Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The 3 second (\~90m) Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) version 1.0 was derived from resampling the 1 arc second (\~30m) gridded DEM (ANZCW0703013355). The DEM represents ground surface topography, and excludes vegetation features. The dataset was derived from the 1 second Digital Surface Model (DSM; ANZCW0703013336) by automatically removing vegetation offsets identified using several vegetation maps and directly from the DSM. The 1 second product provides substantial improvements in the quality and consistency of the data relative to the original SRTM data, but is not free from artefacts. Man-made structures such as urban areas and power line towers have not been treated. The removal of vegetation effects has produced satisfactory results over most of the continent and areas with defects are identified in the quality assessment layers distributed with the data and described in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010). A full description of the methods is in progress (Read et al., in prep; Gallant et al., in prep). The 3 second DEM was produced for use by government and the public under Creative Commons attribution.

The 3 second DSM and smoothed DEM are also available (DSM; ANZCW0703014216,

DEM-S; ANZCW0703014217).

Dataset History

Source data

1. SRTM 1 second Version 2 data (Slater et al., 2006), supplied by Defence Imagery and Geospatial Organisation (DIGO) as 813 1 x 1 degree tiles. Data was produced by NASA from radar data collected by the Shuttle Radar Topographic Mission in February 2000.

2. GEODATA 9 second DEM Version 3 (Geoscience Australia, 2008) used to fill voids.

3. SRTM Water Body Data (SWBD) shapefile accompanying the SRTM data (Slater et al., 2006). This defines the coastline and larger inland waterbodies for the DEM and DSM.

4. Vegetation masks and water masks applied to the DEM to remove vegetation.

5. 1 second DEM resampled to 3 second DEM.

1 second DSM processing

The 1 second SRTM-derived Digital Surface Model (DSM) was derived from the 1 second Shuttle Radar Topographic Mission data by removing stripes, filling voids and reflattening water bodies. Further details are provided in the DSM metadata (ANZCW0703013336).

1 second DEM processing (vegetation offset removal)

Vegetation offsets were identified using Landsat-based mapping of woody vegetation. The height offsets were estimated around the edges of vegetation patches then interpolated to a continuous surface of vegetation height offset that was subtracted from the DSM to produce a bare-earth DEM. Further details are provided in the 1 second DSM metadata (ANZCW0703013355).

Void filling

Voids (areas without data) occur in the data due to low radar reflectance (typically open water or dry sandy soils) or topographic shadowing in high relief areas. Delta Surface Fill Method (Grohman et al., 2006) was adapted for this task, using GEODATA 9 second DEM as infill data source. The 9 second data was refined to 1 second resolution using ANUDEM 5.2 without drainage enforcement. Delta Surface Fill Method calculates height differences between SRTM and infill data to create a "delta" surface with voids where the SRTM has no values, then interpolates across voids. The void is then replaced by infill DEM adjusted by the interpolated delta surface, resulting in an exact match of heights at the edges of each void. Two changes to the Delta Surface Fill Method were made: interpolation of the delta surface was achieved with natural neighbour interpolation (Sibson, 1981; implemented in ArcGIS 9.3) rather than inverse distance weighted interpolation; and a mean plane inside larger voids was not used.

Water bodies

Water bodies defined from the SRTM Water Body Data as part of the DSM processing were set to the same elevations as in the DSM.

Edit rules for land surrounding water bodies

SRTM edit rules set all land adjacent to water at least 1m above water level to ensure containment of water (Slater et al., 2006). Following vegetation removal, void filling and water flattening, the heights of all grid cells adjacent to water was set to at least 1 cm above the water surface. The smaller offset (1cm rather than 1m) could be used because the cleaned digital surface model is in floating point format rather than integer format of the original SRTM.

Some small islands within water bodies are represented as voids within the SRTM due to edit rules. These voids are filled as part of void filling process, and their elevations set to a minimum of 1 cm above surrounding water surface across the entire void fill.

Overview of quality assessment

The quality of vegetation offset removal was manually assessed on a 1/8 ×1/8 degree grid. Issues with the vegetation removal were identified and recorded in ancillary data layers. The assessment was based on visible artefacts rather than comparison with reference data so relies on the detection of artefacts by edges.

The issues identified were:

\* vegetation offsets are still visible (not fully removed)

\* vegetation offset overestimated

\* linear vegetation offset not fully removed

\* incomplete removal of built infrastructure and other minor issues

DEM Ancillary data layers

The vegetation removal and assessment process produced two ancillary data layers:

\* A shapefile of 1/8 × 1/8 degree tiles indicating which tiles have been affected by vegetation removal and any issue noted with the vegetation offset removal

\* A difference surface showing the vegetation offset that has been removed; this shows the effect of vegetation on heights as observed by the SRTM radar

instrument and is related to vegetation height, density and structure.

The water and void fill masks for the 1 second DSM were also applied to the DEM. Further information is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Resampling to 3 seconds

The 1 second SRTM derived Digital Elevation Model (DEM) was resampled to 3 seconds of arc (90m) in ArcGIS software using aggregation tool. This tool determines a new cell value based on multiplying the cell resolution by a factor of the input (in this case three) and determines the mean value of input cells with the new extent of the cell (i.e. Mean value of the 3x3 input cells). The 3 second SRTM was converted to integer format for the national mosaic to make the file size more manageable. It does not affect the accuracy of the data at this resolution. Further information on the processing is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_aac46307-fce9-449d-e044-00144fdd4fa6/SRTM-derived+3+Second+Digital+Elevation+Models+Version+1.0

Dataset Citation

Geoscience Australia (2010) Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/12e0731d-96dd-49cc-aa21-ebfd65a3f67a.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

This Mat-Su Borough road centerlines dataset contains assigned official road names, address ranges, and cartographic classifications. This data is used to create the MSAG table for the Enhanced 9-1-1 program and is suitable for geo-coding purposes. Note: Cartographic classification of roads now includes a classification of "NOT CONST'D" which denotes roads that have been platted but not yet constructed. Original data was aggregated by a consultant (McLane Consulting of Soldotna, AK) as a part of the original addressing/911 project. Centerlines were interpolated from existing digital CAD drawings of property and ROW lines. Consultant (McClane) then did field work to append the centerline file to include additional road segments not represented as part of ROW within the property maps. Additional segments were input using GPS and "heads up" 85 digitizing methods. Each was adjusted to fit with the existing data. Data was originally stored in MapInfo (MIF) format and later converted to ESRI shapefile (SHP) format. Additional data related to the state highway system was collected using GPS technology between 1997 and 1999 by the Alaska Department of Transportation. This data was used to supplement the Borough data set for portions of the Parks Highway, Glenn Highway, Old Glenn Highway, Petersville Road, Denali Highway, and Lake Louise Road. Replacement of those street segments based upon property map interpolation but now available within the AK-DOT GPS collection is planned for Summer 2001. Data is maintained in an ongoing basis, primarily taken from subdivision plats, right-of-way plats, or other similar documentation of road existence. Data is input based on road centerlines as shown on subdivision plats and using "heads up" digitizing from aerial imagery.

Global dataset of downscaled future projections developed by applying a statistical method for climate model downscaling and bias correction. We applied the delta method, which comprises the sum of interpolated anomalies of each GCM to the WorldClim 1-km spatial resolution dataset. The GCMs were the 35 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, for four representative concentrations pathways (RCPs). For each of these, we used the 30-year future periods named as 2030s, 2050s, 2070s and 2080s with three climate variables (mean monthly maximum and minimum temperatures and monthly rainfall). From these, we also derive a set of bioclimatic indices. We divided the global surface in 18 geographical tiles. The primary downscaling resolution is 30 arc-s (~1 km at the Equator) but we aggregate the data to other three resolutions using nearest neighbor interpolation, including: 2.5 arc-m (~5 km), 5 arc-m (~10 km), and 10 arc-m (20 km).

General Bathymetric Chart of the Oceans - GEBCO One Minute Grid

Released: 2003, updated: 2008.

The GEBCO One Minute Grid is an ArcInfo Grid version (one arc-minute resolution) of the original data source gridone.grd NetCDF file which is part of the GEBCO Digital Atlas (GDA) Centenary Edition.

The GEBCO One Minute Grid is largely based on the most recent set of bathymetric contours contained within the GEBCO Digital Atlas. Additional control contours and sounding data were used in many regions, particularly shallow water areas and semi-enclosed seas, to constrain the gridding process. It is a continuous digital terrain model for ocean and land, with land elevations derived from the Global Land One-km Base Elevation (GLOBE) database.

It must be stressed that although the GEBCO One Minute Grid is presented at one minute intervals of latitude and longitude, this does not imply that knowledge is available on sea floor depth at this resolution. It is important to note that, in most places, many miles exist between adjacent ship tracklines and that the grid is an interpolation based upon the input data.