The Great Lakes Environmental Database (GLENDA) houses environmental data collected by EPA Great Lakes National Program Office (GLNPO) programs that sample water, aquatic life, sediments, and air to assess the health of the Great Lakes ecosystem. GLENDA is available to the public on the EPA Central Data Exchange (CDX). A CDX account is required, which anyone may create. GLENDA offers “Ready to Download Data Files” prepared by GLNPO or a “Query Data” interface that allows users to select from predefined parameters to create a customized query. Query results can be downloaded in .csv format. GLNPO programs providing data in GLENDA include the Great Lakes Water Quality Survey and Great Lakes Biology Monitoring Program (1983-present, biannual monitoring throughout the Great Lakes to assess water quality, chemical, nutrient, and physical parameters, and biota such as plankton and benthic invertebrates), the Great Lakes Fish Monitoring and Surveillance Program (1977-present, annual analysis of top predator fish composites to assess historic and emerging persistent, bioaccumulative, or toxic chemical contaminants), the Cooperative Science and Monitoring Initiative (2002-present, intensive water quality and biology sampling of one lake per year focusing on key challenges and data gaps), the Great Lakes Integrated Atmospheric Deposition Network (1990-present, monitoring Great Lakes air and precipitation for persistent toxic chemicals), the Lake Michigan Mass Balance Study (1993-1996, analyzed the atmosphere, tributaries, sediments, water column, and biota of Lake Michigan for nutrients, atrazine, PCBs, trans-nonachlor, and mercury modelling), and the Great Lakes Legacy Act (1996-present, evaluations of sediment contamination in Areas of Concern). GLENDA is updated frequently with new data.

The Global Lakes and Wetlands Database (GLWD) includes the best available data sources and GIS functionality for global lakes and wetlands focused on three scales (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands. The map scaless provided range from 1:1 to 1:3 million resolution. Level 1 (GLWD-1) comprises the 3,067 largest lakes (area ≥ 50 km2) and 654 largest reservoirs (storage capacity ≥ 0.5 km3) worldwide, and includes extensive attribute data. Level 2 (GLWD-2) comprises permanent open water bodies with a surface area ≥ 0.1 km2 excluding the water bodies contained in GLWD-1. Level 3 (GLWD-3) comprises lakes, reservoirs, rivers and different wetland types in the form of a global raster map at 30-second resolution.

The database brings together physical, chemical and biological data from approximately 350 European mountain lakes. These were collected as part of a one off survey undertaken in 2000. Lakes were sampled for a range of contemporary and sub-fossil organisms including planktonic crustaceans, rotifers, littoral invertebrates, chironomids, diatoms and cladocerans. Survey and cartographic data were used to determine environmental characteristics at each site. Organic pollutants and trace metal concentrations were measured in the lake sediment. More information on this dataset can be found in the Freshwater Metadatabase - BF16 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BF16).

The SWOT Level 2 Lake Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a PLD-oriented feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format. This dataset is the parent collection to the following sub-collections: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_2.0 https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_2.0 https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_2.0

The lake map for the State of Alaska was generated from selected Landsat acquired during summer seasons of circa 2000. Nearly 400 30-m resolution Enhanced Thematic Mapper Plus (ETM+) images were used to produce the lake map. The database contains over 38,000 lakes larger than 0.1 km2. The spatial coverage of the product is nearly the entire state except the Aleutian islands. The lake product is released at three different levels in response to lake size classes:

Level 1: large lakes greater than 10 km2;

Level 2: medium-sized lakes between 1 and 10 km2;

Level 3: small lakes between 0.1 and 1 km2. The Alaskan lake products are released in ArcView shapefile format.

The Global Lakes and Wetlands Database (GLWD) version 2 provides a comprehensive and seamless global map of inland surface waters distinguished into 33 waterbody and wetland types. GLWD v2 was developed by harmonizing the best available ground- and satellite-based data sources and has been designed to represent the maximum non-overlapping extents of aquatic ecosystems over the broad contemporary period of 1990-2020.

GLWD v2 represents a total of 18.2 million km2 of wetlands at a grid cell resolution of 15 arc-seconds (approximately 500 m at the equator). The data consist of a map of the dominant waterbody or wetland type in each grid cell, as well as 33 individual class layers which represent the sub-cell fraction of each specific class within each grid cell.

Version 2 of GLWD (Lehner et al., 2025) is the successor of the widely-used GLWD version 1 (Lehner & Döll, 2004). The quality, resolution, and format of GLWD v2 significantly improves upon GLWD v1 and supersedes the older version.

This dataset has compiled to provide a comprehensive database of macrophytes, along with lakes and survey information from the literature, to provide a historical record macrophyte biodiversity for lakes in Japan. The present data contains occurrence records of 2,779 taxa, of which 2,689 have been identified at species/infraspecies (forma, variety, and subspecies), whereas the remaining 90 taxa could only be identified at the section, subgenus, genus, or higher taxonomic ranks. The taxa consisted of 2,474 angiosperms, 33 gymnosperms, 138 pteridophytes, 45 bryophytes, and 89 algae, including green algae (charophytes, zygnematophytes, and Chlorophyta), red algae (Rhodophyceae), and brown algae (Phaeophyceae). This dataset is published as a data paper in Ecological Research (see https://doi.org/10.1111/1440-1703.12449).

This dataset consists of water quality and phytoplankton community structure data collected at 85 Swedish Lakes. This dataset is associated with the following publication: Eason, T., A. Garmestani, and D. Angeler. Spatiotemporal variability in Swedish lake ecosystems. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 17(3): e0265571, (2022).

Database containing details of chemical and physical profile data from Vestfold Hills lakes. The database has a user interface which allows the user to either (a) generate a table specific to a particular lake, parameter and date, or (b) generate an array of lake vs depth vs date (any combination of two) vs parameter, which presents the results of the lakes which fits the specifications. In both options the results are presented in a table, with additional information including ice cover, record date, parameter, data source, lake name, measurement method and equipment and units.

NOTE: This database has now been taken offline, and the data have been extracted into a series of csv files, which are available for download from the provided URL.

See the readme file in the download file for more information.

In the Great Lakes basin, there are numerous organizations undertaking scientific monitoring and research efforts with the goal of identifying threats and evaluating management strategies that will protect and restore the Great Lakes ecosystem. Coordination among all these stakeholders is a challenge, and having a centralized _location where researchers and managers can identify relevant scientific activities and access fundamental information about these activities is crucial for efficient management. The Science in the Great Lakes (SiGL) Mapper was a map-based discovery tool that spatially displayed basin-wide multidisciplinary monitoring and research activities conducted by both USGS and partners from all five Great Lakes. It was designed to help Great Lakes researchers and managers strategically plan, implement, and analyze monitoring and restoration activities by providing easy access to historical and on-going project metadata while allowing them to identify gaps (spatially and topically) that have been underrepresented in previous efforts or need further study. SiGL provided a user-friendly and efficient way to explore Great Lakes projects and data through robust search options while also providing a critical spatial perspective through its interactive mapping interface.

Property Description

Hylak_id Unique lake identifier. Values range from 1 to 1,427,688.

**Lake_name ** Name of lake or reservoir. This field is currently only populated for lakes with an area of at least 500 km2; for large reservoirs where a name was available in the GRanD database; and for smaller lakes where a name was available in the GLWD database.

Country Country that the lake (or reservoir) is located in. International or transboundary lakes are assigned to the country in which its corresponding lake pour point is located and may be arbitrary for pour points that fall on country boundaries.

Continent Continent that the lake (or reservoir) is located in. Geographic continent: Africa, Asia, Europe, North America, South America, or Oceania (Australia and Pacific Islands)

Poly_src The name of datasets that were used in the column. Source of original lake polygon: CanVec; SWBD; MODIS; NHD; ECRINS; GLWD; GRanD; or Other More information on these data sources can be found in Table 1.

Lake_type Indicator for lake type: 1: Lake 2: Reservoir 3: Lake control (i.e. natural lake with regulation structure) Note that the default value for all water bodies is 1, and only those water bodies explicitly identified as other types (mostly based on information from the GRanD database) have other values; hence the type ‘Lake’ also includes all unidentified smaller human-made reservoirs and regulated lakes.

Grand_id ID of the corresponding reservoir in the GRanD database, or value 0 for no corresponding GRanD record. This field can be used to join additional attributes from the GRanD database.

Lake_area Lake surface area (i.e. polygon area), in square kilometers.

Shore_len Length of shoreline (i.e. polygon outline), in kilometers.

Shore_dev Shoreline development, measured as the ratio between shoreline length and the circumference of a circle with the same area. A lake with the shape of a perfect circle has a shoreline development of 1, while higher values indicate increasing shoreline complexity.

Vol_total Total lake or reservoir volume, in million cubic meters (1 mcm = 0.001 km3). For most polygons, this value represents the total lake volume as estimated using the geostatistical modeling approach by Messager et al. (2016). However, where either a reported lake volume (for lakes ≥ 500 km2) or a reported reservoir volume (from GRanD database) existed, the total volume represents this reported value. In cases of regulated lakes, the total volume represents the larger value between reported reservoir and modeled or reported lake volume. Column ‘Vol_src’ provides additional information regarding these distinctions.

Vol_res Reported reservoir volume, or storage volume of added lake regulation, in million cubic meters (1 mcm = 0.001 km3). 0: no reservoir volume

Vol_src 1: ‘Vol_total’ is the reported total lake volume from literature 2: ‘Vol_total’ is the reported total reservoir volume from GRanD or literature 3: ‘Vol_total’ is the estimated total lake volume using the geostatistical modeling approach by Messager et al. (2016)

Depth_avg Average lake depth, in meters. Average lake depth is defined as the ratio between total lake volume (‘Vol_total’) and lake area (‘Lake_area’).

Dis_avg Average long-term discharge flowing through the lake, in cubic meters per second. This value is derived from modeled runoff and discharge estimates provided by the global hydrological model WaterGAP, downscaled to the 15 arc-second resolution of HydroSHEDS (see section 2.2 for more details) and is extracted at the location of the lake pour point. Note that these model estimates contain considerable uncertainty, in particular for very low flows. -9999: no data as lake pour point is not on HydroSHEDS landmask

Res_time Average residence time of the lake water, in days. The average residence time is calculated as the ratio between total lake volume (‘Vol_total’) and average long-term discharge (‘Dis_avg’). Values below 0.1 are rounded up to 0.1 as shorter residence times seem implausible (and likely indicate model errors). -1: cannot be calculated as ‘Dis_avg’ is 0 -9999: no data as lake pour point is not on HydroSHEDS landmask

Elevation Elevation of lake surface, in meters above sea level. This value was primarily derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution by calculating the majority pixel elevation found within the lake boundaries. To remove some artefacts inherent in this DEM for northern latitudes, all lake values that showed negative elevation for the area north of 60°N were substituted with results using the coarser GTOPO30 DEM of USGS at 1 km pixel resolution, which ensures land surfaces ≥0 in this region. Note that due to the remaining uncertainties in the EarthEnv-DEM90 some small negative values occur along the global oce...

Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: ice_duration_days, ice_on_date, ice_off_date, winter_dur_0-4, coef_var_30-60, coef_var_0-30, stratification_onset_yday, stratification_duration, sthermo_depth_mean, peak_temp, gdd_wtr_0c, gdd_wtr_5c, gdd_wtr_10c, bottom_temp_at_strat, schmidt_daily_annual_sum, mean_surf_jas, max_surf_jas, mean_bot_jas, max_bot_jas, mean_surf_jan, max_surf_jan, mean_bot_jan, max_bot_jan, mean_surf_feb, max_surf_feb, mean_bot_feb, max_bot_feb, mean_surf_mar, max_surf_mar, mean_bot_mar, max_bot_mar, mean_surf_apr, max_surf_apr, mean_bot_apr, max_bot_apr, mean_surf_may, max_surf_may, mean_bot_may, max_bot_may, mean_surf_jun, max_surf_jun, mean_bot_jun, max_bot_jun, mean_surf_jul, max_surf_jul, mean_bot_jul, max_bot_jul, mean_surf_aug, max_surf_aug, mean_bot_aug, max_bot_aug, mean_surf_sep, max_surf_sep, mean_bot_sep, max_bot_sep, mean_surf_oct, max_surf_oct, mean_bot_oct, max_bot_oct, mean_surf_nov, max_surf_nov, mean_bot_nov, max_bot_nov, mean_surf_dec, max_surf_dec, mean_bot_dec, max_bot_dec, which are defined below.

The Global Lake and River Ice Phenology Database contains freeze and breakup dates and other ice cover descriptive data for 748 lakes and rivers. Of the 429 water bodies that have records longer than 19 years, 287 are in North America and 141 are in Eurasia; 170 have records longer than 50 years; and 28 longer than 100 years. A few have data prior to 1845. These data, from water bodies distributed throughout the Northern Hemisphere, allow analysis of broad spatial patterns as well as long-term temporal patterns. The data set was prepared by the North Temperate Lakes Long-Term Ecological Research program at the Center for Limnology at the University of Wisconsin-Madison from data submitted by participants in the Lake Ice Analysis Group (LIAG). LIAG is an international ad hoc group of scientists who participated in a 1996 workshop sponsored by the Center of Limnology, University of Wisconsin-Madison and the National Science Foundation Division of Environmental Biology (Long-Term Studies Program). The group would be happy to receive additional data on these lakes or rivers or others around the world for inclusion in the database. NSIDC has developed a Web-based user interface to the database that allows users to search the database and retrieve data by the available parameters. The interface also includes a link to more general information about the lakes and rivers in the database. The output can be directed to a Web browser, a gzipped file, or a tab-separated ASCII text file. Note: The term 'phenology' in the data set title refers to the seasonal phenomenon of the freezing and thawing of lake and river ice.

This dataset records Cladophora and associated benthic algae, collectively Cladophora community or submerged aquatic vegetation (SAV), biomass collected during the growing season of 2022 at stations located along the U.S. shoreline of Lakes Michigan, Huron, Erie, and Ontario. It also records a variety of supporting data collected at Cladophora measurement stations. These supporting data include: - seasonal time series of light, currents, wave action, temperature, specific conductivity, turbidity, pH, phycocyanin, chlorophyll, and dissolved oxygen from moored sensors at a subset of stations; - measurements of Secchi disk depth and water chemistry; - water column profiles of PAR, temperature, specific conductivity, turbidity, pH, phycocyanin, chlorophyll, and dissolved oxygen; - diver observations of SAV, dreissenid mussels, round goby abundance, and substrate properties; - measurements of dreissenid mussel abundance and size class distribution coincident with SAV biomass; - nutrient content of SAV, dreissenid mussels, and sediments; - and information about sampling locations and operations. Similar data were collected at several of the same transects within four Great Lakes in 2018, 2019, 2020, and 2021 are available at (2018) https://doi.org/10.5066/P9E570JS, (2019) https://doi.org/10.5066/P99O4QXB, (2020) https://doi.org/10.5066/P9O9FSTT, and (2021) https://doi.org/10.5066/P9449EUF.

Contact: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)

Citation

If you use Lake-TopoCat, please cite the following ESSD preprint for now:

Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data Discussion, in review, https://doi.org/10.5194/essd-2022-433.

Data description and components
This version of Lake-TopoCat was constructed using the HydroLAKES v1.0 (Messager et al., 2016) lake mask and the 3-arc-second-resolution hydrography dataset MERIT Hydro v1.0.1 (Yamazaki et al., 2019). The drainage type of each HydroLAKES lake, such as isolated, inflow-headwater, headwater, flow-through, terminal, and coastal, was determined with assistance of MERIT Hydro-Vector (Lin et al., 2021), a high-resolution river network dataset with spatially-variable drainage densities.

For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins or regions, and the Lake-TopoCat data products were also organized based on these 68 regions, with their region or basin IDs shown in the Fig. 'Pfaf2_basins.jpg', attached to this database.

Lake-TopoCat consists of five feature components, each with multiple attributes depicting lake drainage relationships. The five features are:

1. Lake boundaries: polygons of 1,426,967 HydroLAKES lakes, larger than 10 ha.

File name: Lakes_pfaf_xx where, 'pfaf_xx' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg')

2. Lake outlets: points representing outlet or pour points of each individual lake. There are multiple outlets from a multifurcation lake. We identified 1,459,201 outlets for 1,426,967 lakes, where 29,190 lakes (~2% of the global lakes) show bi/multifurcation.

File name: Outlets_pfaf_xx

3. Unit catchment: boundary polygons of catchment defining the drainage areas between cascading (i.e., immediately upstream and downstream) lake outlets. The count of unit catchments equal to the count of lake outlets, and bifurcation or multifurcation lakes have multiple local catchments. In total, the delineated catchments in Lake-TopoCat cover about 77.5 million km2, which is about 57% of the Earth’s land mass excluding the Antarctic.

File name: Catchments_pfaf_xx

4. Inter-lake reaches: line features defining the drainage networks that connect the lake outlets to the inland sinks or the ocean. About 3 million connecting reaches were generated among ~1.4 million outlets. The total length of these inter-lake connecting reaches is ~10 million km.

File name: Reaches_pfaf_xx

5. Lake-network basins: boundary polygons of the entire drainage area containing each inter-lake network (i.e., a complete basin from the headwater to an inland sink or the ocean for all basins containing lakes). A total of 47,340 lake-network basins were identified. Among them, endorheic basins account for 5.1% by count and 18% by area of all lake-network basins. These endorheic basins cover ~15.4% of global surface excluding Antarctica.

File name: Basins_pfaf_xx

The attribute tables for each of the feature components are explained in Section 4 of the product description document. For user convenience, we release the preliminary Lake-TopoCat lake outlets, unit catchments, and inter-lake reaches, with the affix '_prelim' in the file names (explained in the attached product description document). We also provide the polygon boundaries of the 68 Pfafstetter basins or regions in the file named 'Pfaf2_regions'. All files of Lake-TopoCat are available in both shapefile and geodatabase formats.

Disclaimer
Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of Lake-TopoCat.

The combination of best available sources for lakes and wetlands on a global scale (1:1 to 1:3 million resolution), and the application of GIS functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands. Level 1 (GLWD-1) comprises the 3067 largest lakes (area ≥ 50 km2) and 654 largest reservoirs (storage capacity ≥ 0.5 km3) worldwide, and includes extensive attribute data. Level 2 (GLWD-2) comprises permanent open water bodies with a surface area ≥ 0.1 km2 excluding the water bodies contained in GLWD-1. For GLWD-3, the polygons of GLWD-1 and GLWD-2 were combined with additional information on the maximum extents and types of wetlands. Class ‘lake’ in both GLWD-2 and GLWD-3 also includes man-made reservoirs, as only the largest reservoirs have been distinguished from natural lakes.

This dataset includes information about total nitrogen (TN) concentrations, total phosphorus (TP) concentrations, TN:TP stoichiometry, and 12 driver variables that might predict nutrient concentrations and ratios. All observed values came from LAGOSLIMNO v. 1.054.1 and LAGOSGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes greater than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, we compiled chemistry data from lakes with concurrent observations of TN and TP from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOSLIMNO v. 1.054.1 (2002-2011). We report the median TN, TP and molar TN:TP values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics that might be important controls on lake nutrients, including: land use (agricultural, pasture, row crop, urban, forest), nitrogen deposition, temperature, precipitation, hydrology (baseflow), maximum depth, and the ratio of lake area to watershed area, which is used to approximate residence time. These data were used to identify drivers of lake nutrient stoichiometry at sub-continental and regional scales (Collins et al, submitted). This research was supported by the NSF Macrosystems Biology program (awards EF-1065786 and EF-1065818) and by the NSF Postdoctoral Research Fellowship in Biology (DBI-1401954).

LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows:

HydroLakes_DP.shp: A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015).

LakeExport.py: Python code to extract reflectance values for U.S. lakes from Google Earth Engine.

GEE_pull_functions.py: Functions called within LakeExport.py

01_LakeExtractor.Rmd: An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database.

SceneMetadata.csv: A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID.

srCorrected_us_hydrolakes_dp_20200628: The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Water quality and ecosystem health data collected using a risk-based monitoring approach to support the Great Lakes Water Quality Agreement are included in this dataset. By conducting regular, systematic measurements of the physical, chemical and biological conditions of the Great Lakes Environment and Climate Change Canada (ECCC) is able to: measure the natural changes and conditions of water quality; determine changes over time, at various locations, of water contaminants and/or threats; support development of science-based guidelines for water, fish, and sediment; identify emerging issues and threats; track the results of remedial measures and regulatory decisions; report and assess science results through performance indicators and in an Open Science environment to support an ecosystem approach to environmental and resource management in the Great Lakes. Data are collected by Environment and Climate Change Canada to meet federal commitments related to the Great Lakes as transboundary waters crossing, inter- provincial and international borders under the authorities of the Department of the Environment Act, the Canada Water Act, the Canadian Environmental Protection Act, 1999 and the Boundary Waters Treaty including the commitments under the Canada-United States Great Lakes Water Quality Agreement.