Zoning boundaries for unincorporated King County; WA. Created layers using parcels, cities, and legal descriptions. This is the version with the cities clipped out. This is used for GISMO and by KCGIS. We have another version that is presently being maintained as coverage that includes city areas.

This Zoning feature class is an element of the Oregon GIS Framework statewide, Zoning spatial data. This version is authorized for public use. Attributes include zoning districts that have been generalized to state classes. As of June 30, 2023, this feature class contains zoning data from 229 local jurisdictions. DLCD plans to continue adding to and updating this statewide zoning dataset as they receive zoning information from the local jurisdictions. Jurisdictions included in the latest version of the statewide zoning geodatabase: Cities: Adams, Adrian, Albany, Amity, Antelope, Ashland, Astoria, Athena, Aurora, Banks, Barlow, Bay City, Beaverton, Bend, Boardman, Bonanza, Brookings, Brownsville, Burns, Butte Falls, Canby, Cannon Beach, Carlton, Cascade Locks, Cave Junction, Central Point, Chiloquin, Coburg, Columbia City, Coos Bay, Cornelius, Corvallis, Cottage Grove, Creswell, Culver, Dayton, Detroit, Donald, Drain, Dufur, Dundee, Dunes City, Durham, Eagle Point, Echo, Enterprise, Estacada, Eugene, Fairview, Falls City, Florence, Forest Grove, Fossil, Garibaldi, Gaston, Gates, Gearhart, Gervais, Gladstone, Gold Beach, Gold Hill, Grants Pass, Grass Valley, Gresham, Halsey, Happy Valley, Harrisburg, Helix, Hermiston, Hillsboro, Hines, Hood River, Hubbard, Idanha, Independence, Jacksonville, Jefferson, Johnson City, Jordan Valley, Junction City, Keizer, King City, Klamath Falls, La Grande, La Pine, Lafayette, Lake Oswego, Lebanon, Lincoln City, Lowell, Lyons, Madras, Malin, Manzanita, Maupin, Maywood Park, McMinnville, Medford, Merrill, Metolius, Mill City, Millersburg, Milton-Freewater, Milwaukie, Mitchell, Molalla, Monmouth, Moro, Mosier, Mount Angel, Myrtle Creek, Myrtle Point, Nehalem, Newberg, Newport, North Bend, North Plains, Nyssa, Oakridge, Ontario, Oregon City, Pendleton, Philomath, Phoenix, Pilot Rock, Port Orford, Portland, Prescott, Prineville, Rainier, Redmond, Reedsport, Rivergrove, Rockaway Beach, Rogue River, Roseburg, Rufus, Saint Helens, Salem, Sandy, Scappoose, Scio, Scotts Mills, Seaside, Shady Cove, Shaniko, Sheridan, Sherwood, Silverton, Sisters, Sodaville, Spray, Springfield, Stanfield, Stayton, Sublimity, Sutherlin, Sweet Home, Talent, Tangent, The Dalles, Tigard, Tillamook, Toledo, Troutdale, Tualatin, Turner, Ukiah, Umatilla, Vale, Veneta, Vernonia, Warrenton, Wasco, Waterloo, West Linn, Westfir, Weston, Wheeler, Willamina, Wilsonville, Winston, Wood Village, Woodburn, Yamhill. Counties: Baker County, Benton County, Clackamas County, Clatsop County, Columbia County, Coos County, Crook County, Curry County, Deschutes County, Douglas County, Harney County, Hood River County, Jackson County, Jefferson County, Josephine County, Klamath County, Lane County, Lincoln County, Linn County, Malheur County, Marion County, Multnomah County, Polk County, Sherman County, Tillamook County, Umatilla County, Union County, Wasco County, Washington County, Wheeler County, Yamhill County. R emaining jurisdictions either chose not to share data to incorporate into the public, statewide dataset or did not respond to DLCD’s request for data. These jurisdictions’ attributes are designated “not shared” in the orZDesc field and “NS” in the orZCode field.

This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (ceghmf.ps, ceghmf.pdf, ceghmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:100,000 or smaller.

A comprehensive plan is a generalized; coordinated land use policy statement of the governing body of a county or city that is adopted pursuant to the Growth Management Act. The year dated versions are retained for vested permits. This comes from parcels and is related to all complu layers.

Using the Land-Use Trade-Offs (LUTO) model, this data collection was produced via a comprehensive, detailed, integrated, and quantitative scenario analysis of land-use and sustainability for Australia’s intensive-use agricultural land to 2050, under intersecting global change and domestic policies, and considering key uncertainties. We assessed land use competition between multiple land uses and assessed sustainability of economic returns and multiple ecosystem services at high spatial (1.1 km grid cell) and temporal (annual) resolution.

Results available are for 648 scenarios covering combinations of four global outlooks, three general circulation climate models, three domestic land-use policies, three productivity growth rates, three land-use change adoption hurdle rates, and two capacity constraint settings.

Outputs included for each scenario are: - annual land-use layers - summary data table - graphical dashboard summary - animation of potential land-use change, drivers, and impacts. This analysis was conducted in conjunction with CSIRO’s Australian National Outlook 2015 initiative to assess future potential land-use change and the impacts for the sustainability of ecosystem services. A full description of the methods and synthesis of the results can be found in the papers listed in the Related Information below and freely available via email from the author.

The data is provided to support a national conversation on the future for Australian land systems, public decision-making and policy design, and further scientific research.

Lineage: LUTO is an integrated environmental and economic model which estimates Australian land use futures under alternative global change and policy scenarios. LUTO has been fully described, tested, applied, and evaluated (see Related Information). The LUTO model takes an agricultural land use map as the baseline, and then combines a range of environmental and economic data to identify potential land use change (agriculture, carbon plantings, environmental plantings, bioenergy, and biofuels) and corresponding supply of ecosystem services (agriculture, emissions abatement, water resources, biodiversity services, bioenergy, and biofuel production). The model works at a grid cell resolution of 0.01 degrees (~1.1 km) and an annual time-step from 2013 – 2050.

The LUTO model is a bottom-up model which identifies the location, type, and timing of potential land use change given changes in the relative profitability of land use options as determined by productivity, prices, costs, and adoption behaviour. Key uncertainties were considered including different rates of land use change adoption and productivity growth. Future trajectories in external drivers and domestic policy which influence productivity, prices, and costs were also considered. These included a carbon price, energy price, and food demand as derived by integrated assessment of global outlooks, and the establishment of new markets for biofuel and biodiversity. The biofuel market assumes the availability of nearby demand for wheat crop grain and residue. The biodiversity market includes a discriminatory payment scheme where landholders are paid the opportunity cost of adoption environmental plantings. The payment budget included a 125 $M yr-1 base level plus a levy on carbon plantings.

A range of models were used to estimate the future provision of ecosystem services from each land use. Agricultural yields were derived from census data by SLA, and apportioned to cells in the land use map. To compliment this we used the Agricultural Production Systems Simulator (APSIM) to quantify crop yields under climate change and crop residue availability for biofuel production. The 3PG2 forest growth model was used to estimate carbon sequestration from reforestation in the form of carbon plantings (fast-growing Eucalyptus monocultures) and environmental plantings (mixed local native species), and growth was modified according to climate change effects. A landscape hydrology model—AWRA-L—was used to estimate the change in water resource availability resulting from the increased interception and evapotranspiration of water induced by reforestation compared to crops/pasture. We used a generalised dissimilarity model (GDM) to identify biodiversity priorities for ecological restoration via environmental plantings. Priority areas were those that both increase the area and connectivity of remnant habitat and create new habitat in areas which become important for species conservation given future shifts in climate.

High-performance computing techniques were used to run LUTO, with each of the 648 scenario combinations taking about 40 hours to run. LUTO identified potential land use transitions over space and time, and estimated the resulting impact on supply of the five ecosystem services.

Normally, any FIRM that has associated flood profiles has cross sections. The S_XS table contains information about cross section lines. These lines usually represent the locations of channel surveys performed for input into the hydraulic model used to calculate flood elevations. Sometimes cross sections are interpolated between surveyed cross sections using high accuracy elevation data. Depending on the zone designation (Zone AE, Zone A, etc.), these locations may be shown on Flood Profiles in the FIS report and can be used to cross reference the Flood Profiles to the planimetric depiction of the flood hazards. This information is used in the Floodway Data Tables in the FIS report, as well as on the FIRM panels.