cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.75a62c0ee4d942d995c96d44bae20a53
title: Spatiotemporal variability of gas transfer velocity in a tropical high-elevation stream using two independent methods

description: Streams in high-elevation tropical ecosystems known as páramos may be significant sources of carbon dioxide (CO2) to the atmosphere by transforming terrestrial carbon to gaseous CO2. Studies of these environments are scarce and estimates of CO2 fluxed are poorly constrained. In this study, we use two independent methods for measuring gas transfer velocity (k), a critical variable in the estimation of CO2 evasion and other biogeochemical processes. The first method, ‘Kinematic k600’ (k600-K), is derived from an empirical relationship between temperature-adjusted k (k600) and the physical characteristics of the stream. The second method, ‘Measured k600’ (k600-M), estimates gas transfer velocity in the stream by in situ measurements of dissolved CO2 (pCO2) and CO2 evasion to the atmosphere, adjusting for temperature. Measurements were collected throughout a 5-week period during the wet season of a peatland-stream transition within a páramo ecosystem located above 4,000m in elevation in northeastern Ecuador. We characterized the spatial heterogeneity of the 250-m reach on five occasions, and both methods showed a wide range of variability in k600 at small spatial scales. Values of k600-K ranged from 7.42 to 330 m d-1 (mean =116 ± 95.1 m d-1), whereas values of k600-M ranged from 23.5 to 444 m d-1 (mean = 121 ± 127 m d-1). Temporal variability in k600 was driven by increases in stream discharge caused by rain events, whereas spatial variability was driven by channel morphology, including stream width and slope. The two methods were in good agreement (less than 16% difference) at high and medium stream discharge (above 7.0 L s-1). However, the two methods considerably differed from one another (up to 73% difference) at low stream discharge (below 7.0 L s-1). Our study provides the first estimates of k600 values in a high elevation tropical catchment across steep environmental gradients and highlights the combined effects of hydrology and stream morphology in co-regulating gas transfer velocities in páramo streams.
DOI: 10.4211/hs.3f5bc470cd924b9494dbb429777c4a56
title: Hydrologic Implications of Seasonally Draining Lakes in the Oregon Cascades: Sensor and Planet Data

description: The hydrogeology of volcanic terrain exhibits characteristics that reflect both a legacy of volcanic construction and transient evolution of bedrock hydraulic conductivity on million year timescales. Here we study a drainage basin in the Central Oregon High Cascades in which Holocene lava flows dammed streams, creating seasonal lakes that fill with the spring snowmelt, and drain completely over the summer. The filling and draining of these lakes depends both on the volume of snowmelt and the permeability of their porous lava dams, providing a natural experiment for disentangling multiscale hydraulic properties from climate. We measure the drainage of two seasonal lakes in the watershed of permanent Clear Lake (the highest elevation permanent source of the McKenzie River) with in situ sensors and satellite remote sensing, and compare this with the larger scale summer recession of the McKenzie river at the outlet of Clear Lake. Clear Lake recession is modeled with 2 parallel linear reservoirs, which are linked to aquifer properties and imply watershed scale transmissivity that varies with spatial scale of the aquifer. We also find, using remote sensing-derived timeseries, that all three systems, but particularly the draining lakes, have responded to declining snowpack since 1990. These results suggest that seasonal variations in surface water storage encode the structure of volcanic aquifers generally, and can be used to infer groundwater dynamics in the Cascade Range.

The resource represents Hobo pressure-temperature data and Planet remote sensing data for Lost Lake and Fish Lake.
DOI: 10.15468/dl.xj9j55
title: Occurrence Download

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salix linearistipularis"
 ]
}

 The dataset includes 165 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050012-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.e9e15a82b2ea4a7b98146a99e4b52614
title: Raw Water Quality and Weather Dataset for AI-based Alum Prediction (2011–2024)

description: This dataset contains daily raw water treatment plant operational data and Oklahoma Mesonet weather data collected from 2011–2024. It includes inflow, pH, turbidity, alkalinity, alum dosage, and daily aggregated weather attributes such as TMAX, TMIN, humidity, and pressure. Data is provided in raw, pre-cleaning form for reproducibility.
DOI: 10.4211/hs.fa48c782a7d9417081e60f5a6bc27638
title: Capturing Community Voices: A Regional Survey of Tracer Hydrology across Latin America and the Caribbean

description: A voluntary online survey was coordinated between July and August 2025 as part of the regional Isotope Hydrology Seminar Series. The survey targeted 82 researchers and practitioners across Latin America and the Caribbean, with 45 completed responses (55% response rate). Respondents provided demographic details and answered 21 structured questions covering tracer sampling, water sources, isotopic and geochemical tracers, sampling frequency, modeling approaches, database size, costs, and data accessibility. Additional topics included publication outputs, repository contributions, data end-users, citizen science, policy applications, and resource needs. Responses were compiled and analyzed in R to identify common practices, critical gaps, and opportunities for strengthening tracer hydrology in the region.
DOI: 10.4211/hs.21588429f8a54c39b9ac6ab694e7e756
title: High-resolution soil water content and precipitation dataset, Meshushim Catchment, 2018–2020

description: The dataset includes high-frequency soil water content (30-minute interval) and rainfall (10-minute interval) records collected between October 2017 and July 2020 at four monitoring stations in the Meshushim Catchment, Israel. The catchment covers an area of ~160 km², ranging in elevation from –200 m to 1100 m above sea level.

Soil water content was measured using Decagon sensors: HS10 at the Meshushim Park, Katzrin, and Tznobar stations, and HS5 at the Rasania station. Sensors were installed at depths of 10 cm, 30 cm, and 50 cm below the soil surface at each station.

Rainfall data were obtained from the Israeli Meteorological Service (IMS) for the Fichman, Merom-Golan, Beit Tzaida, and Gamla stations.

The soil water content files contain values expressed as percentages, with timestamps formatted as dd-mm-yyyy hh:mm:ss. Rainfall data are provided in millimeters; intervals without rain are recorded as 0, and missing soil water content data are coded as NaN.
DOI: 10.4211/hs.fb2fb1e511f7456c8379912db441845a
title: Flood Mapping Training Samples from Sentinel-1 and HAND

description: This dataset contains pixel-level training samples used for developing and validating a deep learning (MLP) model for flood inundation mapping. Samples were derived from two sources: (1) 466 manually labeled image chips from the Sen1Floods11 dataset and (2) 1,624 image chips from an in-house dataset of 104 flood events across the continental United States (CONUS). Each sample represents one pixel, with four key variables: Sentinel-1 VV backscatter, Sentinel-1 VH backscatter, Height Above Nearest Drainage (HAND), and flood status label (0 = non-flooded, 1 = flooded), as well as several auxiliary variables: Country and Chip ID for Sen1Flood11 samples while Case ID and Clip ID for In-House samples.
DOI: 10.4211/hs.1fe9975004ce4b5097d41939afa14f84
title: National Water Model RouteLinks CSV

description: This resource contains "RouteLink" files for version 3.0.17 of the National Water Model which are used to associate feature identifiers for computational reaches to relevant metadata. These data are important for comparing NWM feature data to USGS streamflow and lake observations. The original RouteLink files are in NetCDF format and available here: https://www.nco.ncep.noaa.gov/pmb/codes/nwprod

This resource includes the files in a human-friendlier CSV format for easier use, and a machine-friendlier file in HDF5 format which contains a single pandas.DataFrame. The scripts and supporting utilities are also included for users that wish to rebuild these files. Source code is hosted here: https://github.com/jarq6c/NWM_RouteLinks
DOI: 10.4211/hs.3af5e40f1a194692aa9fade87b98b333
title: Hydrologic data compilation from headwater stream case studies for open access

description: Drying has a major impact on pattern and process in streams, particularly in small or headwater streams. Such streams that dry recurrently are called non-perennial streams and represent most of the channel length across river networks. In spite of their prevalence, non-perennial streams are vastly underrepresented in existing stream gaging networks and in maps and hydrographic datasets. However, diverse and spatially extensive datasets of surface water presence observations exist as well as recently developed mobile applications that could help fill the data gap in characterizing the spatial extent of non-perennial streams. Hydrological data from perennial and non-perennial reaches were compiled from a series of studies on headwater streams to expand available data for mapping and modeling efforts in the United States. Hydrologic data within this compilation include visually recorded observations of hydrological status (dry, isolated pools, interstitial flow, and continuous surface flow), point measurements of discharge (cubic meters per second), and logger-based measurements for the timing and duration of streamflow and drying. These data were compiled across a series of studies on headwater streams (drainage area ~2.6 km2 or less) and were used to characterize their hydrology. Hydrologic data within this compilation are organized into files based on type of hydrologic data and study area. The types of hydrologic data include visually recorded observations of hydrological status (dry, isolated pools, interstitial flow, and continuous surface flow), point measurements of discharge (cubic meters per second), and logger-based measurements for the timing and duration of streamflow and drying. The study areas included in the compilation include headwater streams in Kentucky (Robinson Forest), Illinois (Shawnee National Forest), Indiana (Hoosier National Forest), New Hampshire (Dodge Brook), New York (Balsam Lake Mountain), North Dakota (Pipestem), Ohio (Congress Run, Edgewood Preserve, Edge of Appalachia, Wayne National Forest), South Carolina (Carolina Sandhills, Sugarloaf Mountain, Sumter National Forest Enoree and Long Cane Districts), Tennessee (Big Ridge), Vermont (Hinesburg), Washington (Mt. Baker-Snoqualmie), and West Virginia (Coopers Rock). A more detailed description of the data files are included within the Data description.docx and Data Dictionary for logger data compilation.xlsx files. These datasets are also located at the United States Environmental Protection Agency data repository at: https://doi.org/10.23719/1532528
DOI: 10.4211/hs.847b15cd15524b78acd58a2a8be242c1
title: NEON Soil Preferential Flow Database

description: Preferential flow in soil important in supporting water and nutrients transportations. We derivate a preferential flow database using multi-depth soil moisture and precipitation measurements in NEON sites. The database contains water flow velocity for each precipitation event for any given sensor, site characteristics, soil characteristics at each sensor depth etc. The v1.1 expands the last version by incorporating different Ksat estimates from Rosetta as a threshold for velocity thresholding method for identifying preferential flow.  Please also be noted that sites in Alaska  {"BONA", "DEJU", "HEAL", "TOOL", "BARR" } are not recommended to use. Additionally,  "KONA"  is not include in our analysis, as it is closer to "KONZ".
DOI: 10.4211/hs.195fc9b6249f410eb248296f7e04af84
title: Stable isotope analysis of root water uptake in urban endemic plants of north-central Texas using centrifugation

description: This database compiles 414 samples of thoughfall, soils, roots, and stems collected over different seasons in north-central Texas (USA). It records key variables including sample type, sampling date, extracted water (EW, %), total water content (TWC, %), stable isotope compositions (δ¹⁸O, δ²H), deuterium excess (d-excess), and the associated seasonal context. The dataset enables analyses of water sources, plant–soil interactions, and hydrological processes across temporal and ecohydrological variations.
DOI: 10.4211/hs.821a185b2b0b4f8eb920102d1a93171d
title: Peconic Bay Radionuclide and Biogeochemical Data Aug. and Dec. 2023

description: This dataset contains radionuclide and biogeochemical data from Peconic Bay, New York during August 2023 and December 2023. Data includes subtidal/intertidal groundwater, river water, surface water, in-situ physicochemical parameters, radionuclides (223Ra, 224Ra, 226Ra, 228Ra, 222Rn), nutrients (NOx-N, NH4-N, PO4-P, SiO4-Si), and DOC.
DOI: 10.4211/hs.a36c7009ee784d10a23c135b56ee5691
title: Eastern Shore of Virginia Radionuclide and Biogeochemical Data June 2023

description: This dataset contains radionuclide and biogeochemical data from the coastal bays of the Eastern Shore of Virginia from June 2023. Data includes subtidal/intertidal groundwater, creek water, surface water, in-situ physicochemical parameters, radionuclides (223Ra, 224Ra, 226Ra, 228Ra), nutrients (NOx-N, NH4-N, PO4-P, SiO4-Si), and DOC.
DOI: 10.4211/hs.c2bebfeeda8240a2b3711a3432f38a45
title: Hierarchical Classification of the Hydrologic landscapes of the North American Continent (shapefile - v0)

description: NOTE: The shapefile in this resource represents a work in progress. 

This shapefile shows a hierarchical classification of the North American continent into hydrologically meaningful regions. The classification consists of 5 broad domains, subdivided further into 35 hydrologic provinces. The province outlines are derived from existing polygons that outline the Level III Eco-Regions of North America.  The hydrologic domains are color coded (north = blue, west = red, central = yellow, east = green, island = purple); the provinces are named within the shapefile and shown in a shade representative of their domain. A ready-to-use image is provided in the PDF file. The boundaries of the provinces were derived through multiple community workshops and expert consultations. This process, the reasoning for drawing the boundaries where they are, and perceptual models of hydrologic behavior for each province will be described in an upcoming set of publications. 

The files in this resource are provided here to make it possible to start using this classification, with the understanding that this is a work-in-progress. We ask users to check for a final set of citations in due course.

This research was supported by the Cooperative Institute for Research to Operations in Hydrology (CIROH) with funding under award NA22NWS4320003 from the NOAA Cooperative Institute Program. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the opinions of NOAA.
DOI: 10.4211/hs.ddc0b646cb314142a2586421d584d17f
title: Upper White Stream Monitoring -- Prairie Creek Reservoir

description: The headwater region of the White River is the leading contributor of nitrogen to the Gulf of Mexico across the entire Mississippi River Basin. Recent studies from Ball State University (BSU) and conservation partners, e.g., TNC, have determined that there is inadequate flow data from the region, making it difficult to calibrate hydrological models and support detailed conservation practices. As a result, BSU and TNC contracted with the Ecosystems Connections Institute (ECI), and installed gauging stations at the Prairie Creek Reservoir and immediately downstream. Instrumentation was conducted in August - September 2021 by ECI, with permission/support from the Indiana American Water. Hourly data of water level, water temperature, discharge, and turbidity is monitored since 2021. 
Preliminary analysis has been conducted at Ball State University. The authors welcome discussions and collaborations regarding the usage and/or analysis of the dataset.
DOI: 10.5281/zenodo.17916322
title: openfe_analysis_test_data

description: data migrated from figshare:

openfe_analysis_simulation_output:



migrated from https://figshare.com/articles/dataset/OpenFE_1_3_0beta_example_results_files/28263203

500ps complex simulation output


openfe_analysis_skipped:



migrated from https://figshare.com/articles/dataset/OpenFE_1_3_0beta_example_results_files/28263203





Relative free energy of a TYK2 benchmark system transformation using a beta release of OpenFE 1.3.0. Positions are written to file (simulation.nc) once every 100 frames, and velocities are never written.
DOI: 10.4211/hs.94a65edc37c943e490a25347a3744a16
title: USGS Stream Gauge 0204295505 at Little Neck Creek, Virginia Beach, VA, and Associated Weather Data (2016–2022) in CSV Format

description: This resource includes time series records from June 21, 2016, to the end of June 3, 2024, collected at the USGS Little Neck Creek (LNC) stream gauge (Station ID: 0204295505, Coordinates: 36.8593, -75.9845) in Virginia Beach, VA. Data were compiled to support the development of machine learning models to forecast water levels 24 to 72 hours in advance at the LNC location. These models leverage the relationship between the predicted water level and stream gauge measurements, as well as weather data retrieved via the Meteostat API for the Virginia Beach/Gatewood Park station (Coordinates: 36.8227, -76.0319). The LNC stream gauge is a sentinel monitoring site for the City of Virginia Beach, as its water height is directly correlated with flood conditions in surrounding streets. The gauge provides six-minute interval measurements of water level, wind speed/direction, and precipitation. Complementary hourly data, including air pressure, temperature, and dew point, were obtained from the Meteostat API for the same time period. No pre-processing or scaling has been applied to the dataset. The dataset includes eight variables: Date-Time, Water Level (feet), Wind Speed (mph), Wind Direction (Degrees), Precipitation (inches), Temperature (\textdegree F), Dew Point (\textdegree C), and Atmospheric Pressure (hPa).
DOI: 10.4211/hs.4d6a17a1e5834a44a1da04773a89dd54
title: Examining the role of indicators and scale in social vulnerability index construction: A comparative geospatial analysis of inductive and hierarchical models

description: Social vulnerability indices (SVIs) are tools for spatially identifying populations vulnerable to natural hazards. However, their construction involves methodological choices that can introduce epistemic uncertainty. While previous efforts have explored how construction processes influence outcomes, further validation is needed to ensure SVIs accurately capture vulnerability. This study advances validation efforts by examining how scale, both areal units (Census block groups and tracts) and geographic boundaries (state, coastal, and city), impact SVI construction and indicator behavior. We applied two indicator sets, the Centers for Disease Control (CDC) SVI and the Hazards Vulnerability and Resilience Institute SoVI, and compared across three index structures: inductive with z-score standardization, hierarchical with percentile ranking normalization, and hierarchical with z-score standardization. Using geospatial and hotspot mapping, we analyze how interactions across index model stages impact vulnerability rankings and spatial patterns. We also examine how indicators influence shifts across scales in vulnerable areas. Results show that scale and indicator selection shift spatial patterns and reshape indicators' roles in SVIs. Notably, the hierarchical structure with z-score standardization—unlike those used in the CDC SVI or SoVI—produced the most consistent rankings, hotspot identification, and indicator performance. These findings highlight the importance of scale-indicator interactions and model structure selection in SVI design.
DOI: 10.4211/hs.7434014da2094737b8fcc503c758043c
title: Nutrient loads across Fayetteville, Arkansas

description: This dataset supports a study investigating spatial and temporal controls on nutrient export across urban landscapes. With increasing urbanization globally, nutrient enrichment poses major risks to aquatic ecosystems. This research focuses on understanding nutrient export (specifically nitrate [NO₃] and soluble reactive phosphorus [SRP]) across a rapidly urbanizing region. This data includes biweekly sampling across 21 sites, in addition to storm sampling at a subset of 6 sites. In addition to nutrient export data, this data set includes geospatial analysis from ArcGIS.
DOI: 10.4211/hs.9a799ca306954c049457472e263470ae
title: Data and Code release: From Resistivity to Hydraulic Properties: Calibrating a Groundwater Flow Model by Integrating Airborne Electromagnetic and Borehole Data into a Probabilistic Multi-Texture Framework

description: This is the data and code release for the manuscript "From Resistivity to Hydraulic Properties: Calibrating a Groundwater Flow Model by Integrating Airborne Electromagnetic and Borehole Data into a Probabilistic Multi-Texture Framework" by Leland Scantlebury & Thomas Harter.

Manuscript Abstract: Airborne electromagnetic (AEM) surveys offer rapid, cost‑effective subsurface imaging, yet converting their electrical resistivity (ER) models into physically meaningful hydraulic property fields for groundwater models remains a challenge. We develop and demonstrate a data‑driven workflow for an unconsolidated sedimentary aquifer system in Scott Valley, northern California, USA, that incorporates AEM ER data with borehole logs to build and calibrate a heterogeneous groundwater-surface water model. ER and texture observations are first combined through consensus clustering into five meta‑texture classes; log‑normal ER-texture distributions derived with the Knight et al. (2018) method then transform the ER data to cell‑scale texture probabilities. These probabilities and borehole data are combined using Texture2Par to create a three‑dimensional texture model, which is translated to grid-scale hydraulic conductivity and storage via power‑law averaging. During calibration, we parameterized the ER-texture distributions, essentially allowing parameter estimation to adjust the estimated textures along the AEM flight lines. The texture‑based groundwater-surface water model attains the same high goodness‑of‑fit as the previous zonal calibration (Fort Jones streamflow NSE = 0.84; GW heads r2 = 0.98), with more geologically plausible heterogeneity and improved simulation of groundwater-driven seasonally low streamflow. The proposed ER‑to‑texture workflow provides an adaptable workflow for embedding AEM information into basin‑scale groundwater models.
DOI: 10.4211/hs.82adb5bef88f464b9c617b20759779b1
title: ReynoldsCreekCZO_2016 UW Airborne Geophysics (EM & Magnetics) Data

description: The Univ. of Wyoming Dept. of Geology and Geophysics contracted airborne EM and Magnetics data to be flown in the Reynolds Creek CZO. These are the raw data and initial reports for that effort.  Parameters for the acquisition are listed in the accompanying acquisition report by the contractor who flew the survey. An example of the AEM data are also included. Samples of the magnetics data are shown in the preliminary report. It is recommended that anyone who wishes to use the data have access to both WorkBench and Oasis software.