Vector polygon map data of property parcels from Fresno County, California containing 202,076 features.

Property parcel GIS map data consists of detailed information about individual land parcels, including their boundaries, ownership details, and geographic coordinates.

Property parcel data can be used to analyze and visualize land-related information for purposes such as real estate assessment, urban planning, or environmental management.

Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Vector polygon map data of city limits from Fresno, California containing 1 feature.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

This map feeds into a web app that allows a user to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.For more information about the wildfire response efforts, visit the CAL FIRE incident page.

This layer is a component of Service displaying various land use layers.

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 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).

Geospatial data about Fresno County, California Addresses. Export to CAD, GIS, PDF, CSV and access via API.

This layer is a component of Address and Parcels for viewing.

Except as provided in Section 366, bear may be taken only as follows:(a) Areas:(1) Northern California: In the counties of Del Norte, Humboldt, Plumas, Shasta, Siskiyou, Tehama and Trinity; and those portions of Lassen and Modoc counties west of the following line: Beginning at Highway 395 and the Sierra-Lassen county line; north on Highway 395 to the junction of Highway 36; west on Highway 36 to the junction of Highway 139; north on Highway 139 to Highway 299; north on Highway 299 to County Road 87; west on County Road 87 to Lookout-Hackamore Road; north on Lookout-Hackamore Road to Highway 139; north on Highway 139 to the Modoc-Siskiyou county line; north on the Modoc-Siskiyou county line to the Oregon border.(2) Central California: In the counties of Alpine, Amador, Butte, Calaveras, Colusa, El Dorado, Glenn, Lake, Mendocino, Nevada, Placer, Sacramento, Sierra, Sutter, Yolo and Yuba and those portions of Napa and Sonoma counties northeast of Highway 128.(3) Southern Sierra: That portion of Kern County west of Highway 14 and east of the following line: Beginning at the intersection of Highway 99 and the Kern-Tulare county line; south on Highway 99 to Highway 166; west and south on Highway 166 to the Kern-Santa Barbara county line; and those portions of Fresno, Madera, Mariposa, Merced, Stanislaus, Tulare and Tuolumne counties east of Highway 99.(4) Southern California: In the counties of Los Angeles, Santa Barbara and Ventura; that portion of Riverside County north of Interstate 10 and west of Highway 62; and that portion of San Bernardino County south and west of the following line: Beginning at the intersection of Highway 18 and the Los Angeles-San Bernardino county line; east along Highway 18 to Highway 247; southeast on Highway 247 to Highway 62; southwest along Highway 62 to the Riverside-San Bernardino county line.(5) Southeastern Sierra: Those portions of Inyo and Mono counties west of Highway 395; and that portion of Madera County within the following line: Beginning at the junction of the Fresno-Madera-Mono county lines; north and west along the Madera-Mono county line to the boundary of the Inyo-Sierra National Forest; south along the Inyo-Sierra National Forest boundary to the Fresno-Madera county line; north and east on the Fresno-Madera county line to the point of beginning. Also, that portion of Inyo county west of Highway 395; and that portion of Mono county beginning at the intersection of Highway 6 and the Mono county line; north along Highway 6 to the Nevada state line; north along the Nevada state line to the Alpine county line; south along the Mono-Alpine county line to the Mono-Tuolumne county line and the Inyo National Forest Boundary; south along the Inyo National Forest Boundary to the Inyo-Sierra Forest boundary; south along the Inyo-Sierra Forest boundary to the Fresno-Madera county line; north and east along the Fresno-Madera county line to the junction of the Fresno-Madera-Mono county line; south along the Mono-Fresno county line to the Mono-Inyo County line; east along the Mono-Inyo county line to the point of beginning.

This geodatabase includes the boundaries of the California Natural Resource Agency’s State Conservancies. The collection of the State Conservancies boundaries was initiated in January 2012 by the Sierra Nevada Conservancy, and the geodatabase was updated in 2022 to include Sierra Nevada Conservancy boundary expansion. The geodatabase was constructed from GIS data requested from each of the State Conservancies. The following documentation describes the contacts who provided data, and where available, the type of spatial information provided. Baldwin Hills Conservancy: A shapefile of the Baldwin Hills Conservancy boundary was requested and received from Amanda Recinos, amanda@greeninfo.org, of GreenInfo Network on behalf of the Executive Officer of the Baldwin Hills Conservancy, David McNeill, on 10 January 2012. This boundary has not been modified from the original boundary provided. California State Coastal Conservancy: The California State Coastal Conservancy was updated by San Jenniches, sjenniches@scc.ca.gov, of the Coastal Conservancy in Fiscal Year 2014-2015. The SNC did not receive the boundary directly from the Coastal Conservancy; the feature class was provided by Nickolas Perez, Nickolas.Perez@water.ca.gov, of the California Natural Resources Agency to the SNC on 30 April 2015. Coachella Valley Mountains Conservancy: A shapefile of the Coachella Valley Mountains Conservancy boundary was requested and received fromKerrie Godrey, kgodfrey@cvmc.ca.gov, of the Coachella Valley Mountains Conservancy on 10 January 2012. This boundary has not been modified from the original boundary provided. Delta Conservancy: A shapefile of the legal Delta and Suisun Marsh boundaries were provided by Elisa Sabatina with the Delta Conservancy, Elisa.Sabatini@deltaconservancy.ca.gov, on 10 January 2012. This boundary has not been modified from the original boundary provided. Rivers and Mountains Conservancy (San Gabriel/Lower LA): A shapefile of the Rivers and Mountains Conservancy was provided by Luz Torres, ltorres@rmc.ca.gov, of the Rivers and Mountains Conservancy on 10 January 2012. This boundary has not been modified from the original boundary provided. San Diego River Conservancy: Michael Nelson, mnelson@sdrc.ca.gov, the Executive Officer of the San Diego River Conservancy reported via email on 11 January 2012 that no prior GIS boundary existed for the Conservancy. Mr. Nelson provided written consent to the SNC, via an email dated 11 January 2012, to develop the San Diego River Conservancy GIS boundary from a PDF document supplied by Mr. Nelson that showed the general location of the San Diego River Conservancy’s boundary as occupying a one half mile buffer from the San Diego River. This boundary has not been modified from the original boundary provided. San Joaquin River Conservancy: The San Joaquin River Conservancy boundary was created from using both the legislation description of the boundary and a pdf version of the San Joaquin River Conservancy boundary provided by Marile Colindres, marile.colindres@sjrc.ca.gov, of the San Joaquin Conservancy on 24 February 2012. This boundary has not been modified from the since the creation of the boundary from the legal description in 2012. Santa Monica Mountains Conservancy: The SNC was not able to acquire GIS data from the Santa Monica Mountains Conservancy staff; therefore, the SNC created a boundary to represent the Santa Moninca Mountains Conservancy by using the description of the Conservancy from their website. Specifically, the SNC used the text from their website to select watersheds for GIS boundary: “the Santa Monica Mountains Conservancy zone covers an area from the edge of the Mojave Desert to the Pacific Ocean. The zone encompasses the whole of the Santa Monica Mountains, the Simi Hills, the Verdugo Mountains and significant portions of the Santa Susana and San Gabriel Mountains. In addition, the Mountains Recreation and Conservation Authority also owns or manages thousands of acres in the Sierra Pelona Mountains and in the Whittier-Puente Hills. From north to south, these areas drain into the Santa Clara River, Calleguas Creek, numerous smaller coastal watersheds in the Santa Monica Mountains, and the Los Angeles River and Rio Hondo.The Sierra Nevada Conservancy (SNC) boundary was mapped to correspond with statute AB 2600 (2004) and as re-defined in SB 208 (2022). Work on the boundary was completed by CalFire, GreenInfo Network, and the California Department of Fish and Game. Meets and bounds description of the area as defined in statute: PRC Section 33302 (f) defines the Sierra Nevada Region as the area lying within the Counties of Alpine, Amador, Butte, Calaveras, El Dorado, Fresno, Inyo, Kern, Lassen, Madera, Mariposa, Modoc, Mono, Nevada, Placer, Plumas, Shasta, Sierra, Siskiyou, Tehama, Trinity, Tulare, Tuolumne, and Yuba, described as the area bounded as follows: On the east by the eastern boundary of the State of California; the crest of the White/Inyo ranges; and State Routes 395 and 14 south of Olancha; on the south by State Route 58, Tehachapi Creek, and Caliente Creek; on the west by the line of 1,250 feet above sea level from Caliente Creek to the Kern/Tulare County line; the lower level of the western slope’s blue oak woodland, from the Kern/Tulare County line to the Sacramento River near the mouth of Seven-Mile Creek north of Red Bluff; the Sacramento River from Seven-Mile Creek north to Cow Creek below Redding; Cow Creek, Little Cow Creek, Dry Creek, and up to the southern boundary of the Pit River watershed where Bear Creek Mountain Road and Dry Creek Road intersect; the southern boundary of the Pit River watershed; the western boundary of the upper Trinity watershed in the County of Trinity; on the north by the boundary of the upper Trinity watershed in the County of Trinity and the upper Sacramento, McCloud, and Pit River watersheds in the County of Siskiyou; and within the County of Modoc, the easterly boundary of the Klamath River watershed; and on the north in the County of Modoc by the northern boundary of the State of California; excluding both of the following: (1) The Lake Tahoe Region, as described in Section 6605.5 of the Government Code, where it is defined as "region" (2) The San Joaquin River Parkway, as described in Section 32510. According to GreenInfo Network and the California Department of Fish and Game, the blue oak woodland used to define a portion of the Sierra Nevada Conservancy's western boundary was delineated using referenced vegetation and imagery data.The Tahoe Conservancy boundary was created by using the Tahoe Regional Planning Agency (TRPA) boundary received from the Tahoe Conservancy staff and clipping the TRPA boundary to the State of California boundary, using the Teale Albers projection. The TRPA boundary was received by the SNC from the Tahoe Conservancy staff in 2011, and the Tahoe Conservancy boundary was created by the SNC in 2012. Notes:Some conservancy boundaries overlap.

The 2000 Fresno County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. The finalized data include a shapefile of central and western Fresno County (land use vector data) and JPG files (raster data from aerial imagery). Important Points about Using this Data Set: 1. The land use boundaries were either drawn on-screen using developed photoquads, or hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to the extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. If the data is compared to the previous digital survey (i.e. the two coverages intersected for change detection determination) there will be land use changes that may be unexpected. The linework was created independently, so even if a field’s physical boundary hasn’t changed between surveys, the lines may differ due to difference in digitizing. Numerous thin polygons (with very little area) will result. A result could be UV1 (paved roads) to F1 (cotton). In reality, paved roads are not converted to cotton fields, but these small polygons would be created due to the differences in digitizing the linework for each survey. Additionally, this kind of comparison may yield polygons of significant size with unexpected changes. These changes will almost always involve non-cropped land, mainly U (urban), UR1 (single family homes on 1 – 5 acres), UV (urban vacant), NV (native vegetation), and I1 (land not cropped that year, but cropped within the past three years). The unexpected results (such as U to NV, or UR1 to NV) occur mainly because of interpretation of those non-cropped land uses with aerial imagery. Newer surveys or well funded surveys have had the advantage of using improved quality (higher resolution) imagery or additional labor, where more accurate identification of land use is possible, and more accurate linework is created. For example, an older survey may have a large polygon identified as UR, where the actual land use was a mixture of houses and vacant land. A newer survey may have, for that same area, delineated separately those land uses into smaller polygons. The result of an intersection would include changes from UR to UV (which is normally an unlikely change). It is important to understand that the main purpose of DWR performing land use surveys is to aid in development of agricultural water use data. Thus, given our goals and budget, our emphasis is on obtaining accurate agricultural land uses with less emphasis on obtaining accurate non-agricultural land uses (urban and native areas). 5. Water source information was not collected for this survey. 6. Not all land use codes will be represented in the survey.

Geospatial data about Fresno County, California Streets. Export to CAD, GIS, PDF, CSV and access via API.

The 1994 Fresno County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. Important Points about Using this Data Set: 1. The land use boundaries were hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method information was not collected for this survey. 5. Not all land use codes will be represented in the survey.The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.3, dated April 13, 2022. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. See the CADWR Land User Viewer (gis.water.ca.gov/app/CADWRLandUseViewer) for the most current contact information. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.

The raw dataset consisted of GPS way points collected from Advanced Telemetry Solutions (ATS) Iridium LITE/GPS model G2110L collars with SureDrop collar break off mechanisms, or Tellus small iridium collars equipped with Tellus RL-Drop off on mule deer in the upper San Joaquin River watershed. Migratory deer within the San Joaquin Watershed occupy most of the watershed above Kerckhoff Reservoir, Fresno and Madera Counties, California. The data was collected from 2013-2016 by Tim Kroeker. Fix rates varied between 2 and 12 hours. Human infrastructure in the watershed is widespread and includes residential, water control, hydroelectric power, and recreational use developments. Steep topography between winter and summer range limit crossing points along the San Joaquin River. Habitat conditions favoring deer declined from a peak around 1950, resulting in a reduction in the deer population. The current deer population is believed to be about 4,000. A massive wildfire burned through most of the watershed in 2020, dramatically changing habitat conditions in some areas. To improve the quality of the data set as per Bjørneraas et al. (2010), theGPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or Iii) fixed in 2D space and visually assessed as a bad fix by the analyst.The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 30 deer, including location, date, time, and average location error as inputs in Migration Mapper. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 30 m with a fixed motion variance parameter of 1000 using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 32 individual deer. A separate BBMM was created for all deer locations designated as winter range using a fixed motion variance parameter of 1000. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell in the BBMMs, with greater than 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 6 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.

Under contract to the California Department of Fish and Wildlife (CDFW), California Native Plant Society (CNPS) created a fine-scale vegetation map of portions of the Millerton Lake East quadrangle, including protected areas of Big Table Mountain and the McKenzie Preserve at Table Mountain. CNPS conducted field reconnaissance assistance for this project, as well as accuracy assessment (AA) field data collection. CDFW’s Vegetation Classification and Mapping Program (VegCAMP) provided in-kind service to allocate and score the AA. The mapping study area, consists of approximately 11,505 acres, of Fresno and Madera Counties. Work was performed on the project between 2008 and 2010. The primary purpose of the project was to generate an accurate and detailed basemap of a focus area within the southern Sierra Nevada Foothills, supported by field surveys, that would assist in long-term management of sensitive plant communities. Additionally, this map was created to further CDFW’s goal of developing fine-scale digital vegetation maps as part of the California Biodiversity Initiative Roadmap of 2018. CNPS under separate contract and in collaboration with CDFW VegCAMP developed the floristic vegetation classification used for the project. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). The vegetation map was produced applying heads-up digitizing techniques using a 2009 base of one-meter National Agricultural Imagery Program (NAIP) imagery (true-color and color infrared), in conjunction with ancillary data and imagery sources. Map polygons are assessed for Vegetation Type, Percent Cover, Exotics, Development Disturbance, and other attributes. The minimum mapping unit (MMU) is 1 acre. Field reconnaissance and accuracy assessment enhanced map quality. There was a total of 24 mapping classes.

Mapped hunt boundary is an approximation of regulations using best available data as of September 2015. Hunters are responsible for knowing the exact current boundary locations as described within the California Code of Regulations, Title 14, Section 300(a)(1)(E)3:3. Area Open Zone: The open hunting zone for sooty and ruffed grouse includes the following counties: Alpine, Amador, Butte, Calaveras, Del Norte, El Dorado, Fresno, Glenn, Humboldt, Inyo, Lake, Lassen, Madera, Mariposa, Mendocino, Modoc, Mono, Nevada, Placer, Plumas, Shasta, Sierra, Siskiyou, Tehama, Trinity, Tulare, Tuolumne, and Yuba. All other counties are closed to the taking of sooty or ruffed grouse.

DWR has a long history of studying and characterizing California’s groundwater aquifers as a part of California’s Groundwater (Bulletin 118). The Basin Characterization Program provides the latest data and information about California’s groundwater basins to help local communities better understand their aquifer systems and support local and statewide groundwater management.

Under the Basin Characterization Program, new and existing data (AEM, lithology logs, geophysical logs, etc.) will be integrated to create continuous maps and three-dimensional models. To support this effort, new data analysis tools will be developed to create texture models, hydrostratigraphic models, and aquifer flow parameters. Data collection efforts will be expanded to include advanced geologic, hydrogeologic, and geophysical data collection and data digitization and quality control efforts will continue. To continue to support data access and data equity, the Basin Characterization Program will develop new online, GIS-based, visualization tools to serve as a central hub for accessing and exploring groundwater related data in California.

Additional information can be found on the Basin Characterization Program webpage.

DWR's Evaluation of Groundwater Resources: Maps and Models

DWR will undertake local and regional investigations to evaluate California's groundwater resources and develop state-stewarded maps and models. New and existing data will be combined and integrated using the analysis tools described below to develop maps and models to be developed will describe the grain size, the hydrostratigraphic properties, and hydrogeologic conceptual properties of California’s aquifers. These maps and models help groundwater managers understand how groundwater is stored and moves within the aquifer. The models will be state-stewarded, meaning that they will be regularly updated, as new data becomes available, to ensure that up-to-date information is used for groundwater management activities. The first iterations of the following maps and models will be published as they are developed:

• Texture Models
• Hydrostratigraphic Models
• Aquifer Recharge Potential Maps
• Extent of Important Aquifer Units
• Depth to Basement
• Depth to Freshwater

Local Investigations:

• Madera & North Kings
• Pajaro
• Western San Joaquin Valley

Regional Investigations:

• Sacramento Valley
• Four County Area of San Joaquin Valley (Madera, Fresno, Kings, and Tulare)
• San Joaquin Valley

Data Collection, Compilation, and Digitization

Data Collection

As a part of the Basin Characterization Program, advanced geologic, hydrogeologic, and geophysical data will be collected to improve our understanding of groundwater basins. Data collected under Basin Characterization are collected at a local, regional, or statewide scale depending on the scope of the study.

Datasets collected under the Basin Characterization Program can be found under the following resource:

• Geophysical, Hydrogeologic, & Geologic Datasets

Digitized Existing Lithology and Geophysical Logs

Lithology and geophysical logging data have been digitized to support the Statewide AEM Survey Project and will continue to be digitized to support Basin Characterization efforts. All digitized lithology logs with Well Completion Report IDs will be imported back into the OSWCR database.

Digitized lithology and geophysical logging can be found under the following resource:

• Digitized Lithology and Geophysical Logs.

Analysis Tools and Process Documents

To develop the state-stewarded maps and models outlined above, new tools and process documents will be created to integrate and analyze a wide range of data, including geologic, geophysical, and hydrogeologic information. By combining and assessing various datasets, these tools will help create a more complete picture of California's groundwater basins. All tools, along with guidance documents, will be made publicly available for local groundwater managers to use to support development of maps and models at a local scale. All tools and guidance will be updated as revisions to tools and process documents are made.

Analysis tools and process documents can be found under the following resource:

• Data Analysis Tools and Process Documents.

Data Visualization

Data access equity is a priority for the Basin Characterization Program. To ensure data access equity, the Basin Characterization Program has developed applications and tools to allow data to be visualized without needing access to expensive data visualization software. This list below provides links and descriptions for the Basin Characterization's suite of data viewers.

SGMA Data Viewer: Basin Characterization tab: Provides maps, depth slices, and profiles of Basin Characterization maps, models, and datasets, including the following:

• Aquifer Recharge Potential Maps
• Subsurface Texture Model Depth Slices
• Statewide AEM Survey Texture Depth Slices
• Lithology Log Location Maps
• Geophysical Logs Location Maps
• Statewide AEM Survey Profile Images

3D AEM Data Viewer: Displays the Statewide AEM Survey electrical resistivity and coarse fraction data, along with lithology logs, in a three-dimensional space.

DWR's Subsurface Viewer: Provides a map view and profile view of the Statewide AEM Survey electrical resistivity and coarse fraction data, along with lithology logs. The map view dynamically shows the exact location of AEM data displayed.

Basin Characterization Exchange

The Basin Characterization Exchange (BCX) is a meeting series and network space for the Basin Characterization community to exchange ideas, share lessons learned, define needed guidance, and highlight research topics. The BCX is open to federal, state, and local agencies, consultants, NGOs, academia, and interested parties who participate in Basin Characterization efforts. The BCX also plays a pivotal role in advancing the Basin Characterization Program’s activities and goals. BCX meetings will include regular updates from the Basin Characterization Program and participants can provide feedback and recommendations. Participants will also be provided with early opportunities to test data analysis tools and submit comments on draft process and guidance documents. BCX meetings are (generally) held the 3rd Tuesday of the month from 12:30 - 1:30 pm (PST).

Please email your contact information to Basin.Characterization@water.ca.gov if you’re interested in attending BCX meetings and to join the BCX listserv.

Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone.

The numbers used in the climate zone map don't have a title or legend. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.

This is explained in the Title 24 energy efficiency standards glossary section:

"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year."

"The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone.

"Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:

• CZ 1: Arcata
• CZ 2: Santa Rosa
• CZ 3: Oakland
• CZ 4: San Jose-Reid
• CZ 5: Santa Maria
• CZ 6: Torrance
• CZ 7: San Diego-Lindbergh
• CZ 8: Fullerton
• CZ 9: Burbank-Glendale
• CZ10: Riverside
• CZ11: Red Bluff
• CZ12: Sacramento
• CZ13: Fresno
• CZ14: Palmdale
• CZ15: Palm Spring-Intl
• CZ16: Blue Canyon