Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

A raster model with a cell size of 1 with values representing the direction of water flowing downslope from that cell. The flow direction tool used D8 modeling algorithm where one of 8 values represents the direction of flow to the steepest downhill neighboring cell.

The U.S. Geological Survey (USGS), in cooperation with the New Jersey Department of Environmental Protection (NJDEP), prepared hydro-conditioned geographic information systems (GIS) data layers for use in the updated New Jersey StreamStats 2022 application (U.S. Geological Survey, 2022). This update features improvements in base-elevation resolution from 10 meters to 10 feet and stream centerline hydrography from 1:24,000 to 1:2,400 scale. Hydro conditioning is the process of burning single-line stream centerlines at the 1:2,400 scale into a digital elevation model to produce flow direction and flow accumulation grids. This data release contains raster digital datasets for a 10-foot digital elevation model, a flow direction grid, and a flow accumulation grid for the updated New Jersey Streamstats 2022 application. The eleven 8-digit Hydrologic Unit Codes (HUCs) represented by this dataset are 02020007, 02030103, 02030104, 02030105, 02040104, 02040105, 02040201, 02040202, 02040206, 02040301, and 02040302 (U.S. Geological Survey, 2016). The updated New Jersey StreamStats 2022 application provides access to spatial analytical tools that are useful for water-resources planning and management, as well as engineering and design purposes. The map-based user interface can be used to delineate drainage areas, determine basin characteristics, and estimate flow statistics, including instantaneous flood discharge, monthly flow-duration, and monthly low-flow frequency statistics for ungaged streams. References cited: U.S. Geological Survey, 2016, National Hydrography: U.S. Geological Survey, accessed February 4, 2022, at https://www.usgs.gov/national-hydrography. U.S. Geological Survey, 2022, StreamStats v4.6.2: U.S. Geological Survey, accessed February 4, 2022, at https://streamstats.usgs.gov/ss/.

The Travel Time Tool was created by the MN DNR to use GIS analysis for calculation of hydraulic travel time from gridded surfaces and develop a downstream travel time raster for each cell in a watershed. This hydraulic travel time process, known as Time of Concentration, is a concept from the science of hydrology that measures watershed response to a precipitation event. The analysis uses watershed characteristics such as land-use, geology, channel shape, surface roughness, and topography to measure time of travel for water. Described as Travel Time, it calculates the elapsed time for a simulated drop of water to migrate from its source along a hydraulic path across different surfaces of the replicated watershed landscape, ultimately reaching the watershed outlet. The Travel Time Tool creates a raster whereas each cell is a measure of the length of time (in seconds) that it takes water to flow across it, and then accumulates the time (in hours) from the cell to the outlet of the watershed.

The Travel Time Tool creates an impedance raster from Manning's Equation that determines the velocity of water flowing across the cell as a measure of time (in feet per second). The Flow Length Tool uses the travel time Grid for the impedance factor and determines the downstream flow time from each cell to the outlet of the watershed.

The toolbox works with ArcMap 10.6.1 and newer and ArcGIS Pro. Latest version of the toc2.py script is Version 2.0.1 published 2025/11/07.

For step-by-step instructions on how to use the tool, please view MN DNR Travel Time Guidance.pdf

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

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

Residual contributing catchments for selected stream gauges in the Hunter subregion. Generated from the Geoscience Austraila 9 second flow direction grid using selected stream gauges and model simulation nodes as pour points with the ArcGIS Watershed tool. Catchments exist as a shapefile file.

Purpose

Residual catchment boundaries are used in river system modelling.

Dataset History

Selected gauge locations were used as pour points to generate contributing catchment areas from the GA 9 second flow direction raster. The catchment delineation was achieved using the ArcGIS 10.1 Spatial Analyst WATERSHED tool from the Hydrology toolbox. Resultant watershed raster was also converted to a shapefile (no generalisation).

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-R calibration catchments v01. Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/d419aae0-1cb3-48a8-82de-941398a80e3a.

Dataset Ancestors

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From Bioregional Assessment areas v05

• Derived From HUN AWRA-R calibration nodes v01

• Derived From National Surface Water sites Hydstra

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

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

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

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

Cet ensemble de données fournit les limites des bassins versants dérivés du LiDAR pour toutes les îles Calvert et Hecate, en Colombie-Britannique. Les bassins versants ont été délimités à partir d'un modèle altimétrique numérique de 3 m. Pour chaque polygone de bassin versant, le jeu de données comprend un identificateur unique et des statistiques sommaires simples pour décrire la topographie et l'hydrologie. Polygones de bassin versant Cet ensemble de données a été produit à partir des résultats de la modélisation hydrologique « traditionnelle » menée à l'aide du MNT de terre nue complet topographiquement complet basé sur lidar de 2012 + 2014 avec une zone tampon de 10 m autour de la côte pour s'assurer que tous les bassins versants modélisés atteignent l'océan. Les bassins versants ont été délimités à l'aide de points d'coulée créés à l'intersection des cours d'eau modélisés et du littoral. Après la délimitation du bassin versant, ceux-ci ont été coupés sur le rivage de l'île.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This data is an extract from the 1 second hydrologically enforced SRTM, clipped to a rectangle to take in the extent of the BA_SYD bioregion and its contributing subcatchments (BA_SYD_DEMh1sec).

The data is extracted from

Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

GUID: 9a9284b6-eb45-4a13-97d0-91bf25f1187b

Three additional layers are derived from this data for hydrological analysis and catchment delineation. They are:

BA_SYD_DEMh1sec_fill: as above but with sinks filled

BA_SYD_FDIR: Flow direction

BA_SYD_FACC: Flow accumulation.

Purpose

Hydrological analysis and contribution catchment delineation

Dataset History

This data is an extract from the 1 second hydrologically enforced SRTM, clipped to a rectangle to take in the extent of the BA_SYD bioregion and its contributing subcatchments (BA_SYD_DEMh1sec).

The data is extracted from Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) using the Spatial Analyst "Extract by Rectangle" tool snapped to the source raster.

GUID: 9a9284b6-eb45-4a13-97d0-91bf25f1187b

Three additional layers are derived from the extracted DEM using the respective Tools in the ArcGIS Spatial Analyst-> Hydrology toolset. They are:

BA_SYD_DEMh1sec_fill: as above but with sinks Filled

BA_SYD_FDIR: Flow Direction

BA_SYD_FACC: Flow Accumulation.

Dataset Citation

Bioregional Assessment Programme (2014) BA SYD 1 sec SRTM (h) DEM and hydrological derivatives. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/90f5ef3c-2141-4450-b44c-1afbd843236f.

Dataset Ancestors

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Contributing areas (residual catchments) for 61 node catchments in the Galilee subregion.

Dataset History

Node locations were snapped to GA DEM 9s derived flow accumulation lines representing their respective rivers. ArcGIS WATERSHED tool was run on the GA 9s flow direction raster using the snapped node locations as pour points to produce catchments areas for the nodes. Raster outputs were converted to vector polygon shapefiles to delineate node catchment boundary.

Dataset Citation

Bioregional Assessment Programme (2014) Node catchment for Galilee surface water modelling. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/2907c473-8824-42a1-9b6e-3ed4ba2aedc6.

Dataset Ancestors

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

• Derived From Alpha Coal Project Environmental Impact Statement

• Derived From QLD Exploration and Production Tenements (20140728)

• Derived From China Stone Coal Project initial advice statement

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee surface water modelling nodes

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From China First Galilee Coal Project Environmental Impact Assessment

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

• Derived From Seven coal mines included in Galilee surface water modelling

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

Defra Network WMS server provided by the Environment Agency. See full dataset here.The Most Probable Overland Flow Pathway dataset is a polyline GIS vector dataset that describes the likely flow routes of water along with potential accumulations of diffuse pollution and soil erosion features over the land.It is a complete network for the entire country (England) produced from a hydro-enforced LIDAR 1-metre resolution digital terrain model (bare earth DTM) produced from the 2022 LIDAR Composite 1m Digital Terrain Model. Extensive processing on the data using auxiliary datasets (Selected OS Water Network, OS MasterMap features as well as some manual intervention) has resulted in a hydro-enforced DTM that significantly reduces the amount of non-real-world obstructions in the DTM. Although it does not consider infiltration potential of different land surfaces and soil types, it is instructive in broadly identifying potential problem areas in the landscape.The flow network is based upon theoretical one-hectare flow accumulations, meaning that any point along a network feature is likely to have a minimum of one-hectare of land potentially contributing to it. Each segment is attributed with an estimate of the mean slope along it.The product is comprised of 3 vector datasets; Probable Overland Flow Pathways, Detailed Watershed and Ponding and Errors. Where Flow Direction Grids have been derived, the D8 option was applied. All processing was carried out using ARCGIS Pro’s Spatial Analyst Hydrology tools. Outlined below is a description of each of the feature class.Probable Overland Flow Pathways The Probable Overland Flow Pathways layer is a polyline vector dataset that describes the probable locations accumulation of water over the Earth’s surface where it is assumed that there is no absorption of water through the soil. Every point along each of the features predicts an uphill contribution of a minimum of 1 hectare of land. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Every effort has been used to digitally unblock real-world drainage features; however, some blockages remain (e.g. culverts and bridges. In these places the flow pathways should be disregarded. The Ponding field can be used to identify these erroneous pathways. They are flagged in the Ponding field with a “1”. Flow pathways are also attributed with a mean slope value which is calculated from the Length and the difference of the start and end point elevations. The maximum uphill flow accumulation area is also indicated for each flow pathway feature.Detailed Watersheds The Detailed Watersheds layer is a polygon vector dataset that describes theoretical catchment boundaries that have been derived from pour points extracted from every junction or node of a 1km2 Flow Accumulation dataset. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer.Ponding Errors The Ponding and Errors layer is a polygon vector dataset that describes the presence of depressions in the landscape after the hydro-enforcing routine has been applied to the Digital Terrain Model. The Type field indicates whether the feature is Off-Line or On-Line. Off-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. On-line features do not intersect with watercourses and are more likely to be depressions in the landscape where standing water may accumulate. Only features of greater than 100m2 with a depth of greater than 20cm have been included. The layer was derived by filling the hydro-enforced DTM then subtracting the hydro-enforced DTM from the filled hydro-enforced DTM.Please use with caution in very flat areas and areas with highly modified drainage systems (e.g. fenlands of East Anglia and Somerset Levels). There will occasionally be errors associated with bridges, viaducts and culverts that were unable to be resolved with the hydro-enforcement process.

This dataset includes sub-watershed delineations created for ~66 stream sites across the three iUTAH GAMUT watersheds, Logan River, Red Butte Creek, and Provo River (including GAMUT Aquatic stations: gamut.iutahepscor.org ), where monthly water quality samples were collected 2013-2014. Sub-watershed delineations will allow for analysis of the landscape contributing to these sites, which can then be used in models of water quality for the stream sites. This will contribute to the iUTAH goal of modeling the impact of land-use changes on in-stream water quality.

Delineations were generated from 10 m DEMs (Utah GIS portal) using ArcMap tools: mosiac, fill, flow direction, flow accumulation, snap pour point, watershed and raster to polygon. Default settings were used for all tools.

Abstract

The dataset was derived by the Bioregional Assessment Programme from Geodata: 9 second DEM and the National surface water sites Hydstra. The source datasets for this dataset are identified in the Lineage field of the metadata statement. The history field in the metadata describes the processes undertaken to produce this dataset.

Contributing areas (residual catchments) for selected stream gauges in the Galilee subregion.

Dataset History

Gauge locations were snapped to GA DEM 9s derived flow accumulation lines representing their respective rivers. ArcGIS WATERSHED tool was run on the GA 9s flow direction raster using the snapped gauge locations as pour points to produce catchments areas for the selected gauges. Output rasters were converted to vector polygon shapefiles to delineate catchment boundaries for individual gauges.

Dataset Citation

Bioregional Assessment Programme (2014) Galilee gauge contributing area. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/2e01c3cf-8aa6-45a6-8c77-b25f025fe629.

Dataset Ancestors

• Derived From National Surface Water sites Hydstra

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

USGS delineation: Rivers and Streams. Originally harvested from Inside Idaho (https://insideidaho.org), and clipped to the RCEW watershed. Lauer delineations: Streams were delineated from a 1m DEM derived from the 2014 LiDAR. First, the DEM was prepared for hydrologic processing by smoothing the model with a low-pass filter, filling NODATA holes with FocalStatistics, and filling sinks with the Fill tool. The cleaned DEM was then used to produce flow direction and accumulation maps using their respective tools in ArcMap. The flow accumulation raster was reduced to areas of accumulation greater than 0.1km^2 to avoid delineating small drainages without likely surficial flow. Then stream links and stream order maps were produced from the reduced flow map and converted to polylines using the Stream to Feature tool. Finally, lines were smoothed with a sensitivity of 3m using the PAEK algorithm in the Smooth Line tool.For the stream features, two stream networks were created with differing minimum accumulation areas, 0.1km^2 (this layer) and 1km^2 . The 1km^2 stream network likely has the closest accuracy to consistently flowing streams, but a careful evaluation by researchers more familiar with field work in the area is prudent to eliminate or label intermittent or ephemeral stream segments.

The Most Probable Overland Flow Pathway dataset is a polyline GIS vector dataset that describes the likely flow routes of water along with potential accumulations of diffuse pollution and soil erosion features over the land.

It is a complete network for the entire country (England) produced from a hydro-enforced LIDAR 1-metre resolution digital terrain model (bare earth DTM) produced from the 2022 LIDAR Composite 1m Digital Terrain Model. Extensive processing on the data using auxiliary datasets (Selected OS Water Network, OS MasterMap features as well as some manual intervention) has resulted in a hydro-enforced DTM that significantly reduces the amount of non-real-world obstructions in the DTM. Although it does not consider infiltration potential of different land surfaces and soil types, it is instructive in broadly identifying potential problem areas in the landscape.

The flow network is based upon theoretical one-hectare flow accumulations, meaning that any point along a network feature is likely to have a minimum of one-hectare of land potentially contributing to it. Each segment is attributed with an estimate of the mean slope along it.

The product is comprised of 3 vector datasets; Probable Overland Flow Pathways, Detailed Watershed and Ponding and Errors. Where Flow Direction Grids have been derived, the D8 option was applied. All processing was carried out using ARCGIS Pro’s Spatial Analyst Hydrology tools. Outlined below is a description of each of the feature class.

Probable Overland Flow Pathways The Probable Overland Flow Pathways layer is a polyline vector dataset that describes the probable locations accumulation of water over the Earth’s surface where it is assumed that there is no absorption of water through the soil. Every point along each of the features predicts an uphill contribution of a minimum of 1 hectare of land. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Every effort has been used to digitally unblock real-world drainage features; however, some blockages remain (e.g. culverts and bridges. In these places the flow pathways should be disregarded. The Ponding field can be used to identify these erroneous pathways. They are flagged in the Ponding field with a “1”. Flow pathways are also attributed with a mean slope value which is calculated from the Length and the difference of the start and end point elevations. The maximum uphill flow accumulation area is also indicated for each flow pathway feature.

Detailed Watersheds The Detailed Watersheds layer is a polygon vector dataset that describes theoretical catchment boundaries that have been derived from pour points extracted from every junction or node of a 1km2 Flow Accumulation dataset. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer.

Ponding Errors The Ponding and Errors layer is a polygon vector dataset that describes the presence of depressions in the landscape after the hydro-enforcing routine has been applied to the Digital Terrain Model. The Type field indicates whether the feature is Off-Line or On-Line. Off-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. On-line features do not intersect with watercourses and are more likely to be depressions in the landscape where standing water may accumulate. Only features of greater than 100m2 with a depth of greater than 20cm have been included. The layer was derived by filling the hydro-enforced DTM then subtracting the hydro-enforced DTM from the filled hydro-enforced DTM.

Please use with caution in very flat areas and areas with highly modified drainage systems (e.g. fenlands of East Anglia and Somerset Levels). There will occasionally be errors associated with bridges, viaducts and culverts that were unable to be resolved with the hydro-enforcement process.

Stormwater Pipe/Conveyance Lines in Fuquay-Varina. Please note that many of the stormwater line features represent privately owned and maintained pipes, and these are essential for mapping and understanding the stormwater drainage network sub-systems at the neighborhood level. Please pay attention to the Subtype field to identify the different categories of public vs. private and culvert type stormwater lines. Directionality (start vs. end vertices) of these line features reflects real world flow direction. The GIS data in the area of Downtown Fuquay-Varina has a lot of old and erroneous stormwater features. A project is currently underway to correct much of this inaccurate stormwater data. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersWater Meter VaultsRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)

Abstract

The dataset was derived by the Bioregional Assessment Programme from the GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Contributing catchments for selected locations in the Hunter subregion. Generated from the Geoscience Austraila 9 second flow direction grid. Catchments exist as both a grid and a shapefile version. The pour points used in the delineation of these catchments are included in the dataset.

Dataset History

Selected locations were used as pour points to generate contributing catchment areas from the GA 9 second flow direction raster. The catchment delineation was achieved using the ArcGIS 10.1 Spatial Analyst WATERSHED tool from the Hydrology toolbox. Resultant watershed raster was also converted to a shapefile (no generalisation).

Dataset Citation

Bioregional Assessment Programme (2015) HUN Greta And Goulburn At Hunter Junction catchments v01. Bioregional Assessment Derived Dataset. Viewed 07 June 2018, http://data.bioregionalassessments.gov.au/dataset/d4ed336a-7b68-446e-8f0c-fb670731ef90.

Dataset Ancestors

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

National Hydrologic Dataset downloaded from USGS on 2/4/2022. This data is also available from the USGS as a service at https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServerAbstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Use the metadata link, http://nhdgeo.usgs.gov/metadata/nhd_high.htm, for additional information. Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Nested contributing catchments for selected stream gauges in the Hunter subregion. Generated from the Geoscience Austraila 9 second flow direction grid. Catchments exist as both a geodatabase grid and a shapefile version. ## Dataset History Selected gauge locations were used as pour points to generate contributing catchment areas from the GA 9 second flow direction raster. The catchment delineation was achieved using the ArcGIS 10.1 Spatial Analyst WATERSHED tool from the Hydrology toolbox. Resultant watershed raster was also converted to a shapefile (no generalisation). ## Dataset Citation CSIRO (XXXX) HUN DEM derived catchment boundaries v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/8c3d6222-20f9-4a0b-b6af-59a61f5200ca. ## Dataset Ancestors * Derived From SYD ALL Raw Stream Gauge Data BoM v01 * Derived From Hunter Surface Water data v2 20140724 * Derived From Selected streamflow gauges within and near the Hunter subregion * Derived From SYD ALL Unified Stream Gauge Data v01 * Derived From Hunter Surface Water data extracted 20140718 * Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 * Derived From SSB Hydstra gauges v01