghan, David, Dr. &rft.creator=Kim, Catherine, Dr. &rft.date=2022&rft.coverage=144.1078245,-10.908236099999996 143.2457739,-10.892122999999984 143.2538304,-12.334244999999981 143.3585656,-12.9465426 143.5841489,-14.018063499999997 143.688884,-14.308099200000001 143.8902977,-14.300042599999998 144.3334078,-14.340325399999998 144.5187084,-14.251703399999997 144.6395566,-14.4531171 145.3163066,-14.920396800000006 145.4452114,-16.571989099999996 145.8158126,-16.73312 146.1139048,-17.240682500000005 146.057509,-18.336372999999995 146.4442233,-18.674747999999994 146.6134108,-19.182310500000014 146.9759554,-19.246762899999993 147.6204792,-19.222593200000006 148.087759,-19.55291170000001 148.6839435,-19.585137900000007 148.7967352,-20.374679600000007 148.7161697,-20.680828399999996 148.7564525,-20.850015900000002 149.191506,-21.0594861 149.4493156,-21.6153879 149.4170894,-21.945706299999998 149.529881,-22.2679682 150.0132739,-22.026271800000018 150.206631,-22.396873000000014 150.78670250000002,-22.71107839999999 150.73030659999998,-23.1461319 150.7705894,-23.3958849 151.5037352,-24.104861099999994 151.7857144,-24.0968046 152.5188602,-23.967899799999984 151.72126199999997,-22.76747420000001 151.6004138,-22.799700400000006 151.47956559999997,-23.621468199999995 151.00422919999997,-22.839983099999984 150.8672679,-22.155176600000004 150.3274792,-21.5912182 149.60239,-21.067542599999996 149.8118602,-20.672771799999992 150.7464197,-21.494539700000004 151.82599709999997,-21.937649800000017 152.2932769,-22.38881649999999 152.736387,-22.509664700000002 152.8975179,-22.2679682 152.67193460000001,-21.631501 152.8894614,-21.003090299999997 151.8823929,-20.785563499999995 151.71320539999996,-20.858072399999998 150.8914376,-20.189379000000002 150.2227441,-19.73015570000001 149.4170894,-19.287045600000013 148.6356042,-18.9486706 148.0716459,-18.682804599999997 147.411009,-18.1832986 147.0001251,-18.070506899999998 146.6698066,-17.49043549999999 146.5650715,-16.950646800000015 146.0333394,-16.24972719999998 145.9205477,-15.991917599999994 145.7916429,-14.775379000000001 145.6627382,-14.477286700000008 145.2760239,-14.2194772 144.6234435,-13.873045599999998 144.3011816,-13.703858100000005 144.0192025,-13.26880460000001 143.9789197,-12.914316500000012 143.8741846,-12.4309236 144.18839,-11.7702867 144.1642203,-11.190215300000006 144.1078245,-10.908236099999996&rft_rights=Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/&rft_subject=environment&rft_subject=oceans&rft_subject=biota&rft_subject=CORAL REEFS&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/

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Full description

This vulnerability map results from integrating the spatial pattern of the following stressors: cyclonic waves, currents, cyclone tracks, heatwaves (DHW), crown of thorns starfish, and tsunamis. After summarizing historical data and reclassifying each variable into a common scale, they were integrated into a single vulnerability score whose spatial distribution is represented in this map. Methods The coral rubble vulnerability map was calculated by combining GBR wide datasets on currents (2015-2019), cyclone wave statistics (synthetic cyclones), Degree Heating Weeks (DHW) (1986-2021), crown of thorns starfish outbreaks (1985-2017), cyclone tracks (1950-2022), and tsunamis hazard (Probabilistic Tsunami Hazard Assessment, PTHA18). Each of these datasets were converted onto a common 0.005° raster grid (matching the final vulnerability map) using inverse distance weighting (IDW) interpolation for point format datasets (cyclone track points, COTs, tsunamis) and resampling (currents) for raster datasets. For wave and DHW the data was reprojected and resampled on to the common grid. These grids were then clipped to the GBRMPA GBR features reef boundaries. In order to obtain the vulnerability score, each variable was reclassified in ArcGIS Pro, using five equally spaced categories (quantising the data into discrete levels between the minimum and maximum values). As result, all variables ranged from 1 to 5. Finally, all six layers were added up, ignoring no-data pixels. In the case of currents, cyclonic waves and DHW, multiple measures were reviewed and considered. In the index maximum surface current, maximum significant wave height from cyclones and mean DHW were used. Further details of the input data can be found in the ‘Supplementary information - Maps of input data for integrated coral rubble vulnerability map of the Great Barrier Reef’. Limitations This first vulnerability map assumes equal weight for all variables included. There is an ongoing study aiming to test and identify which variables are more correlated with reef damage. This model does not consider the likely accumulation of rubble based on the topology of the reefs. It does not consider the production rate of coral that can result in coral rubble or that some of the inshore reefs particularly in Broad Sound are not coral reefs. Format The spatial model is 1988x2707 pixels with a spatial reference of WGS84 (EPSG:2346). The original dataset is stored in ESRI GRID format (6322 KB), which was converted to a GeoTiff for use in the eAtlas (22 MB). Data dictionary Pixel values in the raster correspond to low vulnerability (7) through to high vulnerability (23)

Lineage

Maintenance and Update Frequency: asNeeded

Data time period: 1950-01-01 to 2022-12-01

This dataset is part of a larger collection

Click to explore relationships graph

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148.0716459,-17.49849205

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Identifiers
  • global : 4992ccca-2aa0-429a-9573-58d36c88a23f
ACN 633 798 857