Overview This instrument will be testing the data transfer process before deploying the campaign. The netCDF L3 data file has level 3 (L3) data that have gone through the calculation service and contains all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_YYYYMMDDHHMM_.nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined Data Quality This data have been passed through a processing algorithm to determine cloud layer heights and backscatter intensity profiles.

Overview

Purpose and Benefits A polygon feature service intended to serve as a repository to store daily wildfire perimeters for fires occurring within National Park Service parks. This service can be used to generate fire progressions.

Layer

Daily Wildfire Perimeter Attributes and their definitions can be found below.

Attributes:

        Fire Occurrence ID
        The Fire Occurence ID field is a unique identifier linking the daily wildfire perimeter to the wildland fire location feature class.


        Perimeter Date
        The Perimeter Date field is intended for users to capture the date the perimeter was collected.


        Feature Category
        The Feature Category field is intended for users to identify the type of event that occurred..


        GIS Acres
        The GIS Acres field is intended for users to capture the acres for the fire history or fuel treatment perimeter using GIS to calculate the acres.


        Public Display
        The Public Display field is intended for users to determine if the data can be used for public display – i.e any data representing sensitive information such as cultural resources should not be displayed on a public map.


        Data Access
        The Data Access field is intended for users to capture the accessibility of the data – i.e. most fire data is considered unrestricted, however, if cultural resources are included then the data would be restricted from sharing or use with others.


        Unit Code
        The UnitCode field is intended to allow users to identify the National Park that a particular resource may lie within. Some data collected and maintained by National Park Service may inventory resources outside NPS property or responsibility. To make data entry easier, the UnitCode field may select park unit names from a domain that contains all of the park unit 4-letter acronyms. All park units, associated monuments, memorials, seashores, etc., are represented in the domain values.



        Map Method 
        The Map Method field is intended for users to define how the geospatial feature was derived.


        Data Source 
        The Data Source field is intended for users to define the source of the data.


        Date of Source 
        The Source Date field is intended for users to define the date of the source data.


        XY Accuracy 
        The XY Accuracy field is intended to allow users to document the accuracy of the data.


        Notes 
        The Notes field is intended for users to add any additional information describing the feature.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

The Preliminary Assessment Extent (PAE) is a spatial layer that defines the land surface area contained within a bioregion over which coal resource development may have potential impact on water-dependent assets and receptors associated with those assets (Barrett et al 2013).

Purpose

The role of the PAE is to optimise research agency effort by focussing on those locations where a material causal link may occur between coal resource development and impacts on water dependent assets. The lists of assets collated by the Program are filtered for "proximity" such that only those assets that intersect with the PAE are considered further in the assessment process. Changes to the PAE such as through the identification of a different development pathway or an improved hydrological understanding may require the proximity of assets to be considered again. Should the assessment process identify a material connection between a water dependent asset outside the PAE and coal resource development impacts, the PAE would need to be amended.

Dataset History

The PAE is derived from the intersection of surface hydrology features; groundwater management units; mining development leases and/or CSG tenements; and, directional flows of surface and groundwater.

The following 5 inputs were used by the Specialists to define the Preliminary Assessment Extent:

1. Bioregion boundary

2. Geology and the coal resource

3. Surface water hydrology

4. Groundwater hydrology

5. Flow paths (Known available information on gradients of pressure, water table height, stream direction, surface-ground water interactions and any other available data)

Dataset Citation

Bioregional Assessment Programme (2014) CLM Preliminary Assessment Extent Definition & Report( CLM PAE). Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/2cdd0e81-026e-4a41-87b0-ec003eddc5c1.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From QLD Petroleum Leases, 28/11/2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From QLD Current Exploration Permits for Minerals (EPM) in Queensland 6/3/2013

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

A line shapefile of the Hunter subregion boundary with line segments attributed with the biophysical feature/dataset that defines that section of the boundary. This dataset is derived from the Bioregional Assessment areas and links to the source datasets are in the lineage field of this metadata statement.

Purpose

To identify the underlying source used to define the boundary. Mostly the Bioregion boundary was used but some sections are defined by geology and CMA boundaries.For report map purposes.

Dataset History

A polygon shapefile of the Hunter subregion was converted to a line shapefile. The subregion boundary was then compared with the datasets that the subregion metadata listed as boundary sources (see lineage).

The subregion boundary line was split (ArcGIS Editor Split tool) into sections that coincided with the source boundary layers and attributed accordingly.

Dataset Citation

Bioregional Assessment Programme (2014) Hunter bioregion boundary definition sources. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/3052c699-3b0d-4504-95e3-18598147c5ae.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Australian Coal Basins

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset contains 10,000 replicates of AWRA model pre-processing outputs (streamflow Qtot and baseflow Qb), used for calculating additional coal resources development impacts on hydrological response variables in 30 simulation nodes (Zhang et al., 2016). References Zhang Y Q, Viney N R, Peeters L J M, Wang B, Yang A, Li L T, McVicar T R, Marvanek S P, Rachakonda P K, Shi X G, Pagendam D E and Singh R M (2016) Surface water numerical modelling for the Gloucester subregion. Product 2.6.1 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.1. Purpose This pre-processing data is used for estimating AWRA post-processing streamflow outputs under CRDP and baseline conditions, respectively. Dataset History The dataset has all files and scripts necessary to execute the 10,000 runs on the linux platform of the CSIRO High Performance Cluster computers. The AWRA-L model version 4.5 has been used for all BA surface water simulations. The application is developed with the C# language. All execution and class (dll) files can be found at \OSM-07-CDC.it.csiro.au\OSM_CBR_LW_BA_working\Disciplines\SurfaceWater\Modelling\AWRA-LG\Bin. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations. The AWRA preprocessing data are the inputs for estimating AWRA post-processing model outputs (GUID: http://data.bioregionalassessments.gov.au/dataset/15ca8f9d-84b4-4395-87db-ab4ff15b9f07). The dataset was uploaded to \lw-osm-01-cdc.it.csiro.au\OSM_CBR_LW_BAModelRuns_app\GLO\AWRA_ScalingChange_rerun on 03 September 2016 This dataset were further used to compute daily streamflow post-processing outputs under CRDP and baseline conditions, respectively. Dataset Citation Bioregional Assessment Programme (XXXX) GLO AWRA Model Pre-Processing Data v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/51079bcc-96a8-409d-a951-3671fbbad6a2. Dataset Ancestors Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW) Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014 Derived From GLO SW Receptors 20150828 withRivers&CatchmentAreas Derived From Groundwater Economic Assets GLO 20150326 Derived From Gloucester digitised coal mine boundaries Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From GLO SW receptor total catchment areas V01 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Gloucester subregion on 12 September 2014 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From National Groundwater Information System (NGIS) v1.1 Derived From GLO Receptors 20150518 Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved Derived From Natural Resource Management (NRM) Regions 2010 Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From EIS Gloucester Coal 2010 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GEODATA TOPO 250K Series 3 Derived From Asset database for the Gloucester subregion on 28 May 2015 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Geofabric Surface Cartography - V2.1 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014 Derived From EIS for Rocky Hill Coal Project 2013 Derived From Bioregional Assessment areas v03 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Asset database for the Gloucester subregion on 8 April 2015 Derived From Gloucester - Additional assets from local councils Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Asset database for the Gloucester subregion on 29 August 2014 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From Groundwater Modelling Report for Stratford Coal Mine Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Freshwater Fish Biodiversity Hotspots Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014 Derived From GLO climate data stats summary Derived From Australia - Species of National Environmental Significance Database Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From GLO Receptors 20150828 Derived From Report for Director Generals Requirement Rocky Hill Project 2012 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

AWRA-L model calibration outputs reulting from modelling based on observation data from 16 streamflow gauges. The model outputs include high streamflow calibration results and low streamflow calibration results.

The AWRA-L model version 4.5 has been used for all BA surface water simulations. The application is developed with the C# language. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations.

Purpose

This data used as a reference to evaluate uncertainty analysis results.

Dataset History

This is the model calibration results obtained from AWRA-L model.The model outputs include high streamflow calibration results and low streamflow calibration results.The model calibration was carried out against historical streamflow data obtained from 16 catchments, which was obtained in in 2 February 2014.

The model calibration outputs were generated in 20 November 2014.

The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations.

Dataset Citation

Bioregional Assessment Programme (2015) GLO AWRA-L Model calibration v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/fb6fb884-93df-4fac-bd8f-d1f394e52308.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From GLO SW Receptors 20150828 withRivers&CatchmentAreas

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From Gloucester Surface Water Discharge & Quality extract v1 060314

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Geofabric Surface Catchments - V2.1

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Asset database for the Gloucester subregion on 12 September 2014

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From GLO Receptors 20150518

• Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved

• Derived From Asset database for the Gloucester subregion on 8 April 2015

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From EIS Gloucester Coal 2010

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Selected catchment boundaries and their SILO cell percentages for AWRA modelling for the Gloucester subregion

• Derived From GEODATA TOPO 250K Series 3

• Derived From GLO AWRA Model Pre-Processing Data v01

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Geofabric Surface Cartography - V2.1

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014

• Derived From EIS for Rocky Hill Coal Project 2013

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Asset database for the Gloucester subregion on 28 May 2015

• Derived From Gloucester - Additional assets from local councils

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From BA SYD 1 sec SRTM (h) DEM and hydrological derivatives

• Derived From Asset database for the Gloucester subregion on 29 August 2014

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From Groundwater Modelling Report for Stratford Coal Mine

• Derived From Groundwater Economic Assets GLO 20150326

• Derived From GLO SW Model Calibration Gauges v01

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From Freshwater Fish Biodiversity Hotspots

• Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014

• Derived From [GLO SW receptor total catchment areas

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Fall Gulf Coast (Louisiana to Florida) lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Southwest Florida Division of Emergency Management lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

Note: Geoscience Australia no longer supports users' external hard drives. The data can either be downloaded from the ELVIS Portal or from the Related links. The 1 second Shuttle Radar Topography Mission (SRTM) Digital Elevation Models Version 1.0 package comprises three surface models: the Digital Elevation Model (DEM), the Smoothed Digital Elevation Model (DEM-S) and the Hydrologically Enforced Digital Elevation Model (DEM-H). The DEMs were derived from the SRTM data acquired by NASA in February 2000 and were publicly released under Creative Commons licensing from November 2011 in ESRI Grid format.

DEM represents ground surface topography, with vegetation features removed using an automatic process supported by several vegetation maps. This provides substantial improvements in the quality and consistency of the data relative to the original SRTM data, but is not free from artefacts. Man-made structures such as urban areas and power line towers have not been treated. The removal of vegetation effects has produced satisfactory results over most of the continent and areas with defects identified in supplementary layers distributed with the data, and described in the User Guide.

DEM-S represents ground surface topography, excluding vegetation features, and has been smoothed to reduce noise and improve the representation of surface shape. An adaptive smoothing process applied more smoothing in flatter areas than hilly areas, and more smoothing in noisier areas than in less noisy areas. This DEM-S supports calculation of local terrain shape attributes such as slope, aspect and curvature that could not be reliably derived from the unsmoothed 1 second DEM because of noise.

DEM-H is a hydrologically enforced version of the smoothed DEM-S. The DEM-H captures flow paths based on SRTM elevations and mapped stream lines, and supports delineation of catchments and related hydrological attributes. The dataset was derived from the 1 second smoothed Digital Elevation Model (DEM-S) by enforcing hydrological connectivity with the ANUDEM software, using selected AusHydro V1.6 (February 2010) 1:250,000 scale watercourse lines and lines derived from DEM-S to define the watercourses. The drainage enforcement has produced a consistent representation of hydrological connectivity with some elevation artefacts resulting from the drainage enforcement.

Further information can be found in the supplementary layers supplied with the data and in the User Guide.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

A single instance of the surface water model for the Clarence Moreton region, as documented in Gilfedder et al. 2016. This dataset contains a Global definition file for AWRA-L model, i.e. GlobDefinition.xml, which is the only input file for an AWRA-L model run, and the output baseflow (Qb) and streamflow (Qtot) netcdf files for each run, i.e. Qg.nc and Qtot.nc.

A technical description of the AWRA-L 4.5 model can be found at https://publications.csiro.au/rpr/download?pid=csiro:EP162100&dsid=DS1

Dataset History

The AWRA-L model version 4.5 has been used for all Bioregional Assessments surface water simulations. The application is developed with the C# language. All execution and class (dll) files can be found at \OSM-07-CDC.it.csiro.au\OSM_CBR_LW_BA_working\Disciplines\SurfaceWater\Modelling\AWRA-LG\Bin. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files (GlobDefinition.xml) which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 workstations.

Dataset Citation

Bioregional Assessment Programme (2016) CLM AWRA model. Bioregional Assessment Derived Dataset. Viewed 09 October 2017, http://data.bioregionalassessments.gov.au/dataset/abfefbbf-4cc3-4b05-a4ea-1a79e916e72b.

Dataset Ancestors

• Derived From CLM - AWRA Calibration Gauges SubCatchments

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From CLM16swg Surface water gauging station data within the Clarence Moreton Basin

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Climate model 0.05x0.05 cells and cell centroids

Scientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model’s ability to predict events within the data. The ROC curve is made by sliding the event-definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and pre-classified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous-valued data set. ;Data and code were created using IDL, but can also be accessed with the open-source Gnu Data Language (GDL; see https://github.com/gnudatalanguage/gdl)

The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2022 New York and New Jersey United States Army Corps of Engineers (USACE) U.S. Geological Survey (USGS) topobathymetric (topobathy) lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

Abstract The dataset was derived by the Bioregional Assessment Programme from Geofabric data provided by the Bureau of Meteorology. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This datasets contains AHGFContractedCatcments occuring within the Bremer-Warrill catchment that have been further aggregated to define major sub-catchments. …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from Geofabric data provided by the Bureau of Meteorology. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This datasets contains AHGFContractedCatcments occuring within the Bremer-Warrill catchment that have been further aggregated to define major sub-catchments. AGHFContractedCatchments are based upon the fully disaggregated stream segment level catchments derived from GEODATA 9 second DEM foundation data. These data define a catchment for every stream segment in the Surface Network product. The AHGFContractedCatchments is the set of predefined aggregated catchments. This is the component of the product whose outflow node is a Contracted Node that is guaranteed to persist. Purpose To support the Geofabric and provide a topographic basis for classification and reporting of analyses of the subcatchments. Dataset History The AHGFContractedCatchments are aggregations of the GEODATA 9 second DEM catchments that participate in a relationship of common areal extent based upon the location of a Contracted Node from both the Geofabric Surface Cartography and Geofabric Surface Network products. Levels of Contracted Confidence is further described in the Geofabric User Guide. The AHGF contracted stream segment level catchments were selected based on the major streams that occur within the Bremer-Warrill. These stream segment level catchments were then aggregated using ArcGIS to create AHGF sub-catchments for the Bremer-Warrill. Dataset Citation Bioregional Assessment Programme (2014) AHGFContractedCatchment - V2.1 - Bremer-Warrill. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/23825b8f-0f58-417b-ad54-1074d4863b03. Dataset Ancestors Derived From Geofabric Surface Catchments - V2.1

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 University of Texas Post-Fay lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Maryland U.S. Army Corps of Engineers (USACE) lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Federal Emergency Management Agency (FEMA) Oahu lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (L=lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2019 United States Army Corps of Engineers (USACE) North Carolina and Virginia lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.