Macquarie Island Terrain Class Model

• Other Authors: BERGSTROM, DANA M. (hasPrincipalInvestigator), BERGSTROM, DANA M. (processor), DICKSON, CATHERINE R (hasPrincipalInvestigator), DICKSON, CATHERINE R (processor), WHINAM, JENNIFER (hasPrincipalInvestigator), MCGEOCH, MELODIE A. (hasPrincipalInvestigator), BAKER, DAVID J. (processor), Australian Antarctic Data Centre (publisher)
• Format: Dataset
• Language: unknown
• Published: Australian Antarctic Data Centre
• Subjects: biota; environment; EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL ECOSYSTEMS > ALPINE/TUNDRA > ALPINE TUNDRA; TOPOGRAPHY; EARTH SCIENCE; LAND SURFACE; ISLANDS; BIOSPHERE; TERRESTRIAL ECOSYSTEMS; TOPOCLIMATE MODEL; TOPOCLIMATE; MAPPING; Computer > Computer; MODELS; EARTH SCIENCE SERVICES; GEOGRAPHIC REGION > POLAR; OCEAN > SOUTHERN OCEAN > MACQUARIE ISLAND; Southern Ocean; Macquarie Island; Tundra
• Online Access: https://researchdata.ands.org.au/macquarie-island-terrain-class-model/1357177; https://doi.org/10.26179/5bf38bdda2375; https://data.aad.gov.au/metadata/records/AAS_4312_MI_Terrain_Class_Model; http://nla.gov.au/nla.party-617536

A spatially explicit terrain class model (TCM) was produced of the full extent of Macquarie Island to determine areas on Macquarie Island with broadly the same microclimate and ensure that potential microrefugia could be identified. Non-correlated terrain variables likely to affect evapotranspiration rates were included in the model (elevation, surface curvature, topographic wetness index, solar radiation and topographically deflected wind speed). Terrain variables were derived from the Macquarie Island digital elevation model (DEM, Brolsma 2008), see Bricher et al. 2013 for methods. A fuzzy c-means clustering was applied to identify broadly similar climatic conditions and three different metrics (Xie-Beni Index, cluster stability, PCA) determined the optimum number of terrain clusters to be five. See Dickson et al. 2019 and Baker et al. in prep for full methodology. A ‘2nd Membership’ was also produced that only contains cells with at least double that of the second highest cluster centre membership association, which acknowledges that the highest membership ‘hard clustering’ contains some noise from the DEM and cells that may exist on the boundary of two memberships. ArcGIS shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .shx) in a zipped file: MI_TCM_2nd_Membership.zip MI_TCM_Highest_Membership.zip More details in the referenced publication.