Among the Great Lakes, Lake Erie was the shallowest, with an average depth of 62 feet. In comparison, Lake Superior, the deepest of these five lakes, accounted for an average depth of around 483 feet.

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.

Depth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Zmax; n = 16,831) and mean (Zmean; n = 5,881) depth observations to create new depth models, focusing on lakes < 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (Zmax R^2 = 0.35; RMSE = 8.1 m and Zmean R^2 = 0.36; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. Additionally, despite orders of magnitude variation in surface area, most size classes had a modeled modal maximum depth of ~5 m. Concave (bowl-shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel-shaped) as surface area increased. Across the conterminous US, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous US is 1,068 to 1,298 km3, depending on whether Zmax or Zmean was modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern US.

The goal of this project was to test the application of spectral depth models to quantify lake water volumes on the Inner Arctic Coastal Plain of Alaska. Lakes in this area tend to be deeper than those nearby and store a significant amount of the region’s water, making them important in terms of energy balance, as ecological habitat, and as water sources for infrastructure (e.g. ice roads). Due in part to their depth, these lakes have often been neglected by the limited number of prior studies that also focus on lakes in northern Alaska. Techniques employed by these studies limit the magnitude, precision, and/or scale at which depths can be assessed. By analyzing remotely sensed lake color using a method typically reserved for coastal bathymetry mapping, we are able to predict deeper depths and thus more accurately evaluate deep Arctic lake water volumes. This dataset includes 30 meter resolution bathymetry rasters for 17 lakes on the Alaskan Arctic Coastal Plain. These depth maps were created using linear and exponential models tuned with sonar depth measurements (depth dataset: doi:10.18739/A2SN01440) and applied to a Landsat-8 image (image ID: LC08_L1TP_077011_20160805_20170222_01_TI). The 12 model variants we tested were each distinguished by the two Red-Green-Blue (RGB) bands used, linear or exponential growth pattern, and simple or transform ratio. We were able to successfully tune models at the scale of an individual lake, predicting depth with an average model uncertainty (based on root mean squared error) of 1.44 meters. Volumes derived from the provided bathymetry rasters may be useful for understanding the distribution of water across northern Alaska and providing context for future changes in the landscape. Models were derived in part from: Stumpf, R. P., Holderied, K., and Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1), 547-606. Sonar depth points provided by: Simpson, C. and Arp, C. (2018). Sonar Depth Measurements at Lakes on the Inner Arctic Coastal Plain of Alaska, July 2017. Arctic Data Center. doi:10.18739/A2SN01440 [https://doi.org/10.18739/A2SN01440]

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

The dams and reservoirs database is a global databank of 633 large impoundments from a series of world dam registers published by ICOLD and IWPDC (ICOLD, 1984;1988; IWPDC, 1994; 1989), with the following attributes: - Dam name - Year - River Nam - Nearest City - Province - Country - Continent - Dam Type - Length - Volume of dam body - Reservoir Capacity - Area - Latitude and Longitude

 Documented lake volumes were identified for 87 out of the total natural 6 392
 georeferenced lakes with known area (ESRI 1995; Lerman et al. 1995) as
 developed in Green et al. (2004). The small number of known lake volumes
 actually corresponds to approximately 90% of the total volume of global fresh
 water lakes since the largest lakes (Baikal, Tanganyika, North American Great
 Lakes, Victoria) are well documented. For the remaining lakes lacking
 documented volume information, average lake depth was estimated as a function
 of lake area class (ESRI 1995) depending on glacial or tectonic origin (Meybeck
 1995). Lake volume was calculated as the product of estimated lake depth and
 surface area.

These images represent LiDAR (Light Detection & Ranging) data collected by NOAA from the shoreline of southwestern Puerto Rico to about 50 meters in depth. Reflectivity was calculated for each sounding as the ratio of returned energy to transmitted energy, normalized for losses in a single wavelength (green/blue 532nm). This data was critical for creating benthic habitat maps.

Lake morphometry metrics are known to influence productivity in lakes and are important for building various types of ecological and environmental models of lentic systems. The lake morphometry dataset included here contains estimates of Surface Area, Shoreline Length, Shoreline Development, Maximum Depth, Mean Depth, Lake Volume, Maximum Lake Length, Mean Lake Width, Maximum Lake Width, and Fetch for each of the lakepond waterbodies in the NHDPlus V2. The current release of the datasets is version 0.1 and future refinements to the data are expected.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Global nutrient cycles have been altered by the use of fossil fuels and fertilizers resulting in increases in nutrient loads to aquatic systems. In the United States, excess nutrients have been repeatedly reported as the primary cause of lake water quality impairments. Setting nutrient criteria that are protective of a lakes ecological condition is one common solution; however, the data required to do this are not always easily available. A useful solution for this is to combine available field data (i.e., The United States Environmental Protection Agency (USEPA) National Lake Assessment (NLA)) with average annual nutrient load models (i.e., USGS SPARROW model) to estimate summer concentrations across a large number of lakes. In this paper we use this combined approach and compare the observed total nitrogen (TN) and total phosphorus (TN) concentrations in Northeastern lakes from the 2007 National Lake Assessment to those predicted by the Northeast SPARROW model. We successfully adjusted the SPARROW predictions to the NLA observations with the use of Vollenweider equations, simple input-output models that predict nutrient concentrations in lakes based on nutrient loads and hydraulic residence time. This allows us to better predict summer concentrations of TN and TP in Northeastern lakes and ponds. On average we improved our predicted concentrations of TN and TP with Vollenweider models by 18.7% for nitrogen and 19.0% for phosphorus. These improved predictions are being used in other studies to model ecosystem services (e.g., aesthetics) and dis-services (e.g. cyanobacterial blooms) for ~18,000 lakes in the Northeastern United States.

We present mean individual weights for common benthic invertebrates of the Great Lakes collected from over 2,000 benthic samples and eight years of data collection (2012-2019), both as species-specific weights and average weights of larger taxonomic groups of interest. The dataset we have assembled is applicable to food web energy flow models, calculation of secondary production estimates, interpretation of trophic markers, and for understanding how biomass distribution varies by benthic invertebrate species in the Great Lakes. A corresponding data paper describes comparisons of these data to benthic invertebrates in other lakes. Methods Data Collection Benthic invertebrates were collected from the EPA R/V Lake Guardian from 2012-2019 as part of the EPA Great Lakes National Program Office GLBMP and Cooperative Science and Monitoring Initiative (CSMI) benthic surveys. GLBMP samples are collected in all five of the Great Lakes annually and CSMI samples are collected in one of the Great Lakes annually. GLBMP includes 57-63 stations each year: 11 in Lake Superior (and 2-7 additional stations since 2014), 11 in Lake Huron, 16 in Lake Michigan, 10 in Lake Erie, and 10 (9 since 2015) in Lake Ontario. The number of CSMI stations vary by year. CSMI surveys for each lake took place in the following years: Erie 2014 (97 stations), Michigan 2015 (140 stations), Superior 2016 (59 stations), Huron 2017 (118 stations), and Ontario 2018 (46 stations). Additional CSMI surveys have occurred since 2019, however, we did not include these survey data in our analysis because samples would be unbalanced with some lakes sampled twice and other lakes sampled only once. We followed EPA Standard Operating Procedures for Benthic Invertebrate Field Sampling SOP LG406 (U.S. EPA, 2021). In short, triplicate samples were collected from each station using a Ponar grab (sampling area = 0.0523 m2 for all surveys except Lake Michigan CSMI, for which sampling area = 0.0483 m2) then rinsed through 500 µm mesh. Samples were preserved with 5-10% neutral buffered formalin with Rose Bengal stain. Lab Processing Samples were processed in the lab after preservation following EPA Standard Operating Procedure for Benthic Invertebrate Laboratory Analysis SOP LG407 (U.S. EPA, 2015). Briefly, organisms were picked out of samples using a low-magnification dissecting microscope then each organism was identified to the finest taxonomic resolution possible (usually species). Individuals of the same species, or size category, were blotted dry on cellulose filter paper to remove external water until the wet spots left by animal(s) on the absorbent paper disappeared. Blotting time varied based on the surface area/volume ratio of the organisms but was approximately one minute for large and medium chironomids and oligochaetes and less time (0.6 min) for smaller chironomids and oligochaetes. Care was taken to ensure that the procedure did not cause damage to the specimens. Larger organisms (e.g., dreissenids) often took longer to blot dry. All organisms in a sample within a given taxonomic unit were weighed together to the nearest 0.0001 g (WW). Dreissena were weighed by 5 mm size category (size fractions: 0-4.99 mm, 5-9.99 mm, etc.) to nearest 0.0001 g (shell and tissue WW). Data Analysis To calculate the total weight for each species that was mounted on slides by size groups for identification (e.g., Oligochaeta, Chironomidae), we multiplied the number of individuals of the species binned into each size category by the average weight of individuals in that category. If a species was found in more than one size category, we summed the weight of the species across all categories per sample. Oligochaetes often fragment in samples, and thus, were counted by tallying the number of oligochaete heads (anterior ends with prostomium) present in the sample. Oligochaete fragments were also counted and weighed for inclusion in biomass calculations. We set the cutoff for the minimum number of samples to calculate individual weights to ten samples (see companion data paper for details). Therefore, in our further analysis we only calculated individual weights when a taxonomic unit was found in at least ten samples. Species that were found in fewer than ten samples were excluded from the analysis. We calculated wet weights by species whenever possible. If species were closely related, had similar body size (based on our previous experience), and were found in few samples, they were grouped together to achieve our minimum sample size of ten. For some taxa (e.g., Chironomidae), individual species could not be identified so calculations were made at the finest taxonomic resolution possible (usually genus). We hereafter refer to the two taxonomic groupings of closely related species and taxa that could not be identified to species as “taxonomic units.” For each taxonomic unit, we calculated several summary statistics on wet weight: mean, minimum, and maximum weight, median weight, standard error of mean weight, and sample size (number of samples in which a taxonomic unit was present). We performed Kruskal-Wallis tests (Kruskal & Wallis, 1952) to determine when individuals within a species could be grouped by depth zone and/or lake when sample size was large enough (species found in ≥10 samples per group) to permit splitting because we expected species weight to differ by depth zone and/or lake. In all five Great Lakes, benthic density and species richness are greater at stations ≤70 m than at stations deeper than 70 m (Burlakova et al., 2018; Cook & Johnson, 1974). The 70 m depth contour separation of benthos mirrors a breakpoint in spring chlorophyll concentrations observed for these stations, suggesting that lake productivity is likely the major driver of benthic abundance and diversity across lakes (Burlakova et al., 2018). Therefore, we used two categories of depth zones: ≤70 m and > 70 m. If Kruskal-Wallis tests showed that weights did not differ by lake or depth, the average weight for a species was calculated as an average of all lakes and depths. If Kruskal-Wallis tests showed significant separation (α < 0.05) by lake or depth, then means were calculated for each group and we also compared the group means. Individuals in different lakes or depth zones were combined if the mean difference between most groups was less than 25%, even when Kruskal-Wallis tests were significant because small differences were likely not biologically significant. Oligochaete fragments for finer taxonomic units were reported separately from oligochaete species because it was rarely apparent which species the fragments came from. Mean individual wet weights were calculated for a total of 187 groupings within taxonomic units (data file “IndividualWeights_AllData.csv”). For 117 taxonomic units, weights were calculated across all lakes, depths, and basins because weights were similar in all regions or because of small sample size, for seven taxonomic units, weights were calculated by lake, and for the rest summary statistics were calculated by both lake and depth zone. In addition, five species were considered as “special cases” where some areas were similar while others were not. For example, some species had similar weights in multiple lakes, thus those lakes were grouped together while other were kept separate. Dreissena rostriformis bugensis weights were calculated by lake and depth zone except for Lake Erie, where the western, central, and eastern basins were separated because previous research demonstrated that D. rostriformis bugensis size structure is drastically different in each of Lake Erie’s basins (Karatayev et al., 2021). Other special cases were: Heterotrissocladius marcidus group (Huron, Michigan, and Ontario were similar and grouped together, while mean weight in Lake Superior was different), Pisidium spp. (grouped as Ontario/Michigan, Erie, and Huron/Superior), Unidentified Chironomidae (Lake Erie was separated and all other lakes were grouped together), and Spirosperma ferox (Lake Erie was separated and all other lakes were grouped together). To calculate mean individual weights for commonly reported larger taxonomic groups (e.g., Oligochaeta, Chironomidae), we combined species or taxonomic units that belonged to this group (see “SpeciesList.csv” for information on groupings). Summary statistics were calculated on the mean individual weight for all individuals within a group in a given sample, i.e., total biomass for a given group was divided by total density for that group, repeated for each sample. Results are given for each major group as a mean/minimum/maximum for each lake, and for each depth zone within each lake as groups are often made up of different species with different body sizes in each lake and depth zone. Because densities of oligochaetes were counted based on the number of oligochaetes with heads in a sample (excluding fragments), but the fragments were weighed to calculate biomass, the mean individual weight for oligochaetes within a sample was calculated by dividing the weight of all oligochaetes (including fragments) in a sample by the number of oligochaetes (not including fragments). Calculations of mean individual weight by major group were performed both by lake and lake plus depth zone (data file “IndividualWeights_MajorGroups.csv”). Summary statistics were reported for 14 major taxa and were broken down by depth zone when sample size was sufficient (data file “IndividualWeights_MajorGroups.csv”). REFERENCES Burlakova, L. E., Barbiero, R. P., Karatayev, A. Y., Daniel, S. E., Hinchey, E. K., & Warren, G. J. (2018). The benthic community of the Laurentian Great Lakes: Analysis of spatial gradients and temporal trends from 1998 to 2014. Journal of Great Lakes Research, 44(4), 600–617. https://doi.org/10.1016/j.jglr.2018.04.008 Cook, D. G., & Johnson, M. G. (1974). Benthic Macroinvertebrates of the St. Lawrence Great Lakes.

This data shows the depth of the seabed around Ireland between 0 and 5000 metre depths. The data was collected between 1996 and 2022.Bathymetry is the measurement of how deep is the sea. Bathymetry is the study of the shape and features of the seabed. The name comes from Greek words meaning "deep" and “measure".Bathymetry is collected on board boats working at sea and airplanes over land and coastline. The boats use special equipment called a multibeam echosounder. A multibeam echosounder is a type of sonar that is used to map the seabed. Sound waves are emitted in a fan shape beneath the boat. The amount of time it takes for the sound waves to bounce off the bottom of the sea and return to a receiver is used to determine water depth.LiDAR (Light Detection and Ranging) is another way to map the seabed, using airplanes. Two laser light beams are emitted from a sensor on-board an airplane. The red beam reaches the water surface and bounces back; while the green beam penetrates the water hits the seabed and bounces back. The difference in time between the two beams returning allows the water depth to be calculated. LiDAR is only suitable for shallow waters (up to 30m depth).The data are collected as points in XYZ format. X and Y coordinates and Z (depth). The boat travels up and down the water in a series of lines (trackline). An XYZ file is created for each line and contains thousands of points. The line files are merged together and converted into gridded data to create a Digital Terrain Model of the seabed.Colours are also used to show depth ranges. Reds and browns show heights above sea-level. Yellows and greens are shallow waters up to 45m deep. Blues (up to 110m deep) and purple show deeper waters up to 200m deep.This is a raster dataset. Raster data stores information in a cell-based manner and consists of a matrix of cells (or pixels) organised into rows and columns. The format of the raster is a grid. The grid cell size is 25m by 25m. This means that each cell (pixel) represents an area on the seabed of 25 metres squared. Each cell has a depth value which is the average depth of all the points located within that cell.This data shows areas that have been surveyed. There are plans to fill in the missing areas between 2020 and 2026. The deeper offshore waters were mapped as part of the Irish National Seabed Survey (INSS) between 1999 and 2005. INtegrated Mapping FOr the Sustainable Development of Ireland's MArine Resource (INFOMAR) is mapping the inshore areas. (2006 - 2026)

This dataset is about: Water depth, average flow velocity, average suspended sediment concentration, ship speed, bottom speed, and bed velocity for selected transects. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.842199 for more information.

Global change affects gross primary production (GPP) in benthic and pelagic habitats of northern lakes by influencing catchment characteristics and lake water biogeochemistry. However, how changes in key environmental drivers manifest and impact total (i.e., benthic + pelagic) GPP and the partitioning of total GPP between habitats, here represented by the benthic share (autotrophic structuring) is unclear. This dataset presentes compiled data on summer gross primary productivity (GPP) in benthic and pelagic habitats sampled in situ between 2005-2017, together with water chemistry, from 26 shallow lakes (maximum 3.7-15.8 m deep) from three different sites in northern Sweden, located in the Arctic (Norrbotten), subarctic (Jämtland) and boreal (Västerbotten) biomes. The three study regions have variable elevation gradients and vegetation cover. The study lakes cover a wide range of DOC (1.5-16.3 mg·L-1) and accompanied water physico-chemistry. Using this dataset, we investigate how catchment properties (air temperature, land cover, hydrology) affect lake physico-chemistry and patterns of total GPP and autotrophic structuring. We find that total GPP was mostly light limited, due to high dissolved organic carbon (DOC) concentrations originating from catchment soils with coniferous vegetation and wetlands, which is further promoted by high catchment runoff. In contrast, autotrophic structuring related mostly to the relative size of the benthic habitat, and was potentially modified by CO2 fertilization. Across Arctic and subarctic sites, DIC and CO2 were unrelated to DOC, indicating that external inputs of inorganic carbon can influence lake productivity patterns independent of terrestrial DOC supply. By comparison, DOC and CO2 were correlated across boreal lakes, suggesting that DOC mineralization acts as an important CO2 source for these sites. Our results underline that GPP as a resource is regulated by landscape properties, and is sensitive to large-scale global changes (warming, hydrological intensification, recovery of acidification) that promote changes in catchment characteristics and aquatic physico-chemistry. Our findings aid in predicting global change impacts on autotrophic structuring, and thus community structure and resource use of aquatic consumers in general. Given the similarities of global changes across the Northern hemisphere, our findings are likely relevant for northern lakes globally. Methods Database accompanying arcticle "Landscape determinants of pelagic and benthic primary production in northern lakes" with pelagic and benthic GPPz rates, and GPPlake-average with physico-chemical data for each lake. Sampling, lake water physico-chemistry and bathymetry Water chemistry, PAR and temperature were measured on the same dates as GPP measurements. PAR and temperature were measured from the surface to the bottom with 1m depth intervals at the deepest part of the lake, with additional measurements at 0.25m and 0.5m using a handheld probe. Light attenuation coefficients (Kd) of the lake water were calculated as the absolute slope of natural logarithmically transformed photosynthetically active radiation (PAR) against depth. The sum of incoming PAR over the day was retrieved from stations we installed next to the lake. We used the water temperature measured at 0.2m depth (Twater) as proxy for lake epilimnion temperatures. Average air temperatures one month before sampling (Tair) were retrieved from weather stations (extracted from https://www.smhi.se) situated closest (within a range of 60km) to the sampling sites, and we included a temperature decrease of 0.57°C per 100m elevation difference between station and sampling site (sensu Karlsson et al., 2005 and references therein). Water samples for measuring pH, dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), total nitrogen (TN) and phosphorus (TP) were taken at 1m depth (epilimnion), or in nine cases from composite water samples (unstratified lakes). pH was measured directly after sampling and CO2 concentrations in the lake water were calculated from DIC, pH and temperature . In brief, DOC was filtered through a 0.45µm filter (Sarstedt Filtropur), acidified with HCl to an end concentration of 12 mM, and stored in a refrigerator before analyzed. TN and TP (unfiltered) samples were kept frozen until analysis. The DIC concentration was calculated from the headspace CO2 concentration in closed vials containing acidified lake water according to Åberg et al. (2007), and were analyzed as soon as possible. More details and specific lab operating procedures afterwards can be found in Appendix S1 accompanying the msnuscript. Detailed lake bathymetry was acquired through integrated GPS and echo-sounding depth measurements, from which we calculated lake average depth (zavg), lake volumes and areas (as a whole, or in different sections), as well as the relative areal size of the littoral benthic habitat (%Alittoral). Gross primary production (GPP) GPP was measured between 21 June and 28 July in variable years between 2005-2017 in situ in the benthic and pelagic on the same date. Benthic GPP was measured using the Dome-method (subarctic) or the DIC-method (Arctic and boreal). For the DIC-method, intact sediment cores with overlaying water were collected in incubation tubes using a gravity corer on three or five depths, and incubated for about 24h at the depth of collection. GPP rates at the discrete depths were measured by tracking changes in DIC concentrations between the onset and end of the incubation period in sealed off dark (respiration (R)) or transparent (R+GPP) tubes. In the Dome-method, three transparent domes equipped with a miniDOT oxygen logger were gently placed on the sediment at a different depth each, and O2 metabolism of the separated sediment area was measured for 24 hours. Benthic GPP rates at the three discrete depths were derived from net ecosystem production (NEP) using the R-package Lake Metabolizer (Winslow et al., 2016) and by assuming that GPP equals NEP plus R (by assuming R is oxygen loss during dark period; 24h metabolism). Pelagic GPP was measured at the surface, 0.25m, 0.5m and at following 1m depth intervals, where the deepest measurement depended on the lake depth and water turbidity. Measurements were done by incubating transparent glass bottles in situ filled with water from the sampling depth, with additional incubations in dark bottles at the most shallow and deepest measurements, for about four hours around noon using a 14C isotopic tracer. The GPP values measured for 4 hours midday at varying depths were converted to daily values by relating the midday measurements to the ratio of incident PAR during incubation time in relation to the daily PAR (24h). We used averages for duplicate or triplicate measurements of pelagic and benthic rates. An average lake GPP (mg C·m-2·day-1) was calculated for the benthic (benthic GPPlake-average) and pelagic (pelagic GPPlake-average) habitat. Benthic and pelagic GPP daily rates at discrete depths were upscaled to a lake average per m2 (benthic- and pelagic GPPlake-average; mg C·m-2·day-1) by integrating the GPP rates over the corresponding lake surface (benthic) or lake volume (pelagic) per depth interval, and relating the sum to the total lake area. The total average GPP of the lake (total GPPlake-average) is expressed as the sum of benthic and pelagic GPPlake-average (mg C·m-2·day-1), and autotrophic structuring is expressed as the relative amount (%) of GPP that takes place in the benthic habitat.

Physical description of marine lakes including surface area, distance to ocean, tidal efficiency, depth, and stratification. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Lake Bathymetry Mapping

The bathymetry of the lakes was recorded with a purpose-built lightweight, low-power echosounder unit with integrated GPS and AHRS (attitude and heading reference system), towed behind a kayak. (Data courtesy of Herwig Stibor, Thomas Stieglitz.)

Lake Bathymetry Analysis

Raw bathymetry data were down-sampled to include one sample every 2\u00a0m distance along the survey track. Data were visually inspected and false soundings removed. In some lakes, GPS reception was partially compromised along the steep shorelines. Where required to not compromise data density, the survey track was reconstructed from field notes and AHRS data (less than 5% of data where applicable). During the bathymetry surveys, tidal water level was not recorded. Therefore, tidal water level variations were corrected for by modeling tidal water level in each lake. The water level for each lake was correlated with tidal water level measured at a reference station at CRRF or predicted tide (x-tide database), by determining tidal lag time and tidal efficiency (see below) from data previously collected concurrently in the respective lake and at this reference station. Subsequently, bathymetry data was reduced to a grid with 2m resolution by kriging of a further down-sampled subset of data using every third data point. Lake-specific variograms were applied, and contour lines and total lake volumes were calculated in a GIS. (Data courtesy of Thomas Stieglitz.)

Distance from lake to ocean & lake surface area

Lake shorelines and island coastlines were manually extracted from satellite data (Microsoft Bing). The nearest, mean and median distance of each lake to the respective island\u2019s coastline as well as lake surface area was calculated in a GIS. The perimeter was measured in meters using the \u2018Measure Line\u2019 tool in QGIS 3.4.

Habitable surface area for benthic organisms was estimated either as (1) a multiple of the perimeter and depth of the chemocline, i.e. assuming a cylindrical model for the lake, or (2) as the area of a frustum, i.e. a truncated cone, using the perimeter of the lake at the surface, the average length of transects, and the average angle of the transect from the vertical (estimated using the sine of maximum depth / transect length).

Tidal lag time and tidal efficiency were calculated from concurrently measured tidal water level in a lake and the adjacent ocean. The tidal lag time \u2014 the time between high tide in the adjacent ocean and high tide in the lake \u2014 was determined by least-square fit between lake and ocean tide measured using HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti).\u00a0 Tidal efficiency was calculated as the ratio between amplitude of ocean tide and lake tide (e.g. Ayers, JF and Vacher, HL, 1986.\u00a0 Hydrogeology of an Atoll Island: A Conceptual Model from a Detailed Study of a Micronesian Example. Groundwater 24(2) 185-198.). Larger tidal lag time and smaller tidal efficiency respectively indicate a less efficient hydrological connection between ocean and lake.

Error-checking: Estimates of basic dimensions (depth, distance, length, surface area) were double-checked manually for a subset of measurements by a second person using using Google Earth.\u00a0 awards_0_award_nid=55103 awards_0_award_number=OCE-1241255 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1241255 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Lake physical Palau marine lake physical descriptions M. Dawson (UC-Merced) version date: 2019-05-13 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.768110.1 geospatial_vertical_max=22.56 geospatial_vertical_min=0.91 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/768110 institution=BCO-DMO instruments_0_dataset_instrument_description=Used to determine tidal lag time, the time between high tide in the adjacent ocean and high tide in the lake. instruments_0_dataset_instrument_nid=768137 instruments_0_description=Electronic devices that record data over time or in relation to location either with a built-in instrument or sensor or via external instruments and sensors. instruments_0_instrument_name=Data Logger instruments_0_instrument_nid=731353 instruments_0_supplied_name=HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti) metadata_source=https://www.bco-dmo.org/api/dataset/768110 param_mapping={'768110': {'max_actual_depth_m': 'master - depth'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/768110/parameters people_0_affiliation=University of California-Merced people_0_affiliation_acronym=UC Merced people_0_person_name=Michael N Dawson people_0_person_nid=51577 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Nancy Copley people_1_person_nid=50396 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=PaPaPro projects_0_acronym=PaPaPro projects_0_description=This project will survey the taxonomic, genetic, and functional diversity of the organisms found in marine lakes, and investigate the processes that cause gains and losses in this biodiversity. Marine lakes formed as melting ice sheets raised sea level after the last glacial maximum and flooded hundreds of inland valleys around the world. Inoculated with marine life from the surrounding sea and then isolated to varying degrees for the next 6,000 to 15,000 years, these marine lakes provide multiple, independent examples of how environments and interactions between species can drive extinction and speciation. Researchers will survey the microbes, algae, invertebrates, and fishes present in 40 marine lakes in Palau and Papua, and study how diversity has changed over time by retrieving the remains of organisms preserved in sediments on the lake bottoms. The project will test whether the number of species, the diversity of functional roles played by organisms, and the genetic diversity within species increase and decrease in parallel; whether certain species can greatly curtail diversity by changing the environment; whether the size of a lake determines its biodiversity; and whether the processes that control diversity in marine organisms are similar to those that operate on land. Because biodiversity underlies the ecosystem services on which society depends, society has a great interest in understanding the processes that generate and retain biodiversity in nature. This project will also help conserve areas of economic importance. Marine lakes in the study region are important for tourism, and researchers will work closely with governmental and non-governmental conservation and education groups and with diving and tourism businesses to raise awareness of the value and threats to marine lakes in Indonesia and Palau. projects_0_end_date=2017-12 projects_0_geolocation=Western Pacific; Palau; Indonesia (West Papua) projects_0_name=Do Parallel Patterns Arise from Parallel Processes? projects_0_project_nid=2238 projects_0_project_website=http://marinelakes.ucmerced.edu/ projects_0_start_date=2013-01 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 version=1 xml_source=osprey2erddap.update_xml() v1.3

Lake morphometry metrics are known to influence productivity in lakes and are important for building various types of ecological and environmental models of lentic systems. The lake morphometry dataset included here contains estimates of Surface Area, Shoreline Length, Shoreline Development, Maximum Depth, Mean Depth, Lake Volume, Maximum Lake Length, Mean Lake Width, Maximum Lake Width, and Fetch for each of the “lakepond” waterbodies in the NHDPlus V2. The current release of the datasets is version 0.1 and future refinements to the data are expected.

This dataset provides the interpolated bathymetry of Lake Lucerne used for the numerical wave propagation simulation performed in the study “Shallow-Water Tsunami Deposits: Evidence from Sediment Cores and Numerical Wave Propagation of the 1601 CE Lake Lucerne” by Nigg et al. (in review). The multibeam echo-sounder bathymetry dataset of Lake Lucerne acquired by Hibe et al. (2011) was resampled from a grid size of 1x1 m to 5x5 m to reduce computational time using ArcMap (version 10.8.1). In addition, large artificial shoreline modifications were cropped and interpolated based on historical maps. Shallow-water areas (water depth 0-4m), which are not entirely covered by the bathymetrical data were linearly interpolated to the shoreline. The original bathymetry was acquired using a Geo-Acoustics GeoSwath Plus 125 kHz interferometer by Hilbe et al. (2011). Positioning was acquired with a Leica SR 530 GPS receiver with real time kinematic positioning (RTK; swipos GIS/GEO from swisstopo). Acquisition control and data processing were conducted using the GeoAcoustics GS+ software package. Swiss basic hydrological monitoring network (BAFU, 2008) were used to normalize water depths to the mean lake level (433.6 m a.s.l.). See Hilbe et al. (2011) for further information. The original bathymetry dataset is available from swisstopo and should be referenced as Hilbe, M., Anselmetti, F. S., Eilertsen, R. S., Hansen, L., & Wildi, W. (2011). Subaqueous morphology of Lake Lucerne (Central Switzerland): implications for mass movements and glacial history. Swiss Journal of Geosciences, 104(3), 425-443.

Typology of waters is defined as a group of water bodies having common natural ecological conditions in terms of geo-morphological, hydrological, physico-chemical, and biological characteristics. The type descriptors are permanent characteristics that do not respond to human activities and represent the fixed abiotic conditions that explain natural variability. For the need of large-scale analysis of ecological status, multiple pressures on rivers and lakes, linkages of water body types to habitat types and for comparison of type-specific limit values for nutrients and other quality elements across countries in Europe, a broad river and lake typology was developed. Descriptors categories are dominant geology, region, river catchment, river altitude, river flow, lake size and mean lake depth. The ranges of descriptors largely follow the system A of Water Framework Directive (WFD) (EC, 2000) and are described in Lyche Solheim et al. (2019). Various European data sources were used for spatial allocation of rivers and lakes broad types. The starting point was the European Catchments and Rivers Network System (Ecrins) (EEA, 2012), which is organised into sets of spatial thematic layers: lake polygons, river segments (drains), nodes representing intersection of river and catchments and almost 180,000 “Functional Elementary Catchments (FECs)”. Catchments include “main drains” (connecting together the FECs) and “secondary drains” (internal within a FEC). We assigned one broad type to all segments belonging to “main drain” of each FEC and named them “river segment". The catchment size of river segments in each FEC is defined as the sum of the upstream drainage area and FEC surface area. The upstream drainage area has been derived using data in “Code Arbo” in Ecrins database (Globevnik et al., 2017). The altitude of the lower end points of river segments in each FEC is available in Ecrins river database. Lake surface area is obtained from Ecrins lake area attribute “Area”. Data on mean lake depth were obtained from Waterbase – Water Quality database (EEA, 2016) or estimated from terrain data. The basic map of five geological (geochemical) categories was produced from two thematic maps: bedrock map “International Hydrogeological Map of Europe (IHME 1500_v11)” (Dutcher et al, 2015) and the soil map of the European Union “SGDBE4” (JRC, 2016). The dominant geology for lakes was derived from this map with the overlay procedure. For each FEC we then defined dominant catchment geology (geochemistry) and assigned this geology type to all river segments forming the FEC's main drain. Spatial extent of the Mediterranean region is obtained from spatial layer 'Biogeographical regions of Europe» (EEA, 2019). More details on methodology are in Lyche Solheim et al. (2019).

Paleo-water depth is an important component of modeling surface temperatures through time. Paleo-water depth values represent the elevation of the sediment-water interface relative to global mean sea level at a particular point in geologic time. In most of the model time steps, paleo-water depth values were treated uniformly (single value) across the modeled area of interest, as a simplifying assumption. Most of the model layers were deposited in marine conditions, where the sediment-water interface was below mean sea level (positive paleo-water depths); however, the ground surface of the Williston Basin is now several thousand feet above sea-level, and the Cenozoic model layers were likely deposited in continental conditions, where the sediment-water interface represents paleo-topography (negative paleo-water depths). The model uses the present-day topography to interpolate paleo-water depth between the present-day topography and the uniform paleo-water depth at the 70 Ma time step in the model. The interpolation was generated at three time steps: 50, 43, and 20 Ma time steps, where each of these interpolations is describe with an ASCII grid of map-varying values of paleo-water depth. This is a child item of a larger data release titled "Data release for the 3D petroleum systems model of the Williston Basin, USA".

Water quality time series files for ~30 lakes on the North Slope of Alaska in the western transect of our project. Separate research teams work on two transects (Eastern and Western), and the lakes are subdivided by these regional groupings. This dataset contains lake code names, field notes, and subdirectories of the data separated by year collected. These include time series of water temperature and lake depth, and dissolved oxygen and conductivity for a select group of lakes ("focus lakes"). ReadMe files provide the instrumentation and logging details, and there are files that contain the location of each site. Details: These folders contain .csv files of lake water temperature time series collected during the period April to August. Data collected at the same locations using the same configuration will be found in the yearly files, where the year is defined as that year when the data were downloaded. At the first subfolder level, the lakes are differentiated by the Eastern and Western Transects; see accompanying jpg image (CALON_sites_overview.jpg) and list of Lake Float Locations. The instrumentation configuration is identical in most cases. Generally, a rope is attached to an anchor. The other end of the rope is attached to a float. An Onset computer U12-015 water temperature data logger is affixed to the rope 30 cm below the float. Another data logger (U20) is attached to the rope just above the anchor. The U20 is a water level/water temperature logger, so records the change in the water depth over the period of record. The water level is calibrated and corrected using the barometric pressure record from the nearest terrestrial met station. This is done in the Onset Hoboware software. All sensor specifications are shown below. In April, a 10” diameter hole is augured through the ice and the instrument string is lowered to the lake bottom. A pole is used to push the float down the hole and beneath the ice, where it remains until the ice cover melts in spring. Thus, the upper logger is measuring the temperature near the base of the ice slab, which moves toward the surface as the flow melts and thins. The loggers are retrieved in August, and a new instrument package is deployed. The floats are redeployed in the original location the following April. It should be noted that there is often some movement of the floats that get entrained by the moving ice slab in spring or by strong wave activity in summer. All loggers record hourly measurements on the hour using Alaska Daylight Time (ADT). In each data file, column headings are Date, Time, ADT, TempC(deployment depth of upper logger), TempC (deployment depth of lower logger), Water Level, m. In a few cases, additional sensor strings were deployed at a different location in the same lake. This typically occurred when the lake was especially deep (>4 m), and was designed to obtain measurements of the entire water column. In these cases, an Onset Computer U23 water temperature logger was used. These data are shown as additional columns in the .csv files. Finally, there are also some stand alone files for a few select lakes (“focus lakes”) that report time series of water quality including dissolved oxygen and conductivity. Loggers were manufactured by Onset Computer; Hobo U24-001 for conductivity and U26-001 for the dissolved oxygen (see specs below). These loggers were attached to the base of the instrument string described below for a few lakes. Loggers are deployed in August, recovered the following August, serviced, and redeployed. Measurements are collected hourly. Pressure and Water Level Measurements U20-001-01 andU20-001-01-Ti Operation Range 0 to 207 kPa (0 to 30 psia); approximately 0 to 9 m (0 to 30 ft) of water depth at sea level, or 0 to 12 m (0 to 40 ft) of water at 3,000 m (10,000 ft) of altitude Factory Calibrated Range 69 to 207 kPa (10 to 30 psia), 0? to 40?C (32? to 104?F) Burst Pressure 310 kPa (45 psia) or 18 m (60 ft) depth Water Level Accuracy* Typical error: ?0.05% FS, 0.5 cm (0.015 ft) water Maximum error: ?0.1% FS, 1.0 cm (0.03 ft) water Raw Pressure Accuracy** ?0.3% FS, 0.62 kPa (0.09 psi) maximum error Resolution < 0.02 kPa (0.003 psi), 0.21 cm (0.007 ft) water Pressure Response Time (90%) < 1 second Thermal Response Time (90%)*** Approximately 10 minutes in water to achieve full temperature compensation of the pressure sensor Temperature Measurements (All Models) Operation Range -20? to 50?C (-4? to 122?F) Accuracy ?0.44?C from 0? to 50?C (?0.79?F from 32? to 122?F), see Plot A Resolution 0.10?C at 25?C (0.18?F at 77?F), see Plot A Response Time (90%) 3.5 minutes in water (typical) Stability (Drift) 0.1?C (0.18?F) per year HOBO U12 Stainless Temperature Data Logger - U12-015 Measurement range: -40? to 125?C (-40? to 257?F) Accuracy: ? 0.25?C from 0? to 50?C (? 0.45?F from 32? to 122?F), see Plot A Resolution: 0.03?C at 25?C (0.05?F at 77?F), see Plot A Drift: 0.05?C/year + 0.1?C/1000 hrs above 100?C (0.09?F/year + 0.2?F/1000 hrs above 212?F) Response time in 1 m/s (2.2 mph) airflow: U12-015: < 10 minutes, typical to 90% U12-015-02: 2.25 minutes, typical to 90% Response time in water: U12-015: < 3.5 minutes, typical to 90% U12-015-02: 20 seconds, typical to 90% Time accuracy: ? 2 minute per month at 25?C (77?F), see Plot B Operating environment: Air, water, steam, 0 to 100% RH Operating temperature: Logging: -40? to 125?C (-40? to 257?F) Launch/readout: 0? to 50?C (32? to 122?F), per USB specification HOBO Water Temperature Pro v2 Data Logger - U22-001 Operation range†: -40? to 70?C (-40? to 158?F) in air; maximum sustained temperature of 50?C (122?F) in water Accuracy: ?0.21?C from 0? to 50?C (?0.38?F from 32? to 122?F) Resolution: 0.02?C at 25?C (0.04?F at 77?F) Response time: (90%) 5 minutes in water; 12 minutes in air moving 2 m/sec (typical) Stability (drift): 0.1?C (0.18?F) per year HOBO Conductivity Data Logger - U24-001 Memory - 18,500 temperature and conductivity measurements when using one conductivity range; 14,400 sets of measurements when using both conductivity ranges (64kbytes) Sample rate - 1 second to 18 hrs, fixed or multiple-rate sampling with up to 8 user-defined sampling intervals Battery life - 3 years (@ 1 min logging) Maximum Depth - 70 m (225') Operating Range - -2 to 36?C (28? to 97?F) - non freezing Weight - 193 gm (6.82 ounces), buoyancy in freshwater: -59.8 gm (-2.11 ounces) Size - 3.18 cm diameter x 16.5 cm, with 6.3 mm mounting hole (1.25" diameter x 6.5", ?" hole) Calibrated Range - Conductivity: Low Range: 0 to 1,000 ?S/cm; Full Range: 0 to 10,000 ?S/cm - Temperature: 5 to 35C (41 to 95F) Accuracy - Conductivity: 3% of reading, or 5 ?S/cm, whichever is greater / Temperature: 0.1C (0.2F) Resolution -Conductivity: 1 uS/cm - Temperature: 0.01?C (0.02?F) Response time - 1 second to 90% of change HOBO Dissolved Oxygen Logger - U26-001 Dissolved Oxygen Sensor Type Optical Measurement Range 0 to 30 mg/L Calibrated Range 0 to 20 mg/L; 0 to 35?C (32 to 95?F) Accuracy 0.2 mg/L up to 8 mg/L; 0.5 mg/L from 8 to 20 mg/L Resolution 0.02 mg/L Response Time To 90% in less than 2 minutes DO Sensor Cap Life 6 months, cap expires 7 months after initialization Temperature Temperature Measurement/Operating Range -5 to 40?C (23 to 104?F); non-Freezing Temperature Accuracy 0.2?C (0.36?F) Temperature Resolution 0.02?C (0.04?F) Response Time To 90% in less than 30 minutes Logger Memory 21,700 sets of DO and temperature measurements (64 KB total memory) Logging Rate 1 minute to 18 hours Time Accuracy ?1 minute per month at 0 to 50?C (32 to 122?F) Battery 3.6 V lithium battery; factory replaceable Battery Life 3 years (at 5 minute logging) Download Type Optical Depth Rating 100 m (328 ft) Wetted Materials Black Delrin?, PVC, EPDM o-rings, silicone bronze screws; rated for saltwater use Size 39.6 mm diameter x 266.7 mm length (1.56 x 10.5 inches) Weight 464 g (16.37 oz)