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)

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 resource contains flood extent maps of the Huallaga River near Chazuta, Peru derived from Height Above Nearest Drainage (HAND). MERIT Hydro global hydrography datasets (Hydrologically Adjusted Elevations, Flow Direction, and Upstream Drainage Pixel) were used as the input terrain data. The HAND raster and corresponding rating curve were creating using ArcHydro Pro tools in ArcGIS Pro. Specifically, this resource has a shapefile of flood extents for HAND values between 0-15, catchment and drainage line shapefiles, the HAND raster, and the rating curve as a csv. Using a flowrate, the HAND value can be determined from the rating curve. Then, the flood extent is the feature in the Chazuta_FloodExtent_HAND shapefile with the FloodValue attribute matching the Height value from the rating curve. As an example, this resource also includes a shapefile of the 5-m (Height value) flood extent along with the corresponding flood depth raster.

Stormwater collection/conveyance point features in Fuquay-Varina (e.g. inlets and outlets, and stormwater manholes/junction boxes). Please note that many of the stormwater point features represent privately owned and maintained stormwater features, 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; inlet vs. outlet; and manhole types of stormwater features. 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)

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.

Gravity Sewer Pipe Lines in Fuquay-Varina. These line features primarily represent gravity sewer mains. Directionality (start vs. end vertices) of these line features should reflect real world flow direction. The mapping of sewer service lines began recently -- those features are currently rather limited in this dataset. There are also some privately owned and maintained pipes that are mapped for modeling and informational purposes, which also started only recently, most often from as-built utility data from large development projects since 2015. Please pay attention to the Subtype field to identify the different categories of gravity sewer lines. 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)

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of ’very high’ and ’high’ class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Forced Sewer Pipe Lines in Fuquay-Varina. Please note that there are some forced sewer line features that are not abandoned, but are not in service at this time. Please note the 'TYPE' field for the value distinguishing pipes out of service. Directionality (start vs. end vertices) of these line features reflects real world flow direction.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)

Datová sada je součástí dat zaměřujících se na hydrogeologii území. Jsou zde obsaženy vrstvy znázorňující průběh vodních toků Svratky, Svitavy a Ponávky v současnosti a zároveň původní koryta těchto vodních toků před jejich napřímením a regulací. Vzhledem ke snížené kvalitě historických podkladů je původní trasa toků pouze orientační. Další vrstvy obsahují informace o hloubkovém intervalu, ve kterém se nachází hladina podzemní vody v místech hydrogeologických vrtů a studní a údaje o podmínkách pro výstavbu zasahující pod hladinu podzemní vody, která může dlouhodobě změnit režim podzemních vod, a to s uvedením odkazů na použité materiály a studie. Dále je součástí vrstva obsahující graficky znázorněné předpokládané směry proudění podzemních vod.

Data jsou součástí pasportu geologie, hydrogeologie a inženýrské geologie (nyní Georizika) města Brna, který byl vytvořen v roce 2004 a je vždy ke konci roku aktualizován externí firmou. Generel byl vytvořen pro pracovníky úřadů, kteří se odborně vyjadřují k investičním akcím, projektové dokumentaci nebo vydávají rozhodnutí dle příslušných zákonů (stavební zákon, vodní zákon). Nyní je využíván i pracovníky příspěvkových organizací (např. KAM) a poskytován jako podklad pro zpracovatele studií zadávaných Statutárním městem Brno (SMB). Podrobnější informace najdete v dokumentaci. Data jsou v souřadnicovém systému GCS WGS84.

The WA State National Hydrography Dataset (NHD) is the standard hydrography for Washington. NHD Flowline contains linear watercourses representing streams, rivers, canals, ditches, coastline, pipelines, and artificial paths (centerlines through water polygon features such as lakes, estuaries, or double-banked streams). Flowline data for Washington are developed at a resolution of 1:4,800 to 1:24,000. This dataset was extracted and projected into WA State Plane Coordinates South. The coastline in the Puget Sound was updated in 2022 and references the Mean High Water (MHW) datum. Strahler Stream Order has been calculated and added using Esri ArcGIS Pro tools. A trace network was created from simplified flowlines and has also been added. 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. This high-resolution NHD, generally is developed at 1:24,000/1:12,000 scale, but many areas of Washington State have been improved to 1:4800 scale. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. 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. Stream Orders in Washington State range from 1 to 10. The smallest headwater streams are a 1, and the Columbia River is a 10. A detailed data dictionary is available at https://nhd.usgs.gov/userguide.html?url=NHD_User_Guide/Feature_Catalog/NHD_Feature_Catalog.htm

A 2D Hydraulic model (HEC-RAS) for below Tuttle Creek Reservoir at the confluence of the Kansas River and the Big Blue River near Manhattan, KS is presented. Model geometry is based on United States Geological Survey (USGS) 3DEP data (2015), with underwater bathymetry “burned” in using cross-sections sampled in the field in April of 2023. The model was calibrated based on water surface measured during data collection. The hydraulic simulations correspond to streamflows during which fish monitoring data were collected by researchers at Kansas State University (L. Rowley and K. Gido, to be published). Results from the hydraulic model, coupled with a sediment transport model, will be used to study fish and macroinvertabrate ecological response to streamflow. Methods The following is a summary of data utilized for developing a bathymetric terrain for 2D hydraulic modeling using HEC-RAS. Data used for model calibration and validation is also discussed.

Available Data Cross-section elevation data were collected by the United States Army Corps of Engineers (USACE) Kansas City District at approximately 200-foot to 1000-foot increments at the confluence of the Big Blue River and the Kansas River near Manhattan, Kansas. The following equipment was used by two complete surveying teams: • Ohmex SonarMite single beam echo sounder SFX @ 200khz, • Ohmex SonarMite single beam echo sounder DFX @ 28kHz & 200kHZ, • Trimble R12i 0096 & 0098, • Trimble R8 1984 & 6282

The cross-section elevation data were collected by boat and supplemented by hand-carried, pole-mounted Trimbles on April 10 to 14, 2023. The USGS gage on the Big Blue River near Manhattan, KS (06887000) had an average discharge of 425 cfs during the field collection time period (Figure 1). A USGS gage downstream of the confluence, Kansas River at Wamego, KS (06887500) shows an average discharge of 780 cfs at the same time period (Figure 2).

Figure 1 (Refer to supplemental information file). USGS gage Big Blue R NR Manhattan, KS – 06887000 discharge data for the week of April 11, 2023 – April 15, 2023. The average flow was taken as 425 cfs.

Figure 2 (Refer to supplemental information file). USGS gage Kansas River at Wamego, KS (06887500) discharge data for the week of April 11, 2023 – April 15, 2023. The average flow was taken as 780 cfs. Wamego, KS is downstream of the Big Blue River and Kansas River confluence and represents combined flow for both tributaries.

Figure 3 (Refer to supplemental information file). Map of bathymetric cross-sections collected in April 2023 near Manhattan, KS. Arrows show flow direction. Inset is the data collection location relative to the state of Kansas.

Terrain The field data collection featured 56 cross-sections. HEC-RAS 6.3.1 was utilized to create a bathymetric surface by interpolating 1-D cross-sections, while a 1-m resolution USGS 3DEP terrain (2015) was used for the floodplain and surrounding areas. A more recent USGS 3DEP (2018) data was available but featured higher stream flow than the 2015 data collection and therefore, more of the channel was submerged. Overall, the difference between 2015 and 2018 had a mean deviation of ~0.04 feet, with a majority of the differences in the channel ranging between +/-0.5 feet. Islands in this reach are unvegetated and prone to movement, and therefore the exact channel form is uncertain. However, it is assumed that relative island areas are consistent throughout the reach, and 2015 LiDAR was used to delineate the most island area as possible.

To build the bathymetric terrain, a similar process as what was discussed in Harris et al. (2023), field collected data were imported into ArcGIS Pro 3.0 as a point shapefile. To preserve georeferencing, the point shapefile was segmented into groups of 3-4 cross-sections and these cross-sections were interpolated into mini-surfaces using the Inverse Distance Weighted (IDW) spatial analysis tool. These mini-surfaces were brought into HEC-RAS and cross-sections were drawn to intersect with these field surveyed locations. The 1-D cross-sections were then used to create a TIFF for the entire channel area. The 1D interpolation captures the channel centerline between measured cross-sections but meanders and channel widening may not be covered by the interpolated channel. The channel raster was broken into its component objects or “exploded”, in ArcGIS Pro using the Raster to Point tool. The points were then interpolated using the Inverse-Distance-Weighted interpolation tool (IDW). This creates a terrain that covers meanders and channel expansion while maintaining fidelity to the original channel raster.

Areas where the terrain was inundated at the time of LiDAR data collection are “flat” and referred to as a hydro-flattened surface. The Slope tool in ArcMap was used to delineate these hydro-flattened areas and a shapefile tracing unsubmerged islands was used. The IDW surface was clipped to the hydro-flattened extents and then mosaicked with the original 3DEP terrain to create a seamless bathymetric and topographic surface.

The field data collected in April 2023 (Figure 3) required supplemental information to cover a fish monitoring instance upstream of the bridge at Pillsbury Drive/177. In September 2021, the USACE Kansas City District collected sediment samples with XY-georeference and depth measurements. The LiDAR hydro-flattened surface was used to estimate the energy grade slope from the new cross-section to the recent field monitoring extents. The model scenario or “plan” on the April 2023 extents was run at a similar flow as was occurring in September 2021. The combination of water surface elevation at that flow (780 cfs), the energy grade slope in the 3DEP data and field measured depth in 2021 were used to estimate the elevation at the channel bed.

Land Cover Land cover was delineated using the Multi-Resolution Land Characteristic (MRLC) Consortium’s 2019 National Land Cover Data (NLCD) (MRLC 2016). Fifteen types of landcover were identified for this study area by the NLCD: Hay-Pasture, Shrub-Scrub, Developed Low Intensity, Developed Medium Intensity, Cultivated Crops, Deciduous Forest, Herbaceous, Develop Open Space, Developed High Intensity, Woody Wetlands, Emergent Herbaceous Wetland, Open Water, Mixed Forest, Barren Land, and Evergreen Forest. Manning’s n values were selected based on a range of n values along with a “Suggested Initial n” provided by Krest Engineers (2021) (Table 1). Table 1. A table representing a range of Manning’s n values, a suggested Manning’s n value, and percent imperviousness for each NLCD land cover type. (Krest Engineers, 2021)

Model Settings The 2D HEC-RAS mesh was set to 40-feet square, with breaklines to orient cell edges along areas of steep elevation change or to support model convergence. Boundary conditions were placed at three locations in the 2D flow area: the inflow of the Big Blue River (boundary condition type: flow hydrograph), the upstream end of the Kanas River (flow hydrograph), and the downstream end of the Kanas River (normal depth). An energy grade slope was given as 0.0005 ft/ft for the Big Blue River and 0.0003 ft/ft for the Kansas River. Advanced time step control adjustments were implemented using Courant’s Criterion, with a minimum Courant of 0.75 and a maximum of 3.

Calibration The suggested value from Krest Engineers (2021) was the initial Manning’s n used for each land cover type (Table 1). The hydraulic model was then run, and the Manning’s n was changed to better conform to water surface elevations observed during field data collection. Flows corresponding to the field collection dates were 415 cfs for the Big Blue River and 360 cfs for the Kansas River. These streamflows were determined by cross-referencing the field collection dates (April 10 to 14, 2023) to continuous monitoring data available from USGS at gages Big Blue R NR Manhattan, KS (06887000) and Kansas R at Fort Riley, KS (06879100). The 2D model simulation results were compared to the field-measured water surface elevations at each channel cross-section with the ArcGIS Zonal Statistics as Table tool. Model improvement was determined by calculating the Root Mean Square Error (RMSE) of the simulated water surface elevation to the field observed water surface elevation, and the Manning’s n values resulting in the lowest error were selected. Following calibration, the model has overall RMSE of 0.29 ft for depth. The final Manning’s n values used for all the following simulations are included in Table 2.

Land Cover

Mannings n

Open Water

0.025

Emergent Herbaceous Wetlands

0.05

Woody Wetlands

0.045

Herbaceous

0.025

Mixed Forest

0.08

Evergreen Forest

0.08

Deciduous Forest

0.1

Scrub-Shrub

0.07

Hay-Pasture

0.025

Cultivated Crops

0.02

Baren Land

0.023

Developed, Open Space

0.03

Developed, Low Intensity

0.06

Developed, Medium Intensity

0.08

Developed, High Intensity

0.12

Table 2. The selected Manning’s n per Landcover classification after calibration

Simulations Apart from the calibration simulations, further simulations were conducted to match additional fish data collection from July 17 – 21, 2023 and October 2- 6, 2023. USGS gages, Big Blue R NR Manhattan, KS (06887000) and Kansas R at Fort Riley, KS (06879100), were used to find the discharge rates (in cfs) during those fish sampling periods. While discharge was consistent throughout the weeks for some gages (Figures 4 and 7), others showed differences greater than 10% or 100 cfs (Figures 5 and 6). The gages that showed significant differences were divided into two sub-simulations for the lower and higher flows during that week.

USGS Streamflow Data for July 17 - 21, 2023

HEC RAS Scenario Description River Simulation Flow (cfs)

July_KS_LF July lower flow Big

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.

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

These data describe longitudinal (upstream to downstream) patterns of dewatering during summer baseflow (July-September) conditions in nine watersheds in Shenandoah National park. In July-August of 2016 all nine watersheds (Jeremy's Run, Hazel River, Piney River, Hughes River, Staunton River, Whiteoak Canyon Run, Paine Run, Meadow Run, and Big Run) were evaluated for dewatering. In September of 2019, dewatering surveys were repeated in three watersheds (Pine Run, Piney River, and Staunton River) to evaluate annual variation in dewatering patterns. Data were collected by team of investigators walking each stream from an upstream point defined by the point along the stream draining 75-hectares (determined using watershed tools in ArcGIS Pro, version 2.6.0) to the bottom of each watershed near the park boundary, and mapping transition points between three hydrologic categories: Wet, dry, or isolated pools based upon investigator observation. “Wet” segments were defined as reaches where entire channel was wet with flow between pools; “Dry” segments were defined as reaches containing no water, or water of insufficient depth to sustain age 1+ brook trout; and “Isolated Pools” were defined as reaches containing pools of sufficient depth to hold 1+ brook trout but these pools were hydrologically disconnected from other parts of the channel. Because "Isolated Pool" type contains components of other two drying types (i.e., wet and dry), the boundaries were determined by the first transition/change encountered in the downstream direction of assessment. For example, when transitioning from a wet reach to isolated pools, the transition point marking the upstream boundary of the "isolated pool" would be the first dry segment encountered. Similarly, when transitioning from dry reach to isolated pool, the first encounter of a wet section would mark the upstream boundary of the isolated pool. Spatial coordinates of transition points were mapped using a Trimble R2 GNSS receiver for <1-meter accuracy.

This data contains general information about Pedestrian Network in Hong Kong. Pedestrian Network is a set of 3D line features derived from road features and road furniture from Lands Department and Transport Department. A number of attributes are associated with the pedestrian network such as spatially related street names. Besides, the pedestrian network includes information like wheelchair accessibility and obstacles to facilitate the digital inclusion for the needy. Please refer to this video to learn how to use 3D Pedestrian Network Dataset in ArcGIS Pro to facilitate your transportation analysis.The data was provided in the formats of JSON, GML and GDB by Lands Department and downloaded via GEODATA.GOV.HK website.

The original data files were processed and converted into an Esri file geodatabase. Wheelchair accessibility, escalator/lift, staircase walking speed and street gradient were used to create and build a network dataset in order to demonstrate basic functions for pedestrian network and routing analysis in ArcMap and ArcGIS Pro. There are other tables and feature classes in the file geodatabase but they are not included in the network dataset, users have to consider the use of information based on their requirements and make necessary configurations. The coordinate system of this dataset is Hong Kong 1980 Grid.

The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.

For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Lands Department at https://geodata.gov.hk/gs/download-datadict/201eaaee-47d6-42d0-ac81-19a430f63952.

For details about the data, source format and terms of conditions of usage, please refer to the website of GEODATA STORE at https://geodata.gov.hk.Dataset last updated on: 2022 Oct

Shows the line feature class built from the Lake Simcoe Protection Act Watershed Boundary (LSPAWB) is owned by the Ontario Ministry of Environment (MOE), in cooperation with Lake Simcoe Region Conservation Authority (LSRCA) and the Ontario Ministry of Natural Resources (MNR), and was created to support the Lake Simcoe Protection Act. The boundary was first created by LSRCA in 2005 as part of a Source Water Protection boundary project using the LSRCA 2002 5-metre Digital Elevation Model (DEM) and the best available data at the time. The portions of the watershed where the boundary shared a border with the Toronto and Region Conservation Authority and the Nottawasga Valley Conservation Authority were manually reviewed and updated though a series of consultations between the agencies. The boundary was created in ArcHydro using following procedures: 1) The updated watercourse was burned into the DEM, which generates Re-conditioned DEM; 2) Sinks in the Re-conditioned DEM were filled; 3) Flow direction grid was generated from the DEM updated in Steps 2); 4) Flow accumulation grid was generated; 5) Stream Definition grid was generated; 6) Stream Definition Segmentation grid was generated from the result in Step 5); 7) Catchment Grid was generated based on the Stream Definition Segmentation grid; 8) Catchment Grid was then converted to catchment polygons; 9) Drainage lines were generated; 10) Then we generated adjoint catchments and drainage points feature classes; 11) Subwatershed outlet points was digitized in a new feature class layer for the all sub watersheds for which we need to delineate boundaries 12) Run subwatershed delineation process; 13) The subwatersheds where dissolved to create one polygon. In 2008-09 in preparation for the Lake Simcoe Protection Plan data collection was performed on the hydrology layers for the Ramara Creeks, Talbot River, Upper Talbot River, and Whites Creek Subwatersheds of the watershed and the watershed boundary in that area was revised. The following steps were used in the delineation of the boundary in the north eastern portion of the watershed. The same Arc Hydro steps were followed as in the original boundary creation except for one additional step in which the culverts were burned into the DEM at the same time the watercourse was. The dataset was in turn converted to geodatabase format and additional attribute information was added to support the Act and the utility of the product for the end-user.

This wetland mapping project was funded by the King County Water and Land Services, Ecological Restoration and Engineering Services Unit, as part of a Best Available Science update. Wetlands within the King County boundary were mapped and classified, and reviewed by King County team members and National Wetland Inventory Staff. Wetlands were mapped and classified using: the National Wetlands Inventory (NWI) classification system (Cowardin et al., 1979) and the Landscape Position, Landform, Water Flow Path, and Water Body Type (LLWW) classification developed for the Western U.S. (Lemly et al. 2018).

The main objective for this project was to improve the knowledge of wetland extent and value within King County. In all, more than approximately 6,600 square miles of land comprise the county. King County contracted with Geospatial Services (GSS) at Saint Mary's University of Minnesota to create of high-quality National Wetlands Inventory Plus (NWIPlus) level mapping for the county. Program staff will conduct some ground truthing of data. NWIPlus is an enhanced NWI product with hydrogeomorphic-type descriptors that can facilitate predicting wetland functions. The enhanced attributes describe wetland landform, water flow path and water body type. The updated mapping will be utilized by developers and landowners to avoid wetland impacts, and may be incorporated into other GIS models which would identify potential wetland restoration projects and conservation priorities. Finalized mapping was made available through the county’s online map applications and submitted to the US Fish and Wildlife Service for addition to the National Wetlands Inventory.

King County completed this work as part of a Landscape Level 1 wetlands assessment. This work fits into the counties Wetland Program Plan (“The Plan”) and its goal of providing greater projection of wetlands and aquatic resources statewide. This work is overseen and is supported by the King County Wetland Program, within the Water and Land Services Department. The project, entitled “King County Wetland Inventory Update, King County, WA ” used geospatial techniques and image interpretation processes to remotely map and classify wetlands (includes deepwater habitats) and riparian areas in King County, WA. Wetlands for the project area were mapped and classified using on-screen digitizing methods in a Geographical Information System (GIS). This process was supported by development of a selective image interpretation key that resulted from field verification of image signatures and wetland classifications. Wetland image interpretation employed a variety of input image and collateral data sources, as well as field verification techniques. All mapping was completed at an on-screen scale of 1:5,000 or larger in compliance with national wetland mapping standards. The primary source imagery for mapping consisted of Eagleview, 2021, one-quarter foot, true-color pictometry. 8-bit, tiled orthophotography in TIFF format published by King County and mosaiced by GSS. Collateral data used in the mapping process included Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM) 1.5 ft resolution and LiDAR derived products such as hillshade, contours, depth grids, and synthetic flow networks; King County Digital Surface Model Vegetation Height; King County Coho intrinsic potential stream layer; Beaver Intrinsic Potential (BIP); Historic National Wetland Inventory (NWI); National Hydrography Dataset (NHD) springs and watershed boundaries; ESRI basemap imagery; and Google Earth Time Slider True Color Imagery (GE); King County wetland layers; King County Stormwater features; King County wetland mitigation sites; King County Habitat Restoration sites; and Wetland Intrinsic Potential (WIP). All feature creation and attribution were completed with on-screen digitization procedures using ESRI, ArcGIS Pro 3.2.0 with advanced editing tools. For wetland mapping and classification projects at the landscape level, a desktop computer heads-up digitizing process is performed referencing the Federal Geographic Data Committee (FGDC) Wetlands Mapping Standard (FGDC-STD-015-2009, FGDC 2009) and the FGDC Classification of Wetlands and Deepwater Habitats of the United States Standard (FGDC-STD-004-2013, FGDC 2013). Field reviews are used to address questions regarding image interpretation, land use practices, classification of wetland type and verification of preliminary mapping. The King County inventory of wetlands used source imagery and collateral data to identify and classify features within the FGDC Standards (FGDC-STD-015-2009, FGDC 2009; FGDC-STD-004-2013, FGDC 2013). The projects Target Mapping Unit was 0.25 acres; however, features mapped beyond this TMU by request of King County and at the interpreters discretion. Following this process, the King County inventory went through a standardized Quality Assurance and Quality Control (QA/QC) process with the United States Fish and Wildlife Service (USFWS) NWI program, King County, and GSS’s internal QAQC review.