The goal of this project is to create a map of the planet Mars, by using ESRI software. For this, a 3D project was developed using ArcGIS Pro, considering a global scene, to be published in an online platform. All the various data from Mars will be available in a single website, where everyone can visualize and interact. The Red Planet has been studied for many decades and this year marks the launch of a new rover, Mars2020, which will happen on the 17th of July. This new rover will be continuing the on-going work of the Curiosity Rover, launched in 2012. The main objective for these rovers is to determine if Mars could have supported life, by studying its water, climate and geology. Currently, the only operational rover in Mars is Curiosity and with that in mind, this project will have a strong focus on the path taken by this rover, during almost 8 years of exploration. In the web application, the user will be able to see the course taken by Curiosity in Mars’ Gale Crater, from its landing until January 2020. The map highlights several points of interest, such as the location after each year passed on MarsEarth year and every kilometer, which can be interacted with as well as browse through photos taken at each of the locations, through a pop-up window. Additionally, the application also supports global data of Mars. The two main pieces, used as basemaps, are the global imagery, with a pixel size of 925 meters and the Digital Elevation Model (DEM), with 200 meters per pixel. The DEM represents the topography of Mars and was also used to develop Relief and Slope Maps. Furthermore, the application also includes data regarding the geology of the planet and nomenclature to identify regions, areas of interest and craters of Mars. This project wouldn’t have been possible without NASA’s open-source philosophy, working alongside other entities, such as the European Space Agency, the International Astronomical Union and the Working Group for Planetary System Nomenclature. All the data related to Imagery, DEM raster files, Mars geology and nomenclature was obtained on USGS Astrogeology Science Center database. Finally, the data related to the Curiosity Rover was obtained on the portal of The Planetary Society. Working with global datasets means working with very large files, so selecting the right approach is crucial and there isn’t much margin for experiments. In fact, a wrong step means losing several hours of computing time. All the data that was downloaded came in Mars Coordinate Reference Systems (CRS) and luckily, ESRI handles that format well. This not only allowed the development of accurate analysis of the planet, but also modelling the data around a globe. One limitation, however, is that ESRI only has the celestial body for planet Earth, so this meant that the Mars imagery and elevation was wrapped around Earth. ArcGIS Pro allows CRS transformation on the fly, but rendering times were not efficient, so the workaround was to project all data into WGS84. The slope map and respective reclassification and hillshading was developed in the original CRS. This process was done twice: one globally and another considering the Gale Crater. The results show that the crater’s slope characteristics are quite different from the global panorama of Mars. The crater has a depression that is approximately 5000 meters deep, but at the top it’s possible to identify an elevation of 750 meters, according to the altitude system of Mars. These discrepancies in a relatively small area result in very high slope values. Globally, 88% of the area has slopes less than 2 degrees, while in the Gale Crater this value is only 36%. Slopes between 2 and 10 degrees represent almost 60% of the area of the crater. On the other hand, they only represent 10% of the area globally. A considerable area with more than 10 degrees of slope can also be found within the crater, but globally the value is less than 1%. By combining Curiosity’s track path with the DEM, a profile graph of the path was obtained. It is possible to observe that Curiosity landed in a flat area and has been exploring in a “steady path”. However, in the last few years (since the 12th km), the rover has been more adventurous and is starting to climb the crater. In the last 10 km of its journey, Curiosity “climbed” around 300 meters, whereas in the first 11 km it never went above 100 meters. With the data processed in the WGS84 system, all was ready to start modelling Mars, which was firstly done in ArcGIS Pro. When the data was loaded, symbology and pop-ups configured, the project was exported to ArcGIS Online. Both the imagery and elevation layer were exported as “hosted tile service”. This was a key step, since keeping the same level of detail online and offline would have a steep increase in imagery size, to hundreds of Terabytes, thus a lot of work was put into balancing tile cache size and the intended quality of imagery. For the remaining data, it was a straight-forward step, exporting these files as vectors. Once all the data was in the Online Portal, a Global Web Scene was developed. This is an on-going project with an outlook to develop the global scene into an application with ESRI’s AppBuilder, allowing the addition of more information. In the future, there is also interest to increment the displayed data, like adding the paths taken by other rovers in the past, alongside detailed imagery of other areas beyond the Gale Crater. Finally, with 2021 being the year when the new rover Mars2020 will land on the Red Planet, we might be looking into adding it to this project.https://arcg.is/KuS4r

This dynamic image service provides float values representing ground heights in meters, based on 3DEP seamless 1 arc-second data from USGS 3D Elevation Program (3DEP). Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: AnnuallyCoverage: conterminous United States, Hawaii, Alaska, Puerto Rico, Territorial Islands of the United States; Canada and Mexico.Data Source: The data for this layer comes from 3DEP seamless 1 arc-second dataset from the USGS's 3D Elevation Program with original source data in its native coordinate system.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as hillshade, slope, consider using the appropriate server-side function defined on this service.

Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. The layer is restricted to a 24,000 x 24,000 pixel limit.

NOTE: The image service uses North America Albers Equal Area Conic projection (WKID: 102008) and resamples the data dynamically to the requested projection, extent and pixel size. For analyses requiring the highest accuracy, when using ArcGIS Desktop, you will need to use native coordinates (GCS_North_American_1983, WKID: 4269) and specify the native resolutions (0.0002777777777779 degrees) as the cell size geoprocessing environment setting and ensure that the request is aligned with the source pixels.

Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates. Slope Degrees Slope Percentage Aspect Hillshade Slope Degrees MapThis layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Dataset Summary: This datasheet describes three 2-foot resolution rasters that cover the extent of Santa Cruz County – slope percent, slope degrees, and aspect. These rasters are derived directly from the 2-foot resolution Santa Cruz County Digital Terrain Models (DTMs), which were derived from 2020 QL1 lidar. These rasters represent the state of the landscape when countywide lidar data was collected in 2020. QL1 lidar was collected in western Santa Cruz County by Quantum Spatial and in eastern Santa Cruz County by the Sanborn Map Company. Figure 1 shows the respective areas of lidar collection. This deliverable is a combination of these two lidar datasets. The horizontal coordinate system of these rasters, State Plane CA Zone III (WKID 6420), is the native horizontal resolution of the 2020 point clouds. Figure 1. Sources of lidar data for Santa Cruz County

Table 1 provides links to download these lidar derived rasters.
These three lidar derivatives provide information about the surface of the earth. The two slope rasters depict the steepness of the ground for each 2-foot x 2-foot cell of the raster surface. One of the slope rasters represents slope in degrees, the other in percent. Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each cell to its neighbors.
The two slope and 1 aspect rasters were derived from the Santa Cruz County Digital Terrain Model using the ‘slope’ and ‘aspect’ functions in ArcGIS Pro Spatial Analyst. Table 1. lidar derivatives for Santa Cruz County

Dataset

Description

Link to Datasheet

Link to Data

Slope (Percent)

Steepness of the ground in percent for each 2-foot x 2-foot raster cell. Units in percent.

https://vegmap.press/scz_slope_and_aspect_datasheet

https://vegmap.press/scz_slope_percent

Slope (Degrees)

Steepness of the ground in degrees for each 2-foot x 2-foot raster cell. Units in degrees.

https://vegmap.press/scz_slope_and_aspect_datasheet

https://vegmap.press/scz_slope_degrees

Aspect

Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each cell to its neighbors.

https://vegmap.press/scz_slope_and_aspect_datasheet

https://vegmap.press/scz_aspect

Related Datasets: Other related lidar derived topography derivatives are available for Santa Cruz County. The following is a list of those rasters, with links to their datasheets: HillshadeDigital Terrain Model (western areas of the county)Digital Terrain Model (eastern areas of the county)5-meter resolution slope and aspect

Pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. Recently discovered, pockmarks along the Aquitaine slope within the French EEZ, were manually mapped although two semi-automated methods were tested without convincing results. In order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition GAZCOGNE1, 2013. Bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the Kongsberg EM302 ship-borne multibeam echosounder of the R/V Le Suroît at a speed of ~8 knots, operated at a frequency of 30 kHz and calibrated with ©Sippican shots. Precision of seafloor backscatter amplitude is +/- 1 dB. Multibeam data, processed using Caraibes (©IFREMER), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. The present table includes 11 morphological attributes extracted from a Geographical Information System project (Mercator 44°N conserved latitude in WGS84 Datum) and additional parameters related to seafloor backscatter amplitudes. Pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on GAZCOGNE1 and BOBGEO2 bathymetric datasets. The pockmark area and its perimeter were calculated with the “Calculate Geometry” tool of Arcmap 10.2 (©ESRI) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). A first method to calculate pockmark internal depth developed by Gafeira et al. was tested (Gafeira J, Long D, Diaz-Doce D (2012) Semi-automated characterisation of seabed pockmarks in the central North Sea. Near Surface Geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). This method is based on the “Fill” function from the Hydrology toolset in Spatial Analyst Toolbox Arcmap 10.2 (©ESRI), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. The difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. Thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. For the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark. Latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “Zonal Geometry as Table” tool from Spatial Analyst Toolbox in ArcGIS 10.2 (©ESRI) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). Pockmark elongation was calculated as the ratio between the major and minor axis length. Cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). Cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “Zonal Statistics as Table” tool from Spatial Analyst Toolbox in ArcGIS 10.2 (©ESRI). Slope was calculated from bathymetry with “Slope” function from Spatial Analyst Toolbox in ArcGIS 10.2 (©ESRI) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). Seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

To link physiographic variables to lithology and trail characteristics (Objective 2), we created process domain maps and used principal component analysis considering physiographic variables such as slope, topographic position index (TPI), and concavity as a function of lithology and trail type. All metrics were calculated using ArcGIS Pro and lidar collected 2013 by McKim & Creed Inc. for City of Boulder. High-resolution lidar data can be useful in determining different types of processes, ranging from slow creep to landslides (Booth et al. 2009; Booth et al. 2013) and variables like slope highlight areas where mass movements are likely to occur. Similarly, concavity, or landscape curvature, indicates where advective versus diffusive processes occur, correlated to convex versus concave landscapes, respectively (Dietrich and Perron 2006; Sweeney et al., 2015). Concave processes are dominated by diffusive movement (slope-dependent transport; Dietrich and Perron 2006). These processes are competing against one another on the hillslope and give rise to diffusion-dominated ridges and advection-dominated valleys (Dietrich and Perron 2006; Sweeney et al., 2015). The TPI is a landform classification used to determine roughness indices like valleys and ridges in the study area. The TPI was calculated at different resolutions (5-m, 10-m, 50-m, 100-m) to see if different hillslope attributes were identifiable at the different scales. To calculate the TPI, the mean for each resolution was subtracted from the Digital Elevation Model (DEM). Derived physiographic variables and GIS data products can be found in this repository.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) from various authoritative sources from across the globe. Heights are orthometric (sea level = 0), and bathymetric values are negative downward from sea level. The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. Height/Depth units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the max extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapMosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks. Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

Aim: Geographic variation in metabolic resources necessary for functional trait expression can set limits on species distributions. For species that need to produce and maintain biomineralized traits for survival, spatial variation in mineral macronutrients may constrain species’ distributions by limiting the expression of biomineralized traits. Here, we examine whether threatened, heavily biomineralized Oreohelix land snails are restricted to CaCO3 rock regions, if they incorporate greater amounts of CaCO3 rock carbon in their shell than less biomineralized smooth forms, and if ornamentation increases shell strength. Location: Western United States Methods: We used random forest (RF) classification models at multiple spatial resolutions to evaluate the contribution of topographic, vegetation, climate, and geologic variables in predicting the presence of heavily biomineralized shell ornaments. We then measured and compared shell biometric variables, 14C/12C ratios, and peak force for fr..., RF predictors: Predictor Dataset Creation The predictors used in this study came from a variety of sources (Supplementary Table A1). In this section, we will detail how they were made to facilitate replication of our results. All predictors were reprojected in ArcGIS Pro v.2.6.0 to WGS1984 and clipped to the same raster resolution. Predictor names used in the R code are shown in parentheses. See Supplemental Table A1 for references.

Elevation (elevation): This layer was sourced from the publicly available ASTER Global Digital Elevation data reprojected to 90m resolution using the Project tool and clipped to the desired extent using the Clip Raster tool.

Slope (slope): This layer was created using the Slope tool in ArcGIS on the 90m elevation data using a z-factor of 0.00001171 appropriate for 40 degrees latitude (https://pro.arcgis.com/en/pro-app/latest/tool-reference/3d-analyst/applying-a-z-factor.htm) which is close to the mean latitude of our study area.

Compound topographic inde..., ArcGIS Pro/QGIS to modify layers R for scripts

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

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.

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.

The Terrain Ruggedness Index (TRI) is used to express the amount of elevation difference between adjacent cells of a DEM. This raster function template is used to generate a visual representation of the TRI with your elevation data. The results are interpreted as follows:0-80m is considered to represent a level terrain surface81-116m represents a nearly level surface117-161m represents a slightly rugged surface162-239m represents an intermediately rugged surface240-497m represents a moderately rugged surface498-958m represents a highly rugged surface959-4367m represents an extremely rugged surfaceWhen to use this raster function templateThe main value of this measurement is that it gives a relatively accurate view of the vertical change taking place in the terrain model from cell to cell. The TRI provides data on the relative change in height of the hillslope (rise), such as the side of a canyon.How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual TRI representation of your imagery. This index supports elevation data.References:Raster functionsApplicable geographiesThe index is a standard index which is designed to work globally.

This layer shows urban bike corridors for York County, Pennsylvania. They are dedicated or prioritized pathways designed to enhance cyclist safety, accessibility, and connectivity within city transportation networks. Bike corridors may include protected bike lanes, shared roadways, multi-use trails, and greenways, often integrated with public transit and urban land uses. They are typically designed with features such as traffic calming, signage, pavement markings, and physical barriers to promote safe, continuous, and comfortable cycling for all age and ability levels. The analysis incorporates spatial data layers such as road networks, digital elevation models (DEM), land use/land cover (LULC), vehicular traffic volumes, cycling crash incidents, and demographic data within GIS platforms like ArcGIS Pro and QGIS. Advanced network analysis, including least-cost path modeling and service area analysis, identifies optimal routing options based on factors such as slope, traffic density, and road hierarchy. Kernel Density Estimation and hotspot analysis help locate high-risk zones for cyclists, informing the placement and design of safer corridors. Suitability analysis is conducted through a weighted overlay of criteria—proximity to key destinations (schools, parks, employment centers), existing cycling infrastructure, equity indicators, and environmental considerations. GIS tools such as buffering, intersecting, and spatial joins are used to model catchment areas and potential demand. A Multi-Criteria Decision Analysis (MCDA) framework within GIS enables the prioritization of corridor segments based on user-defined planning goals. The output includes thematic maps, spatial prioritization models, and a comprehensive geodatabase of recommended bike corridor alignments. This GIS-based approach offers city planners and policymakers a robust, data-driven toolset for designing bike-friendly cities that promote active transportation, reduce carbon emissions, and support healthier, more equitable communities.