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47 results for “global lakes”
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
Globally distributed lake surface water temperatures collected in situ and by satellites; 1985-2009
Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global dataset of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. This dataset accompanies a data publication in the journal Scientific Data
The Extended Global Lake area, Climate, and Population Dataset (GLCP)
A changing climate and increasing human population necessitate understanding global freshwater availability and temporal variability. To examine lake freshwater availability from local-to-global and monthly-to-decadal scales, we created the Global Lake area, Climate, and Population (GLCP) dataset, which contains annual lake surface area for 1.42 million lakes with paired annual basin-level climate and population data. Building off an existing data product infrastructure, the next generation of the GLCP includes monthly lake ice area, snow basin area, and more climate variables including specific humidity, longwave and shortwave radiation, as well as cloud cover. The new generation of the GLCP continues previous FAIR data efforts by expanding its scripting repository and maintaining unique relational keys for merging with external data products. Compared to the original version, the new GLCP contains an even richer suite of variables capable of addressing disparate analyses of lake water trends at wide spatial and temporal scales.
Global data set of long-term summertime vertical temperature profiles in 153 lakes
Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.
Global glacial lake bathymetry data
<p>This dataset collects globally published bathymetric data for glacial lakes, recording attributes such as glacial lake name, location, type, year of survey, corresponding area, volume, maximum water depth, and source.</p>
Population-adjusted pseudo global warming simulations over the Great Lakes Region
<p>Summaries of pseudo global warming simulations over the Great Lakes Region. Specifics below:</p> <p> </p> <p>Each CSV file provides regional summaries from PGW simulations, either over land, for urban grids, or rural grids. This includes cumulative hours above critical heat stress threholds as well as their population-adjusted values.</p> <p>The 'Future_sensitivities_land.csv' file calculates heat stress in the future by keeping all variables except one the same value as the historical run.</p> <p>Among the geotiffs, _perc_land files include percentage of hours in summer above the National Weather Service heat index and wet bulb globe temperature thresholds, _pop_land incorporate population adjusted heat stress above those thresholds, the TEMP_contribution files estimate changes in heat stress if only air temperature changed and all variables remained the same as historical values and the HI_ and WBGT_pop rasters include summer average heat stress and their corresponding populations. </p> <p>See Chakraborty et al. Under Review (will be updated on paper publication) for more details.</p>
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013
Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
Global Lake Ecological Observatory Network: Long term chloride concentration from 529 lakes and reservoirs around North America and Europe: 1940-2016
This dataset compiles long term chloride concentration data from 529 freshwater lakes and reservoirs in Europe and North America. All lakes in the dataset had greater than or equal to ten years of data. For each lake the following landscape and climate metrics were calculated: mean annual precipitation, mean monthly air temperatures, road density and impervious surface in 100 to 1500 m buffer zones, sea salt deposition. The dataset includes three files: 1) Descriptive data of lake sites (physical lake metrics, climate, land-cover characteristics), 2) Chloride time-series, and 3) GIS shapefiles.
Global LAke Surface water Temperature (GLAST): Compound thermal extremes in lakes
<p>This database contains daily maximum temperature, daily minimum temperature, and daily mean temperature for 92,245 lakes globally from 1981 to 2020. These daily time series were derived from hourly simulation data.</p> <p>The database also includes annual statistics of six types of thermal extreme events calculated from the daily lake temperature time series:</p> <ul> <li>daytime hot extreme events (hot day–mild night)</li> <li>nighttime hot extreme events (mild day–hot night)</li> <li>compound hot extreme events (hot day–hot night)</li> <li>daytime cold extreme events (cold day–mild night)</li> <li>nighttime cold extreme events (mild day–cold night)</li> <li>compound cold extreme events (cold day–cold night)</li> </ul> <p> </p> <p>The annual statistics provided for these events include metrics such as frequency, intensity, duration, and total days. Additionally, annual statistics of extreme air temperature events over the lakes are included.</p> <p>Details about the hourly-scale lake temperature simulation methodology can be found in the paper <em>"Global lakes are warming slower than surface air temperature due to accelerated evaporation"</em> (Tong et al., 2023, Nature Water). Definitions and calculation methods for thermal extreme events in lakes and atmosphere are provided in <em>"Day-night compound thermal extremes in lakes"</em> (Tong et al., 2025).</p> <p>For detailed information about the contents of each data file, please refer to the accompanying <strong>readme.docx</strong> file.</p> <p>For more datasets on global aquatic environments, please visit the official website of the Global Aqua Remote Sensing (GARS) Laboratory, led by Prof. Lian Feng: <a href="https://garslab.com/?cat=1">https://garslab.com/?cat=1</a>.</p>
Surface Water Area Variations of Global Lakes and Reservoirs
<p>Monthly surface area timeseries of large lakes and reservoirs generated from Sentinel-1 SAR backscatter data from January 2017 through December 2019.</p>
Global lake evaporation volume (GLEV) dataset
<p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>For an interactive interface of the dataset (Google Earth Engine App), please see <a href="https://zeternity.users.earthengine.app/view/glev">https://zeternity.users.earthengine.app/view/glev</a></strong></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>There are three csv files in this dataset. Each file has 409 columns and 1427687 rows.</p> <p><strong>1. 0_evaporation_rate.csv</strong><br> The first column is Hylak_id from <a href="https://www.hydrosheds.org/page/hydrolakes">HydroLAKES v1.0 dataset</a>.<br> The rest 408 columns contain monthly evaporation rate (mm per day) from Jan 1985 to Dec 2018.<br> <strong>2. 1_openwater_area.csv</strong><br> The first column is Hylak_id.<br> The rest 408 columns contain monthly open water area (square meters) from Jan 1985 to Dec 2018.<br> <strong>Note </strong>that this is not the surface area of lake as shown in the above GEE App.<br> It is the open water area by removing the lake ice coverage.<br> The surface area dataset is available <a href="https://drive.google.com/file/d/1ltWmB_Gj8jcFeDU3mpdGJXOx3uyVYcqN/view?usp=sharing">here</a>.<br> <strong>3. 2_evaporation_volume.csv</strong><br> The first column is Hylak_id.<br> The rest 408 columns contain monthly evaporation volume (thousand cubic meter per month) from Jan 1985 to Dec 2018.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>To use this dataset, citation of the following paper is recommended:</strong><br> Zhao, G., Li, Y., Zhou, L., Gao, H. (2022) Evaporative water loss of 1.42 million global lakes. <em>Nature Communications</em>. <a href="https://doi.org/10.1038/s41467-022-31125-6">https://doi.org/10.1038/s41467-022-31125-6</a></p> <p>The detailed algorithms associated with the development of GLEV can be found in:<br> Zhao, G., and H. Gao (2019), Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches, <em>Remote Sensing of Environment</em>, 226, 109-124. <a href="https://doi.org/10.1016/j.rse.2019.03.015">https://doi.org/10.1016/j.rse.2019.03.015</a><br> Zhao, G., and H. Gao (2018), Automatic correction of contaminated images for assessment of reservoir surface area dynamics. <em>Geophysical Research Letters</em>, 45, 6092-6099. <a href="https://doi.org/10.1029/2018GL078343">https://doi.org/10.1029/2018GL078343</a></p>
ReaLSAT, a global dataset of reservoir and lake surface area variations
<p>Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at a global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2020) of 681,137 water bodies below 50°N and sizes greater than 0.1 square kilometers.</p> <p> The dataset contains the following files:</p> <p>1) ReaLSAT.zip: A shapefile that contains the reference shape of waterbodies in the dataset.</p> <p>2) monthly_timeseries.zip: contains one CSV file for each water body. The CSV file provides monthly surface area variation values. The CSV files are stored in a subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contain CSV files of lakes that lie between 60 E and 70 E longitude, and 50S and 40 S. </p> <p>3) monthly_shapes_<bottom_left_lon>_<bottom_left_lat>.zip: contains a geotiff for each water body that lie within the 10 degree by 10 degree cell. Please refer to the visualization notebook on how to use these geotiffs. </p> <p>4) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data.</p> <p>6) generate_realsat_timeseries.ipynb: a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody.</p> <p>Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset:</p> <p>[1] Khandelwal, Ankush, Anuj Karpatne, Praveen Ravirathinam, Rahul Ghosh, Zhihao Wei, Hilary A. Dugan, Paul C. Hanson, and Vipin Kumar. "ReaLSAT, a global dataset of reservoir and lake surface area variations." <em>Scientific data</em> 9, no. 1 (2022): 1-12.</p> <p>[2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).</p> <p> </p> <p><strong>Version Updates</strong></p> <p>Version 2.0:</p> <p>- extends the datasets to 2020.</p> <p>- provides geotiffs instead of shapefiles for individual lakes to reduce dataset size.</p> <p>- provides a notebook to visualize the updated dataset. </p> <p>Version 1.4: added 1120 large lakes to the dataset and removed partial lakes that overlapped with these large lakes.</p> <p>Version 1.3: fixed visualization related bug in generate_realsat_timeseries.ipynb</p> <p>Version 1.2: added a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody in ReaLSAT database.</p>
GLARE: global lake atlas in research
<p>Lakes are vital landscape units with ecological, economic, and social importance, and there has been a surge in scientific research aimed at understanding and addressing the challenges to ensure the preservation and sustainable management of lakes. However, there is limited knowledge regarding the geographical distribution of the studied lakes, the specific issues and driving factors in the research. There is still a need for comprehensive datasets that combine lake morphology and watershed characteristics with research topics.</p> <p>In this dataset, we assembled metadata from 58,024 peer-reviewed publications on 2,542 lakes and geometric, meteorological, and socioeconomic attributes from multisource databases, including HydroATLAS, NOAA PSL, GCP, and HILDA+. These datasets were aggregated into the Global Lake Atlas in REsearch (GLARE) by parsing the literature documents, extracting the lake entities based on deep learning, geocoding the lakes, and allocating 17 topics and 54 subtopics.</p> <p>The open-source GLARE dataset is essential for addressing global lake challenges and strengthening research in underrepresented regions by linking literature information with physical and biogeochemical data. Furthermore, the methodology applied in GLARE can be employed to extract any unstructured data, making it highly applicable to various other academic fields.</p>
Predicted lake dissolved organic carbon at a global scale
<p>The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of <em>in situ</em> DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km<sup>2</sup>. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L<sup>-1</sup>. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.</p>
Global nitrous oxide emissions from rivers, lakes, and reservoirs during 1850-2019
<p>Data for paper "Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era", accepted at Nature Communications 2023.</p><p>Data are in ascii-format at a spatial resolution of 30 arcmin. Header for ascii-format files - ncols: 720 - nrows: 354 - xllcorner: -180 - yllcorner: -88.5 - cellsize: 0.5 - NODATA_value: -9999. The unit of the datasets is g N /yr.</p>
Lake-TopoCat: A global Lake drainage Topology and Catchment database
<p><strong>Contact</strong>: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use Lake-TopoCat, please cite the following paper:</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. <em>Earth System Science Data</em>, 15, 3483-3511, <a href="https://doi.org/10.5194/essd-15-3483-2023">https://doi.org/10.5194/essd-15-3483-2023</a>.</p> <p> </p> <p><strong>Data description and components</strong><br>This version of Lake-TopoCat was constructed using the SWOT Prior Lake Database (PLD) v106 (<em>Wang et al.</em>, 2023) lake mask and the 3-arc-second-resolution hydrography dataset MERIT Hydro v1.0.1 (<em>Yamazaki et al.</em>, 2019). The drainage type of each PLD lake, such as isolated, inflow-headwater, headwater, flow-through, terminal, and coastal, was determined with assistance of MERIT Hydro-Vector (<em>Lin et al.</em>, 2021), a high-resolution river network dataset with spatially-variable drainage densities.</p> <p><br>For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins or regions, and the Lake-TopoCat data products were also organized based on these 68 regions, with their region or basin IDs shown in the Fig. 'Pfaf2_basins.jpg', attached to this database.</p> <p><br>Lake-TopoCat consists of five feature components, each with multiple attributes depicting lake drainage relationships. The five features are:</p> <p><strong>1. Lake boundaries:</strong> polygons of 5,893,363 PLD lakes, larger than 1 ha.</p> <p> File name: <em>Lakes_pfaf_xx </em>where, 'pfaf_xx' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg')</p> <p><strong>2. Lake outlets:</strong> points representing outlet or pour points of each individual lake. There are multiple outlets from a multifurcation lake. We identified 5,983,642 outlets for 5,893,363 lakes, where 83,819 lakes (~1.4% of the global lakes) show bi/multifurcation.</p> <p> File name: <em>Outlets_pfaf_xx</em></p> <p><strong>3. Unit catchment:</strong> boundary polygons of catchment defining the drainage areas between cascading (i.e., immediately upstream and downstream) lake outlets. The count of unit catchments equal to the count of lake outlets, and bifurcation or multifurcation lakes have multiple local catchments. In total, the delineated catchments in Lake-TopoCat cover about 85.1 million km2, which is about 63% of the Earth’s land mass excluding the Antarctic.</p> <p> File name: <em>Catchments_pfaf_xx</em></p> <p><strong>4. Inter-lake reaches:</strong> line features defining the drainage networks that connect the lake outlets to the inland sinks or the ocean. About 11 million connecting reaches were generated among ~6 million outlets. The total length of these inter-lake connecting reaches is ~19 million km, which is at least 8.75 times longer than the SWOT-visible river reaches as depicted in the SWOT River Database (SWORD) v16 (<em>Altenau et al.</em>, 2021).</p> <p> File name: <em>Reaches_pfaf_xx</em></p> <p><strong>5. Lake-network basins:</strong> boundary polygons of the entire drainage area containing each inter-lake network (i.e., a complete basin from the headwater to an inland sink or the ocean for all basins containing lakes). A total of 108,985 lake-network basins were identified. Among them, endorheic basins account for 2.75% by count and 19.5% by area of all lake-network basins. These endorheic basins cover ~17.5% of global surface excluding Antarctica.</p> <p> File name: <em>Basins_pfaf_xx</em></p> <p>The attribute tables for each of the feature components are explained in Section 4 of the product description document. For user convenience, we release the preliminary Lake-TopoCat lake outlets, unit catchments, and inter-lake reaches, with the affix '_prelim' in the file names (explained in the attached product description document). We also provide the polygon boundaries of the 68 Pfafstetter basins or regions in the file named 'Pfaf2_regions'. All files of Lake-TopoCat are available in both shapefile and geodatabase formats.</p> <p> </p> <p><strong>Disclaimer</strong><br>Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of Lake-TopoCat.</p>
Coupling nitrogen removal and watershed management to improve global lake water quality
<p>Updated dataset and code for the article "Coupling nitrogen removal and watershed management to improve global lake water quality" after removing deep lakes.</p>
Daily NOAA Global Ensemble Forecasting System forecasts for six National Ecological Observatory Network lakes (2021--05-18 to 2021-10-24)
<p>NOAA Global Ensemble Forecasting System output generated at 00 UTC that has been subsetted and temporally downscaled from 6-hr to 1-hr for six lakes in the National Ecological Observatory Network. The files include all ensembles and the set of variables required to run the General Lake Model. The NEON siteID for the lakes are BARC, SUGG, CRAM, LIRO, PRLA, PRPO. See https://www.neonscience.org for more information about each lake.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.