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163 results for “LAGOS”
LAGOS-US RESERVOIR: Data module classifying conterminous U.S. lakes 4 hectares and larger as natural lakes or reservoirs
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.
LAGOS-US GEO v1.0: Data module of lake geospatial ecological context at multiple spatial and temporal scales in the conterminous U.S.
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.
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
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.
LAGOS - Chlorophyll, TP, and water color summer epilimnetic concentrations and lake and catchment data for inland lakes in WI, MI, NY, and ME – a subset of lake data from LAGOSLimno v.1.040.1
This dataset includes lake total phosphorus (TP), true water color, and chlorophyll a (CHLa) concentrations from summer, epilimnetic water samples and is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.040.0 for lake water chemistry data and LAGOSGEO version 1.02 for lake catchment geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. Lake catchments, defined as 'The area of land that drains directly into a lake, and into all upstream-connected, permanent streams to that lake exclusive of any upstream lake watersheds for lakes greater than or equal to 10 ha that are connected via permanent streams', were delineated for lakes greater than or equal to 4 ha. Lake-stream connectivity type was assigned to lakes greater than or equal to 4 ha using GIS tools that use the National Hydrology Dataset (See Soranno et al. 2015 for LAGOS geographic processing steps). A subset of lake and geographic data was created to examine spatial variation in TP and water color relationships with CHLa across broad geographic extents using spatially-varying coefficient models with a Bayesian framework. Lakes were selected that had complete records for summer epilimnetic total TP, true water color, and CHLa. In addition we selected lakes with surface area greater than or equal to 4 ha and less than 10,000 ha to exclude very small and very large lakes from the analyses. The resulting dataset includes 838 lakes in Wisconsin, Michigan, New York, and Maine with 7395 observations. The majo
LAGOS-US LIMNO: Data module of surface water chemistry from 1975-2021 for lakes in the conterminous U.S.
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.
LAGOS - Predicted and observed maximum depth values for lakes in a 17-state region of the U.S.
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.
LAGOS - Lake nitrogen, phosphorus, stoichiometry, and geospatial data for a 17-state region of the U.S.
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).
LAGOS-NE v.1.054.1 - Lake water quality time series and geophysical data from a 17-state region of the United States
Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix “iws”) and HUC 4 (variable prefix “hu4”) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.
LTER-Italy site Lago Braies figure
<p>Geographical representation of the LTER-Italy site Lago Braies (LTER_EU_IT_092) - DEIMS-ID <a href="https://deims.org/c54a2c21-2079-400d-b169-5e2de8dfdf06">https://deims.org/c54a2c21-2079-400d-b169-5e2de8dfdf06</a></p>
LTER-Italy site Lago di Tovel figure
<p>Geographical representation of the LTER-Italy site Lago di Tovel (LTER_EU_IT_090) - DEIMS-ID <a href="https://deims.org/f3146959-ae18-4b4e-a9be-16634b0b530a">https://deims.org/f3146959-ae18-4b4e-a9be-16634b0b530a</a></p>
LTER-Italy site Lago Paione Superiore figure
<p>Geographical representation of the LTER-Italy site Lago Paione Superiore (LTER_EU_IT_089) - DEIMS-ID <a href="https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313">https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313</a></p>
LTER-Italy site Lago Paione Inferiore figure
<p>Geographical representation of the LTER-Italy site Lago Paione Inferiore (LTER_EU_IT_088) - DEIMS-ID <a href="https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f">https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f</a></p>
LTER-Italy site Lago Anterselva figure
<p>Geographical representation of the LTER-Italy site Lago Anterselva (LTER_EU_IT_091) - DEIMS-ID <a href="https://deims.org/e8342e5a-849b-4eba-8a99-249d285b5094">https://deims.org/e8342e5a-849b-4eba-8a99-249d285b5094</a></p>
LTER-Italy site Lago Monte Lerno figure
<p>Geographical representation of the LTER-Italy site Lago Monte Lerno (LTER_EU_IT_051) - DEIMS-ID <a href="https://deims.org/625a2aac-4b37-4366-8693-7d97b95759dc">https://deims.org/625a2aac-4b37-4366-8693-7d97b95759dc</a></p>
LTER-Italy site Lago Temo figure
<p>Geographical representation of the LTER-Italy site Lago Temo (LTER_EU_IT_053) - DEIMS-ID <a href="https://deims.org/5bd7ec0b-8215-4764-8f4a-9b1d42c95e24">https://deims.org/5bd7ec0b-8215-4764-8f4a-9b1d42c95e24</a></p>
LTER-Italy site Lago Cuga figure
<p>Geographical representation of the LTER-Italy site Lago Cuga (LTER_EU_IT_050) - DEIMS-ID <a href="https://deims.org/3b9c3c88-6774-49cb-a0fa-687e6ab1ce61">https://deims.org/3b9c3c88-6774-49cb-a0fa-687e6ab1ce61</a></p>
LTER-Italy site Lago Scuro Parmense figure
<p>Geographical representation of the LTER-Italy site Lago Scuro Parmense (LTER_EU_IT_047) - DEIMS-ID <a href="https://deims.org/da7069d6-4d3e-4b31-a8aa-a606d4814eb3">https://deims.org/da7069d6-4d3e-4b31-a8aa-a606d4814eb3</a></p>
LTER-Italy site Lago di Candia figure
<p>Geographical representation of the LTER-Italy site Lago di Candia (LTER_EU_IT_043) - DEIMS-ID <a href="https://deims.org/c7fe4203-24b1-4d11-a573-99b99204fede">https://deims.org/c7fe4203-24b1-4d11-a573-99b99204fede</a></p>
LTER-Italy site Lago Sos Canales figure
<p>Geographical representation of the LTER-Italy site Lago Sos Canales (LTER_EU_IT_052) - DEIMS-ID <a href="https://deims.org/e8374da3-1644-460b-bd4c-bf669514dd22">https://deims.org/e8374da3-1644-460b-bd4c-bf669514dd22</a></p>
LTER-Italy site Lago Piramide Inferiore figure
<p>Geographical representation of the LTER-Italy site Lago Piramide Inferiore (LTER_EU_IT_054) - DEIMS-ID <a href="https://deims.org/81535ac1-1401-495e-a786-17be3a95f1c6">https://deims.org/81535ac1-1401-495e-a786-17be3a95f1c6</a></p>
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