The County of Oxford Official Plan document outlines the policies and objectives that have been established in an effort to guide and manage the use of land and resources within the county. The Official Plan was adopted by Oxford County Council in 1995, and has since been amended and expanded in response to Provincial Policy Statements and the Official Plan Review process. The digital Official Plan data layer was created to represent lands in Oxford County with designations as defined in the Official Plan document. The data is used to produce numerous schedules in the map portion of the document. It is accessible from within the county's corporate Geographic Information System (GIS) applications for use in site specific queries, analyses, etc. The data is changed through the process of application and subsequent Official Plan Amendments (OPAs) and Ontario Municipal Board (OMB) orders.

These layers are polar climatological and other summary environmental layers that may be useful for purposes such as general modelling, regionalisation, and exploratory analyses. All of the layers in this collection are provided on a consistent 0.1-degree grid, which covers -180 to 180E, 80S to 30S (Antarctic) and 45N to 90N (Arctic). As far as practicable, each layer is provided for both the Arctic and Antarctic regions. Where possible, these have been derived from the same source data; otherwise, source data have been chosen to be as compatible as possible between the two regions. Some layers are provided for only one of the two regions.

Each data layer is provided in netCDF and ArcInfo ASCII grid format. A png preview map of each is also provided.

Processing details for each layer:

Bathymetry File: bathymetry Measured and estimated seafloor topography from satellite altimetry and ship depth soundings. Antarctic: Source data: Smith and Sandwell V13.1 (Sep 4, 2010) Processing steps: Depth data subsampled from original 1-minute resolution to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation. Reference: Smith, W. H. F., and D. T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277:1957-1962. http://topex.ucsd.edu/WWW_html/mar_topo.html Arctic: Source data: ETOPO1 Processing steps: Depth data subsampled to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation on polar stereographic projection. Reference: Amante, C. and B. W. Eakins, ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp, March 2009. http://www.ngdc.noaa.gov/mgg/global/global.html

Bathymetry slope File: bathymetry_slope Slope of sea floor, derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Slope calculated on 0.1-degree gridded depth data (above). Calculated using the equation given by Burrough, P. A. and McDonell, R.A. (1998) Principles of Geographical Information Systems (Oxford University Press, New York), p. 190 (see http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=How%20Slope%20works)

CAISOM model-derived variables Variables derived from the CAISOM ocean model. This model has been developed by Ben Galton-Fenzi (AAD and ACE-CRC), and is based on the Regional Ocean Modelling System (ROMS). It has circum-Antarctic coverage out to 50S, with a spatial resolution of approximately 5km. The values here are averaged over 12 snapshots from the model, each separated by 2 months. These parameters should be treated as experimental.

Reference: Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214

Floor current speed File: caisom_floor_current_speed Current speed near the sea floor.

Floor temperature File: caisom_floor_temperature Potential temperature near the sea floor.

Floor vertical velocity File: caisom_floor_vertical_velocity Vertical water velocity near the sea floor.

Surface current speed File: caisom_surface_current_speed Near-surface current speed (at approximately 2.5m depth)

Chlorophyll summer File: chl_summer_climatology Source data: Near-surface chl-a summer climatology from MODIS Aqua Antarctic: Climatology spans the 2002/03 to 2009/10 austral summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Arctic: Climatology spans the 2002 to 2009 boreal summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Reference: Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. https://oceancolor.gsfc.nasa.gov/

Distance to Antarctica File: distance_antarctica Distance to nearest part of Antarctic continent (Antarctic only) Source data: A modified version of ESRI's world map shapefile Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to nearest seabird breeding colony (Antarctic only) File: distance_colony Antarctic source data: Inventory of Antarctic seabird breeding sites, collated by Eric Woehler. http://data.aad.gov.au/aadc/biodiversity/display_collection.cfm?collection_id=61. Processing steps: The closest distance of each grid point to the colonies was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to maximum winter sea ice extent File: distance_max_ice_edge Source data: SMMR-SSM/I passive microwave estimates of daily sea ice concentration from the National Snow and Ice Data Center (NSIDC). Processing steps: Antarctic: Mean maximum winter sea ice extent was derived from daily estimates of sea ice concentration as described at https://data.aad.gov.au/metadata/records/sea_ice_extent_winter. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Arctic: The median March winter sea ice extent was obtained from the NSIDC at http://nsidc.org/data/g02135.html. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Reference: Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated 2008. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I passive microwave data. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. tp://nsidc.org/data/nsidc-0051.html

Distance to shelf break File: distance_shelf Distance to nearest area of sea floor of depth 500m or less. Derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points in less than 500m of water (i.e. over the shelf) were assigned negative distances. See also distance to upper slope

Distance to subantarctic islands (Antarctic only) File: distance_subantarctic_islands Distance to nearest land mass north of 65S (includes land masses of e.g. South America, Africa, Australia, and New Zealand). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to canyon File: distance_to_canyon Distance to the axis of the nearest canyon (Antarctic only) Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances to nearest canyon axis calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. NOTE: source data extend only as far north as 45S. Do not rely on this layer near or north of 45S. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Distance to polynya File: distance_to_polynya Distance to the nearest polynya area (Antarctic only) Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: The seaice_gt_85 layer (see below) was used. Pixels which were (on average) covered by sea ice for less than 35% of the year were identified. The distance from each grid point on the 0.1-degree grid to the nearest such polynya pixel was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. (NB the threshold of 35% was chosen to give a good empirical match to the polynya locations identified by Arrigo and van Dijken (2003), although the results were not particularly sensitive to the choice of threshold. Reference: Arrigo KR, van Dijken GL (2003) Phytoplankton dynamics within 37 Antarctic coastal polynya systems. Journal of Geophysical Research, 108, 3271. http://dx.doi.org/10.1029/2002JC001739

Distance to upper slope (Antarctic only) File: distance_upper_slope Distance to the "upper slope" geomorphic feature from the Geoscience Australia geomorphology data set. This is probably a better indication of the distance to the Antarctic continental shelf break than the "distance to shelf break" data (above). Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points inside of an "upper slope" polygon were assigned negative distances. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Fast ice File: fast_ice The average proportion of the year for which landfast sea ice is present in a location Source data: 20-day composite records of East Antarctic landfast sea-ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: The average proportion of the year for which each pixel was covered by landfast sea ice was calculated as an average across 2001--2008. Data were regridded to the 0.1-degree grid using bilinear interpolation.

Distance to fast ice File: distance_to_fast_ice Distance to the nearest location where fast ice is typically present. Source data: 20-day composite records of East Antarctic landfast sea ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: Pixels in the landfast sea ice data that were associated with fast ice presence for more than half of the

Abstract: Climate time series for Germany derived from observations of the German Meteorological Service (Deutscher Wetterdienst / DWD) provided in daily resolution at a grid width of 250 meters for the period from 1961 to 2020 (current status February 2023). The following variables were processed: Daily total global radiation, separately for a horizontal and an inclined plane; daily total precipitation; daily mean, minimum and maximum 2m-air temperature; daily mean water vapor saturation deficit; daily mean wind speed. The temperature data sets are available in two different versions: V5 including a residual correction and V6 without.

TableOfContents: Daily total global radiation at horizontal plane (grhds); daily total global radiation at inclined plane (grids); daily total precipitation (rrds); daily mean water vapor saturation deficit (sddm); daily mean 2m-air temperature (tadm); daily minimum 2m-air temperature (tadn); daily maximum 2m-air temperature; daily mean wind speed (wsdm)

TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate: 1961-01-01 00:00:00; temporalExtent_endDate: 2020-12-31 23:59:59; temporalDuration: 60; temporalDurationUnit: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none

Methods: Spatialization of gridded climate fields is performed, merging Model Output Statistics (MOS) downscaling with surface parameterization techniques (Böhner and Antonic, 2009; Böhner and Bechtel, 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg and Böhner (2023).

A description of the methods used can be found in:

Dietrich, H.; Wolf, T.; Kawohl, T.; Wehberg, J.; Kändler, G.; Mette, T.; & Röder, A. & Böhner, J. (2019). Temporal and Spatial High-Resolution Climate Data from 1961 to 2100 for the German National Forest Inventory (NFI). Annals of Forest Science 76, 6. https://doi.org/10.1007/s13595-018-0788-5

Kawohl, T.; Dietrich, H.; Wehberg, J.; Böhner, J.; Wolf, T. & Röder, A. (2017). Das Klima in 80 Jahren – Wein- statt Waldbau? – AFZ-Der Wald 15: 32-35.

For GIS-based Terrain-parameterization methods and their application in statistical-dynamical downscaling see, e.g.:

Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015.

Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0.

Quality: --

Units: MJ/m2; MJ/m2; mm; hPa; degC; degC; degC; m/s

GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32

Size: Files are first packed into zip-archives and then further grouped together into one tar-archive per variable and 10-year period. The original file size is between about 4 and 7.5 GB per year and variable. The file size of the tar archives ranges between 3 GB and 70 GB.

Format: SAGA-Grid (.sgrd), https://saga-gis.sourceforge.io/en/index.html

DataSources: DWD Climate Data Center (CDC): Historical daily station observations (temperature, pressure, precipitation,sunshine duration, etc.) for Germany, version v21.3, 2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-kl-historical and DWD Climate Data Center (CDC): Historical daily precipitation observations for Germany, version v21.3,2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-more_precip-historical. http://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/

Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html

Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php

Abstract: Gridded climate time series for Germany derived through downscaling of EURO-CORDEX historical simulations and climate projections from following ensemble members (www.euro-cordex.net)::

MPI-M-MPI-ESM-LR(r1)_CLMcom-CCLM4-8-17: RCPs 8.5, 4.5, 2.6 and historical (MPI_CLM)

ICHEC-EC-EARTH(r12)_KNMI-RACMO22E(v1): RCP 8.5 and historical (ECE_RAC)

CCCmaCanESM2_r1i1p1_CLMcomCCLM4817_v1: RCP 8.5 and historical (CA2_CLM)

All time series were consistently calculated at daily resolution and a grid cell spacing of 250 × 250 meter. Historical 1950–2005 data sets and 2006–2100 RCP projections comprise of mean temperature, minimum temperature, maximum temperature, precipitation, global radiation, air pressure, wind speed, specific humidity and delineated variables (relative humidity, potential evapotranspiration, water vapor pressure). All data sets except specific humidity and surface air pressure are available twice, as downscaled but non-bias corrected EURO-CORDEX data, and as bias corrected data sets. Correction terms for empirical adjustment of downscaling results were computed according to Sachindra et al. (2014) using gridded WP-KS-KW data as observational reference (Dietrich et al. 2019).

Dietrich, H., Wolf, T., Kawohl, T., Wehberg, J., Kändler, G., Mette, T., Röder, A. & Böhner, J. (2019): Temporal and spatial high-resolution climate data from 1961-2100 for the German National Forest Inventory (NFI). – Annals of Forest Science 76: 6, https://doi.org/10.1007/s13595-018-0788-5.

Sachindra, D.A., Huang, F., Bartona, A. & Pereraa, B.J.C. (2014): Statistical downscaling of general circulation model outputs to precipitation – part 2: bias-correction and future projections. – Int. J. Climatol. 34: 3282–3303, https://doi.org/10.1002/joc.3915.

TableOfContents: daily mean 2m-air temperature (tav); daily minimum 2m-air temperature (tmn), daily maximum 2m-air temperature (tmx); daily sum of precipitation (prz); daily sum of global radiation (sgz); daily surface air pressure (psz); daily mean 10m wind speed (wsp); daily mean specific humidity (hus); daily mean relative humidity (rhm); potential evapotranspiration (pet); daily mean water vapor pressure (vap)

TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2005-12-31 23:59:59; temporalDuration_Historical: 56; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2006-01-01 00:00:00; temporalExtent_endDate_RCPs: 2100-12-31 23:59:59; temporalDuration_RCPs: 95; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none

Methods: Statistical downscaling of EURO-CORDEX data is performed, merging MOS (Model Output Statistics) downscaling with surface parameterization techniques (Böhner & Antonic 2009; Böhner & Bechtel 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg & Böhner (2023).

Böhner, J. & Antonic, O. (2009): Land-Surface Parameters Specific to Topo-Climatology. – In: Hengl, T & Reuter, H.I. [Eds.]: Geomorphometry: Concepts, Software, Applications. – Developments in Soil Science, Elsevier, Volume 33, 195-226, https://doi.org/10.1016/S0166-2481(08)00008-1.

Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0.

Böhner, J. & Wehberg, J.-A. (2022): Schlussbericht zum Verbundvorhaben Standortsfaktor Wasserhaushalt im Klimawandel (WHH-KW); Teilvorhaben 4: Klimadaten. Universität Hamburg/Centrum für Erdsystemforschung und Nachhaltigkeit (CEN)/Institut für Geographie/Abt. Physische Geographie. Waldklimafonds, Bundesministerium für Ernährung und Landwirtschaft, Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit. 14 Seiten.

Wehberg, J.-A. & Böhner, J. (2023): Hochaufgelöste Klimaprojektionen für Deutschland. Forstliche Forschungsberichte München 224. Schriftenreihe des Zentrums Wald-Forst-Holz Weihenstephan, ISBN 3-933506-55-7, pp. 69-78.

Quality: --

Units: degC; degC; degC; mm; MJ/m2; hPa; m/s; kg/kg; percent; mm; hPa

ScaleFactors: 0.1; 0.1; 0.1; 0.1; 0.1; 0.1; 0.1; 1; 1; 0.1; 1

GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32

Size: Files are stored into one NetCDF-file per year and variable and uploaded as tar-archives - one per variable, model and run. The file size of the netCDF files differs between 36 and 206 GB per future scenario simulation and variable (95 years) and between 21 and 113 GB per historical run and variable (56 years).

Format: netCDF

DataSources: EURO-CORDEX data published via ESGF (https://cordex.org/data-access/esgf/). Jacob, D., Petersen, J., Eggert, B. et al. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14, 563–578 (2014). https://doi.org/10.1007/s10113-013-0499-2

Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html

Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php

The purpose of this study is to characterize the current state of the agricultural resources, identify areas where the resource is at risk and to provide a snapshot of their status for public information. An upgraded provincial soil data base is available for much of the Lake Erie Basin. A major gap in the database for this area is Oxford County. The existing soil map for Oxford County was upgraded so that it was compatible with adjacent county/regional municipality information. The upgrade was undertaken according to present standards outlined by the Ontario Centre for Soil Resource Evaluation (OCSRE). The study focused on slope information generation, soil reliability checking and development of information and maps in electronic format. The soil polygons produced during the original Oxford County soil survey were used. The digitally generated slope polygons were then overlain over the original soil map. To generate the slope information for Oxford County, a Geographic Information System (GIS) was used to produce a digital terrain model. A triangulated irregular network (TIN) with a set of adjacent non-overlapping triangles was used to perform the digital terrain modelling. A stratified, random transect method was used for field verification purposes. This sampling procedure was used to verify slope mapping and provide an estimate of soil reliability for each soil polygon. The information compiled facilitated the production of an upgraded 1:50,000 map in electronic format. The field verification of the digitally generated slope information showed that the GIS generated slope information was accurate. Sixty-five percent of the slopes verified in the field agreed with the digitally slope class polygons, while an additional 30 percent were within one slope class. The original soil information was also fairly accurate. Field studies showed that 56 percent of the soils were identified as mapped, while 74 percent were found within the correct soil catena. Most of the discrepancies were due to differences in drainage class. In counties where no slope information is available, this method of digitally generating slope information can be used to upgrade soil maps to include slope information.