This map presents a tour of the City of Redlands, California using the detailed map of Redlands included in the community basemap. The City of Redlands is located in Southern California, about 65 miles east of Los Angeles. The map tour highlights some of the unique features in the history of Redlands as well as several of the places and events that make it a very livable community today.The map features a detailed basemap for the City of Redlands, California, including buildings, parcels, vegetation, land use, landmarks, streets, and more. The map features special detail for areas of high interest within the City, including local parks, landmarks, and the ESRI campus.The map references detailed GIS data provided by the City of Redlands, Department of Innovation and Technology, GIS Division. The map was authored using map templates available from ESRI, including:Topographic Map Template - Large ScalesCampus Basemap TemplateThe map was published as part of ESRI's Community Maps Program and is one of several detailed maps of cities and counties in the World Topographic Map.

Indoors Demo for Building L in Esri Redlands Campus.

To download:1. Click the Download button above.2. A side panel will appear showing download options. Under Shapefile, click the Download button.3. When the download completes, browse to the location of the downloaded .zip, copy it to the location where you manage your redistricting files, then right-click to extract the contents. You will then be able to use the file in GIS software.If, rather than downloading the data, you wish the reference online versions of these datasets directly to ensure you are always using the most up-to-date data, please contact the County of San Bernardino Innovation and Technology Departments at 909-884-4884 or by emailing OpenData@isd.sbcounty.gov for informations and instructions for doing so.This dataset should only be used for the purpose of establishing election divisions within a district. It will be removed once the redistricting process has concluded.

This feature layer is the clipped Census Tracts of the City of Redlands. It is enriched with data from ESRI 2023.Enriched fields include:Education levels (High School/No Diploma, High School Diploma, GED, Some College/No Degree, Associate's Degree, Bachelor's Degree, and Grad/Professional Degree)Total HouseholdsHousing Affordability IndexMedian and Average Home ValueMedian and Average Household IncomeOccupationsGenerations (Generation Z, Millennial, Generation X, and Baby Boomer)Total PopulationHousehold PopulationPopulation Density2020-2023 Growth Rate: PopulationDaytime Population (Workers and Residents)Hispanic and Non-Hispanic groupsDominant Tapestry (lifestyles)Senior and Age 0-4 PopulationPersons with DisabilityHouseholds with 0 CarsHouseholds with Income Below Poverty LevelHave a Working Cell Phone Total Crime IndexHouseholds with No Internet AccessHousehold with Food Stamps/SNAPWorking, Professional, and Service Classes *calculated by adding associated occupations then dividing by employed 16+ civilian population number field and multiplying by 100.Employed 16+ Civilian Population (number and percentage)

New Group Layer

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a GIS-usable format employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 16, using North American Datum of 1983 (NAD83). To produce a polygon vector layer for use in ArcGIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcGIS (Version 9.2, © 2006 Environmental Systems Research Institute, Redlands, California). In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map attribute codes (both map class codes and physiognomic modifier codes) to the polygons, and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer of INDU and immediate environs. At this stage, the map layer has only map attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map class names, physiognomic definitions, link to NVC association and alliance codes), we produced a feature class table along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature class layers produced from this project, including vegetation sample plots, accuracy assessment sites, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

Redlands, California provides a template for showcasing urban planning and design capabilities using Esri CityEngine. This example includes CyberCity3D buildings along with a fully redeveloped site using new 3D City Design rules.CityEngine is able to simulate the impact of design decisions in near real time, enabling decision makers to meet or exceed project goals, whether they be sustainability metrics, regulatory compliance or cost reduction.Click here to download the Example Redlands Redevelopment.Check out these web scenes created in the Redlands Example:New CGA Rule Features3D Transect / SmartCode ExamplesThis example requires CityEngine Version 2012.1. Click here to download a free 30-day trial of CityEngine.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Tracklines and associated observations were mapped and analyzed using ArcMap (ESRI, Redlands, CA). GPS data were recorded in NAD27 map datum and projected to an USGS Albers Equal Area Conic map projection for presentation and subsequent density analyses. Concatenated GPS and observation data were then used to generate point and line coverages in ArcMap (ESRI, Redlands, CA). We designed a custom analytic tool using ArcMap Model Builder that allows for the construction and export of user-specified and effort-adjusted spatial binning of species observations along continuous trackines. For the purposes of this report, we calculated seabird density estimates and marine mammal counts along continuous 3.0-kilometer and 7.7-kilometer trackline segments (i.e., 3.0 kilometer and 7.7 kilometer bins). Therefore, marine bird densities (at 3-kilometer scale, for example) are based on a composite strip area ranging from 0.15 per kilometer squared (one observer on effort) to 0.30 per kilometer squared (two observers on effort). We made no effort to adjust densities such that they would be proportional to variations in the area of buffered transect strip bin (i.e., weighted offset variable). These data are associated with the following publication: Mason, J.W., McChesney, G.J., McIver, W.R., Carter, H.R., Takekawa, J.Y., Golightly, R.T., Ackerman, J.T., Orthmeyer, D.L., Perry, W.M., Yee, J.L. and Pierson, M.O. 2007. At-sea distribution and abundance of seabirds off southern California: a 20-Year comparison. Cooper Ornithological Society, Studies in Avian Biology Vol. 33. References- ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.

New Group Layer

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Hydrologically conditioned digital elevation model (DEM) generated from lidar data clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA). The DEM was hydrologically corrected by breaching through pits with no downslope neighboring cells to force surface flow to continuously move downslope using Whitebox Geospatial Analysis Tools (Lindsay and Dhun 2015, Lindsay 2016). Pits that were not properly breached were manually adjusted using elevation information from the DEM and aerial imagery to locate culverts under roadways.

This line shapefile represents the major rivers within the world at 1:15,000,000 scale. This layer is part of the 2014 ESRI Data and Maps collection for ArcGIS 10.2.World Rivers provides a base map layer of major rivers of the world.

Aerial light detection and ranging (lidar) data were collected over the study site between April 12 – 14, 2012 as part of the Fauquier, Fairfax, Frederick (MD), and Jefferson County acquisition for FEMA Region 3 FY12 VA lidar (Dewberry 2012). Lidar points classified as ground and water were used to create a 3-m digital elevation model (DEM) clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA).

Dataset of net, winter and summer mass balances of Austre Gronfjordbreen, a valley glacier in western part of Nordenskiold land. Following dataset covers the period of 2012/2013 - 2018/2019. The data is planned to be published in a peer-reviewed journal in 2020. The dates of field surveys, the number of stakes and methods of winter measurements are also attached.

Quality

An annual mass balance was calculated by summing the winter balance and summer balance. Using observations from stake data, the glaciological mass balance of the entire glacier was calculated as sum of weighted mean values of separate elevation bands. The interval of elevation difference is 50 m. A mean mass balance was for each individual elevation zone using Topo To Raster interpolation in ArcGIS software (Esri, Redlands, CA, USA) (© ESRI). The mass balance value of each elevation zone is multiplied by the zone area, summed and then divided by the total glacier area to obtain the mass balance of the entire glacier. The total glacier area was estimated for each year by available satellite images.

This geodatabase includes spatial datasets that represent the Mississippian aquifer in the States of Alabama, Illinois, Indiana, Iowa, Kentucky, Maryland, Missouri, Ohio, Pennsylvania, Tennessee, Virginia and West Virginia. The aquifer is divided into three subareas, based on the data availability. In subarea 1 (SA1), which is the aquifer extent in Iowa, data exist of the aquifer top altitude and aquifer thickness. In subarea 2 (SA2), which is the aquifer extent in Missouri, data exist of the aquifer top and bottom aquifer surface altitudes. In subarea 3 (SA3), which is the aquifer area of the remaining States, no altitude or thickness data exist. Included in this geodatabase are: (1) a feature dataset "ds40MSSPPI_altitude_and_thickness_contours that includes aquifer altitude and thickness contours used to generate the surface rasters for SA1 and SA2, (2) a feature dataset "ds40MSSPPI_extents" that includes a polygon dataset that represents the subarea extents, a polygon dataset that represents the combined overall aquifer extent, and a polygon dataset of the Ft. Dodge Fault and Manson Anomaly, (3) raster datasets that represent the altitude of the top and the bottom of the aquifer in SA1 and SA2, and (4) georeferenced images of the figures that were digitized to create the aquifer top- and bottom-altitude contours or aquifer thickness contours for SA1 and SA2. The images and digitized contours are supplied for reference. The extent of the Mississippian aquifer for all subareas was produced from the digital version of the HA-730 Mississippian aquifer extent, (USGS HA-730). For the two Subareas with vertical-surface information, SA1 and SA2, data were retrieved from the sources as described below. 1. The aquifer-altitude contours for the top and the aquifer-thickness contours for the top-to-bottom thickness of SA1 were received in digital format from the Iowa Geologic Survey. The URL for the top was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_topography.zip. The URL for the thickness was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_isopach.zip Reference for the top map is Altitude and Configuration, in feet above mean sea level, of the Mississipian Aquifer modified from a scanned image of Map 1, Sheet 1, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 1 Reference for the thickness map is Distribution and isopach thickness, in feet, of the Mississipian Aquifer, modified from a scanned image of Map 1, Sheet 2, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 2 The altitude contours for the top and bottom of SA2 were digitized from georeferenced figures of altitude contours in U.S. Geological Survey Professional Paper 1305 (USGS PP1305), figure 6 (for the top surface) and figure 9 (for the bottom surface). The altitude contours for SA1 and SA2 were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derive the altitude from the thickness (depth from the land surface), and merge the subareas into a single surface. The primary tool was an enhanced version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute. The raster surfaces were corrected in areas where the altitude of the top of the aquifer exceeded the land surface, and where the bottom of an aquifer exceeded the altitude of the corrected top of the aquifer.

1) Spatial data: This dataset includes raw shapefiles used for an overlay analysis of digitized imagery, pre- and post-herbicide treatment from a remotely piloted aircraft system to invasive Phragmites australis. Images were digitized in ArcGIS Pro (v. 3.1.0, ESRI Inc., Redlands, CA). Files include the targeted treatment area, pre-treatment (2022) digitized P. australis classification, and post-treatment (2023) digitized vegetation damage. Site codes correspond to the following sites: BDD- Baie du Doré, RPP- Rondeau Provincial Park, SL- Spongy Lake, WD- Wood Drive. Note: there is no post-treatment classification at Wood Drive. 2) Data: This dataset includes outputted area and calculated percentage values from the overlay analysis, maximum drift measurements, and vegetation transect data/metadata.

Abundance of rivers and wetlands, by freshwater ecoregion.

The abundance of rivers and wetlands describes the degree to which a freshwater ecoregion is covered with these habitats. We calculated this abundance by combining selected classes of the Global Lakes and Wetlands Database (Level 3 of Lehner and Döll 2004), together with ESRI Rivers (2005) and the perennial rivers from ArcWorld 1:3million (ESRI 1992), all gridded to one-kilometer cell resolution. It should be noted that some ecoregions (e.g., Australia’s Arafura and Carpentaria Drainages and Great Diving Range on the northern and eastern coasts) are dominated by small rivers and streams that are not reflected properly in global river and wetland data sets; thus, abundance in these ecoregions may be underrepresented. We classified an ecoregion as “dominated by lakes and reservoirs” when it met thresholds set by regions of known lake and reservoir dominance versus river and wetland dominance. Known ecoregions were used to determine the algorithms to set the cutoffs between abundance classes.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

ESRI 1992 and 2005. ESRI ArcWorld Database [CD] and ESRI Data & Maps [CD]. Redlands, CA: Environmental Systems Research Institute. Digital media.

Lehner, B., and P. Döll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296: 1-22.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

The U.S. Geological Survey (USGS) is providing online maps of water-table and potentiometric-surface altitude in the upper glacial, Magothy, Jameco, Lloyd, and North Shore aquifers on Long Island, New York, April May 2016. Also provided is a depth-to-water map for Long Island, New York, April May 2016. The USGS makes these maps and geospatial data available as REST Open Map Services (as well as HTTP, JSON, KML, and shapefile), so end-users can consume them on mobile and web clients. A companion report, U.S. Geological Survey Scientific Investigations Map 3398 (Como and others, 2018; https://doi.org/10.3133/sim3398) further describes data collection and map preparation and presents 68x22 in. Portable Document Form (PDF) versions, 4 sheets, scale 1:125,000.

The USGS, in cooperation with State and local agencies, systematically collects groundwater data at varying measurement frequencies to monitor the hydrologic conditions on Long Island, New York. Each year during April and May, the USGS completes a synoptic survey of water levels to define the spatial distribution of the water table and potentiometric surfaces within the three main water-bearing units underlying Long Islandthe upper glacial, Magothy, and Lloyd aquifers (Smolensky and others, 1989)and the hydraulically connected Jameco (Soren, 1971) and North Shore aquifers (Stumm, 2001). These data and the maps constructed from them are commonly used in studies of the hydrology of Long Island and are used by water managers and suppliers for aquifer management and planning purposes. Sheets 1 4 in U.S. Geological Survey Scientific Investigations Map 3398 (Como and others, 2018; https://doi.org/10.3133/sim3398) were prepared using water-level data measured at 424 groundwater monitoring wells (observation and supply) and 15 streamgages across Long Island during April and May of 2016. Additionally, digital datasets were derived from the water-level observations that include (1) contour lines and a continuous raster of the depth to water table in the upper glacial and Magothy aquifers, (2) contour lines of the potentiometric surface in the middle to deep Magothy aquifer and the hydraulically connected Jameco aquifer, (3) contour lines of the potentiometric surface in the Lloyd aquifer and hydraulically connected North Shore aquifer, and (4) point feature classes for the 424 groundwater-monitoring wells and 15 streamgages where water levels were collected.

Data Sources

Water-level measurements made in 424 monitoring wells (observation and supply wells), 13 streamgages, and 2 lake gages across Long Island during April May 2016 were used to prepare the maps in this report. Groundwater measurements were made by the wetted-tape or electric-tape method to the nearest hundredth of a foot. Many of the supply wells are in continuous operation and, therefore, were turned off for a minimum of 24 hours before measurements were made to allow the water levels in the wells to recover to ambient (nonpumping) conditions. Full recovery time at some of these supply wells can exceed 24 hours; therefore, water levels measured at these wells are assumed to be less accurate than those measured at observation wells, which are not pumped (Busciolano, 2002). In addition to pumping stresses, density differences (saline water) also lower the water levels measured in certain wells (Lusczynski, 1961). In this report, all water-level altitudes are referenced to the National Geodetic Vertical Datum of 1929 (NGVD 29).

Analysis

Contours of water-table and potentiometric-surface altitudes were created using the groundwater measurements. The water-table contours were interpreted using water-level data collected from 13 streamgages, 2 lake gages, 275 observation wells, and 1 supply well screened in the upper glacial aquifer or the shallow Magothy aquifer. The potentiometric-surface contours of the Magothy aquifer were interpreted from measurements at 88 wells (61 observation wells and 27 supply wells) screened in the middle to deep Magothy aquifer and the contiguous and hydraulically connected Jameco aquifer. The potentiometric-surface contours of the Lloyd aquifer were interpreted from measurements at 60 wells (55 observation wells and 5 supply wells) screened in the Lloyd aquifer and the contiguous and hydraulically connected North Shore aquifer. Editing of the geospatial data was done in ArcGIS Desktop (Esri, 2015a) using geographic information system (GIS) tools.

Depth to water map

A GIS was used to create a continuous surface of the water table using an iterative finite-difference interpolation technique (Esri, 2015b) with measurements from 13 streamgages, 2 lake gages, 275 observation wells, 1 supply well, interpreted 10-foot (ft) contour intervals, and the coastline.

The land surface altitude, or topography, was obtained from National Oceanic and Atmospheric Administration (2016). The data were collected using light detection and ranging (lidar) and were used to produce a three-dimensional digital elevation model (DEM). The lidar data have a horizontal accuracy of 1.38 ft and a vertical accuracy of 0.40 ft at a 95-percent confidence level for the open terrain land-cover category. The DEM was developed jointly by the National Oceanic and Atmospheric Administration and the U.S. Geological Survey as part of the Disaster Relief Appropriations Act of 2013 (Pub.L. 113 2, H.R. 152, 127 Stat. 4). Land surface altitude is referenced to the North American Vertical Datum of 1988 (NAVD 88). On Long Island, NAVD 88 is approximately 1 foot higher than NGVD 29.

The continuous surface of the water table was adjusted for the vertical datum differences across Long Island. This surface was then subtracted from the land surface elevation (from the DEM) at the same location. The results are shown as a continuous depth to water-table map.

References Cited

Busciolano, Ronald, 2002, Water-table and potentiometric-surface altitudes of the upper glacial, Magothy, and Lloyd aquifers on Long Island, New York, in March April 2000, with a summary of hydrogeologic conditions: U.S. Geological Survey Water-Resources Investigations Report 01 4165, 17 p., 6 pl.

Como, M.D., Finkelstein, J.S., Simonette L. Rivera, Monti, Jack, Jr., and Busciolano, Ronald, 2017, Water-table and potentiometric-surface altitudes in the upper glacial, Magothy, and Lloyd aquifers of Long Island, New York, April May 2016: U.S. Geological Survey Scientific Investigations Map 3398, 4 sheets, scale 1:125,000, 6-p. pamphlet, https://doi.org/10.3133/sim3398.

Esri, 2015a, ArcMap 10.3.1: Redlands, Calif., Esri, software.

Esri, 2015b, Topo to Raster Tool: Redlands, Calif., Esri, software.

Lusczynski, N.J., 1961, Head and flow of ground water of variable density: Journal of Geophysical Research, v. 66, no. 12, p. 4247 4256.

National Oceanic and Atmospheric Administration, 2016, NCEI Hurricane Sandy digital elevation models: National Centers for Environmental Information, digital data, accessed January 8, 2016, at https://www.ngdc.noaa.gov/mgg/inundation/sandy/sandy_geoc.html

Smolensky, D.A., Buxton, H.T., and Shernoff, P.K., 1989, Hydrologic framework of Long Island, New York: U.S. Geological Survey Hydrologic Investigations Atlas HA 709, 3 sheets, scale 1:250,000.

Soren, Julian, 1971, Results of subsurface exploration in the mid-island area of western Suffolk County, Long Island, New York: Oakdale, N.Y., Suffolk County Water Authority, Long Island Resources Bulletin 1, 60 p.

Stumm, Frederick, 2001, Hydrogeology and extent of saltwater intrusion of the Great Neck peninsula, Great Neck, Long Island, New York: U.S. Geological Survey Water-Resources Investigations Report 99 4280, 41 p.