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Lake chloride concentrations and model predictions for 49,432 lakes in the Midwest and Northeast United States.
Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but we have little knowledge of the prevalence and spatial distribution of the problem. The majority of salt pollution in north temperate regions stems from road salt application but other chloride sources include water softeners, synthetic fertilizers, and livestock excretion. Although chloride contamination of lakes is well documented, it is unknown how many lakes are at risk of long-term salinization. We used a quantile regression forest to leverage information from 2,773 lakes to predict the chloride concentration of all 49,432 lakes greater than 4 ha in a 17-state area. The QRF used 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest interstate and road. Model predictions had an r2 of 0.94 for all chloride observations, and 0.87 for predictions of the mean chloride concentration observed at each lake.
Freshwater insect occurrences and traits for the contiguous United States, 2001 - 2018
Freshwater insects comprise 60% of the diversity of freshwater animals and are essential indicators of ecosystem health. Yet our knowledge of the distribution of freshwater insect diversity in the United States is incomplete because we lack comprehensive data on freshwater insect distributions and traits at a continental scale. We fill this knowledge gap by presenting a database of occurrence records and functional traits for freshwater insects in the contiguous US. We compiled over 2.05 million genus occurrence records for 932 genera in the major freshwater insect orders, at 51,044 stream locations sampled between 2001 and 2018 by federal and state biological monitoring programs. We assembled life history, dispersal, morphology, and ecology traits from existing databases, scientific books, and the primary literature, and present traits and trait affinities for 1,072 insect genera. Our freshwater insects CONUS database can be used to map freshwater insect taxonomic and functional diversity, and when paired with environmental data, will provide a powerful resource for quantifying how the environment shapes diversity patterns as well as taxon-specific distributions across the contiguous US.
Data from Herbarium Specimens Reveal Delays in Autumn Maple Coloration in the Northeastern United States Over the Past 150 Years
Data from "Herbarium Specimens Reveal Delays in Autumn Maple Coloration in the Northeastern United States Over the Past 150 Years". Herbarium specimens annotated for damage and phenological status paired with climate and locality information to investigate long-term trends in species-interactions and autumn phenology.
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Hydrology-mediated ecological function of a large wetland threatened by an invasive predator"
Asian swamp eels (Monopterus albus/javanensis complex) were first reported from Florida in 1997 and the Everglades in 2007; swamp eels have been established in Taylor Slough of Everglades National Park since 2014. This dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 24 sites across four regions of the Everglades: Taylor Slough, Shark River Slough, Water Conservation Area 3, and the C-111 Panhandle. Prey species included are the six most common small fishes prior to swamp eel invasion of Taylor Slough (1996–2009) and the three common decapod species (two crayfish species and grass shrimp). An annual index of mean wet season electrofishing catch-per-unit-effort of swamp eels, Mayan cichlids (Mayaheros uruphthalmus), and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) is included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses are included.
Data to explore circular manureshed management in beef supply chains of the United States and western Canada
Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.
Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets
<p>*********** Please view the README.txt file for detailed documentation of data. ***********</p> <p><strong>Title:</strong> Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets</p> <p><strong>Version: </strong>1.0</p> <p><strong>Date of Release: </strong>2019/12/16</p> <p><strong>Identifier: </strong>10.5281/zenodo.3576251</p> <p><strong>Associated publication:</strong> Austermann, J., Chen, C.Y., Lau, H.C.P., Maloof, A.C., and Latychev, K. (2019) Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States. <em>Earth and Planetary Science Letters</em>. doi: 10.1016/j.epsl.2019.116006</p> <p><strong>Link to publication: </strong><a href="https://doi.org/10.1016/j.epsl.2019.116006">https://doi.org/10.1016/j.epsl.2019.116006</a></p> <p><strong>Suggested citation: </strong>Please reference the associated publication above when using any datasets or materials described in the README file.</p> <p><strong>Contact information:</strong> Jacky Austermann (jackya@ldeo.columbia.edu) and Christine Y. Chen (cychen.earth@gmail.com)</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This directory contains the following datasets:</p> <p>SHORELINE FEATURE ELEVATION DATA</p> <ul> <li><strong>Bonneville_Provo_Sehoo_shoreline_feature_elev_Austermann2019_EPSL.xlsx</strong>: shoreline feature elevation measurements of the Bonneville, Provo, and Sehoo lake stages of Lake Bonneville and Lake Lahontan; original measurements were made by Adams et al. (1999), Chen and Maloof (2017), and Currey (1982)</li> </ul> <p>MODELED RECONSTRUCTIONS OF LAKE VOLUME AND PALEOTOPOGRAPHY</p> <ul> <li><strong>LakeBonneville_NAICE_l20.ump02p25.lmp5VM5.mat:</strong> model output for Lake Bonneville, including reconstructions of lake volume and paleotopography</li> <li><strong>LakeLahontan_NAICE_l20.ump02p25.lmp5VM5.mat</strong>: model output for Lake Lahontan, including reconstructions of lake volume and paleotopography</li> </ul>
ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009. </p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km<sup>-1</sup>) data (Hubbert and Bringi 1995). </p> <p> </p> <p>ARMOR file naming conventions are as follows: </p> <p> </p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates a full or sector volume plan position indicator </p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p> </p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format </p> <p> </p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup> value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). </p> <p> </p> <p>Hourly file naming conventions are as follows:</p> <p> </p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>Acknowledgments: </p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p>
Simulated river flow and temperature in regulated river systems in the southeastern United States
<p>Streamflow and stream temperature are important water resources variables, and regional-scale simulations are essential for water resources management and multi-sector assessment for large regions, e.g., regional ecological assessment and power system planning. In large-scale stream temperature modeling practices, reservoir thermal stratification is mostly ignored. We have synthesized a process-based modeling approach, consisting of a series of established models, to simulate streamflow and stream temperature for a complicated river-reservoir system, which explicitly considers thermal stratification. This approach consists of a large-scale, spatially-distributed hydrological model (Variable Infiltration Capacity or VIC; Liang et al., 1994; Hamman et al., 2018), a river routing model (Model for Scale Adaptive River Transport or MOSART; Li et al., 2013), coupled to a spatially-distributed water management model (WM; Voisin et al., 2013, 2017), and a stream temperature model (River Basin Model or RBM; Yearsley, 2009; 2012) that includes a two-layer reservoir thermal stratification module (2L; Niemeyer et al., 2018). To generate this dataset, we applied this modeling approach at a temporal resolution of 1 day and a spatial resolution of 1/8º to river systems in the southeastern United States that include 271 major reservoirs. We used an ensemble of downscaled meteorological forcing data from 20 global climate models (GCM) based on RCP8.5 to simulate potential climate change impacts. This dataset includes simulated river flow and temperatures for both historical (1980-2009; 1980s) and future periods (2070-2099; 2080s). The simulations for the 1980s are based on the gridMet data set (Abatzolglou, 2013), which also forms the basis for the statistical downscaling that is applied to each of the climate models. All simulations for the 2080s are based on downscaled climate model outputs. This dataset includes streamflow and stream temperature using both unregulated and regulated model setups to quantify the impacts of reservoir regulations. The unregulated setup does not account for withdrawals and impoundments in the river system. The stream temperature in the unregulated model setups is constant in each river cross section. In the regulated setup, we explicitly considered reservoir regulation, thermal stratification, and water withdrawal. For a more detailed description of the model configuration, please see Cheng et al. (2020).</p> <p> </p> <p>File structure and filenames: The archive includes two directories, named “streamflow/” and “stream_temperature/”, which contain model output for streamflow and stream temperature, respectively. Within each directory, subdirectories named “regulated/” and “unregulated/” contain model output for the regulated and unregulated model setups, respectively. All data files are in netCDF format and provide model outputs at a temporal resolution of 1 day and a spatial resolution of 1/8º. The unit for streamflow is m<sup>3</sup>/s and the unit for stream temperature is °C.</p> <p> </p> <p>Files are constructed as follows:</p> <p>SERC.<climate simulation>.RCP85.<model setup>.<variable>.nc</p> <p>where</p> <ul> <li><climate simulation> is either ‘historical’ for the simulation that represents the 1980s or an abbreviation that indicates the climate model for the simulations that represent the 2080s. The abbreviations for the climate models are shown in column 1 in the Table below.</li> <li><model setup> is either ‘regulated’ or ‘unregulated’ for the regulated and unregulated model setups, respectively.</li> <li><variable> is either ‘streamflow’ or ‘stream_temperature’ for streamflow and stream temperature, respectively.</li> </ul> <p>More details please see README.pdf</p>
ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below. </p> <p> </p> <p>The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004). Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi<sup>2</sup> value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset. </p> <p> </p> <p>The hourly fine naming conventions are as follows for the February 2008 data:</p> <p>LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz</p> <p>LMA_NA: LMA file designator corresponding to the NALMA</p> <p>2008-02-06: year (YYYY)-month (MM)-day (DD)</p> <p>10-00-00: UTC time, (HH)-minute (MM)-second (SS)</p> <p> </p> <p>And for the April 2008 data:</p> <p>LYLOUT_080411_180000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>080411: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>180000: UTC time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km<sup>-1</sup>) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).</p> <p> </p> <p>Acknowledgments: </p> <p>NALMA data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p> <p>Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. <em>Geophysical Research Letters</em>, <em>28</em>(1), 143–146. https://doi.org/10.1029/2000GL011464</p> <p> </p>
Environmental and Mineralogical Characteristics of Tourmaline-bearing localities in the United States
<p><strong>Background</strong></p> <p>This dataset contains the compiled mineralogical records of 458 tourmaline-bearing sites in the United States. Environmental parameters associated with each locality such as surface temperature, precipitation, and geothermal gradient have also been recorded. Geological processes were identified for each locality using site and mineral descriptions from mineral databases and they were also interpreted using machine learning algorithms. Market basket analysis algorithms were utilized to identify patterns within the mineralogical data and these patterns were attributed to the geological processes listed in the dataset.</p> <p><strong>Data Sources</strong></p> <p>Mineral occurrence data were retrieved from Mindat.org for the tourmaline minerals schorl, dravite, elbaite, uvite, and foitite. Mineral descriptions listed in the Handbook of Mineralogy (Anthony et al., 2004) web database aided in the identification of different geological processes. Geothermal gradient data are interpolated from values listed in geothermal temperature studies conducted by Batir et al. (2013), Kron and Stix (1982), and Nathenson and Guffanti (1987). Elevation data were retrieved from the USGS Elevation Point Query Service and the climatic data from the WorldClim dataset. Köppen-Geiger climatic zones were assigned to each locality using data published by Kottek et al. (2006) and made available at http://koeppen-geiger.vu-wien.ac.at/</p> <p><strong>Version Two</strong></p> <p>The second version of this dataset (TourmalineLocalitiesV2.csv) contains the initial tourmaline-bearing sites retrieved from the Mindat mineral database plus 389 randomly generated test localities. The randomly generated locations have all of the same parameters as the initial dataset with the mineralogy being extracted from mineral surface models. Natural neighbor interpolation was used to generate surface models for each individual mineral in the dataset and the mineralogy for each test locality was extracted from the models. The first version of the dataset also did not have coordinates for each site and those have been added in this version. </p>
A 30m Topographic Wetness Index Dataset for the Continental United States
<p>The topographic wetness index was computed at a 30m resolution for the continental United States. This dataset was computed using the triangular multiple flow direction algorithm for computing upslope areas (see https://doi.org/10.1029/2006WR005128). We used the Shuttle Radar Topography Mission (SRTM; https://doi.org/10.1029/2005RG000183) digital elevation model (DEM) for all computations. This analysis was conducted in the R programming language using the System for Automated Geoscientific Analyses (SAGA; https://doi.org/10.5194/gmd-8-1991-2015) for back end computations. This dataset is in the NAD83(NSRS2007) / Conus Albers projection system, EPSG 5072.</p>
UWSCatCH: Urban Water Supply Catchment Contributions and Hydrological Statistics for large cities of the conterminous United States.
<p>UWSCatCH extends and enhances the Urban Water Blueprint (McDonald et al., 2014) for a selection of 116 cities (population > 150,000) and their associated surface water supply catchments in the conterminous United States. The two major enhancements to the Urban Water Blueprint are: [1] estimates of the relative contributions of each surface water catchment to each city's average water supply (as well as updated estimates of any contributions from groundwater); [2] NHDplusV2 reach codes for each water supply intake stream location and associated average flow estimates (regulated and unregulated) (local upstream USGS gage IDs are also provided). UWSCatCH also features a raster file with spatially distributed (1/24° grid) runoff (average of 1980 - 2012 reanalysis simulation) which is masked to watershed polygons (included as a shapefile) to explore spatial distribution of average runoff generation affecting each city. UWSCatCH is designed for use in the R package "gamut" (https://github.com/IMMM-SFA/gamut), and may be applied in a variety of regional and national scale research studies concerning drinking water supply to major US cities.</p>
USHighPM₂.₅: Daily Seamless 1 km Ground-Level PM₂.₅ Dataset for the United States (2000–Present)
<p>USHighPM<sub>2.5</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for the United States (i.e., USHighAirPollutants, USHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>2.5</sub> dataset for the United States from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.92 and a normalized root-mean-square error (NRMSE) of 0.4 on a daily basis.</p> <p>If you use the USHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., LPH, 2023):</p> <ul> <li>Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. <a href="https://weijing-rs.github.io/publications/Wei_et_al-LPH-2023.pdf">Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study</a>. <em>The Lancet Planetary Health</em>, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8</li> </ul> <p><strong>More USHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous United States
<p>Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.</p> <p>Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022). </p> <p><strong>At-a-station Hydraulic Geometry</strong></p> <p>We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:</p> <p>\(w={aQ^b}\)</p> <p>\(d=cQ^f\)</p> <p>\(v=kQ^m\)</p> <p>where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:</p> <ol> <li>We considered only measurements which reported Q, v, w and A.</li> <li>For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.</li> <li>We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q\neq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.</li> <li>Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables. <ol> <li>We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.</li> <li>Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.</li> <li>Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).</li> </ol> </li> <li>If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.</li> </ol> <p>Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.</p> <p><strong>At-a-station Manning's n</strong></p> <p>We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by</p> <p>\(n = {{R^{2/3}S^{1/2}} \over v}\)</p> <p>where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10<sup>-5</sup> m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q<sub>50</sub>; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10<span>—</span>which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.</p> <p><strong>Daily discharge percentiles</strong></p> <p>We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:</p> <ol> <li>For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.</li> <li>A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.</li> </ol> <p>We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q<sub>1</sub> and Q<sub>5</sub>, increments of 5% (e.g. Q<sub>10</sub>, Q<sub>15</sub>, Q<sub>20</sub>, etc.) between Q<sub>5</sub> and Q<sub>95</sub>, increments of 1% between Q<sub>95</sub> and Q<sub>99</sub>, and increments of 0.1% between Q<sub>99</sub> and Q<sub>100</sub> in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.</p> <p><strong>HG Regionalization</strong></p> <p>We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:</p> <p>\(log(Q_i) = \beta_1log(DA) + \beta_0\)</p> <p>where Q<sub>i</sub> is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r<sup>2</sup> value of the regression relationship for Q percentiles Q<sub>10</sub>, Q<sub>25</sub>, Q<sub>50</sub>, Q<sub>75</sub>, Q<sub>90</sub>, Q<sub>95</sub>, Q<sub>99</sub>, and Q<sub>99.9</sub>. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).</p> <p><strong>Dataset description</strong></p> <p>We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r<sup>2</sup> values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.</p> <p> </p> <p><strong>Errata</strong></p> <p>In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.</p>
Fall advances in the timing of molt in birds in the southwestern United States
<p><span>Molt is a critical event in the annual cycle of birds. Although we know an increasing amount about the impacts of climate change on the timing of other avian events</span><span>, there has been relatively limited work conducted on changes in molt phenology over time. In this study, we utilized a thirteen-year bird banding dataset from southeastern Utah to examine long-term trends in the molt timing of body and flight feathers during both the spring and fall migratory seasons, accounting for temporal trends in non-molting birds and how trends may vary between different sexes and ages of birds. We found that there were no significant temporal trends in molt timing in the spring but there were significant trends in the fall, such that birds were advancing the timing of their body and flight feather molt over time. Finally, we highlight the significant influence of climate on molt phenology: El Niño/Southern Oscillation and maximum temperature were both associated with advances in spring body molt, maximum temperature was associated with delays in fall flight feather molt timing, and precipitation was associated with advances in both fall body and flight feather molt timing. This study provides the first examination of long-term trends in the molt phenology of North American birds, showing that, over the past decade, birds in the western United States are advancing their feather molt timing in the fall at a rate of roughly 1 day/year.</span></p>
Point and Nonpoint Proportion of Potentially Contaminated Supply (PPCS) for 116 United States cities
<p>Point and nonpoint PPCS metrics (and additional metrics) computed for 116 United States cities using gamut (Geospatial Analytics for Multisectoral Urban Teleconnections)---<a href="https://doi.org/10.5281/zenodo.5590217">https://doi.org/10.5281/zenodo.5590217</a>.</p> <p>These results are described in the following publication:</p> <p>Turner, S.W.D., Rice, J., Nelson, K., Vernon, C., McManamay, R., Dickson, K., and Marston, L. (accepted manuscript) Comparison of potential drinking water source contamination across one hundred U.S. cities. <em>Nature Communications</em>.</p> <p> </p>
United States tornado reports in landfalling tropical cyclones used in Paredes et al. (2021)
<p>These data include all tropical cyclone tornado reports used in Paredes et al. (2021) plus an additional year (e.g., 2020). These data will not be updated regularly. For the latest version, users should refer to https://www.spc.noaa.gov/misc/edwards/TCTOR/ or contact roger.edwards@noaa.gov.<br> <br> Each specific tropical cyclone tornado record has been extracted from the broader Storm Prediction Center tornado database, for all Atlantic and Gulf of Mexico tropical cyclones to affect the continental United States from 1995–2020. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. Unlike other climatologies previously published in the literature, the chosen time period for this examination essentially covers only the full national deployment of the WSR-88D radar network in the United States. This permits consistent comparisons of a very large sample size of tropical cyclone tornado events (>1600) during the era of modernized National Weather Service warning and verification practices.</p>
19th Century United States Newspaper images predicted as Photographs with labels for "human", "animal", "human-structure" and "landscape"
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (<a href="https://chroniclingamerica.loc.gov/">chroniclingamerica.loc.gov/</a>). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'photographs'. This dataset contains a sample of these images with additional labels indicating if the photograph has one or more of the following labels: "human", "animal", "human-structure" and "landscape"</p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in `images.zip`</li> <li>`newspaper-navigator-sample-metadata.csv` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>`multi_label.csv` contains the labels for the images as a CSV file</li> <li>`annotations.csv` conains the labels for the images with additional metadata</li> </ul> <p>This dataset was created for use in an under-review Programming Historian tutorial (<a href="http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2">http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2</a>) The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material. The data is shared here since it may be useful for others. <strong>This data documentation is a work in progress and will be updated when the Programming Historian tutorial is released publicly. </strong></p> <p>The metadata CSV file contains the following columns:</p> <p>- filepath<br> - pub_date<br> - page_seq_num<br> - edition_seq_num<br> - batch<br> - lccn<br> - box<br> - score<br> - ocr<br> - place_of_publication<br> - geographic_coverage<br> - name<br> - publisher<br> - url<br> - page_url<br> - month<br> - year<br> - iiif_url</p>
A collection of fully-annotated soundscape recordings from the Western United States
<p>This collection contains 33 hour-long soundscape recordings, which have been annotated with 20,147 bounding box labels for 56 different bird species from the Western United States. The data were recorded in 2018 in the Sierra Nevada, California, USA. This collection has partially been featured as test data in the 2021 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>Measuring the effects of forest management activities in the Sierra Nevada, California, USA can reveal a potential correlation with avian population density and diversity. For this dataset, passive acoustic surveys were conducted in the Lassen and Plumas National Forests in May-August 2018. Survey grid cells (4 km<sup>2</sup>) were randomly selected from a 6,000-km<sup>2</sup> area, and SWIFT recording units were deployed at locations conducive to sound propagation (e.g., ridges rather than gullies) within those cells. The sensitivity of the used microphones was -44 (+/-3) dB re 1 V/Pa. The microphone's frequency response was not measured, but is assumed to be flat (+/- 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 38 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of -/+ 0.9 V. Recording units recorded continuously 17:00 - 23:59, 0:00 - 10:00, one-hour files were stored as uncompressed WAVE sampled at 32 kHz and later converted to FLAC. Parts of this dataset have previously been used in the 2021 BirdCLEF competition.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>We subsampled data for this collection by selecting locations that spanned the full elevational and latitudinal gradients of our study area (~840 – 1700 m asl and 39.41 – 40.71°N), and thus represent a broad range of plant communities. A single annotator boxed every bird call he could recognize, ignoring those that are too faint or unidentifiable. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. The annotator was allowed to combine multiple consecutive calls of one species into one bounding box label if pauses between calls were shorter than five seconds. We use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list).</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date and timestamp in PDT. As an example, the file “SNE_001_20180509_050002.flac” has sequential ID 001 and was recorded on May 9th 2018 at 05:00:02 PDT. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements </strong></p> <p>The collection and annotation of this dataset was funded by the U.S. Forest Service Region 5 and the California Department of Fish and Wildlife.</p>
A collection of fully-annotated soundscape recordings from the Northeastern United States
<p>This collection contains 285 hour-long soundscape recordings, which have been annotated by expert ornithologists who provided 50,760 bounding box labels for 81 different bird species from the Northeastern USA. The data were recorded in 2017 in the Sapsucker Woods bird sanctuary in Ithaca, NY, USA. This collection has (partially) been featured as test data in the 2019, 2020 and 2021 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>As part of the Sapsucker Woods Acoustic Monitoring Project (SWAMP), the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology deployed 30 first-generation SWIFT recorders in the surrounding bird sanctuary area in Ithaca, NY, USA. The sensitivity of the used microphones was -44 (+/-3) dB re 1 V/Pa. The microphone's frequency response was not measured, but is assumed to be flat (+/- 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 33 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of -/+ 0.9 V. This ongoing study aims to investigate the vocal activity patterns and seasonally changing diversity of local bird species. The data are also used to assess the impact of noise pollution on the behavior of birds. Recordings were recorded 24 h/day in 1-hour uncompressed WAVE files at 48 kHz, converted to FLAC and resampled to 32 kHz for this collection. Parts of this dataset have previously been used in the 2019, 2020 and 2021 BirdCLEF competition.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>We subsampled data for this collection by randomly selecting one 1-hour file from one of the 30 different recording units for each hour of one day per week between Feb and Aug 2017. For this collection, we excluded recordings that were shorter than one hour or did not contain a bird vocalization. Annotators were asked to box every bird call they could recognize, ignoring those that are too faint. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. Annotators were allowed to combine multiple consecutive calls of one species into one bounding box label if pauses between calls were shorter than five seconds. We use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list).</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date, and timestamp in UTC. As an example, the file “SSW_001_20170225_010000Z.flac” has sequential ID 001 and was recorded on Feb 25th, 2017 at 01:00:00 UTC. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz, and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. Unidentifiable calls have been marked with “????” and are included in the ground truth annotations. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements </strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection (individual contributors in alphabetic order): Jessie Barry, Sarah Dzielski, Cullen Hanks, W. Alexander Hopping, Robert Koch, Jim Lowe, Jay McGowan, Ashik Rahaman, Yu Shiu, Laurel Symes, and Matt Young. </p> <p><strong>Version history</strong></p> <p>Version 2: Unidentifiable calls have been marked with “????” and added as bounding box labels to the ground truth annotations.<br> Version 1: Initial release.</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.