Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR is collecting AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data are collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and will continue over the next several years. Visit the AEM Survey Schedule Webpage to get up-to-date information on the survey schedule: https://gis.water.ca.gov/app/AEM-schedule.

Additional information about the Statewide AEM Surveys can be found at the project website: https://water.ca.gov/Programs/SGMA/AEM.

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area: https://data.cnra.ca.gov/dataset/aem/resource/a6286b07-5597-49e6-9cac-6a3a98b904df

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology Logs
• Digitized Geophysical Logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page: https://data.cnra.ca.gov/dataset/aem/resource/29c4478d-fc34-44ab-a373-7d484afa38e8

AEM 3D Viewer (Beta) (computer only): https://dwr.maps.arcgis.com/apps/instant/3dviewer/index.html?appid=f781b14f42ab45e5b72f32cf07af899c

AEM Profile Viewer: https://dwr.maps.arcgis.com/apps/instant/attachmentviewer/index.html?appid=65f0aa6db8124aeda54e1f33c5dfe66c

SGMA Data Viewer (Basin Characterization tab): https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#basincharacter

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps: https://data.cnra.ca.gov/dataset/aem/resource/7d115ac3-d7b8-47fa-ab8b-a078b2525bbe

Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps: https://data.cnra.ca.gov/dataset/aem/resource/0952506a-1ad8-4c04-9372-ded45148e6a6

SGMA Data Viewer (Basin Characterization tab): https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#basincharacter

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal: https://data.cnra.ca.gov/dataset/aem/resource/42e5798e-c633-4a7a-8398-fc96c2afaced

• SkyTEM Instrument Comparison for Airborne EM:https://data.cnra.ca.gov/dataset/aem/resource/d38f1284-71f3-45e3-9af5-676ebe22f61b

2018-2020 AEM Pilot Studies

Three pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin. All pilot study reports and data are available on the California Natural Resources Agency Open Data Portal: https://data.cnra.ca.gov/dataset/aem-pilot-studies.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods: Each project map is developed using the following steps: 1. The project map was drawn based on the information provided in the research project proposals. 2. The map was refined based on feedback during the first data discussions with the project leader. 3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.

Limitations of the data: The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data: The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.

Data dictionary: NAME - Title of the layer PROJ - Project code of the project relating to the layer NODE - Whether the project is part of the Northern or Southern Nodes TITLE - Title of the project P_LEADER - Name of the Project leader and institution managing the project PROJ_LINK - Link to the project metadata MAP_DESC - Brief text description of the map area MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities MOD_DATE - Last modification date to the individual map layer (prior to merging)

Updates & Processing: These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated: 20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)

Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

This data is visualized in the Michigan Orphan Wells Dashboard. Michigan’s Orphan Well Program was created in 1994. Orphan wells are abandoned or improperly plugged wells for which there is no known solvent existing owner or operator. EGLE’s Oil, Gas, and Minerals Division (OGMD) worked with the oil and gas industry for the passage of Act 308, P.A. 1994, which established an Orphan Well Fund within the Michigan Department of Treasury. OGMD utilizes the fund, paid for by revenue created by a severance tax on the oil and gas industry, to plug and remediate orphan well sites. Since the passage of Act 308 nearly 30 years ago, approximately 400 sites have either been plugged or remediated. As of October 1, 2022, Michigan had 447 documented orphan sites as well as additional associated orphaned facilities, piping, and contamination that remained to be addressed.Beginning October 1, 2022, OGMD began utilizing federal grant funds awarded under the Infrastructure Investment and Jobs Act (IIJA), 2021, also known as the Bipartisan Infrastructure Law Section 40601, to provide additional financial assistance for the Orphan Well Program. The IIJA created three types of grants for states to apply for to help address orphan wells and orphan facilities on federal, state, tribal, and private lands.This data also includes state funded orphan well cleanups. The orphan well data is updated nightly. A NULL in a field means that the field does not have any value.EGLE makes every attempt to ensure data accuracy but cannot guarantee the completeness or accuracy of the information contained within this dataset. For content related questions or to submit feedback, reach out to EGLE-Maps@Michigan.gov. Please visit the orphan well program in Michigan webpage for more information.

See below for a description of the data fields. Notes: MIRBDMSNET is the Michigan EGLE-OGMD Oil and Gas Well database, which stands for: Michigan Risk Based Data Management System .net. Field Name Field Description

OrphanGroup Funding groups for Orphan well/facility derived from MIRBDMSNET database. State= State fundedIIJA= funded through the Federal Infrastructure Investment and Jobs Act (IIJA)

AssignedStaff OGMD staff code from MIRBDMSNET database. Codes may change as staff changes. Reach out to EGLE for current staff code list.

OrphanCategory Category of Orphan well/facility derived from MIRBDMSNET database. Categories are 1, 2, or 3.Category 1= wells are abandoned, leaking wellsCategory 2= wells are abandoned, non-leaking wellsCategory 3= environmental remediation investigations and cleanups

ProjectName Name of Orphan project from MIRBDMSNET database.

ProjectType Type of Orphan Project from MIRBDMSNET databaseindividual=individual plugging projectmultiple=multiple well plugging projectwellandfac= well and facility decommissioning and site restoration projects

ProjectScore Score for orphan project from MIRBDMSNET database. Scores are derived from separate form filled out by the orphan well team to take into account many risk factors to gauge the overall risk. A higher score indicates a higher risk and therefore a higher priority level.

IndividualScore Individual well/facility score from MIRBDMSNET database. Scores are derived from separate form filled out by the orphan well team to take into account many risk factors to gauge the overall risk. A higher score indicates a higher risk and therefore a higher priority level.

MichiganPermitNumber Well permit number from MIRBDMSNET.

PN_WELLSN Well permit number from MIRBDMSNET.

PN_WELLST Well permit number from MIRBDMSNET. For labeling purposes.

Well_Name Well name from MIRBDMSNET.

MichiganFacilityNumber Facility number from MIRBDMSNET.

FACN2 Facility number from MIRBDMSNET. For labeling purposes.

FacilityName Facility name from MIRBDMSNET.

LABEL Combined labels for both wells and facilities. This is derived from other columns.

STATUS Status of well or facility derived from MIRBDMSNET.

Data_Element Type of object in Orphan list- well or facility- derived from MIRBDMSNET. Either an orphan facility or an orphan well.

Element_Status Status of orphan object- well or facility- derived from MIRBDMSNET.Active= not plugged or removedPlugged=well is pluggedComplete=facility is removed.

US_Well_ID_API API number for wells, derived from MIRBDMSNET

WellID_DataSource Source of well ID- always "API".

WellType Type of well, derived from MIRBDMSNET.

CountyName County location of well/facility, derived from MIRBDMSNET.

TownshipName Township location of well/facility, derived from MIRBDMSNET.

TRS Section, township, range location of well/facility, derived from MIRBDMSNET.

Quarter Quarter location of well/facility, derived from MIRBDMSNET.

QuarterQuarter Quarter quarter location of well/facility, derived from MIRBDMSNET.

QuarterQuarterQuarter Quarter quarter quarter location of well/facility, derived from MIRBDMSNET.

DTD Drilled total depth of well in ft, derived from MIRBDMSNET.

TVD Total vertical depth of well in ft, derived from MIRBDMSNET.

FieldName Field name associated with well/facility, derived from MIRBDMSNET.

FieldType Type of field associated with well/facility, derived from MIRBDMSNET.G=gasO=oilGS= gas storage

ProducingFormation The geologic formation associated with well/facility, derived from MIRBDMSNET.

Year Year well or facility established, derived from MIRBDMSNET.

Company Last company to own well/facility, derived from MIRBDMSNET.

Longitude Longitude location of well/facility, derived from MIRBDMSNET.

Latitude Latitude location of well/facility, derived from MIRBDMSNET.

HorizontalDatum Horizontal datum of well/facility. Always "Nad83", derived from MIRBDMSNET.

LocationAccuracy Source of location information, derived from MIRBDMSNET.

RemedialAction Describes if remedial action required. derived from MIRBDMSNET database. Options are Yes, No, NA.

StateHouse State house number where well/facility is located, derived from MIRBDMSNET database.

StateSenate State senate number where well/facility is located, derived from MIRBDMSNET database.

SurfaceManagEntityType Surface managing entity type, derived from MIRBDMSNET database. Options are:PrivateStateFederalTribeMixed

SubsurfaceManagEntityType Subsurface managing entity type, derived from MIRBDMSNET database. Options are:PrivateStateFederalTribeMixed

State Always "Michigan", derived from MIRBDMSNET database.

Tribe Tribe involved in orphan project, derived from MIRBDMSNET database.

BayMills: Bay Mills Indian CommunityGTraverse: Grand Traverse Band of Ottawa and ChippewaHannahville: Hannahville Indian CommunityKeweenaw: Keweenaw Bay Indian CommunityLacVieux: Lac Vieux Desert Band of Lake Superior ChippewaLittleRiver: Little River Band of OttawaLTraverse: Little Traverse Bay Bands of OdawaMatch: Match E Be Nash She Wish Band of PotawatomiNottaw: Nottawaseppi Huron Band of PotawatomiPokagon: Pokagon Band of PotawatomiSaginaw: Saginaw ChippewaSaultSteMarie: Sault Ste Marie Tribe of Chippewa

PrePlugMethaneMeas Measurement of methane pre-plugging, derived from MIRBDMSNET database. Unit is grams/hour or g/hr.

PostPlugMethaneMeas Measurement of methane post-plugging, derived from MIRBDMSNET database. Unit is grams/hour or g/hr.

MethaneMeasMethod Method for measuring methane, derived from MIRBDMSNET database. Options are:AVO: Audio, Visual, and OlfactoryOGI: Optical Gas ImageryHandheldMass SamplingNA

HabitatRestored Was habitat restored?, derived from MIRBDMSNET database. Options are Yes, No, NA.

AmountHabitatRestored Amount of habitat restored, derived from MIRBDMSNET database. In Acres.

RestorationEndpoint Description of final restoration, derived from MIRBDMSNET database.

DateRestoreComplete Date restoration was completed, derived from MIRBDMSNET database.

SurfWtrContamination Does surface water contamination exist?, derived from MIRBDMSNET database. Options are Yes, No, TBD.

SurfWtrContamIndicator Kind of testing indicating surface water contamination, derived from MIRBDMSNET database. Options are AVO (Audio, Visual, and Olfactory), Sample, or NA.

SurfWtrRemediation Is surface water remediation needed?, derived from MIRBDMSNET database. Options are Yes, No, TBD, or NA.

SurfWtrRemedMethod Describe method for surface water remediation, derived from MIRBDMSNET database.

SurfWtrRemedCompleteDate Date surface water remediation was completed, derived from MIRBDMSNET database.

GroundWtrContamination Does ground water contamination exist?, derived from MIRBDMSNET database. Options are Yes, No, TBD.

GroundWtrContamIndicator Kind of testing indicating ground water contamination, derived from MIRBDMSNET database. Options are AVO (Audio, Visual, and Olfactory), Sample, or NA.

GroundWtrRemediation Is ground water remediation needed?, derived from MIRBDMSNET database. Options are Yes, No, TBD, or NA.

GroundWtrRemedMethod Describe method for ground water remediation, derived from MIRBDMSNET database.

GroundWtrRemedCompleteDate Date ground water remediation was completed, derived from MIRBDMSNET database.

CommunityImpact Does this location have community impact, derived from MIRBDMSNET database. Options are Yes, No, TBD, NA. From the US-DOI: " Indicates whether this

2022 STATEWIDE CROP MAPPING - PROVISIONALLand use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2022 water year (WY 2022). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, 2018, 2019, 2020 and 2021 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018, 2019, 2020, 2021 and 2022 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification method using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 98.1% at the DWR Class level and 96.7% at the DWR Subclass level. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. The column 'MAIN_CROP' was added in 2019 and has been continued through the 2022 dataset. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, continued in the 2022 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.Prior to WY 2021 final mapping release, pasture areas that where mechanically harvested during a water year were classified as P6-Miscellaneous Grasses. Starting with the WY 2021 final mapping release and moving forward these harvested pasture areas are classified as P3-Mixed Pasture.

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR is collecting AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data are collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and will continue over the next several years. Visit the AEM Survey Schedule Webpage to get up-to-date information on the survey schedule: https://gis.water.ca.gov/app/AEM-schedule.

Additional information about the Statewide AEM Surveys can be found at the project website: https://water.ca.gov/Programs/SGMA/AEM.

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area: https://data.cnra.ca.gov/dataset/aem/resource/a6286b07-5597-49e6-9cac-6a3a98b904df

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology Logs
• Digitized Geophysical Logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page: https://data.cnra.ca.gov/dataset/aem/resource/29c4478d-fc34-44ab-a373-7d484afa38e8

AEM 3D Viewer (Beta) (computer only): https://dwr.maps.arcgis.com/apps/instant/3dviewer/index.html?appid=f781b14f42ab45e5b72f32cf07af899c

AEM Profile Viewer: https://dwr.maps.arcgis.com/apps/instant/attachmentviewer/index.html?appid=65f0aa6db8124aeda54e1f33c5dfe66c

SGMA Data Viewer (Basin Characterization tab): https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#basincharacter

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps: https://data.cnra.ca.gov/dataset/aem/resource/7d115ac3-d7b8-47fa-ab8b-a078b2525bbe

Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps: https://data.cnra.ca.gov/dataset/aem/resource/0952506a-1ad8-4c04-9372-ded45148e6a6

SGMA Data Viewer (Basin Characterization tab): https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#basincharacter

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal: https://data.cnra.ca.gov/dataset/aem/resource/42e5798e-c633-4a7a-8398-fc96c2afaced

• SkyTEM Instrument Comparison for Airborne EM:https://data.cnra.ca.gov/dataset/aem/resource/d38f1284-71f3-45e3-9af5-676ebe22f61b

2018-2020 AEM Pilot Studies

Three pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin. All pilot study reports and data are available on the California Natural Resources Agency Open Data Portal: https://data.cnra.ca.gov/dataset/aem-pilot-studies.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.

The European Marine Observation and Data Network (EMODnet) consists of more than 100 organisations assembling marine data, products and metadata to make these fragmented data resources more available to public and private users relying on quality-assured, standardised and harmonised marine data which are interoperable and free of restrictions on use. EMODnet is currently in its fourth phase. BGR participates in the EMODnet Geology theme and is coordinating the “seafloor geology” work package from the beginning. In cooperation with the project partners BGR compiles and harmonises GIS data layers on the topics geomorphology, pre-Quaternary and Quaternary geology and provides those, based on INSPIRE principles, via the EMODnet Geology portal https://www. emodnet-geology.eu/map-viewer/. These map layers present the pre-Quaternary and Quaternary sea-floor geology and Geomorphology of the European Seas, semantically harmonized based on the INSPIRE data specifications including the terms for lithology, age, event environment, event process and geomorphology. The data are compiled from the project partners, the national geological survey organizations of the participating countries. The data set represents the most detailed available data compilation of the European Seas using a multiresolution approach. Data completeness depending on the availability of data and actual mapping campaigns. This open and freely accessible product was made available by the EMODnet Geology project (https://www.emodnet-geology.eu/), implemented by EMODnet Geology Phase IV partners, and funded by the European Commission Directorate General for Maritime Affairs and Fisheries. These data were compiled by BGR from the EMODnet IV Geology partners. All ownership rights of the original data remain with the data originators, who are acknowledged within the attribute values of each map feature.

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2019 water year (WY 2019). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, and 2018 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018 and 2019 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.9% using the Land IQ legend and 98.1% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. A new column for the 2019 dataset is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.The 2019 Crop Mapping dataset has been updated as of August 2022 and includes the following changes:- Slightly shifted Urban polygons were relocated to their original correct positions.- The following new rule has been included for ‘X’ Unclassified Fallow: “Unclassified Fallow is also used when indicating the planting of Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses. In these scenarios Unclassified fallow would be Crop1, and Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses would be Crop2.”- Some UniqueID’s that were accidentally duplicated have been corrected back to their original UniqueID’s.

This data set is part of an ongoing project to consolidate interagency fire perimeter data. Currently only certified perimeters and new perimeters captured starting in 2021 are included. A process for loading additional perimeters is being evaluated.The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. This data set is an ongoing project with the end goal of providing a national interagency fire history feature service of best-available perimeters.No "fall-off" rules are applied to this service. The date range for this service will extend from present day back indefinitely. Data prior to 2021 will be incomplete and incorporated as an ongoing project.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The