Esri's Water Resources GIS Platform offers a comprehensive suite of tools and resources designed to modernize water resource management. It emphasizes geospatial solutions for monitoring, analyzing, and modeling water systems, helping decision-makers tackle challenges like drought resilience, flood mitigation, and environmental protection. By leveraging the capabilities of ArcGIS, users can transform raw water data into actionable insights, ensuring more efficient and effective water resource management.A central feature of the platform is Arc Hydro, a specialized data model and toolkit developed for GIS-based water resource analysis. This toolset allows users to integrate, analyze, and visualize water datasets for applications ranging from live stream gauge monitoring to pollution control. Additionally, the platform connects users to the ArcGIS Living Atlas of the World, which offers extensive water-related datasets such as rivers, wetlands, and soils, supporting in-depth analyses of hydrologic conditions. The Hydro Community further enhances collaboration, enabling stakeholders to share expertise, discuss challenges, and build innovative solutions together.Esri’s platform also provides training opportunities and professional services to empower users with technical knowledge and skills. Through instructor-led courses, documentation, and best practices, users gain expertise in using ArcGIS and Arc Hydro for their specific water management needs. The combination of tools, datasets, and community engagement makes Esri's water resources platform a powerful asset for advancing sustainable water management initiatives across public and private sectors.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

Our Certification & Restoration Program currently licenses water and wastewater treatment plant operators as well as water distribution plants throughout Florida. Obtaining one of these licenses is a prerequisite to obtaining employment as a plant operator, excluding owner-operators.See Metadata for contact information.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Land cover describes the surface of the earth. Land-cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to the earth's surface is required. Land-cover classification is a complex exercise and is difficult to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.There are a few public datasets for land cover, but the spatial and temporal coverage of these public datasets may not always meet the user’s requirements. It is also difficult to create datasets for a specific time, as it requires expertise and time. Use this deep learning model to automate the manual process and reduce the required time and effort significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band very high-resolution (10 cm) imagery.OutputClassified raster with the 8 classes as in the LA county landcover dataset.Applicable geographiesThe model is expected to work well in the United States and will produce the best results in the urban areas of California.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 84.8%. The table below summarizes the precision, recall and F1-score of the model on the validation dataset: ClassPrecisionRecallF1 ScoreTree Canopy0.8043890.8461520.824742Grass/Shrubs0.7199930.6272780.670445Bare Soil0.89270.9099580.901246Water0.9808850.9874990.984181Buildings0.9222020.9450320.933478Roads/Railroads0.8696370.8629210.866266Other Paved0.8114650.8119610.811713Tall Shrubs0.7076740.6382740.671185Training dataThis model has been trained on very high-resolution Landcover dataset (produced by LA County).LimitationsSince the model is trained on imagery of urban areas of LA County it will work best in urban areas of California or similar geography.Model is trained on limited classes and may lead to misclassification for other types of LULC classes.Sample resultsHere are a few results from the model.

Learn the building blocks of a query expression and how to select features that meet one or more attribute criteria.

Imagery is processed and used for a wide variety of geospatial applications, including geographic context, visualization, and analysis. You may want to apply processing techniques on image data, visually interpret the data, use it as a background to aid interpretation of other data, or use it for analysis. In this course, you will use tools in ArcGIS to perform basic image processing. You will learn how to dynamically modify properties that enhance image display, visualize surface features, and create multiple products.After completing this course, you will be able to:Describe common types of image processing used for analysis.Relate the access of imagery to decisions in processing.Apply on-the-fly display techniques to enhance imagery.Use image-processing functions to modify images for analysis.

This style for ArcGIS Pro contains four north arrows. They have a glassy semitransparent white appearance with a shadow effect for better visibility over highly textured surfaces while muted enough to provide balance.Plus they're a bit of fun sizzle.Will they look good over your map? Maybe! I wouldn't try them over a solid basemap though. They will look pretty bad probably. They are intended for the busy high contrast varied hues of an imagery basemap. But of course you will do what you feel is right, which may include not using them for any map.There is an arrowhead style north arrow and a cardinal ring arrow. These are standard north arrow shapes available in ArcGIS Pro, but given the glassy appearance. A stylized "N" and a minimalist arrow were drawn as custom SVGs then added to ArcGIS Pro and given the glassy appearance.Enjoy! John Nelson

This web map shows the cumulative data of certificate of compliance issued from 2018 onwards in Hong Kong. It is a set of data made available by the Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

Certified businesses enterprises in the District of Columbia as identified by the DC Department of Small and Local Business. A Certified Business Enterprise (CBE) is a business that is headquartered in the District of Columbia and has been certified by the Department of Small and Local Business Development (DSLBD). Businesses with CBE certification receive preferred procurement and contracting opportunities.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

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