CDFW BIOS GIS Dataset, Contact: Melanie Gogol-Prokurat, Description: Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for California's 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.

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.

SummaryA study by the U.S. Geological Survey (USGS), in cooperation with the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC) and the Department of Interior Southeast Climate Adaptation Science Center, evaluated the hydrologic response of a daily time step hydrologic model to historical observations and projections of potential climate and land cover change for the period 1952-2099. An application of the Precipitation Runoff Modeling System (PRMS) was used to develop the hydrologic simulations. The model simulations were used to compute the potential changes in hydrologic response across the southeastern U.S. using historical observations of climate and streamflow, and 13 downscaled general circulation models with four representative concentration pathways representing a range of potential future changes in climate. The PRMS simulated hydrologic response within the entire geographic study area – the model domain. The model domain was subset into small local watersheds delineating areas expected to have a similar hydrologic response due to changes in the model inputs. These local watersheds are called “hydrologic response units” or HRUs. The PRMS computes flow generated locally on each HRU for each time step. These flow components then are directed to stream segments (SEGs) for flow aggregation. These segments connect the network of HRUs to simulate accumulated streamflow from the upstream watershed. Each HRU and SEG has a unique ID. For each HRU and SEG, 52 summary streamflow metrics (Index of Hydrologic Alteration or IHA metrics) were calculated based on the daily flow outputs. A description of each IHA metric may be found here (streamflow_description_table.xlsx). The summary information presented here shows geospatial results from three main components: 1) The future percent difference from historical conditions for each HRU and SEG and for each of 50 IHA metrics (two metrics excluded due to a predominance of missing values). The results are based on the difference between future conditions in 2045-2075 and historical conditions from 1952-2005. Values are expressed as the percent difference based on a median of 45 future scenarios. https://www.sciencebase.gov/catalog/item/597b37bbe4b0a38ca27563d4 Data source - HRU: “Summary of percent change in statistics by GCM/RCP scenario by HRU”stats_difference_hru_gcm_v2_csvData source - SEG: “Summary of percent change in statistics by GCM/RCP scenario by SEG”stats_difference_seg_gcm_v2_csv PurposeThe streamflow statistics were selected to describe streamflow conditions that may be most useful in defining the suitability for each river or stream to support sustaining populations of priority aquatic species across the GCPO LCC. The data presented here are intended to provide more easily accessible landscape scale summary information in support of the USGS flow modeling project.

The purpose and need of the project is derived from the need to move specific aspects of the ecosystem within the project area further towards desired conditions, goals, and objectives in the Payette National Forest Land and Resource Management Plan (forest plan).

Railroad Saddle Forest Restoration Project on the Payette National Forest, New Meadows Ranger District, Idaho.The Forest has developed the proposed action to address the purpose and need of the project. The proposed action includes vegetation management and associated roads related activities.Vegetation management includes commercial timber harvest, non-commercial thinning (NCT), and prescribed fire. NCT and prescribed fire are proposed across the project area, outside of the inner half of riparian conservation areas (RCAs). The silviculture - NIDGS map shows the location of commercial harvest units (plus NCT NIDGS units).Road related activities include temporary road construction, log hauling on roads, the obliteration of undetermined roads, and the conversation of closed roads to two wheeled motorized use

This layer provides median household income estimates, along with margins of error, coefficients of variation (CV), and a data reliability classification based upon the margin of error and its related CV.

Attachment regarding a request by NNP Briar Chapel for a revision to the Conditional Use Permit to (1) revise the civic site at the intersection of Andrews Store Rd and Parker Herndon Rd (possible Chatham County elementary school site) on master plan to allow for full development of the site (rather than just 2 acres as shown), (2) create the possibility of having up to 2,650 residential units (currently approved for 2,500), (3) revise the master plan map to reduce the perimeter buffer (a) from 100’ to 50’ along the frontage with Chapel in the Pines church (at the church’s request); (b) from 100’ to 50’ along the short boundary with Duke Energy RoW at SD-N; and (c) from 100’ to 75’ along Phase 15-S boundary to eliminate the need to build a retaining wall within the perimeter buffer, and (4) revise the color key table on the master plan map to reflect adjustments to residential densities in particular locations.

PUNTOS DE INTERES

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://res1wwwd-o-twildlifed-o-tcad-o-tgov.vcapture.xyz/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.

This is the 2022 version of the Aquifer Risk Map. The 2021 version of the Aquifer Risk Map is available here.This aquifer risk map is developed to fulfill requirements of SB-200 and is intended to help prioritize areas where domestic wells and state small water systems may be accessing raw source groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the risk map is to be made available to the public and is to be updated annually starting January 1, 2021. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding. This is the final 2022 map based upon feedback received from the 2021 map. A summary of methodology updates to the 2022 map can be found here.This map displays raw source groundwater quality risk per square mile section. The water quality data is based on depth-filtered, declustered water quality results from public and domestic supply wells. The process used to create this map is described in the 2022 Aquifer Risk Map Methodology document. Data processing scripts are available on GitHub. Download/export links are provided in this app under the Data Download widget.This draft version was last updated December 1, 2021. Water quality risk: This layer contains summarized water quality risk per square mile section and well point. The section water quality risk is determined by analyzing the long-tern (20-year) section average and the maximum recent (within 5 years) result for all sampled contaminants. These values are compared to the MCL and sections with values above the MCL are “high risk”, sections with values within 80%-100% of the MCL are “medium risk” and sections with values below 80% of the MCL are “low risk”. The specific contaminants above or close to the MCL are listed as well. The water quality data is based on depth-filtered, de-clustered water quality results from public and domestic supply wells.Individual contaminants: This layer shows de-clustered water quality data for arsenic, nitrate, 1,2,3-trichloropropane, uranium, and hexavalent chromium per square mile section. Domestic Well Density: This layer shows the count of domestic well records per square mile. The domestic well density per square mile is based on well completion report data from the Department of Water Resources Online System for Well Completion Reports, with records drilled prior to 1970 removed and records of “destruction” removed.State Small Water Systems: This layer displays point locations for state small water systems based on location data from the Division of Drinking Water.Public Water System Boundaries: This layer displays the approximate service boundaries for public water systems based on location data from the Division of Drinking Water.Reference layers: This layer contains several reference boundaries, including boundaries of CV-SALTS basins with their priority status, Groundwater Sustainability Agency boundaries, census block group boundaries, county boundaries, and groundwater unit boundaries. ArcGIS Web Application

The FGGD CV of LGP map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains an average coefficient of variation of LGP for the pixel area over the period 1901-1996. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

File List Prec_cv.zip (md5: 77b70a2389e6149d159d8e8ff4036d5e) Description The file prec_cv.zip contains a georeferenced tagged image file, prec_cv.tif, which can be imported into ArcGIS. It contains a GIS layer of the CV of annual precipitation on a global scale. The data file is zipped in order to compress its size (38 MB unzipped).

UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY. Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds. Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal. File Name Details: ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif). Elements are separated by underscore (_) in the following sequence: property_r_depth_cm_geometry_model_additional_elements.extension Example: om_r_0_cm_2D_QRF_bt.tif Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling. The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below). _95PI_h: Indicates the layer is the upper 95% prediction interval value. _95PI_l: Indicates the layer is the lower 95% prediction interval value. _95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI. References Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

Comarcas de la Comunitat Valenciana

Important Note: This beta item will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer represents CMIP6 future projections of the variation in monthly precipitation totals over the course of the year. This index is the ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation (also known as the coefficient of variation) and is expressed as a percentage. The larger the percentage, the greater the variability of precipitation. In some regions the CV values exceed 100%. These regions, such as deserts, may have such little rainfall that any variation creates an extreme percentage. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: mmCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate Climate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica. Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal RangeBioclimate 3 IsothermalityBioclimate 4 Temperature SeasonalityBioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual RangeBioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation SeasonalityBioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

This is the AGOL Description. Publication Date: 2024-10-07

The Aquifer Risk Map Web Tool contains all archived maps, including this 2023 Aquifer Risk Map.The Aquifer Risk Map is developed to fulfill requirements of SB-200 (Monning, 2019) and is intended to help prioritize areas where domestic wells and state small water systems may be accessing groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the map is made available to the public and updated annually starting January 1, 2021. This web map is part of the 2023 Aquifer Risk Map. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding.

This web map includes the following layers:Water Quality Risk: water quality risk estimates per square mile section for all contaminants with an MCL. Water quality risk is listed as “high” (average or recent concentration in section is above MCL for one or more contaminants), “medium” (average or recent concentration in section is between 80% - 100% of MCL for one or more contaminants), “low” (average or recent concentration in section is less than 80% of MCL for all measured contaminants) or “unknown” (no water quality data available in section).Individual Contaminant Risk: water quality risk estimates for nitrate, arsenic, 1,2,3-trichloropropane, hexavalent chromium, and uranium per square mile section.State Small Water Systems (DDW): state small water systems (5-14 connections) location from the Division of Drinking Water joined with water quality risk section estimates from the 2023 Aquifer Risk Map.Domestic Well Records (OSWCR): the approximate count and location of domestic well completion reports submitted to the Department of Water Resources. This is used as a proxy to identify domestic well locations.Public Water System Boundaries (DDW): the approximate boundaries of public drinking water systems, from the Division of Drinking Water. For reference only.Census Areas: Census block groups and census tract boundaries containing demographic information from the 2021 American Community Survey (B19013 Median Household Income and B03002 race/ethnicity) joined with summarized water quality risk estimates from the 2023 Aquifer Risk Map (count of high risk domestic wells and state small water systems per census area).Reference Boundaries: Various geographic boundaries including counties, basins, GSA’s, CV-SALTS basin prioritization status, Disadvantaged Community (DAC) status, and legislative boundaries. For reference only.CalEnviroScreen 4.0: CalEnviroScreen scores from OEHHA. For reference only.Groundwater Level Percentiles (DWR): Groundwater depth in various monitoring wells compared to the historic average at that well. For reference only.

The water quality risk is based on depth-filtered, de-clustered water quality results from public and domestic supply wells. The methodology used to determine water quality risk is outlined here. For more information about the SAFER program, please email SAFER@waterboards.ca.gov. For technical questions or feedback on the map please email GAMA@waterboards.ca.gov.

Whole grids from the v3.3 Michigan Great Lakes Grids GIS layer were assigned to statistical districts to match boundaries in existing GIS layers (see maps 1-3 in the 2000 Consent Decree). The Dissolve tool in ArcGIS 10.5 was used to dissolve the Michigan Great Lakes Grid layer (v3.3) to create statistical district boundaries that were then bounded by waterbody descriptions as follows: 1) The MI-8 and MH-1 statistical districts were clipped to end at the boundary of the St. Mary’s River as described in the 2000 Consent Decree. 2) MH-6 was clipped to end at the Blue Water Bridge as described in Fisheries Order 200, and 3) “MICH”in Lake Erie was modified to end at the Lake Erie/Detroit River boundary as described in the 2016-2017 Michigan Fishing Guide. Note that all districts consist of whole or clippped (as described above) 10-minute grids except for Lake Erie which consists of whole aggregated 10-minute grids and two clipped (as described above) 5-minute grids (le_602 and le_603). Also note that statistical district boundaries are almost identical to those of the lake trout management units (where they exist) as described in FO-200.GIS layer last updated 10/02/2019. Metadata last updated 10/2/2019. SourcesConsent Decree, United States of America, and Bay Mills Indian Community, Sault Ste. Marie Tribe of Chippewa Indians, Grand Travers Band of Ottawa and Chippewa Indians, Little River Band of Ottawa Indians, and Little Travers Bay Bands of Odawa Indians v. State of Michigan et al. (Case No. 2:73 CV 26, August, 2000). https://www.michigan.gov/documents/dnr/consent_decree_2000_197687_7.pdfFisheries Order 200. Statewide Trout, Salmon, Whitefish, Lake Herring, and Smelt Regulations. Michigan Natural Resources Commission and the Michigan Department of Natural Resources. https://www.michigan.gov/documents/dnr/FO_200.10_317498_7.pdf?111313Hansen, M. J., 1996. A lake trout restoration plan for Lake Superior. Great Lakes Fishery Commission. Michigan Fishing Guide. Department of Natural Resources, State of Michigan. https://www.michigan.gov/documents/dnr/2019MIFishingGuide-Feb26_647890_7.pdfSmith, S. H., Buettner, H. J., and R. Hile. 1961. Fishery Statistical Districts of the Great Lakes. Great Lakes Fishery Commission Technical Report No. 2, September 1961. https://www.glfc.org/pubs/TechReports/Tr02.pdf

Representação das àreas protegidas em Cabo Verde

【Courtesy of the C. V. Starr East Asian Library University of California, Berkeley】 Copperplate print. In Japanese. Relief shown by hachures. Includes legend.

【Courtesy of the C. V. Starr East Asian Library University of California, Berkeley】 Mounted cover title. In Japanese. Copperplate print. Relief shown by hachures. Includes preface and legend.