Washgington State Parks Regions. WA State Parks or properties can belong to one of three regions - Northwest, Southwest or Eastern.

This priority map is a composite reflecting the overlap of forest health/wildfire risks (Tier 1) and the values at risk (Tier 2). Tier 1 and Tier 2 scores were normalized on a 0-1 range and then added together, this ensured equal weight for each tier in the final composite. A low score does not mean a watershed has no forest issues or values at risk. Instead, it means that the metrics and overall needs are lower relative to other watersheds.

The Digital Surficial Geologic Map of Lake Roosevelt National Recreation Area, Washington is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Washington State Bureau of Reclamation. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (laro_metadata.txt; available at http://nrdata.nps.gov/laro/nrdata/geology/gis/laro_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (laro_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 11N. That data is within the area of interest of Lake Roosevelt National Recreation Area. The data covers all of Lake Roosevelt National Recreation Area except for a small sliver in the eastern part of the Electric City (7.5 minute) quadrangle, and a small portion in the southeast part of the Orient (7.5 minute quadrangle).

The Digital Geologic Map of George Washington Memorial Parkway and parks in the National Capital Area, Virginia, Maryland, and the District of Columbia is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey and Maryland Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (gwmp_metadata.txt; available at http://nrdata.nps.gov/gwmp/nrdata/geology/gis/gwmp_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (gwmp_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 18N. That data is within the area of interest of George Washington Memorial Parkway, Rock Creek Park, National Capital Parks-East, Greenbelt Park and Chesapeake and Ohio Canal National Historical Park.

Geologic data from the geologic map of the Spokane 1:100,000-scale quadrangle compiled by Joseph (1990) were entered into a geographic information system (GIS) as part of a larger effort to create regional digital geology for the Pacific Northwest. The map area is located in eastern Washington and extendds across the state border into western Idaho.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

Due to the dynamic and diverse nature of winter weather and resultant road conditions, WSDOT maintenance personnel use a variety of methods and materials to help prevent snow and ice formation on state highways. Outcomes of snow and ice control treatments will vary, dependent upon severity of winter weather events, topography, traffic levels and speeds, and proximity to support facilities (i.e. liquid chemical storage tanks and salt stockpiles). While outcomes can be measured in a variety of ways, the motoring public most often measures maintenance efforts in terms of road conditions during and immediately after winter weather events. Maintenance personnel also rate roadway conditions during the winter season. This information is used to project expected road conditions associated with snow and ice treatment levels for different events. The unique nature of individual winter weather events limits the relevance of projected expectations on a given storm, but when ratings from an entire winter season are averaged, they become a good indicator of the Level of Service (LOS) provided by maintenance personnel over the entire season. Winter climates differ greatly between Eastern and Western Washington, so road treatment levels may vary on either side of the Cascades to match the ability to respond to those conditions. Limited funding also requires prioritization of roads for snow and ice control, so that different levels of service will be employed for individual roads and sections of roads

The land base of the Pacific Northwest includes large areas that could support hardwoods or a hardwood component. Often, however, site index, the most commonly used measure of a site's potential productivity, is not available for red alder as other species occupy the site. In order to make site-specific management decisions, the suitability for red alder production can be assessed by geographic and topographic position, soil moisture and aeration during the growing season, and soil fertility and physical condition (Harrington 1986). The difficulty of weighing these physical factors to determine site suitability appears to be a major impediment to the establishment of red alder plantations. Additionally, forest managers are lacking a planning tool that would consider red alder in the landscape for long term management plans. To assist forest managers in their planning and site selection efforts, we developed a GIS-based Red Alder Site Suitability Model based on physical criteria identified by Harrington (1986) as most influential on the productivity of red alder. The major components of the model are elevation, topographic position, slope, aspect, soil type, and soil depth. The model was implemented in a GIS (ESRI ArcPro v.3.0) raster environment with topographic position, slope, aspect, and elevation derived from a 10-meter digital elevation model (DEM), using lidar data where available. Topographic position class of valley, lower slope, flat slope, middle slope, upper slope, or ridgetop was derived from the topographic position index (TPI) using standard deviation thresholds as described by Weiss (2001). The soil texture and depth were derived from Washington DNR’s corporate soil data layer. Each pixel was then classified and assigned one of four suitability categories: High, Medium, Low, and No Potential. Because of the level of spatial detail of the model, forest managers can assess the potential of red alder management on any given site, such as planned timber harvest. Additionally, the model can be used at a larger scale, i.e. planning for future product diversification in a watershed.The model has been cursorily field-verified on existing red alder plantations and compared with locations and site index of natural red alder stands for DNR's forest inventory system. Initial results indicate that the model is accurate in identifying sites with potential for intensive red alder management. Local knowledge will still be an important factor in the application of the model. Frost pockets or areas susceptible to other physical damage such as ice damage (i.e. within the east wind drafts of the Columbia River Gorge) are not identified in by this model. The usefulness of this model will be determined by the experience of the field staff over time. References:Harrington, Constance A. 1986. A method of site quality evaluation for red alder. Gen. Tech. Rep. PNW-GTR-192. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 22 p. https://doi.org/10.2737/PNW-GTR-192Weiss, A. 2001. Topographic position and landforms analysis. In Poster presentation, ESRI user conference, San Diego, CA (Vol. 200). http://www.jennessent.com/downloads/tpi-poster-tnc_18x22.pdf

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington.

This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.

There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa et al. 2019.

The Washington State Parcels Project provides a statewide data set of tax parcels attributes that cover those counties that currently have digital tax parcels. Attribute data has been normalized so that the field names are the same across all counties. The data set contains the tax parcel identification number, situs addresses, the Department of Revenue land use codes, improvement and land values, and a link to the county's assessor's website for parcel information (if it exists).

Boundaries (polygons) of NYS Assembly districts in New York State with name and contact info for each member of the NYS Assembly. Districts based on Legislative Task Force redistricting 2024. Information on representative based on assembly website as of 5-8-2025.Please contact Geospatial Services at nysgis@its.ny.gov if you have any questions.All district boundaries have been clipped to the NYS shoreline. This affects the following counties: Bronx, Cayuga, Chautauqua, Clinton, Erie, Essex, Franklin, Jefferson, Kings, Monroe, Nassau, New York, Niagara, Orleans, Oswego, Queens, Richmond, St. Lawrence, Suffolk, Washington, Wayne, Westchester.

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones. Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event. Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are:1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Unstable_Slopes/MapServer/3The siteclass data layer was created for use in implementing Forest Practices' Riparian Management Rules. (See WAC 222-30-021 and 222-30-022.)

The siteclass data layer was derived from the DNR soils data layer's site index codes and major tree species codes for western and eastern Washington soils contained in the layer's Soils-Main table and Soils-Pflg (private forest land grade) table. Site index ranges in the Soils_PFLG took precedence over site index ranges in the Soils-Main table where data existed.The siteclass data layer was created for use in implementing new ForestPractices' Riparian Management Rules. (See WAC 222-30-021 and 222-30-022.) The siteclass information was derived from the DNR soils data layer's site indexcodes and major tree species codes for western and eastern Washington soilscontained in the layer's Soils-Main table and Soils-Pflg (private forest landgrade) table. Site index ranges in the Soils_PFLG took precedence over siteindex ranges in the Soils-Main table where data existed.Siteclass codes as derived from the soil survey:For Western Washington, the 50 year site index is used SITECLASS SITE INDEX RANGE I 137+ II 119-136 III 97-118 IV 76-96 V 1-75For Eastern Washington, the 100 year site index is used SITECLASS SITE INDEX RANGE I 120+ II 101-120 III 81-100 IV 61-80 V 1-60In addition to the coding scheme above, the following codes were added forrule compliance: SITECLASS DESCRIPTION 6 (Red Alder) The soils major species code indicated Red Alder 7 (ND/GP) No data), NA, or gravel pit 8 (NC/MFP) Non-commercial or marginal commercial forest land 9 (WAT) Water body(Rule note: If the site index does not exist or indicates red alder,noncommercial, or marginally commercial species, the following apply:If the whole RMZ width is within those categories, use site class V.If those categories occupy only a portion of the RMZ width, then use thesite index for conifer in the adjacent soil polygon.)WADNR SOILS LAYER INFORMATION LAYER: SOILS GEN.SOURCE: State soils mapping program CODE DOCUMENT: State soil surveys CONTACT: NA COVER TYPE: Spatial polygon coverage DATA TYPE: Primary data Information for the SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five year mapping program completed in 1980 for the purpose of forest land taxation. It was funded by the Washington State Department of Revenue in cooperation with the Department of Natural Resources, Soil Conservation Service (SCS), USDA Forest Service and Washington State University. State and private lands which had the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soil survey project, agricultural and non- commercial forest lands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. Approximately 1100 townships wholly or partially contain digitized soils data (2101 townships would provide complete coverage of the state of Washington). SOILS data are currently stored in the Polygon Attribute Table (.PAT) and INFO expansion files. COORDINATE SYSTEM: WA State Plane South Zone (5626) (N. zone converted to S. zone) COORDINATE UNITS: Feet HORIZONTAL DATUM: NAD27 PROJECTION NAME: Lambert Conformal Conic **** MAJOR CODES USED FOR SITECLASS DATA*****PFLG DATA: ITEM: PFLG.MAJ.SPEC TITLE: Potential major tree species for given soil FORMAT: INPUT OUTPUT DATA DECIMAL ARRAY ARRAY WIDTH WIDTH TYPE PLACES OCCUR. INDEX ------------------------------------------------- 3 3 C 0 0 0 CODE TABLE OR VALUE RANGE: SOIL.MAJ.SPEC.CODE DESCRIPTION: Potentially major tree species for a given soil type. The data carried by this item describes a major commercial tree species that could potentially grow on a specific soil type as identified in the Private Forest Land Grading program (PFLG). Non-tree codes are also included to map non-soil ground cover, e.g. water, gravel pits. ITEM: PFLG.SITE.INDEX TITLE: Mean site index calc.from trees on given soil FORMAT: INPUT OUTPUT DATA DECIMAL ARRAY ARRAY WIDTH WIDTH TYPE PLACES OCCUR. INDEX ------------------------------------------------- 3 3 I 0 0 0 CODE TABLE OR VALUE RANGE: 0-200 DESCRIPTION: Site index data collected for the Private Forest Land Grading soils program (PFLG). It is a designation of the quality of a forest site based on the height of of the tallest trees (dominant and co-dominant trees) in a stand at an arbitrarily chosen age. Usually the age chosen is 50 or 100 years. For example, if the average height attained by the tallest trees in a fully stocked stand at the age of 50 years is 75 feet, the site index is 75 feet. Westside site conditions are estimated by using an index age of 50 years, while eastside site conditions are estimated by using an index age of 100 years.--------------------------------------------------------------------SOILS-MAIN DATA: CODE TABLE NAME: SOIL.MAJ.SPEC.CODE ----------------------------------------------------------------------------- CODE MAP/REPORT MAP CODE DESCRIPTION LABEL SYMB --------- ------------ ---- -------------------------------------------------- AF ALPINE FIR 0 Subalpine fir DF DOUGLAS FIR 0 Douglas fir GF GRAND FIR 0 Grand fir GP GRAVEL PIT 0 Gravel pit LP LODGEPOLE PN 0 Lodgepole pine MFP MAR FOR PROD 0 Marginal forest productivity NA N/A 0 Not applicable NC NON-COMMERC 0 Non-commercial ND NO DATA 0 No data PP PONDEROSA PN 0 Ponderosa pine RA RED ALDER 0 Red alder WAT WATER 0 Water WH W HEMLOCK 0 Western hemlock WL W LARCH 0 Western larch WP W WHITE PINE 0 Western white pine ITEM: SITE.INDEX.SIDE TITLE: Indicates 100 yr or 50 yr soil site index FORMAT: INPUT OUTPUT DATA DECIMAL ARRAY ARRAY WIDTH WIDTH TYPE PLACES OCCUR. INDEX ------------------------------------------------- 1 1 C 0 0 0 CODE FILE OR VALUE RANGE: SITE.INDEX.SIDE.CODE DESCRIPTION: Code used to indicate whether 100 year or 50 year site index tables are used to calculate the site index of a soil type. Note that some site indexes for "eastside" soils are based on the 50 year index table. SITE.INDEX.SIDE Indicates 100 yr or 50 yr soil site index CODE FILE SITE.INDEX.SIDE.CODE IS NOT USED BY OTHER ITEMS CODE MAP/REPORT MAP CODE DESCRIPTION LABEL SYMB --------- ------------ ---- -------------------------------------------------- E 100 YR SITE 0 Soil site index based on 100 year table W 50 YR SITE 0 Soil site index based on 50 year table------------------------------------------------------------------

Polygons depict time of travel estimates for active public drinking water supplies. Source location data were obtained from the Washington State Department of Health, Office of Drinking Water.

Critical aquifer recharge areas are those areas that provide a critical recharging effect on aquifers used for drinking water, including areas that are vulnerable to contamination or reduced recharge.CARAs are divided into three categories depending on sensitivity: Category I - extreme aquifer sensitivity; Category II - high aquifer sensitivity; Category III - moderate aquifer sensitivity. See Thurston County Code Chapter 24.10.010 for full definitions.This data set combines soil-based CARAs with geology-based CARAs. Geology-based CARA categories were developed in conjunction with Nadine Romero, Thurston County's hydrogeologist, using available data from the Washington State Department of Natural Resources. Where an area has conflicting CARA categories between soils and geology, the more restrictive category prevails. Geology mapping at the 1:24,000 scale was only available for some portions of the county at the time of CAO update, the rest is mapped at 1:100,000 scale. Therefore it is important to not to accidentally use the data at a scale beyond its native resolution. The geology data are organized by quads. The 24k quads are: Shelton, Squaxin Island, Longbranch, Summit Lake, Tumwater, Lacey, Nisqually, Maytown, and East Olympia. The 100k quads are: Kamilche Valley, Capitol Peak, Little Rock, Tenalquot Prairie, McKenna, Harts Lake, Oakville, Rochester, Violet Prairie, Bucoda, Vail, Lake Lawrence, Bald Hills, Eatonville, and Elbe. This data was created by the Thurston County Hydrogeologist and Long Range Planning staff in 2014-2015 using USDA NRCS Soils data and Department of Natural Resources Geologic Data.

This map shows the National Park Service (NPS) jurisdictional boundaries in the District of Columbia symbolized by the six administrative NPS administrative units: Chesapeake & Ohio Canal National Historical Park, George Washington Memorial Parkway, National Capital Parks-East, National Mall and Memorial Parks, Rock Creek Park, and White House and President's Park.This web map is used by the InstantApp NCR NPS Land in DC by Administrative Unit Web App.The NPS Land and Jurisdictional data layers represent the land under the jurisdiction of the NPS including which NPS administrative unit manages it. This boundary is equivalent to the type of boundary that is displayed on park brochure maps.These polygons do NOT represent the legal the boundary. For issues regarding land ownership and official boundaries, contact the National Capital Region Land Resources Program Center using the contact form.The National Capital Region NPS jurisdictional boundaries data is available on the National Capital Region NPS OpenData site, along with other NCR-specific NPS datasets. Visit the National Park Service OpenData site for more NPS data or the Integrated Resource Management Application (IRMA) Portal for NPS data, documents, and more.