This data package, LAGOS-US LOCUS v1.0, is one of the core data modules of the LAGOS-US platform that provides an extensible research-ready platform to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). This data module contains information on the location, identifiers, and physical characteristics of lakes and their watersheds. The characteristics in this module include: variables that can be obtained from GIS data such as location and geometry; variables that can be derived using GIS processing such as lake watersheds and their geometry, lake glaciation history, and lake connectivity; and commonly used identifiers from GIS and other data products useful for linking with LAGOS-US. LOCUS is based on a snapshot of the high-resolution National Hydrography Dataset product available at the initiation of the project that provided the basis for locating, identifying, and characterizing the geometry of all lakes in LAGOS-US. The database design that supports the LAGOS-US research platform was created based on several important design features. Lakes are the fundamental unit of consideration, all lakes in the spatial extent must be represented (above a minimum size) and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other 2 core data modules that are part of the LAGOS-US platform: GEO (which includes geospatial ecological context at multiple spatial and temporal scales for lakes and their watersheds) and LIMNO (in situ lake surface-water physical, chemical, and biological measurements through time) that are each found in their own data packages.

The LAGOS-US LAKE DEPTH v1.0 module (hereafter, called DEPTH) contains in situ measurements of lake depth for a subset of all lakes (n = 17,675) in the conterminous U.S. > 1 ha (3.7% of 479,950) that are in the LAGOS-US LOCUS v1.0 data module (Smith et al. 2021). All 17,675 lakes in DEPTH have a maximum depth value and 6,137 lakes have a mean depth. DEPTH includes approximately 65 data sources obtained from community, government, and university monitoring programs, as well as academic reports and commercial websites. DEPTH includes lake identifiers, lake location, lake area, lake depth (both maximum and mean depth when available), source information, and data flags. The unique lake identifier (lagoslakeid) for all lakes is the same one used in LAGOS-US LOCUS v1.0.

Knowing the consistency of water quality sampling in lakes surrounded by a variety of racial and ethnic communities is important when thinking about potential policy uses and community impacts. By using the 2010 US Census race and ethnicity demographic tract data, we analyzed the frequency (i.e., number of years and consistency) of lake water quality sampling according to racial and ethnic demographics in surrounding neighborhoods. Our approach classified human communities near lakes as predominantly White or people of color (POC), and Hispanic or non-Hispanic. Associated R analysis scripts can also be found in this folder. Our data and approach can be used for future studies seeking to analyze environmental monitoring practices in relation to human demographic variables, particularly from US Census data.

This data package, LAGOS-US RESERVOIR, is one of the extension modules of the LAGOS-US platform for studying lakes in the United States. Although naturally-formed lakes and reservoirs are thought to differ in many properties, there is currently no data source that differentiates between lakes and reservoirs in the conterminous US. This absence of data stems from how challenging it is to identify reservoirs at broad scales -- there is a wide variety of dam types and sizes that results in various reservoir shapes and sizes, making a simple classification difficult. Furthermore, reservoirs are understudied compared to natural lakes. The LAGOS-US RESERVOIR data module fills these data and the resulting knowledge gaps by classifying lakes greater than or equal to 4 hectares in the conterminous US (137,465 lakes) into one of two classes: natural lakes (NL) or reservoirs (RSVR). We define RSVRs (using visual interpretation of imagery) as lakes that are likely to be either human-made or highly human-altered by the presence of a relatively large water control structure that significantly changes the flow of water. We define NLs (using visual interpretation of imagery) as lakes that are likely to be either naturally-formed or do not have a relatively large, apparently flow-altering structure on or near it. The RSVR and NL classification is based on high resolution imagery and model predictions. We trained machine learning models using 12,127 manually (i.e., visually) classified lakes. When then used these models to assign NL or RSVR predictions to the remaining 77,604 NLs and 59,861 RSVRs. RESERVOIR also includes model-based prediction probabilities and variables that are commonly used when studying reservoirs (e.g., lake shape). These data can be used for studying reservoirs at the regional to conterminous US scale.

Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.

The LAGOS-US GEO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The GEO module contains data on the geospatial and temporal ecological setting (e.g., land use, terrain, soils, climate, hydrology, atmospheric deposition, and human influence) quantified at multiple spatial divisions (e.g., equidistant buffers around lakes, watersheds, hydrologic basins, political boundaries, and ecoregions) relevant to the LAGOS-US lake population defined in the LAGOS-US LOCUS module. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.

Data are directly from the LAGOS-US platform. The current dataset identifies the lake watershed: the contributing watershed area accumulated upstream to the first connected lake ≥ 10 ha, if present. This definition emphasizes the importance of local inputs,and also acknowledges the potential for larger upstream lakes to function as sinks. The LAGOS-US dataset contains locational, identifying, and physical information of all lakes ≥ 1 ha and their watersheds in the conterminous U.S. Geospatial files for lakes and watersheds as well as tables for linking across other LAGOS modules and broad-scale lake databases are available. LOCUS data can be linked across LAGOS data products using the unique lake identifier (lagoslakeid).Cheruvelil, K.S., Soranno, P.A., McCullough, I.M., Webster, K.E., Rodriguez, L.K. and Smith, N.J. 2021, LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. Limnol Oceanogr Letters 6: 270-292. https://doi-org.proxy1.cl.msu.edu/10.1002/lol2.10203

These datasets were created to quantify how natural lakes (NLs) and reservoirs (RSVRs) across the conterminous U.S. (CONUS) with different levels of hydrologic connectivity respond to drought and how natural and human factors operating at multiple spatial scales affect lake responses. The data used in this study were from the LAGOS-US research platform (Cheruvelil et al. 2021; https://doi.org/10.1002/lol2.10203) that includes lake and landscape data for 479,950 lakes ≥ 1 ha across the CONUS.

Project title: Sun, X., Cheruvelil, K.S., Hanly, P.J., &Soranno, P.A. Lake chlorophyll responses to drought are related to lake type, connectivity, and ecological context across the conterminous United States.

Manuscript citation: Sun, X., Cheruvelil, K.S., Hanly, P.J., &Soranno, P.A. (2025). Lake chlorophyll responses to drought are related to lake type, connectivity, and ecological context across the conterminous United States. Limnology and Oceanography. doi: 10.1002/lno.12817

Three datasets are included:

SPI.csv: Dataset contains the standard precipitation index (SPI) values from January 2009 to December 2018 for 479,950 lakes across the conterminous U.S.

full_dataset_32predictors.csv: Dataset contains the response variables (ΔZ-score-median and directions of CHL responses) and 32 predictors (excluding lake maximum depth) for 62,927 lakes across the conterminous U.S.

depth_subset_33predictors.csv: Dataset contains the response variables (ΔZ-score-median and directions of CHL responses) and 33 predictors (including lake maximum depth) for 8,994 lakes across the conterminous U.S.

Some data were transformed and some variable names in the dataset are different from the names in the manuscript. Please see the Metadata file for the transformation and conversion of variable names. The 'lagoslakeid' (LAGOS-US unique identifier for each lake) is included in the dataset as a variable but was not used as a predictor in analyses.

The LAGOS-US LIMNO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The LIMNO module contains in situ observations of 47 parameters of lake physics, chemistry, and biology (hereafter referred to as chemistry) from lake surface samples (defined as observations taken from the epilimnion of a lake) obtained from the Water Quality Portal, the National Lakes Assessment (2007, 2012, 2017), and NEON programs. LIMNO provides 3,511,020 observations across all parameters collected between 1975 and 2021 from 20,329 lakes; the number of observations per lake ranged from 1 to 20,605 with a median of 32. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other, as well as other comprehensive lake data products such as the USGS NHD), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and GEO (characteristics defining geospatial and temporal ecological setting quantified at multiple spatial divisions) that are each found in their own data packages.

The LAGOS-US RESERVOIR data module (hereafter, RESERVOIR) classifies all 137,465 lakes > 4 hectares in the conterminous U.S. into one of the following three categories using a machine-learning predictive model based on visual interpretation of lake outlines and a classification rule based on lake shape. Natural Lakes (NLs) are defined as lakes that are likely to be entirely or mostly naturally-formed and that do not have large, flow-altering structures on or near them; Reservoir Class A’s (RSVR_A) are defined as lakes that are likely to be either human-made or highly human-altered by the presence of a relatively large water control structure that appears to significantly change the flow of water; and Reservoir Class B’s (RSVR_Bs) are lakes that are likely to be entirely human-made based on isolation from rivers and a highly angular shape that is rarely, if ever, seen in natural lakes also often. We trained the machine learning models on 12,162 manually-classified lakes to assign probabilities of a lake being in 1 of 2 of the categories (NL or RSVR), then we further classified the RSVR classification into either A or B based on NHD Fcodes, isolation, and angularity. The data module includes a detailed User Guide, metadata tables, and a data table that includes information such as location, lake geometry, surface water connectivity class, and official name. Using our definition, our classification indicates that over 46 % of lakes > 4 ha in the conterminous U.S. are reservoir lakes. These data can be combined with other LAGOS-US data modules and U.S. national databases using unique lake identifiers to study both reservoir lakes and natural lakes at broad scales.

This dataset includes predicted and observed values of maximum depth for lakes in the upper Midwest and northeast United States. All observed values came from LAGOS ver 1.040.0 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes great 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 40 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, observed maximum depth values (n = 8164) were used to train and validate a predictive mixed effects model for lake depth using terrestrial and lake morphology as predictors (Oliver et al., submitted). Predicted values (n = 50 607) generated by the model had a root mean squared error of 7.1 m. This research was supported by the NSF Macrosystem Biology awards 1065786, 1065818, and 1065649.

This layer represents a subset of the Reservoir Fish Habitat Partnership data (https://fishhabitat.org/the-partnerships/reservoir-fisheries-habitat-partnership) joined to LAGOS lakes data (https://lagoslakes.org/lagos-research-platforms/) and 2021 National Landcover data (https://www.mrlc.gov/data). The values for "totdev_totag21" represents the percentage of landcover identified as "agriculture" or "developed" in the upstream lakes watersheds (watersheds as defined by LAGOS lakes dataset). This product is intended to be used for a one time evaluation of the two sources of lake quality. Please contact yvonne_allen@fws.gov for further details about this comparison.

This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset includes data for eight major limnological variables in LAGOS-NE_LIMNO v. 1.087.1 that were used to evaluate biases in lake water quality sampling and implications for macroscale research (Stanley et al. In Revision, Limnology and Oceanography). Most observations came from LAGOS-NE_LIMNO v. 1.087.1, an integrated database of lake ecosystems (Soranno et al. 2015, Soranno et al. 2017) but were supplemented with additional data from the State of New Hampshire. LAGOS-NE contains information on lakes great than or equal to 1 ha (originally derived from the U.S. Geological Survey's 2013 National Hydrography Dataset) for a 17-state region of the U.S., and a subset of the lakes has observational data on lake chemistry and productivity. Approximately 87 different sources of data were compiled for the LAGOS-NELIMNO v. 1.087.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled data for eight major limnological variables (Secchi disk depth, chlorophyll, total phosphorus, total nitrogen, nitrate, ammonium, true water color, and dissolved organic carbon) and geographic characteristics of lakes (location, lake area, depth, perimeter, watershed area) to evaluate biases in different limnological properties over space and time.

The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes.

DefinitionThis indicator identifies the availability of lake habitat within the Midwest Landscape. It prioritizes lakes greater than one hectare based on watershed condition and nearshore landcover. Pixels can take the following values:1 – High watershed or shoreland disturbance2 – Medium watershed or shoreland disturbance3 – Lower watershed or shoreland disturbance 4 – Lowest watershed or shoreland disturbance SelectionThis indicator was chosen as a targetable, important feature of the MLI goals that will be used to track conditions over time and prioritize areas for conservation. Indicators were defined through elicitation and prioritization exercises with federal and state participants. Criteria for the indicators includes 1) actionable, 2) measurable, 3) relevant to multiple groups across the region, and/or 4) representative of other social and/or environmental values. Input Data & Mapping StepsThis indicator originates from the LAGOS-US dataset, the National Land Cover Database, and Lapierre et al. Continuous Lake Classifications. To create this layer, MLI partners, members, and staff completed the following mapping steps: projected all input data to NAD83 (2011) UTM Zone 15N, joined LAGOS polygons with the Continuous Lake Classifications table, and converted the polygon layer to a raster based on the total percentage of agriculture and development in each lakeshed. Next, we converted the National Land Cover Database to a natural vs non-natural binary, and used Focal Statistics to calculate the majority landcover type in each lake's nearshore area (defined as 100m from the boundary of the lake). Finally, we mosaicked the lake raster with the nearshore landcover types to emphasize the value of natural nearshore areas and removed highly altered areas using our Highly Altered Areas mask.For full mapping details, please refer to the Midwest Conservation Blueprint 2024 Development Process. For a complete download of all Blueprint input and output data, visit the Midwest Conservation Blueprint 2024 Data Download.