Snow Today

What is Snow Today?

Snow Today is a NASA-funded research project that examines where snow is present, where it has snowed recently, and how much water is in the snow. It also makes comparisons between snow today, snow yesterday, snow last year, and snow over the last few decades.

The Snow Today project publishes the following open access resources on this website:

• Daily data visualizations of snow conditions in near-real time across the western United States with additional regions coming online in 2024
• Scientific data analysis articles published during the winter and spring months of the snow season

Snow Today relies on satellite data collected hundreds of miles above Earth and data from snow monitoring stations in remote mountain areas. To map snow surface properties including snow cover extent and duration as well as snow albedo, our team takes daily satellite data and applies physically based techniques, which have been refined to map snow hidden from satellites, such as beneath clouds and forest canopies. While the satellite data show snow extent, duration, and albedo, they do not provide direct information on snow depth or snow water equivalent (SWE), which is a measure of how much water is stored within snow. To complement the remotely sensed data, our team also analyzes in situ SWE measured at hundreds of snow monitoring stations, many of which have records dating back to the 1980s. Snow Today uses SWE data to determine changes in snow quantity from new snowstorms that add water and from snowmelt events that drain melted snow from the snowpack. For stations with multiple decades of SWE data, Snow Today also compares recent SWE and snowstorm events to average conditions for that time of year.

Data access

In addition to interactive data visualization via the Daily Snow Viewer application, individual data files are available for download via FTP. 

• Currently, only SWE data files are available to download. The team is working on adding snow cover data and other variables to the FTP server for download. We will update this information when the data become available for download.

To access the data using an FTP client (e.g. FileZilla, Cyberduck, FireFTP, WinSCP, etc.), please use the following information as input:

Host Name: dtn23.rc.colorado.edu 
Username: anonymous 
Password: pwd 
Directory: /shares/snow-today 
General instructions to access FTP

Data variables

Remotely sensed snow surface properties

Notes:

1. Cloud-free and canopy-adjusted snow surface properties are updated daily, with a typical one- to two-day lag.
2. Starting March of 2024 Snow Today will use data from the SPIRES algorithm. Data from the STC-MODSCAG/MODDRFS algorithm will be added as an additional data stream pending availability of the input MODSCAG/MODDRFS data. See the Algorithms section of this page for more information.
3. For full bibliographic information on the studies cited in these descriptions, see the References section of this page.

The following variables are or will be available on the Daily Snow Viewer application and are derived from MODIS and VIIRS satellite data. To fill gaps identified as clouds or missing data, this analysis interpolates using spatial and temporal filters. Satellite observations that look straight down are weighted more heavily in the interpolation than satellite views from an angle. To account for distortions caused by satellite angle and forest cover, this analysis uses ancillary information like forest height maps and vegetation cover percent. See the Glossary section of this page for variable definitions and the References section for technical algorithm details.

• Snow cover percent
• Snow albedo
• Snow radiative forcing
• Snow cover duration

In situ snow water equivalent

Note: Snow water equivalent (SWE) data are updated daily.

Snow water equivalent data come from in-situ observations that use snow pillows to measure the amount of water present in the snow. See Data Sources for region-specific information. The following variables are available on the Daily Snow Viewer.

Snow water equivalent

This overlay shows daily snow water equivalent (SWE) on the date indicated on the image, based on snow station data. The data are expressed as a percentage of average over each station’s record, which includes a minimum of 25 years.

Change in snow water equivalent

This overlay shows changes in daily snow water equivalent (SWE) at the for the last 24 hours, based on snow station data.

Percentage of median snow water equivalent

This overlay gives an estimate of snow water equivalent (SWE) calculated by dividing SWE for the current day by the average SWE for the historical record on the same calendar day. The resulting value is multiplied by 100 to convert from a fractional value to a percentage. For Snow Today, we require stations to have measured 25 years of data to make this calculation.

Algorithms

DRFS

The Dust Radiative Forcing in Snow (DRFS) algorithm determines the spectral reflectance differences between measured and modeled clean snow of the same grain size. The algorithm uses these differences along with incoming solar irradiance to estimate snow radiative forcing. MODIS based MOD09GA surface reflectance used in the MODDRFS algorithm has been processed by the Jet Propulsion Laboratory for 2000 to 2023 and by the NSIDC DAAC starting in 2024. For more detail see Painter et al., 2012, in the References section. Data from this algorithm have been used in Snow Today from 2020 through 2023.

SCAG

The Snow Cover And Grain (SCAG) size algorithm, a physically based model for estimating snow cover percent and snow grain size for clear sky surface reflectance using surface reflectance. MODIS based MOD09GA surface reflectance used in the MODSCAG algorithm has been processed by the Jet Propulsion Laboratory for 2000 to 2023 and by the NSIDC DAAC starting in 2024. For more detail see Painter et al., 2009, in the References section. Data from this algorithm has been used in Snow Today from 2020 through 2023.

STC-SCAG/DRFS

Spatially and Temporally Complete (STC) SCAG/DRFS data applies temporal and spatial interpolation to SCAG and DRFS data. The algorithm accounts for off-nadir viewing by weighting observations by satellite view angle. Viewable snow cover from the satellite is corrected to better estimate snow on the ground using vegetation height and concurrently measured viewable vegetation fraction. For more detail see Rittger et al., 2020, in the References section. Data from this algorithm have been used in Snow Today from 2020 through 2023.

SPIReS

The Snow Property Inversion from Remote Sensing (SPIReS) algorithm is a physically based model for estimating snow cover and snow albedo. It improves on previous models by solving for grain size and concentration of light-absorbing impurities simultaneously and uses a background reflectance along with snow endmembers. The algorithm integrates cloud-masking with a machine-based learning approach. For more detail see Bair et al., 2021, in the References section. Starting in 2024 data from this algorithm are displayed in Snow Today for 2001 to present.

Data sources

Moderate Resolution Imaging Spectroradiometer (MODIS) data

The primary input for the algorithms used on Snow Today to create the remotely sensed snow surface properties are surface reflectance MODIS Terra. Historical reflectance data are from the LPDAAC while near-real-time data are from MODAPS/LANCE.

Visible Infrared Imagining Radiometer Suite (VIIRS) data

We plan on adding data from VIIRS Suomi-NPP beginning in 2024. We plan to use historical reflectance data from the LPDAAC and near real time data from MODAPS.

Snow station data

Snow station data in the United States are sourced from the Snow Telemetry (SNOTEL) network by the Natural Resources Conservation Service (NRCS), United States Department of Agriculture (USDA) and the California Department of Water Resources. The data are publicly available from the National Water and Climate Center.

Snow station data in Alberta is sourced from Alberta province with stations operated by the Ministry of Environment, BC Hydro, Rio Tinto Alcan, and Metro Vancouver. They are publicly accessed from the British Columbia Snow survey data site.

Acknowledgements

The Snow Today website led by Karl Rittger is a collaborative effort of many team members at several institutions at the University of Colorado Boulder, including the Institute of Arctic and Alpine Research (INSTAAR), the National Snow and Ice Data Center (NSIDC), and the Cooperative Institute for Research in Environmental Sciences (CIRES). These analyses build upon the legacy of a similar website formerly maintained at NSIDC by the late Drew Slater.

Active team members include Sebastien Lenard, Ross Palomaki, Matt Fisher, Mark Raleigh, Ann Windnagel, Mary J. Brodzik, Mark Serreze, Agnieszka Gautier, Audrey Payne, Daniel Crumley, Jessica Calme, Lisa Booker, Leslie Goldman, Michon Scott, Ned Bair, and Jeff Dozier.

Many others have contributed from CIRES/NSIDC and INSTAAR over the lifetime of this website.

This work uses resources from the University of Colorado Boulder Research Computing Group, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University.

Glossary of terms

Hydrologic Unit Code (HUC)

A hierarchical designation consisting of two additional digits for each level of specificity established by the United States Geological Survey (USGS) to delineate portions of the United States based on surface features related to the distribution and movement of water. The HUC system divides the United States into 21 two-digit regions, 222 four-digit subregions, 370 six-digit basins, and smaller regions designated by additional digits. Snow Today currently includes summaries for HUC2 and HUC4 subregions with potential to include HUC6 and HUC8 in future updates.

HydroSHEDS-HydroBASINS

This global vectorized dataset, HydroBASINS, is a hierarchically nested set of sub-basins consistently sized at different scales from tens to millions of kilometers. It is derived from 90-meter digital elevation models below 60 degrees latitude and from coarser 1-kilometer resolution above 60 degrees latitude. Snow Today utilizes this data set for regions outside of the United States and may display different levels of the hierarchy depending on the region and average basin scale.

Surface reflectance (e.g. MOD09GA or VNP09GA)

Surface spectral reflectance of Terra MODIS visible, near-infrared, and shortwave infrared wavelengths corrected for atmospheric conditions such as gases, aerosols, and Rayleigh scattering. These data are used as input to our snow surface property algorithms. Snow Today plans on including data from Suomi VIIRS in the future.

SNOTEL

A network of snow telemetry sites measuring snow water equivalent (SWE) with snow pillows, as well as other variables of interest related to snow depth, weather, and soil moisture. These sites are maintained by the Natural Resources Conservation Service (NRCS), an agency within the United States Department of Agriculture (USDA).

Snow albedo (snow brightness)

A non-dimensional, unitless quantity that measures how well a surface reflects solar energy, ranging from 0 to 1. A value of 0 means the surface is a perfect absorber, where all incoming energy is absorbed, while a value of 1 means the surface is a perfect reflector, where all incoming energy is reflected and none is absorbed. Fresh, clean snow with a high albedo appears bright, while old or dirty snow tends to have a lower albedo and appears darker. This quantity can also be expressed as a percent with a range from 0 to 100, with zero percent absorbing all incoming energy and 100 percent reflecting all energy.

Snow cover duration

The number of days a region has been covered with snow, identified with snow cover percent greater than a specific snow cover threshold since a specific starting time. In the Daily Snow Viewer maps we use a region-specific start date. For example, the water year in the Western United States uses October 1 as the starting date. The start date for each region can be viewed in the Snow Viewer on the x-axis of the plots after selecting that region.

Snow cover percent

The areal extent of snow-covered ground, expressed as the mathematical percent of a region covered with snow. In the context of Snow Today, the region refers to an Earth-observing satellite’s smallest measurement area. We use data from the Moderate Resolution Imaging Spectroradiometer at roughly 500-meter spatial resolution and plan on using VIIRS data at similar spatial resolution in the future. Note that the Earth’s surface is sometimes covered by clouds.

Snow cover percent threshold

Snow cover percent, below which Snow Today has less confidence, generally due to bright soils or shallow water that cannot be easily separated using surface reflectance data. Typically for snow cover this threshold is used to lower false positives which occur more abundantly below the threshold than true positives.

Snow pillow

A large, flat instrument that measures and reports the water weight of snowpack on the ground. The weight of water is the snow water equivalent (SWE).

Snow radiative forcing

When snow impurities such as dust or soot fall on snow, its surface darkens and absorbs more solar energy. Snow radiative forcing is a measure of the additional absorption of solar radiation from light-absorbing particles (LAP) such as dust or soot. Units of measure are watts per square meter (W/m²) and values can range from 0 to 500 W/m². This maximum value depends on incoming solar radiation (elevation, direct sun versus shaded) and the amount of dust or soot. A value of 0 means no additional radiation is being absorbed. A value of 500 means nearly all of the solar energy is absorbed (depending on latitude, elevation, clouds). Radiative forcing is calculated by the difference between the net (downward minus upward) radiative fluxes (irradiance) with and without LAP.

Snow water equivalent (SWE)

The water content obtained if all snowpack at a location melted instantly. Because snow contains a mix of water (ice and liquid) and air, the snow water equivalent (SWE) is less than the depth of the snow on the ground.

Snowfall

New snow that has fallen out of the atmosphere and accumulated on the ground since the previous day or since the previous observation.

References

Bair, E.H., K. Rittger, S.M. Skiles, and J. Dozier. 2019. An examination of snow albedo estimates from MODIS and their impact on snow water equivalent reconstruction. Water Resources Research 55: 7826-7842, doi: 10.1029/2019wr024810.

Bair, E.H., T. Stillinger, and J. Dozier. 2021. Snow property inversion from remote sensing (SPIReS): A generalized multispectral unmixing approach with examples from MODIS and Landsat 8 OLI. IEEE Transactions on Geoscience and Remote Sensing 59: 7270-7284, doi: 10.1109/TGRS.2020.3040328.

Dozier, J., T.H. Painter, K. Rittger, and J.E. Frew. 2008. Time-space continuity of daily maps of fractional snow cover and albedo from MODIS.  Advances in Water Resources 31(11): 1515-1526, doi:10.1016/j.advwatres.2008.08.011.

Jensen, A. S., K. Rittger, and M. S. Raleigh. 2024. Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western US from 2001 to 2022. Environmental Research: Climate, 3, 025001, doi: 10.1088/2752-5295/ad285a.

Painter, T.H., J. Dozier, D.A. Roberts, R.E. Davis, and R.O. Green. 2003. Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Remote Sensing of Environment 85: 64-77, doi: 10.1016/s0034-4257(02)00187-6.

Painter, T.H., K. Rittger, C. McKenzie, P. Slaughter, R.E. Davis, and J. Dozier. 2009. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment 113(4): 868-879, doi:10.1016/j.rse.2009.01.001.

Painter, T.H., A.C. Bryant, and S.M. Skiles. 2012. Radiative forcing by light absorbing impurities in snow from MODIS surface reflectance data. Geophysical Research Letters 39: L17502, doi: 10.1029/2012gl052457.

Raleigh, M.S., K. Rittger, C.E. Moore, B. Henn, J.A. Lutz, and J.D. Lundquist. 2013. Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada. Remote Sensing of Environment 128: 44-57, doi: 10.1016/j.rse.2012.09.016.

Rittger, K., T.H. Painter, and J. Dozier. 2013. Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources 51: 367-380, doi: 10.1016/j.advwatres.2012.03.002.

Rittger, K., M.S. Raleigh, J. Dozier, A.F. Hill, J.A. Lutz, and T.H. Painter. 2020. Canopy adjustment and improved cloud detection for remotely sensed snow cover mapping. Water Resources Research 55, doi: 10.1029/2019WR024914.

Rittger, K., K.J. Bormann, E.H. Bair, J. Dozier, and T.H. Painter. 2021. Evaluation of VIIRS and MODIS snow cover fraction in High-Mountain Asia using Landsat 8 OLI. Frontiers in Remote Sensing 2(8), doi: 10.3389/frsen.2021.647154.

Stillinger, T., K. Rittger, M.S. Raleigh, A. Michell, R.E. Davis, and E.H. Bair. 2023. Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets. The Cryosphere 17: 567-590, doi: 10.5194/tc-17-567-2023.

Related references

This section lists published research using Snow Today data products for applications ranging from streamflow forecasts to Sierra Bighorn mortality to large-scale climate modeling, and more.

Ackroyd, C., S.M. Skiles, K. Rittger, and J. Meyer. 2021. Trends in snow cover duration across river basins in High Mountain Asia from daily gap-filled MODIS fractional snow covered area. Frontiers in Earth Science 9, doi: 10.3389/feart.2021.713145.

Armstrong, R.L., K. Rittger, M.J. Brodzik, A. Racoviteanu, A.P. Barrett, S.J.S. Khalsa, B. Raup, A.F. Hill, A.L. Khan, A.M. Wilson, R.B. Kayastha, F. Fetterer, B. Armstrong. 2018. Contributions to High Asia runoff from glacier ice and seasonal snow: separating melt water sources in river flow. Regional Environmental Change doi: 10.1007/s10113-018-1429-0.

Bair, E.H., K. Rittger, R.E. Davis, T.H. Painter, and J. Dozier. 2016. Validating reconstruction of snow water equivalent in California's Sierra Nevada using measurements from the NASA Airborne Snow Observatory. Water Resources Research 52: 8437-8460, doi: 10.1002/2016WR018704.

Bair, E.H., A. Abreu Calfa, K. Rittger, and J. Dozier. 2018. Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan. The Cryosphere 12(5): 1579-1594, doi: 10.5194/tc-12-1579-2018.

Bair, E.H., K. Rittger, J.A. Ahmad, and D. Chabot. 2020. Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan. The Cryosphere 14: 331-347, doi: 10.5194/tc-14-311-2020.

Bair, E., T. Stillinger, K. Rittger, and M. Skiles. 2021. COVID-19 lockdowns show reduced pollution on snow and ice in the Indus River Basin. Proceedings of the National Academy of Sciences 118: e2101174118, doi: 10.1073/pnas.2101174118.

Bair, E.H., J. Dozier, K. Rittger, T. Stillinger, W. Kleiber, and R.E. Davis. 2023. How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction? The Cryosphere 17: 2629-2643, doi: 10.5194/tc-17-2629-2023.

Berger, D.J., D.W. German, C. John, R. Hart, T.R. Stephenson, and T. Avgar. 2022. Seeing is be-leaving: perception informs migratory decisions in Sierra Nevada bighorn sheep (Ovis canadensis sierrae). Frontiers in Ecology and Evolution 10, doi: 10.3389/fevo.2022.742275.

Feldman, D.R., A.C. Aiken, W.R. Boos, R.W.H. Carroll, V. Chandrasekar, S. Collis, J.M. Creamean, G. de Boer, J. Deems, P.J. DeMott, J. Fan, A.N. Flores, D. Gochis, M. Grover, T.C.J. Hill, A. Hodshire, E. Hulm, C.C. Hume, R. Jackson, F. Junyent, A. Kennedy, M. Kumjian, E.J.T. Levin, J.D. Lundquist, J. O’Brien, M.S. Raleigh, J. Reithel, A. Rhoades, K. Rittger, W. Rudisill, Z. Sherman, E. Siirila-Woodburn, S.M. Skiles, J.N. Smith, R.C. Sullivan, A. Theisen, M. Tuftedal, A.C. Varble, A. Wiedlea, S. Wielandt, K. Williams, and Z. Xu. 2023. The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. Bulletin of the American Meteorological Society doi: 10.1175/BAMS-D-22-0049.1.

Hao, D., G. Bisht, C. He, E. Bair, H. Huang, C. Dang, K. Rittger, Y. Gu, H.Wang, Y. Qian, and L.R. Leung. 2022. Improving snow albedo modeling in E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau. Geoscientific Model Development Discussions doi: 10.5194/gmd-2022-67.

Hao, D., G. Bisht, K. Rittger, T. Stillinger, E. Bair, Y. Gu, and L.R. Leung. 2023. Evaluation of E3SM land model snow simulations over the western United States. The Cryosphere 17: 673-697, doi: 10.5194/tc-17-673-2023.

Hatchett, B.J., A.L. Koshkin, K. Guirguis, K. Rittger, A.W. Nolin, A. Heggli, A.M. Rhoades, A.E. East, E.R. Siirila-Woodburn, W.T. Brandt, A. Gershunov, and K. Haleakala. 2023. Midwinter dry spells amplify post-fire snowpack decline. Geophysical Research Letters 50: e2022GL101235, doi: 10.1029/2022GL101235.

Hill, A.F., R.F. Stallard, K. Rittger. 2018. Clarifying regional hydrologic controls of the Maranon River, Peru through rapid assessment to inform system-wide basin planning approaches. Elementa: Science of the Anthropocene 6, doi: 10.1525/elementa.290.

Hill, A. F., K. Rittger, T. Dendup, D. Tshering, and T.H. Painter. 2020. How important is meltwater to the Chamkhar Chhu Headwaters of the Brahmaputra River? Frontiers in Earth Science 8(81), doi: 10.3389/feart.2020.0008.

Huang, H., Y. Qian, C. He, E.H. Bair, and K. Rittger. 2022. Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada. Geophysical Research Letters 49: e2022GL098102, doi: 10.1029/2022GL098102.

Khan, A., K. Rittger, P. Xian, J.J. Katich, R.L. Armstrong, R. Kayastha, J. Dana, D.M. McKnight. 2020. Biofuel burning influences refractory black carbon concentrations in seasonal snow at lower elevation of the Dudh Koshi River basin of Nepal. Frontiers in Earth Science doi: 10.3389/feart.2020.00371.

McGrath, D., L. Zeller, R. Bonnell, W. Reis, S. Kampf, K. Williams, M. Okal, A. Olsen-Mikitowicz, E. Bump, M. Sears, and K. Rittger. 2023. Declines in peak snow water equivalent and elevated snowmelt rates following the 2020 Cameron Peak Wildfire in northern Colorado. Geophysical Research Letters 50: e2022GL101294, doi: 10.1029/2022GL101294.

Micheletty, P.D., A.M. Kinoshita, and T.S. Hogue. 2014. Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada. Hydrology and Earth System Sciences Discussions 11: 7513-7549, doi: 10.5194/hessd-11-7513-2014.

Micheletty, P., D. Perrot, G. Day, and K. Rittger. 2021. Assimilation of ground and satellite snow observations in a distributed hydrologic model for water supply forecasting. JAWRA Journal of the American Water Resources Association doi: 10.1111/1752-1688.12975.

Painter, T.H., S.M. Skiles, J.S. Deems, W.T. Brandt, and J. Dozier. 2018. Variation in rising limb of Colorado River snowmelt runoff hydrograph controlled by dust radiative forcing in snow. Geophysical Research Letters 45: 797-808, doi: 10.1002/2017gl075826.

Rittger, K., E. Bair, A. Kahl, J. Dozier. 2016. Spatial estimates of snow water equivalent from reconstruction. Advances in Water Resources 94: 345-363, doi: 10.1016/j.advwatres.2016.05.015.

Rittger, K., M. Krock, W. Kleiber, E.H. Bair, M.J. Brodzik, T.R. Stephenson, B. Rajagopalan, K.J. Bormann, T.H. Painter. 2021. Multi-sensor fusion using random forests for daily fractional snow cover at 30 m. Remote Sensing of Environment 264: 112608, doi: 10.1016/j.rse.2021.112608.

Sarangi, C., Y. Qian, K. Rittger, R. Leung, D. Chand, K. Bormann, T.H. Painter. 2020. Dust dominates high-altitude snow darkening and melt over high-mountain Asia, Nature Climate Change doi: 10.1038/s41558-020-00909-3.

Skiles, S.M., T.H. Painter, J.S. Deems, A.C. Bryant, and C.C. Landry. 2012. Dust radiative forcing in snow of the Upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates. Water Resources Research 48, doi: 10.1029/2012WR011986.

Yang, K., K.N. Musselman, K. Rittger, S.A. Margulis, T.H. Painter, and N.P. Molotch. 2021. Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent. Advances in Water Resources 104075, doi: 10.1016/j.advwatres. 2021.104075.

Yang, K., K. Rittger, K.N. Musselman, E.H. Bair, J. Dozier, S.A. Margulis, T.H. Painter, and N.P. Molotch. 2023. Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations. Frontiers in Earth Science 11, doi: 10.3389/feart.2023.1106621.

Zhao, H., X. Hao, J. Wang, H. Li, G. Huang, S. Donghang, B. Su, L. Huajin, and X. Hu. 2020. The spatial–spectral–environmental extraction endmember algorithm and application in the MODIS fractional snow cover retrieval. Remote Sensing 12: 3693, doi: 10.3390/rs12223693.

Basemaps

NSIDC’s Daily Snow Viewer offers multiple basemaps, which are credited as follows:

USGS Topographic

US Geological Survey. "USGS Topo Base Map Service from The National Map" [basemap]. 2013. Reston, Virginia: US Department of the Interior. https://www.sciencebase.gov/catalog/item/544171f4e4b0b0a643c73c28. (August 20, 2022)

USGS Topographic + Imagery, USGS Imagery, USGS Shaded Relief, and USGS Hydro Cached

US Geological Survey. "USGS Hydro-NHD Base Map Service from The National Map" [basemap]. 2016. Reston, Virginia: US Department of the Interior. https://www.sciencebase.gov/catalog/item/581d051fe4b08da350d523ee

ArcGIS Dark Gray and ArcGIS Dark Gray - Base Only

Esri. "ArcGIS Dark Gray - Base Only" [basemap]. Vector. "Dark Gray Canvas". November 22, 2022. https://www.arcgis.com/home/item.html?id=358ec1e175ea41c3bf5c68f0da11ae2b. (August 20, 2022).

ArcGIS National Geographic

Esri. "ArcGIS National Geographic" [basemap]. Vector. "National Geographic Style". November 22, 2022. https://www.arcgis.com/home/item.html?id=3d1a30626bbc46c582f148b9252676ce. (August 20, 2022).

ArcGIS World Topographic

Esri. "ArcGIS World Topographic" [basemap]. Vector. "World Topographic Map". November 22, 2022. https://www.arcgis.com/home/item.html?id=7dc6cea0b1764a1f9af2e679f642f0f5. (August 20, 2022).