Geographic Information System (GIS) Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to grow from USD 9.1 billion in 2023 to USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is driven by the increasing application of GIS software across various sectors such as agriculture, construction, transportation, and utilities, along with the rising demand for location-based services and advanced mapping solutions.

One of the primary growth factors for the GIS software market is the widespread adoption of spatial data by various industries to enhance operational efficiency. In agriculture, for instance, GIS software plays a crucial role in precision farming by aiding in crop monitoring, soil analysis, and resource management, thereby optimizing yield and reducing costs. In the construction sector, GIS software is utilized for site selection, design and planning, and infrastructure management, making project execution more efficient and cost-effective.

Additionally, the integration of GIS with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) is significantly enhancing the capabilities of GIS software. AI-driven data analytics and IoT-enabled sensors provide real-time data, which, when combined with spatial data, results in more accurate and actionable insights. This integration is particularly beneficial in fields like smart city planning, disaster management, and environmental monitoring, further propelling the market growth.

Another significant factor contributing to the market expansion is the increasing government initiatives and investments aimed at improving geospatial infrastructure. Governments worldwide are recognizing the importance of GIS in policy-making, urban planning, and public safety, leading to substantial investments in GIS technologies. For example, the U.S. governmentÂ’s Geospatial Data Act emphasizes the development of a cohesive national geospatial policy, which in turn is expected to create more opportunities for GIS software providers.

Geographic Information System Analytics is becoming increasingly pivotal in transforming raw geospatial data into actionable insights. By employing sophisticated analytical tools, GIS Analytics allows organizations to visualize complex spatial relationships and patterns, enhancing decision-making processes across various sectors. For instance, in urban planning, GIS Analytics can identify optimal locations for new infrastructure projects by analyzing population density, traffic patterns, and environmental constraints. Similarly, in the utility sector, it aids in asset management by predicting maintenance needs and optimizing resource allocation. The ability to integrate GIS Analytics with other data sources, such as demographic and economic data, further amplifies its utility, making it an indispensable tool for strategic planning and operational efficiency.

Regionally, North America holds the largest share of the GIS software market, driven by technological advancements and high adoption rates across various sectors. Europe follows closely, with significant growth attributed to the increasing use of GIS in environmental monitoring and urban planning. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives in countries like China and India.

Component Analysis

The GIS software market is segmented into software and services, each playing a vital role in meeting the diverse needs of end-users. The software segment encompasses various types of GIS software, including desktop GIS, web GIS, and mobile GIS. Desktop GIS remains the most widely used, offering comprehensive tools for spatial analysis, data management, and visualization. Web GIS, on the other hand, is gaining traction due to its accessibility and ease of use, allowing users to access GIS capabilities through a web browser without the need for extensive software installations.

Mobile GIS is another crucial aspect of the software segment, providing field-based solutions for data collection, asset management, and real-time decision making. With the increasing use of smartphones and tablets, mobile GIS applications are becoming indispensable for sectors such as utilities, transportation, and

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the Goulburn Broken Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this.

The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon.

In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater.

Separate datasets and associated metadata records have been created for GDE species tolerance.

Geotweet Archive v2.0 The Harvard Center for Geographic Analysis (CGA) maintains the Geotweet Archive, a global record of tweets spanning time, geography, and language. The primary purpose of the Archive is to make a comprehensive collection of geo-located tweets available to the academic research community. The Archive extends from 2010 to the present and is updated daily. The number of tweets in the collection totals approximately 10 billion, and it is stored on Harvard University’s High Performance Computing (HPC) cluster. The Harvard HPC supports many applications for working with big spatio-temporal datasets, including two geospatial tools recently deployed by the CGA: OmniSci Immerse, and PostGIS. The Geotweet Archive consists of tweets which carry two types of geospatial signature: 1) GPS-based longitude/latitude generated by the originating device 2) Place-name-centroid-based longitude/latitude from the bounding box provided by Twitter, based on the user-define place designation (typically a town name). Any tweet which carries one or both of these signatures is included in the Archive. Approximately 1-2% of all tweets contain such geographic coordinates, (this percentage needs verification and may vary over time). The current version of the Archive is Version 2.0. The original Version 1.0 archive began in 2012 as part of a project with Ben Lewis of CGA and then Harvard graduate student Todd Mostak, to develop a GPU-powered spatial database called GEOPS. GEOPS formed the basis for technology startup MapD Technologies, which is now OmniSci. OmniSci Immerse software now runs on Harvard’s High Performance Computing (HPC) environment to support interactive exploration and analytics with the Geotweet Archive and any other large datasets. Version 2.0 of the archive represents the results of a merge between the CGA archive, and an archive developed by the Department of Geoinformatics at the University of Salzburg in Austria, as well as several other archives. Clemens Havas and Bernd Resch at University of Salzburg, and Devika Kakkar of Harvard CGA collaborated to deploy Version 2.0. ======================================================== Schema of Geotweet Archive v2.0 Field name_TYPE_Description message_id----BIGINT----Tweet ID tweet_date----TIMESTAMP----Date and time of tweet from Twitter (utc) tweet_text----TEXT ENCODING----Text content of tweet tags----TEXT ENCODING DICT----Tweet hashtags tweet_lang----TEXT ENCODING DICT----Language that the tweet is in source ----TEXT ENCODING DICT----Operating system or application type used to create the tweet place*----TEXT ENCODING NONE----The geographic place as defined by the user, usually a town name. A bounding box determined by Twitter based on this field, from which centroids (see longitude and latitude fields) and the spatial_error field are derived, and used when not overridden by a GPS coordinate. See Twitter tweet object for place. retweets ----SMALLINT----Number of retweets as of last time it was checked tweet_favorites----SMALLINT----Now known as ‘likes’ photo_url----TEXT ENCODING DICT----URL of any image referenced quoted_status_id ----BIGINT----ID number for quote status user_id ----BIGINT----User ID number user_name----TEXT ENCODING NONE----User name user_location*----TEXT ENCODING NONE----User defined location, usually a city or town. See Twitter user object. followers ----SMALLINT----Followers as of the last time checked friends ----SMALLINT----Number of users followed by this user user_favorites----INT----Number of topics the user is interested in status----INT----Code for what user is doing as of last time it was checked user_lang----TEXT ENCODING DICT----User defined language latitude----FLOAT----Latitude from GPS or bounding box based on Place field longitude----FLOAT----Longitude from GPS or bounding box based on Place field data_source*----TEXT ENCODING DICT----The source crawler or dataset for the tweet gps----TEXT ENCODING DICT----Flag for whether lon/lat is from GPS or town name bounding box (SRID – 4326). When both are present, the GPS coordinate takes priority. spatialerror----FLOAT----Estimate in meters horizontal error for lon/lat coordinate. 10m for GPS coordinates, error for bounding boxes calculated as radius of circle with area of bounding box. ===================================================== *data_source_Code U. Salzburg REST API crawler----1 Harvard CGA streaming crawler----2 U. Salzburg streaming API crawler----3 Ryan Qi Wang and Harvard Medical School datasets----4 U. Heidelberg dataset----5 Archive.org dataset----6 ---------------------------------------------------------------------------------------------- Note: Before April of 2015 the default for GPS coordinate capture was turned on for Twitter users. After this date users have had to opt-in to share their precise location. This is one reason for the large decrease in volume of geotweets after this date. A number of automated...

BSUs were revised in May 2016. New data submitted by Montana and Oregon was used to update the earlier version of this feature class. The biologically significant unit (BSU) is a geographical/spatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual states/EISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areas/core habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLM's GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographical/spatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority and/or Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority. Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also. Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding).PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat.PGH is defined as Greater sage-grouse Occupied Range outside of PPH.Datasets used to create PPH and PGH:Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06/09/2012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacant/unknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. Vacant or Unknown Habitat: Suitable habitat for sage-grouse that is separated (not contiguous) from occupied habitats that either: 1) Has not been adequately inventoried, or 2) Has not had documentation of grouse presence in the past 10 years Potentially Suitable Habitat: Unoccupied habitats that could be suitable for occupation of sage-grouse if practical restoration were applied. Soils or other historic information (photos, maps, reports, etc.) indicate sagebrush communities occupied these areas. As examples, these sites could include areas overtaken by pinyon-juniper invasions or converted rangelandsUpdate October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat and management zones, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat.California and Nevada's BSUs were developed by Nevada Department of Wildlife's Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevada's Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) Oregon submitted updated BSU boundaries in May 2016, which were incorporated into this latest version. In Oregon, Core Areas were defined by the Oregon Department of Fish and Wildlife (ODFW) to be used by BLM to map Oregon PACs, Priority Areas of Conservation for the Greater Sage-grouse RMP Amendments. The data was projected to R6 Albers and will be used as is. Core Area Approach to Habitat Mitigation for Greater Sage-Grouse in Oregon: The goal of these recommendations is to protect essential habitats to meet habitat and population objectives identified in this Plan. The objective of these

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for, and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the West Gippsland Catchment Management Authority (CMA) area.

The method used in this research is based upon the characteristics of a potential GDE containing area as one that:

1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community.

2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased.

3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns.

4. Has growth patterns similar to verified GDEs.

The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this.

The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon.

In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to

groundwater. Separate datasets and associated metadata records have been created for GDE species tolerance.

Dataset History

Data Set Source:

The maps are created from base layers of Landsat imagery from 1988 to 2005 that was supplied by the Australian Greenhouse Office (AGO) and time series data from the NASA WIST website supplied the Modis (MOD13Q1) product for 2003. Additional base layers that were used include the Geological 250 series, Geomorphological Management Units, Wetland and EVC Layer Stream Gauge catchments. Expert analyses selected thresholds within the data sets that were combined within a Weighted Overlay Model and converted to Shape files for use.

Collection Method:

Not available (see Abstract for context and background information).

Processing Steps:

Not available (see Abstract for context and background information).

For further information, see:

Dresel PE, Clark R, Cheng X, Reid M, Terry A, Fawcett J and Cochrane D 2010, Mapping Terrestrial Groundwater Dependent Ecosystems: Method Development and Example Output, Department of Primary Industries, Melbourne, Victoria, 66 pp.

Dataset Citation

Victorian Department of Environment and Primary Industries (2014) Potential Groundwater Dependent Ecosystems for West Gippsland Catchment Management Authority. Bioregional Assessment Source Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/e5499022-8e74-4412-8516-c66d163e6e75.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the Wimmera Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this. The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon. In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater. Separate datasets and associated metadata records have been created for GDE species tolerance.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the Mallee Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this. The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon. In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater. Separate datasets and associated metadata records have been created for GDE species tolerance.

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasCoordinate System: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American Samoa.Visible Scale: The layer is limited to scales of 1:1,000,000 and larger. Use the USA Flood Hazard Areas imagery layer for smaller scales.Source: Federal Emergency Management AgencyPublication Date: October 13, 2021This layer is derived from the October 13, 2021 version of the National Flood Hazard Layer feature class S_Fld_Haz_Ar. The data were aggregated into eight classes to produce the Esri Symbology field based on symbology provided by FEMA. All other layer attributes are derived from the National Flood Hazard Layer.The layer was projected to Web Mercator Auxiliary Sphere, then the repair geometry geoprocessing tool was run on it. Its resolution was set to 0.0001 meter.To improve performance Flood Zone values "Area Not Included", "Open Water", "D", "NP", and No Data were removed from the layer. Areas with Flood Zone value "X" subtype "Area of Minimal Flood Hazard" were also removed. An imagery layer created from this dataset provides access to the full set of records in the National Flood Hazard Layer.A web map featuring this layer is available for you to use.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 an imagery 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 change the symbology field to Special Flood Hazard Area and set a filter for = “T” to create a map of only the special flood hazard areas. Add labels and set their propertiesCustomize the pop-upUse in analysis tools to discover patterns in the dataArcGIS 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. Areas up to 1,000-2,000 features can be exported successfully.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.

America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

Fire Stations in the United States Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments not having a permanent location are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Fire fighting training academies are also included. TGS has made a concerted effort to include all fire stations in the United States and its territories. This dataset is comprised completely of license free data. The HSIP Freedom Fire Station dataset and the HSIP Freedom EMS dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. Please see the process description for the breakdown of how the records were merged. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 01/03/2005 and the newest record dates from 01/11/2010.

This database contains distribution maps of 135 eastern U.S. tree species based on Importance Values (IV) derived from Forest Inventory Analysis (FIA) data and a geographical information system (GIS) database of Elbert L. Jr. Little's published ranges. Between 1971 and 1977, Elbert L. Jr. Little, Chief Dendrologist with the U.S. Forest Service, published a series of maps of tree species ranges based on botanical lists, forest surveys, field notes ad herbarium specimens. These published maps have become the standard reference for most U.S. and Canadian tree species ranges.

The USDA Forest Service's FIA units are charged with periodically assessing the extent, timber potential, and health of the trees in the United States. The investigators have created a spatial database of individual species IV based on the number of stems and basal area of understory and overstory trees using FIA data from more than 100,000 plots in the eastern United States. The IV ranges from 0 to 100 and gives a measure of the abundance of the species. (See the investigator's Climate Change Atlas for 80 Forest Tree Species of the Eastern United States at [http://www.fs.fed.us/ne/delaware/atlas/web_atlas.html] for details). The investigators have aggregated the plot-level IV to 20km cells.

Both sets of maps (Little's ranges and IV based on FIA data) are available for download. The Little's range maps (little.shp) are vector based and are provided as shape files. These maps can span United States or United States and Canada in extent depending on the species. The Importance Value (IV) are raster maps (asciigrid) in asciigrid format. This is an ascii file with header information that can be used to import data into ArcInfo GRID or ArcView's Spatial Analyst GIS software. The spatial resolution is 20km. These raster maps span the eastern U.S. (east of the 100th meridian) in extent.

The Sheeprocks (UT) was revised to resync with the UT habitat change as reflected in the Oct 2017 habitat data, creating the most up-to-date version of this dataset. Data submitted by Wyoming in February 2018 and by Montana and Oregon in May 2016 were used to update earlier versions of this feature class. The biologically significant unit (BSU) is a geographical/spatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual states/EISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areas/core habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLM's GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographical/spatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority and/or Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority. Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also. California and Nevada's BSUs were developed by Nevada Department of Wildlife's Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevada's Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding). PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat. PGH is defined as Greater sage-grouse Occupied Range outside of PPH. Datasets used to create PPH and PGH: Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06/09/2012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacant/unknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. Vacant or Unknown Habitat: Suitable habitat for sage-grouse that is separated (not contiguous) from occupied habitats that either: 1) Has not been adequately inventoried, or 2) Has not had documentation of grouse presence in the past 10 years Potentially Suitable Habitat: Unoccupied habitats that could be suitable for occupation of sage-grouse if practical restoration were applied. Soils or other historic information (photos, maps, reports, etc.) indicate sagebrush communities occupied these areas. As examples, these sites could include areas overtaken by pinyon-juniper invasions or converted rangelandsUpdate October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat and management zones, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Oregon submitted updated BSU boundaries in May 2016 and again in October 2016, which were incorporated into this latest version. In Oregon, the Core Area maps and data were developed as one component of the Conservation Strategy for sage-grouse. Specifically, these data provide a tool in planning and identifying appropriate mitigation in the event of human development in sage-grouse habitats. These maps will assist in making

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the Glenelg Hopkins Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this. The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon. In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater. Separate datasets and associated metadata records have been created for GDE species tolerance.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the North East Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this.

The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon.

In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater.

Separate datasets and associated metadata records have been created for GDE species tolerance.

This layer shows total population count by sex and age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population that are considered dependent (ages 65+ and <18). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B01001Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Terms of Use:

Data Limitations Disclaimer

The MassDEP Estimated Sewer System Service Area Boundaries datalayer may not be complete, may contain errors, omissions, and other inaccuracies, and the data are subject to change. The user’s use of and/or reliance on the information contained in the Document (e.g. data) shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.

All sewer service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a sewer service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the wastewater utility – please contact the relevant wastewater system. Not all service areas have been confirmed by the sewer system authorities.

This is an ongoing data development project. Attempts have been made to contact all sewer/wastewater systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some sewer service areas included in this datalayer have not been verified by the POTWs, privately-owned treatment works, GWDPs, or the municipality involved, but since many of those areas are based on information published online by the municipality, the utility, or in a publicly available report, they are included in the estimated sewer service area datalayer.

Please use the following citation to reference these data

MassDEP. Water Utility Resilience Program. 2025. Publicly-Owned Treatment Work and Non-Publicly-Owned Sewer Service Areas (PubV2024_12).

We want to learn about the data uses. If you use this dataset, please notify staff in the Water Resilience program (WURP@mass.gov).

Layers and Tables:

The MassDEP Estimated Sewer System Service Area data layer comprises two feature classes and a supporting table:

Publicly-Owned Treatment Works (POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_POTW_POLY includes polygon features for sewer service areas systems operated by publicly owned treatment works (POTWs)Non-Publicly Owned Treatment Works (NON-POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_NONPOTW_POLY includes polygon features for sewer service areas for operated by NON publicly owned treatment works (NON-POTWs)The Sewer Service Areas Unlocated Sites table SEWER_SERVICE_AREA_USL contains a list of known, unmapped active POTW and NON-POTW services areas at the time of publication.

ProductionData Universe

Effluent wastewater treatment plants in Massachusetts are permitted either through the Environmental Protection Agency’s (EPA) National Pollutant Discharge Elimination System (NPDES) surface water discharge permit program or the MassDEP Groundwater Discharge Permit Program. The WURP has delineated active service areas served by publicly and privately-owned effluent treatment works with a NPDES permit or a groundwater discharge permit.

National Pollutant Discharge Elimination System (NPDES) Permits

In the Commonwealth of Massachusetts, the EPA is the permitting authority for regulating point sources that discharge pollutants to surface waters. NPDES permits regulate wastewater discharge by limiting the quantities of pollutants to be discharged and imposing monitoring requirements and other conditions. NPDES permits are typically co-issued by EPA and the MassDEP. The limits and/or requirements in the permit ensure compliance with the Massachusetts Surface Water Quality Standards and Federal Regulations to protect public health and the aquatic environment. Areas served by effluent treatment plants with an active NPDES permit are included in this datalayer based on a master list developed by MassDEP using information sourced from the EPA’s Integrated Compliance Information System (ICIS).

Groundwater Discharge (GWD) Permits

In addition to surface water permittees, the WURP has delineated all active systems served by publicly and privately owned effluent treatment works with groundwater discharge (GWD) permits, and some inactive service areas. Groundwater discharge permits are required for systems discharging over 10,000 GPD sanitary wastewater – these include effluent treatment systems for public, district, or privately owned effluent treatment systems. Areas served by an effluent treatment plant with an active GWD permit are included in this datalayer based on lists received from MassDEP Wastewater staff.

Creation of Unique IDs for Each Service Area

The Sewer Service Area datalayer contains polygons that represent the service area of a particular wastewater system within a particular municipality. Every discharge permittee is assigned a unique NPDES permit number by EPA or a unique GWD permit identifier by MassDEP. MassDEP WURP creates a unique Sewer_ID for each service area by combining the municipal name of the municipality served with the permit number (NPDES or GWD) ascribed to the sewer that is serving that area. Some municipalities contain more than one sewer system, but each sewer system has a unique Sewer_ID. Occasionally the area served by a sewer system will overlap another town by a small amount – these small areas are generally not given a unique ID. The Estimated sewer Service Area datalayer, therefore, contains polygons with a unique Sewer_ID for each sewer service area. In addition, some municipalities will have multiple service areas being served by the same treatment plant – the Sewer_ID for these will contain additional identification, such as the name of the system, to uniquely identify each system.

Classifying System Service Areas

WURP staff reviewed the service areas for each system and, based on OWNER_TYPE, classified as either a publicly-owned treatment work (POTW) or a NON-POTW (see FAC_TYPE field). Each service area is further classified based on the population type served (see SECTOR field).

Methodologies and Data Sources

Several methodologies were used to create service area boundaries using various sources, including data received from the sewer system in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and wastewater system websites. When MassDEP received sewer line data rather than generalized areas, 300-foot buffers were created around the sewer lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas. Many of the smaller GWD permitted sewer service areas were delineated using parcel boundaries related to the address on file.

Verification Process

Small-scale pdf file maps with roads and other infrastructure were sent to systems for corrections or verifications. If the system were small, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the wastewater system and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all, or nearly all, of the municipality is served by one wastewater system.

To ensure active systems are mapped, WURP staff developed two work flows. For NPDES-permitted systems, WURP staff reviewed available information on EPA’s ICIS database and created a master list of these systems. Staff will work to routinely update this master list by reviewing the ICIS database for new NPDES permits. The master list will serve as a method for identifying active systems, inactive systems, and unmapped systems. For GWD permittees, GIS staff established a direct linkage to the groundwater database, which allows for populating information into data fields and identifying active systems, inactive systems, and unmapped systems.

All unmapped systems are added to the Sewer Service Area Unlocated List (SEWER_SERVICE_AREAS_USL) for future mapping. Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder - the location of these circles are estimated based on the general location of the treatment plant or the general estimated location of the service area - these do not represent the actual service area.

Percent Served Statistics The attribute table for the POTW sewer service areas (SEWER_SERVICE_AREA_POTW_POLY) has several fields relating to the percent of the town served by the particular system and one field describing the percent of town served by all systems in the town. The field ‘Percent AREA Served by System’ is strictly a calculation done dividing the area of the system by the total area of the town and multiplying by 100. In contrast, the field ‘Percent Served by System’, is not based on a particular calculation or source – it is an estimate based on various sources – these estimates are for planning purposes only. Data includes information from municipal websites and associated plans, the 1990 Municipal Priority list from CMR 310 14.17, the 2004 Pioneer Institute for Public Policy Research “percent on sewer” document, information contained on NPDES Permits and MassDEP Wastewater program staff input. Not all POTW systems have percent served statistics. Percentage may reflect the percentage of parcels served, the percent of area within a community served or the population served and should not be used for legal boundary definition or regulatory interpretation.

Sources of information for estimated wastewater service areas:

EEOA Water Assets

This spatial layer displays Range Tenures (grazing and hay cutting licence and permits) administered by the Ministry of Forests, Lands and Natural Resource Operations. A Range Tenure is an area of Crown rangeland where a Range Act tenure applies. Tenure holders access a defined amount of forage through grazing (measured in Animal Unit Months) or hay (tonnage). Range Tenures apply only to Crown Land. In some cases, digital boundaries may overlap Private Land but these lands are not part of the Grazing area (as described in the legal description). Grazing may overlap waterbodies during drawdown (also described legally in the Tenure documents and where applicable, Range Use Plan). Livestock may graze islands and large bodies of water may act as Natural Range Barriers. To guide your use: 1. Layer contains RETIRED, PENDING, and ACTIVE Tenures (=Licenses and Permits); Select ACTIVE under attribute LIFE_CYCLE_STATUS_CODE to isolate all active tenures 2. Unique ID for Polygon is by RAN# in attribute FOREST_FILE_ID. Each tenure has own RAN#. Multiple areas belonging to the same tenure may have same RAN# with unique block ids 3. FILE_TYPE_CODE contains information on the type of Permit: • E01 - Grazing License • E02 - Grazing Permit • E03 - Non-Replaceable Grazing Permit • H01 - Haycutting License • H02 - Haycutting Permit 4. IF YOU ARE DOING SPATIAL ANALYSIS based on AREA --- Please note that tenures overlap either partially or wholly amongst tenure holders. One area may be shared by more than one Tenure holder and therefore there will be multiple congruent or partially overlapping polygons (multipart). If you want to do SPATIAL ANALYSIS based strictly on area, you must collapse or flatten the data using DISSOLVE so that the polygons are 1:1 with the land base (suggested approach). Complete Attribute List: FOREST_FILE_ID: The unique identifier for a Range Tenure (eg. RAN077777). May be autogenerated through the application process in ezlink, or may be pre-determined by resource clerk and entered through ezlink. If a tenure expires, a new RAN number is issued. (Note that practice of issuing new RAN number has been discontinued on a 'go forward' basis); MAP_BLOCK_ID: Unique identifier for multi-part polygons belonging to same polygon (tenure) (eg road separates tenure, each part has identical map_block_id). Each tenure may have one or more map blocks. Ezlink populated; CLIENT_NUMBER: Together with Client_Location_Code, this is a unique identifier for the tenure holder(s); CLIENT_LOCATION_CODE: See above; CLIENT_NAME: Name of Client (one client name only ("A" licensees only, if "B" exists will not show up); first name/last name) *If change is made only to client name, eg. In instance where Tenure is transferred, may need to push change through to spatial as system is broken (see below under Data Quality); FILE_TYPE_CODE: Populated from FTA, this is the type of tenure that the RAN number refers to (E01=grazing licence, E02/E03=grazing permit, H01=haycutting licence etc); HAY_CUTTING_AREA_NO: Unique number that identifies an area that is or was used for haycutting. Some Districts use, others don't; AUTHORIZED_HARVEST_TONNES: Not populated if Grazing. Provided in Tonnes of Hay Populated through ezlink. Previously may have populated if Tenure was grazing (and provided in AUMs); RETIREMENT_DATE: Date of retirement. This field is populated when the tenure is archived and the “Retire” button in FTA is used. The “Retire” button cannot be used if the tenure status is anything but “AR”. Once the button is pushed, the date is put into this field. This has not been populated consistently for all records; MAP_LABEL: Combination of RAN # (Forest_File_ID) and Block ID (Map_Block_ID); CALENDAR_YEAR: The calendar year information entered. May not be populated; TOTAL_AUTHORIZED_GRAZBL_FORAGE: Total number of Animal Unit Months (AUMs) grazed in the year (not including non-use (Non_Use_Forage) but does include private land deduction (Total_Private_Land_Graz_Forage)). It does not populate from the Hay Cutting tenures; TOTAL_PRIVATE_LAND_GRAZ_FORAGE: Where unfenced private land runs adjacent to Crown land, a deduction in animal unit months is determined by considering the forage production capability of the private land; NON_USE_FORAGE: The number of AUMs that have been scheduled for non-use as authorized by the District Manager. Populated when tenure in total or partial non-use; NET_AUTHORIZED: A combination of the Total Authorized (Grazable Forage in AUMs (Total_Authorized_Grazble_Forage)) less the Private Land Deduction (PLD)(Total_Private_Land_Graz_Forage) plus the Non-use (Non_Use_Forage). Populated from FTA calculation. Not populated for Hay Cutting; MAX_NO_OF_CATTLE "The number of cows, bulls, yearlings, or cow/calf pairs that will graze on Crown land during the year for a specific tenure. Populated by each District into FTA but value not required."; MAX_NO_OF_SHEEP "The number of sheep that will graze on Crown land during the year for a specific tenure. Populated by each District into FTA but value not required."; MAX_NO_OF_HORSES "The number of horses that will graze on Crown land during the year for a specific tenure. Populated by each District into FTA but value not required."; MAX_NO_OF_OTHER_LIVESTOCK "The number of other livestock (eg. goats) that will graze on Crown land during the year for a specific tenure. Populated by each District into FTA but value not required."; FILE_STATUS_CODE: The current status of the tenure (eg. A - Active, AR - Archived, P - Pending, AT - Archived, I - Inactive, SU - Suspended). Populated directly from FTA. DO NOT USE Used inconsistently by Districts so therefore use Life_Cycle_Status_Code; ADMIN_DISTRICT_CODE: District code, eg. DCS, of Forest District responsible for tenure administration. May be different than geographic location of tenure; ADMIN_DISTRICT_NAME: District name, eg. 100 Mile House Forest District, of Forest District responsible for tenure administration. May be different than geographic location of tenure; LIFE_CYCLE_STATUS_CODE: The life cycle state of the Tenure. One of Pending, Active or Retired. Populated based on SQL from File_Status_Code. USE THIS FIELD to determine status of Tenure