Proportional change in effective area of similar ecological environments for Mammals as a function of land clearing within the present long term (30 year average) climate (1990 centred) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover.

This metric describes the effects of land clearing on the area of similar environments to each grid cell as a proportion. Each cell is compared with a sample of 60,000 points in both uncleared landscape and degraded landscape (pairwise similarities summed (e.g. a completely similar cell will contribute 1, a dissimilar cell 0, with a range of values in between). The contribution of each cell is then multiplied by a 0 (cleared) to 1 (intact) condition index based on the natural areas layer. By dividing the test area by the current area, we are able to quantify the reduction in area as a function of land use/climate change. Values less than one indicate a reduction, values of 1 no change, and values greater than 1 (rare cases in the north) show an increase in similar environments.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided in two forms: 1. Zipped ESRI float grids: Binary float grids (.flt) with associated ESRI header files (.hdr) and projection files (.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file. 2. ArcGIS layer package (.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.

Additionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.

Layers in this 9s series use a consistent naming convention: BIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS e.g. A_90_CAN85_S or R_90_MIR85_L where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants

Lineage: Proportional change in the area of similar ecological environments was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. Proportional change was calculated by taking the area of baseline ecological environments similar to each present cell as the denominator and the area of present cells with their contribution scaled by the natural areas condition index (0 degraded to 1 intact) as the numerator. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download. GDM Model: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ALA data extracted 28 February 2014 (GDM: REP_r3_v2) Climate data. Models were built and projected using: a) 9-second gridded climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment b) 9-second gridded climatology for continental Australia 2036-2065 CanESM2 RCP 8.5 (CMIP5): Summary variables with elevation and radiative adjustment Natural Areas Layer (intact to degraded land) Australian Government Department of the Environment (2014) Natural areas of Australia - 100 metre (digital dataset and metadata). Available at http://www.environment.gov.au/metadataexplorer/explorer.jsp and up to date information for Western Australia were provided at 25m Albers projection were reprojected to GDA94, merged and aggregated to a continuous measure of proportion of intact area per grid cell at 9s.

Sonoma Water analyzed 3-km downscaled climate projections for daily rainfall from 13 global circulation models (LOCA2. Pierce et.al., 2023) to produce gridded scalars. Those scalars were then applied to NOAA Atlas 14 precipitation frequency rasters for the 24-hour duration storm to produce this raster: the climate model mean + 2 standard deviations of projected 24-hour rainfall depth in inches for mid-century (2046-2075), medium-high emissions scenario (SSP2-4.5), the 100-year storm. Raw data from LOCA2 (Scripps, 2023), CMIP6 (IPCC, 2021) for Sonoma County, Upper Russian River, and Upper Eel River watershed. For additional information, see the Technical Report at [https://www.sonomawater.org/media/PDF/climate/FutureRainfallDatabaseTechnicalReport.pdf].

Sonoma Water analyzed 3-km downscaled climate projections for daily rainfall from 13 global circulation models (LOCA2. Pierce et.al., 2023) to produce gridded scalars. Those scalars were then applied to NOAA Atlas 14 precipitation frequency rasters for the 24-hour duration storm to produce this raster: the climate model mean of projected 24-hour rainfall depth in inches for early century (2016-2045), high emissions scenario (SSP5-8.5), the 25-year storm. Raw data from LOCA2 (Scripps, 2023), CMIP6 (IPCC, 2021) for Sonoma County, Upper Russian River, and Upper Eel River watershed. For additional information, see the Technical Report at [https://www.sonomawater.org/media/PDF/climate/FutureRainfallDatabaseTechnicalReport.pdf].

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Sonoma Water analyzed 3-km downscaled climate projections for daily rainfall from 13 global circulation models (LOCA2. Pierce et.al., 2023) to produce gridded scalars. Those scalars were then applied to NOAA Atlas 14 precipitation frequency rasters for the 24-hour duration storm to produce this raster: the climate model mean of projected 24-hour rainfall depth in inches for early century (2016-2045), medium-high emissions scenario (SSP2-4.5), the 10-year storm. Raw data from LOCA2 (Scripps, 2023), CMIP6 (IPCC, 2021) for Sonoma County, Upper Russian River, and Upper Eel River watershed. For additional information, see the Technical Report at [https://www.sonomawater.org/media/PDF/climate/FutureRainfallDatabaseTechnicalReport.pdf].

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Sonoma Water analyzed 3-km downscaled climate projections for daily rainfall from 13 global circulation models (LOCA2. Pierce et.al., 2023) to produce gridded scalars. Those scalars were then applied to NOAA Atlas 14 precipitation frequency rasters for the 24-hour duration storm to produce this raster: the climate model mean of projected 24-hour rainfall depth in inches for mid-century (2046-2075), high emissions scenario (SSP5-8.5), the 500-year storm. Raw data from LOCA2 (Scripps, 2023), CMIP6 (IPCC, 2021) for Sonoma County, Upper Russian River, and Upper Eel River watershed. For additional information, see the Technical Report at [https://www.sonomawater.org/media/PDF/climate/FutureRainfallDatabaseTechnicalReport.pdf].

Sonoma Water analyzed 3-km downscaled climate projections for daily rainfall from 13 global circulation models (LOCA2. Pierce et.al., 2023) to produce gridded scalars. Those scalars were then applied to NOAA Atlas 14 precipitation frequency rasters for the 24-hour duration storm to produce this raster: the Climate model mean of projected 24-hour rainfall depth in inches for Late century (2070-2099), high emissions scenario (SSP5-8.5), the 25-year storm. Raw data from LOCA2 (Scripps, 2023), CMIP6 (IPCC, 2021) for Sonoma County, Upper Russian River, and Upper Eel River watershed. For additional information, see the Technical Report at [https://www.sonomawater.org/media/PDF/climate/FutureRainfallDatabaseTechnicalReport.pdf].

Sonoma Water analyzed 3-km downscaled climate projections for daily rainfall (LOCA2) from 13 global circulation models to produce gridded scalars of the climate model mean. Those scalars were then applied to NOAA Atlas 14 precipitation frequency rasters for the 24-hour duration storm to produce this raster: the climate model mean of projected 24-hour rainfall depth in inches for early century (2016-2045), medium-high emissions scenario (SSP2-4.5), the 5-year storm. Raw data from LOCA2 (Scripps, 2023), CMIP6 (IPCC, 2021) for Sonoma County, Upper Russian River, and Upper Eel River watersheds. For additional information, see the Technical Report at [https://www.sonomawater.org/media/PDF/climate/FutureRainfallDatabaseTechnicalReport.pdf].