This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

These contour lines were derived and delivered for Pennsylvania from the PAMAP Quality Level 3 (QL3) LIDAR data collection between 2006 and 2008. Some post-processing has been done to the original deliverables, including merging, line smoothing, and eliminating duplicate (overlapping) data between collections. This dataset renders the contour lines with the following scale-dependent visibility: 100 foot increments between 1:200,000 and 1:100,000 | 50 foot increments between 1:100,000 and 1:30,000 | 20 foot increments between 1:30,000 and 1:5,000 | 10 foot increments between 1:5,000 and 1:1,000 | and 2 foot increments between 1:1,000 and 1:10. The lines have been smoothed using the ArcGIS Pro 3.3 Smooth Line geoprocessing tool via the Polynomial Approximation with Exponential Kernal (PAEK) and setting a 10 ft smoothing tolerance distance. The extent of this data extends slightly beyond the Pennsylvania boundary into all surrounding states to ensure complete coverage of Pennsylvania. Duplicate (overlapping) contour data between collection years and north/south state plane zones has been eliminated by splitting the data from adjacent collects at county boundaries to ensure a seamless product with no duplication or overlapping data. The contour line geometries along the county boundaries that separate different years of PAMAP data collection (2006, 2007, and 2008) do not always connect properly.

This layer was derived using the following steps in ArcGIS Pro. The Focal Statistics tool (Neighborhood = circle, with 5 cell radius, Mean statistics type) was used to slightly smooth the bathymetric DEM. The Contour tool was used to generate 10-foot contour polygons, with a base contour of 0. The two lowest contour intervals were merged using the Edit ribbon; this was done to merge the BCB 380 to 390 and BCB 390.5 contours together into the 0 to 10 foot depth contour. New attribute fields were added to convert the Boston City Base (BCB) elevations (used by DCR and MWRA) into bathymetric depth in feet. This was then used to calculate a "Depth Range" attribute field (text). The Erase tool was used to remove any bathymetry contour area that overlapped with the Wachusett Islands layer. This small area of overlap resulted from the Focal Statistics tool and smoothing process. The layer was projected into Massachusetts State Plane coordinates. Finally, to improve drawing performance, the Simplify Shared Edges tool was used with the Douglas-Peucker simplification algorithm, a 2 meter tolerance and a 10 square meter minimum area. A custom symbology was applied using the "Depth Range" attribute field.

This dataset consists of 10-metre-interval contours in proximity to Denman Glacier. The contours were derived from the REMA 2 (Reference Elevation Model of Antarctica version 2) 10-metre mosaic digital elevation model. Features were produced using the ArcGIS Pro Contour tool with default settings. Contour generation was limited to a region extending from approximately 97°E to 104°E and 65°S to 68°S. The contours have not been edited or post-processed. This data is stored in the AAD's relief ln enterprise dataset.

MassGIS derived these contours from the USGS 2021 Central Eastern MA Lidar Project data.The Hydro-enforced digital elevation model (DEM) and water's edge breaklines were processed in ArcGIS Pro 3.0.3 using the Contours with Barriers3D Analyst Tool using a contour interval of 0.3048 (meters).The resultant isolines' meter elevations were then multiplied by 3.28084 in a new field to display the elevations with a vertical resolution of 1.0 foot

This dataset consists of 100-metre-interval contour lines across East Antarctica. The contours are derived from the REMA 2 (Reference Elevation Model of Antarctica version 2) 10-metre mosaic digital elevation model. Features were produced using the ArcGIS Pro 'Generate Topographic Contours' tool with a Raster Smooth Tolerance of 0.6, a Contour Minimum Length of 300 metres, and a Contour Smooth Tolerance of 150 metres based on an output map scale of 1:1 million. Contour generation was limited to a source mosaic tile set extending from the from approximately 31°E to 177°E and from the coast inland to approximately 76°S. The contours were edited to remove holes present in the source DEM and some shorter-length contours were manually removed to improve clarity. This data is stored in the AAD's relief ln enterprise dataset.

This 3D model of Mount Saint Helens shows the topography using wood-textured contours set at 50m vertical spacing, with the darker wood grain color indicating the major contours at 1000, 1500, 2000, and 2500 meters above sea level. The state of the mountain before the eruption of May 13, 1980 is shown with thinner contours, allowing you to see the volume of rock that was ejected via the lateral blast.The process to create the contours uses CityEngine and ArcGIS Pro for data processing, symbolization, and publishing. The steps:Create a rectangular AOI polygon and use the Clip Raster tool on your local terrain raster. A 30m DEM was used for before, 10m for after.Run the Contour tool on the clipped raster, using the polygon output option - 50m was used for this scene.Run the Smooth Polygon tool on the contours. For Mount St. Helens, I used the PAEK algorithm, with a 200m smoothing tolerance. Depending on the resolution of the elevation raster and the extent of the AOI, a larger or smaller value may be needed. Write a CityEngine rule (see below) that extrudes and textures each contour polygon to create a stair-stepped 3D contour map. Provide multiple wood texture options with parameters for: grain size, grain rotation, extrusion height (to account for different contour depths if values other than 100m are used), and a hook for the rule to read the ContourMax attribute that is created by the Contour tool. Export CityEngine rule as a Rule Package (*.rpk).Add some extra features for context - a wooden planter box to hide some of the edges of the model, and water bodies.Apply the CityEngine-authored RPK to the contour polygons in ArcGIS Pro as a procedural fill symbol, adjust parameters for desired look & feel.Run Layer 3D to Feature Class tool to convert the procedural fill to multipatch features. Share Web SceneRather than create a more complicated CityEngine rule that applied textures for light/dark wood colors for minor/major contours, I just created a complete light- and dark-wood version of the mountain (and one with just the water), then shuffled them together.Depending on where this methodology is applied, you may want to clip out other areas - for example, glaciers, roads, or rivers. Or add annotation by inlaying a small north arrow in the corner of the map. I like the challenge of representing any feature in this scene in terms of wood colors and grains - some extruded, some recessed, some inlaid flat.

These contour lines were derived and delivered for Pennsylvania from the PAMAP Quality Level 3 (QL3) LIDAR data collection between 2006 and 2008. Some post-processing has been done to the original deliverables, including merging, line smoothing, and eliminating duplicate (overlapping) data between collections. This dataset renders the contour lines with the following scale-dependent visibility: 100 foot increments between 1:200,000 and 1:100,000 | 50 foot increments between 1:100,000 and 1:30,000 | 20 foot increments between 1:30,000 and 1:5,000 | 10 foot increments between 1:5,000 and 1:1,000 | and 2 foot increments between 1:1,000 and 1:10. The lines have been smoothed using the ArcGIS Pro 3.3 Smooth Line geoprocessing tool via the Polynomial Approximation with Exponential Kernal (PAEK) and setting a 10 ft smoothing tolerance distance. The extent of this data extends slightly beyond the Pennsylvania boundary into all surrounding states to ensure complete coverage of Pennsylvania. Duplicate (overlapping) contour data between collection years and north/south state plane zones has been eliminated by splitting the data from adjacent collects at county boundaries to ensure a seamless product with no duplication or overlapping data. The contour line geometries along the county boundaries that separate different years of PAMAP data collection (2006, 2007, and 2008) do not always connect properly.

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2092 Morse Rd, Bldg G-2Columbus, OH, 43276Telephone: 614-265-6509Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2062 Morse Rd, Bldg G-2Columbus, OH, 43246Telephone: 614-265-6479Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

This file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.

Accurate evaluation of riparian forests depends on precise delineation of both bank to bank (active channel) and single-thread hydrography. Local land use and salmon recovery planners use hydrography as a reliable tool for understanding and managing watershed impacts across the state. Active channel mapping allows practitioners to delineate riparian zones, examine the shading effects of riparian vegetation, map the location, extent, and distribution of anadromous and resident fish as well as locate fish blocking culverts, map protective stream buffers, and accurately inventory existing hydrography (Hyatt et al, 2022).The manual provided in this package describes methods and procedures used to digitize active channel polygons from high resolution elevation data and high-resolution imagery. Methods like this have become necessary, as access to high resolution data has become easier. Included in this method is AC Tools, a Python script-based ArcGIS Pro Toolset that can be used to delineate channel bank and channel island contour lines along river mainstems and larger tributaries. Much of the method involves how to select those contours and create active channel polygons. Methods are also available for download at https://pspwa.box.com/s/3stokaav635odvd8k2dtkcigef5sbkr2Pilot results of this methodology were conducted in Stillaguamish, Queets, and the Entiat River, and are available at the Puget Sound Partnerships Spatial Data Hub.

Active Channel HydrographyThe “active channel” includes the wetted channels of rivers and streams as well as adjacent un-vegetated cobble and gravel bars that are inundated during high flows. In this method, the active channel is analogous to the “bankfull channel” (Leopold and Maddock 1953, Leopold et al 1964, Williams 1978) or the ordinary high-water mark line (OHWM), where the presence and action of waters are “so common and usual, and so long continued in ordinary years as to mark upon the soil or vegetation a character distinct from the abutting upland,”(WAC 220-660-030(111)). In places where this line cannot the delineated the ordinary high water line is delineated along the elevation of the mean annual flood for every three years.

There are many reasons for considering the boundary of the active channel network. A common use for delineating the active channel is to map the inner edge of the riparian zone (eg. Hyatt 2023). Riparian areas are transitional areas between land and aquatic ecosystems that include both lotic and lentic systems (Gregory et al, 1991). These zones can include the surface and subsurface water influences and human induced natural forces, understanding the active channel boundary thereby isn’t just important for managing fish populations and identifying habitat restoration sites, it is also important for land use planning and management.