The Show Low School District Map (ArchD Landscape) outlines the official school district boundary for Show Low Unified School District in Navajo County, Arizona. This district serves K-12 students, ensuring educational access, school zoning, and district administration. The map is used for governmental reference, enrollment planning, and community awareness, supporting school operations and jurisdictional delineation

Groundwater is the water that soaks into the ground from rain and can be stored beneath the ground. Groundwater floods occur when the water stored beneath the ground rises above the land surface. The Historic Groundwater Flood Map shows the observed peak flood extents caused by groundwater in Ireland. This map was made using satellite images (Copernicus Programme Sentinel-1), field data, aerial photos, as well as flood records from the past. Most of the data was collected during the flood events of winter 2015 / 2016, as in most areas this data showed the largest floods on record.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were calculated using data and techniques with various precision levels, and as such, it may not show the true historic peak flood extents.The Winter 2015/2016 Surface Water Flooding map shows fluvial (rivers) and pluvial (rain) floods, excluding urban areas, during the winter 2015/2016 flood event, and was developed as a by-product of the historic groundwater flood map.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were made using remote sensing images (Copernicus Programme Sentinel-1), which covered any site in Ireland every 4-6 days. As such, it may not show the true peak flood extents.The Synthetic Aperture Radar (SAR) Seasonal Flood Maps shows observed peak flood extents which took place between Autumn 2015 and Summer 2021. The maps were made using Synthetic Aperture Radar (SAR) images from the Copernicus Programme Sentinel-1 satellites. SAR systems emit radar pulses and record the return signal at the satellite. Flat surfaces such as water return a low signal. Based on this low signal, SAR imagery can be classified into non-flooded and flooded (i.e. flat) pixels.Flood extents were created using Python 2.7 algorithms developed by Geological Survey Ireland. They were refined using a series of post processing filters. Please read the lineage for more information.The flood maps shows flood extents which have been observed to occur. A lack of flooding in any part of the map only implies that a flood was not observed. It does not imply that a flood cannot occur in that location at present or in the future.This flood extent are to the scale 1:20,000. This means they should be viewed at that scale. When printed at that scale 1cm on the maps relates to a distance of 200m.They are vector datasets. Vector data portray the world using points, lines, and polygons (areas). The flood extents are shown as polygons. Each polygon has information on the confidence of the flood extent (high, medium or low), a flood id and a unique id.The Groundwater Flooding High Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 10%, which correspond with a return period of every 10 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Medium Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 1%, which correspond with a return period of every 100 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Low Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 0.1%, which correspond with a return period of every 1000 years.The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.

ADMMR map collection: Gila County Asbestos Deposits Location Map, Globe to Show Low; 1 in. to 4 miles; 19 x 23 in.

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improve classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. As such, widespread studies to develop and understand the nuances associated with these approaches will enable efficient adoption and application.

Elevation strongly influences soil moisture and patterns of tundra plant communities. Areas less than 100 m above sea level were separated to show low-elevation plains. Areas above 100 m elevation were divided into 333-m intervals to show decreases of about 2 °C, as predicted by the adiabatic lapse rate of 6 °C per 1000 m. This corresponds to the change in mean July temperature between Bioclimate Subzones. Vegetation in mountainous regions changes with elevation, forming distinct elevational belts which correspond approximately to bioclimatic subzones. Vegetation is also modified by local topographic effects such as slope, aspect, and cold-air drainage. This heterogeneity was too detailed to map at this scale, so vegetation in mountainous areas was mapped as a complex, using a diagonal hachure pattern. The background color and the orientation of the hatching represent the pH of the dominant bedrock (magenta for non-carbonate bedrock including sandstone and granite, purple for carbonate bedrock including limestone and dolomite). The color of the hatching represents the bioclimate subzone at the lowest elevation within the polygon (yellow hatching for Subzone D and red hatching for Subzone E). Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI , Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Raynolds, M.K., Walker, D.A., Maier, H.A. 2005. Plant community-level mapping of arctic Alaska based on the Circumpolar Arctic Vegetation Map. Phytocoenologia. 35(4):821-848. http://doi.org/10.1127/0340-269X/2005/0035-0821 Raynolds, M.K., Walker, D.A., Maier, H.A. 2006. Alaska Arctic Tundra Vegetation Map. 1:4,000,000. U.S. Fish and Wildlife Service. Anchorage, AK.

Elevation strongly influences soil moisture and patterns of tundra plant communities. Areas less than 100 m above sea level were separated to show low-elevation plains. Areas above 100 m elevation were divided into 333-m intervals to show decreases of about 2 °C, as predicted by the adiabatic lapse rate of 6 °C per 1000 m. This corresponds to the change in mean July temperature between Bioclimate Subzones. Vegetation in mountainous regions changes with elevation, forming distinct elevational belts which correspond approximately to bioclimatic subzones. Vegetation is also modified by local topographic effects such as slope, aspect, and cold-air drainage. This heterogeneity was too detailed to map at this scale, so vegetation in mountainous areas was mapped as a complex, using a diagonal hachure pattern. The background color and the orientation of the hatching represent the pH of the dominant bedrock (magenta for non-carbonate bedrock including sandstone and granite, purple for carbonate bedrock including limestone and dolomite). The color of the hatching represents the bioclimate subzone at the lowest elevation within the polygon (yellow for Subzone D, red for Subzone E). Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Elvebakk, A. 1999. Bioclimate delimitation and subdivisions of the Arctic. Pages 81-112 in I. Nordal and V. Y. Razzhivin, editors. The Species Concept in the High North - A Panarctic Flora Initiative. The Norwegian Academy of Science and Letters, Oslo. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

The Hydrogeological Overview Map of Lower Saxony 1: 500 000 — Groundwater quality: Iron content shows the evaluation of a representative selection of iron concentrations from the laboratory database of the LBEG. The data collected over a period from 1967 to 2000 have been averaged twice. For groundwater measuring points with multiple analyses, mean values of the available test results were formed. In addition, the values of all sampling points in a radius of 2 000 m were subjected to further averaging.

Classes are classified taking into account the limit values of the Drinking Water Ordinance (TVO) of 0.2 mg/l. Increased concentrations, which are clearly due to point-shaped anthropogenic entries (e.g. old landfills), are not reproduced in this overview map. The iron content is shown at depth levels without reference to the local hydrogeological situation. The rod diagrams in the example shown on the right reflect results for the depths up to 20 meters, over 20 to 50 meters, over 50 to 100 meters and over 100 to 200 meters. A comparison of values is therefore not permissible without taking into account the respective hydrogeological situation (e.g. hydrogeological floor construction) as well as the use of the data for detailed examinations.

The concentration of iron in groundwater is strongly influenced by pH and redox ratios. The highest iron content in Lower Saxony is achieved in acidic and/or greatly reduced water. On the other hand, high concentrations of carbonate and sulphide ions cause the precipitation of siderite or iron sulphides, thus limiting the solubility of iron. At high concentrations of dissolved organic carbon, large proportions of iron are also bound to organocomplexes.

In general, the iron content in the solid rock equifers of the Lower Saxony mountain region is significantly lower than in quaternary loose rocks. Mesozoic limestones contain the lowest iron concentrations of 0.01 to a maximum of 0.1 mg/l. Higher values are observed in mesozoic sandstone. In the paleozoic rocks of the resin there are values in the range of 0.1-0.5 mg/l. The oxygen-containing groundwater in northern Lower Saxony (e.g. Lüneburg Heath) shows iron concentrations in the range of 0.1-1 mg/l. In rare cases, up to 2 mg/l are achieved. In the lowlands in northern Lower Saxony, the limit value of the TVO of 0.2 mg/l is often exceeded.

Iron concentrations of 2-10 mg/l are often observed in ascending groundwater with longer flow paths. Also very high iron content between 10 and 40 mg/l can be found in groundwater, which is influenced by moors (e.g. Vehnemoor southwest of Oldenburg and Teufelsmoor north of Bremen). By contrast, iron-containing groundwaters in the north of Hanover (Isernhagen, Langenhagen) with concentrations of up to 40 mg/l are likely to be traced back to the oxidation of pyrite from lower chalk claystone.

This is the 2022 version of the Aquifer Risk Map. The 2021 version of the Aquifer Risk Map is available here.This aquifer risk map is developed to fulfill requirements of SB-200 and is intended to help prioritize areas where domestic wells and state small water systems may be accessing raw source groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the risk map is to be made available to the public and is to be updated annually starting January 1, 2021. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding. This is the final 2022 map based upon feedback received from the 2021 map. A summary of methodology updates to the 2022 map can be found here.This map displays raw source groundwater quality risk per square mile section. The water quality data is based on depth-filtered, declustered water quality results from public and domestic supply wells. The process used to create this map is described in the 2022 Aquifer Risk Map Methodology document. Data processing scripts are available on GitHub. Download/export links are provided in this app under the Data Download widget.This draft version was last updated December 1, 2021. Water quality risk: This layer contains summarized water quality risk per square mile section and well point. The section water quality risk is determined by analyzing the long-tern (20-year) section average and the maximum recent (within 5 years) result for all sampled contaminants. These values are compared to the MCL and sections with values above the MCL are “high risk”, sections with values within 80%-100% of the MCL are “medium risk” and sections with values below 80% of the MCL are “low risk”. The specific contaminants above or close to the MCL are listed as well. The water quality data is based on depth-filtered, de-clustered water quality results from public and domestic supply wells.Individual contaminants: This layer shows de-clustered water quality data for arsenic, nitrate, 1,2,3-trichloropropane, uranium, and hexavalent chromium per square mile section. Domestic Well Density: This layer shows the count of domestic well records per square mile. The domestic well density per square mile is based on well completion report data from the Department of Water Resources Online System for Well Completion Reports, with records drilled prior to 1970 removed and records of “destruction” removed.State Small Water Systems: This layer displays point locations for state small water systems based on location data from the Division of Drinking Water.Public Water System Boundaries: This layer displays the approximate service boundaries for public water systems based on location data from the Division of Drinking Water.Reference layers: This layer contains several reference boundaries, including boundaries of CV-SALTS basins with their priority status, Groundwater Sustainability Agency boundaries, census block group boundaries, county boundaries, and groundwater unit boundaries. ArcGIS Web Application

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This map shows the locations of the businesses that received funding through the Small Business Stabilization Fund program. The business locations are plotted on top of the Race and Social Equity Composite Index to show proportions of businesses located in areas of higher disadvantage.