cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.267e083484a74866927d91bdef60f8d6
title: USU Great Salt Lake Mass Balance Model Data Collection

description: This resource includes data and model code for a Great Salt Lake mass balance model developed at Utah State University (the USU-MBM) as part of a study titled "Modeling Great Salt Lake Water Levels and Salinities to Capture Current Adaptive Management Actions". The model predicts water surface elevation and salinity of the north section, south section upper brine layer and south section deep brine layer from January 2017 until September 2023. It also predicts UPRR causeway breach bi-directional flow exchange. Datasets include model testing data, forcing data, mineral extraction withdrawal, UPRR causeway breach geometry, WSE/Area/Volume tables for the north and south sections of the lake, and water activity coefficient tables for use in compiling the model.
DOI: 10.4211/hs.b5bcc8ea9e2146e892a5390d9c083d15
title: Multi-scale thermal mapping of submarine groundwater discharge in coastal ecosystems of a volcanic area

description: This resource contains thermal datasets from four different techniques applied in the Philippines: satellite thermal remote sensing, unmanned aerial systems (drones) thermal remote sensing, distributed temperature sensing, and point sensing through thermistors. The data accompanies the manuscript "Multi-scale thermal mapping of submarine groundwater discharge in coastal ecosystems of a volcanic area" by: Ebony Williams, Christopher Kratt, Raymond Rodolfo, Mark Lapus, Ryan Lardizabal, Aya Shika Bangun, Amber Nguyen, Scott Tyler, and M. Bayani Cardenas.
DOI: 10.4211/hs.703cf05242f7455c8dfb072dd072c962
title: AIMS_SOP_waterQualSensorInstall

description: The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams.  An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM  
This protocol will describe the process of installing the water quality sensors (YSI EXO2 and s::can spectro::lyser) in the field. The installation and use of these sensors is for multi-year time series data (Approach 1).
DOI: 10.4211/hs.c738c05278a34bc9848dd14d61cffab9
title: AORC atmospheric forcing data across CAMELS US basins, 1980 - 2024

description: These data are atmospheric forcings from NOAA's Analysis of Record for Calibration (AORC 2024) summarized over the CAMELS catchments (Addor et al. 2017), however, the delineations over those CAMELS basins were updated by Andy Wood (2024) using the basin boundaries produced for the USGS GagesII effort (Falcone 2011). 

Data collected using this repository code: https://github.com/jmframe/CIROH_DL_NextGen/tree/main/forcing_prep, which should be somewhat derivative of this: https://github.com/CIROH-UA/ngen-datastream/tree/main/forcingprocessor.

There are eight variables representing the meteorological conditions:
Total Precipitaion (APCP_surface) - Hourly total precipitation (kgm-2 or mm) for Calibration (AORC) dataset
Air Temperature (TMP_2maboveground) - Temperature (at 2 m above-ground-level (AGL)) (K)
Specific Humidity (SPFH_2maboveground) - Specific humidity (at 2 m AGL) (g g-1)
Downward Long-Wave Radiation Flux (DLWRF_surface) - longwave (infrared) radiation flux (at the surface) (W m-2)
Downward Short-Wave Radiation Flux (DSWRF_surface) - Downward shortwave (solar) radiation flux (at the surface) (W m-2)
Pressure (PRES_surface) - Air pressure (at the surface) (Pa)
U-Component of Wind (UGRD_10maboveground) - (west-east) - components of the wind (at 10 m AGL) (m s-1)
V-Component of Wind (VGRD_10maboveground) - (south-north) - components of the wind (at 10 m AGL) (m s-1)

The data include a timestamp from Coordinated Universal Time (UTC; AORC 2021).

References:
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
AORC. 2021. Analysis of Record for Calibration Version 1.1 - Sources, Methods, and Verification, National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS), Office of Water Prediction (OWP), Silver Spring, MD.https://www.weather.gov/media/owp/operations/aorc_v1_1_methods.pd
AORC. 2024. NOAA Analysis of Record for Calibration (AORC) Dataset was accessed July 2024 from https://registry.opendata.aws/noaa-nws-aorc. 
Falcone, J., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow: U.S. Geological Survey data release, https://doi.org/10.5066/P96CPHOT. https://www.sciencebase.gov/catalog/item/631405bbd34e36012efa304a
Wood. 2024. CAMELS basins delineation.
DOI: 10.4211/hs.5ee190b481c749fb8398f182742720f1
title: Acoustic Doppler Current Profiler (ADCP) data collected near Erie, PA, Lake Erie, 2019

description: This dataset contains Acoustic Doppler Current Profiler (ADCP) data collected near Erie, PA, in Lake Erie during the summer of 2019. The ADCP was deployed in an upward-looking configuration from August 9 to September 9, 2019, at a depth of 5.80 meters, and it recorded 3D water velocities and temperatures. The data collection aimed to support assessments of Cladophora and benthic algae and to validate hydrodynamic models in the region. The deployment location is specified at 42.18863 degrees north latitude and -79.98218 degrees west longitude. The U.S. Environmental Protection Agency funded this project under the Great Lakes Restoration Initiative, with the U.S. Geological Survey (USGS) and Michigan State University (MSU) conducting the data collection, processing, and quality assurance. Data quality was ensured through rigorous checks, with velocity measurement accuracy at 1% of the measured value, and temperature sensor accuracy at ±0.1 ℃. This dataset provides valuable insights for environmental and hydrodynamic studies in Lake Erie, contributing to a better understanding of water flow patterns and temperature variations essential for ecological assessments and management practices.

The ADCP data was used in the following paper:
Saeed Memari, Mantha S. Phanikumar, Vishnu Boddeti, Narendra Das; Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing. Journal of Hydroinformatics 2024; jh2024110. doi: https://doi.org/10.2166/hydro.2024.110
DOI: 10.4211/hs.e47c60a00425431aafeffe9046eaf902
title: Supplementary Data for "Impact of surficial lithology on arsenic mobility in riverbanks of tidally fluctuating rivers: The Hooghly River, West Bengal, India"

description: Arsenic (As) contamination in groundwater persists in South Asia. Precipitated amorphous Fe(III)-oxides regulate
the mobilization of aqueous As and iron (Fe) within the hyporheic zone (HZ). Depending on the chemical stability
of these Fe(III)-oxides, this so-called Natural Reactive Barrier (NRB) can function as a sink or source of
aqueous As and Fe within shallow alluvial aquifers under influences of tidal and seasonal fluctuations of river
stage. The extent to which surficial lithology influences the As mobility along a riverine (upstream) to tidal
(downstream) continuum is uncertain. To explore this process along a tidally fluctuating river, two new study
sites with contrasting surface lithology were characterized along the banks of Hooghly River. The upstream
sandy riverbank aquifer experiences robust mixing with oxygen-rich surface water under influences of tidal fluctuations
which maintain oxic conditions in the riverbank aquifer. Introduced riverine dissolved oxygen (DO) drives
the in-situ precipitation of crystalline Fe(III)-oxides which remove dissolved As and Fe from groundwater before
discharging to the river. Although sediment from the downstream silt-capped riverbank contains higher concentrations
of sedimentary As and Fe compared to the sandy site, lower proportions of crystalline Fe(III)-oxide
minerals were observed. Arsenic was more easily mobilized from the aluminosilicate clay minerals to which the
As was primarily bound at the silt-capped riverbank, compared to the As bound to Fe(III)-oxides at the sandy site.
Thus, aluminosilicates can be an important source of dissolved As. These findings demonstrate that the surficial
lithology of a riverbank along a tidally and seasonally fluctuating river regulates the mobility of As and its mineralogical
association within riverbank sediments in shallow riverbank aquifers.
DOI: 10.4211/hs.1995f35ab6d64f3aa4aee974e830444d
title: MICROPLASTICS IN GROUNDWATER OF SAN DIEGO XOCOYUCAN AND IXTACUIXTLA DE MARIANO MATAMOROS, TLAXCALA, MEXICO.

description: The present study evaluated the amount and types of microplastics (MP) in ten wells where drinking water is extracted for human consumption and domestic activities of two rural communities of the State of Tlaxcala: San Diego Xocoyucan and Ixtacuixtla, Mariano de Matamoros, México. For groundwater sampling, the possible aerial deposition of microplastics was considered; in each well, two samples were taken, one of surface water (from the first 5 cm of depth) and another of water without contact with air; the last one was obtained with a fountain pump submerged into the deepest part of the wells. In both cases, samples were filtered in situ with a number 35 sieve. The average abundance of microplastics was 0.34 MP/L, and there was no difference between the communities. The MPs measured 2,100 to 4,400 μm in length, averaging 2,995 ± 437. 100% of the MP found were fragments. Regarding color abundance, black > white > blue > green > gray coincided in both communities. The polymers found were nylon, High Density Poly Ethylene (HDPE),  polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), polyethylene terephthalate (PET). Seasonally, there were no differences in the abundance of MP by aerial deposition. However, the groundwater deep in the wells presented a statistical difference with the highest abundance in the dry season (Ixtacuixtla 0.49 MP/L; Xocoyucan 0.47 MP/L), which indicates that rainwater dilutes this pollution (Ixtacuixtla 0.36 MP/L; Xocoyucan 0.34 MP/L). The communities of San Diego Xocoyucan, Tlaxcala and Ixtacuixtla de Mariano Matamoros, Tlaxcala, generate domestic, agricultural and industrial waste. Even more, there is no efficient management of urban solid waste, as well as wastewater; All of the above generates the state of plastic pollution in the region and therefore its groundwater.
DOI: 10.4211/hs.47168b0fcecb4e04ae70329cd5c2b4ec
title: Supporting Data and Code for "Evaluating the U.S. National Water Model Retrospective Evapotranspiration Simulation using Eddy-Covariance Flux Tower Measurements"

description: This HydroShare resource contains the data, Python scripts, and documentation used in the study “Evaluating the U.S. National Water Model Retrospective Evapotranspiration Simulation using Eddy-Covariance Flux Tower Measurements” (Chapagain et al., 2025, Journal of Hydrology: Regional Studies).The study evaluates National Water Model (NWM) evapotranspiration (ET) estimates across the contiguous United States (CONUS) using multi-year comparisons with AmeriFlux tower observations at 72 sites spanning diverse hydroclimatic settings.

The analysis assesses NWM ET performance at the site level and across categorical groupings based on National Weather Service River Forecast Centers (RFCs), Köppen–Geiger climate zones, and land cover types. It also examines temperature forcing accuracy and contrasts ET behavior under water- and energy-limited (P/PET) regimes.

Results reveal wide performance variability, with stronger agreement in cold climates and forested basins and lower skill in arid and agricultural regions. ET estimates tend to perform better under water-limited than energy-limited conditions, while temperature forcings show strong agreement with observations.

The resource includes:

- Preprocessed NWM and AmeriFlux ET and meteorological datasets.
- Python scripts for computing performance metrics (e.g., Scaled KGE, PBIAS).
- Group-based and regime-based analysis notebooks.
- Visualization workflows reproducing key figures from the paper.
- ArcGIS files for site-location mapping.

Most analyses were performed in Python, with ArcGIS Pro was used for spatial processing and visualization. These materials enable reproducibility of the paper’s results and facilitate further analysis of NWM ET performance across CONUS.
DOI: 10.4211/hs.0c2f63b1bcec41d8841df522ce7b0b76
title: A Simple Model of Flow Reversals in Florida's Karst Springs

description: This resource contains Matlab code and input data for a simple model of flow reversals in Florida's karst springs used in: Klammler H, Jawitz JW, Cohen MJ. 2024. A simple model for flow reversals in Florida's karst springs. Water Resources Research, in press.
DOI: 10.4211/hs.683db72af79b4c8cb756207da90efa1a
title: Reference Berms in Selected Rangeland Sites in Arizona and Colorado

description: This dataset includes reference berms in selected rangeland study sites in Arizona and Colorado. The berms were manually identified from 1 m lidar DEMs collected during 2015-2020. Berm centerlines were provided as polylines stored as ESRI shapefiles. A berm made of multiple segments is considered as a single berm. Only berms taller than 0.6 m are included in this dataset.
DOI: 10.4211/hs.266099a29929423e9cec8bdb886b6a21
title: ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks

description: ChemLotUS contains water quality information in rivers and streams carefully curated and linked to a high resolution stream network. The dataset covers the contiguous United States (CONUS) with nearly 35 million records in about 290,000 sites. 11 water constituents are included so far: Calcium (Ca), Electrical Conductivity (EC), pH, Total Suspended Solids (TSS), Turbidity (Tu), Total Organic Carbon (TOC), Dissolved Oxygen (DO), Chlorophyll a (Chl-a), Nitrate (NO3), Soluble Reactive P (SRP), and Total P (TP). Raw water quality data comes from the Water Quality Portal (WQP - https://www.waterqualitydata.us/), and network information comes from the National Hydrography Dataset High Resolution (NHD-HR - https://www.usgs.gov/national-hydrography/nhdplus-high-resolution).

This repository contains the data, as well as the code developed to obtain it, according to the readme file.

More details about the dataset are explained in the research paper with the same name "ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks", currently submitted to peer review
DOI: 10.4211/hs.8da6ebf2ee9a491490bb09a6349e70fe
title: Estimation of CONUS stream water quality using LSTM and WRTDS

description: This dataset offers a comprehensive collection of water quality data for approximately 500 stations across the Continental United States (CONUS). It includes 20 common water quality parameters, along with meteorological, hydrological, and land use variables such as streamflow, precipitation, temperature, evapotranspiration, and vegetation indices. To support water quality modeling research, we provide model outputs from both conventional statistical (WRTDS) and advanced deep learning (LSTM) approaches. This dataset is designed to facilitate model development, validation, and application, and to promote reproducible research.
DOI: 10.4211/hs.2c676a5913f44c85befedfcedc6185bc
title: Kampoosa Bog - Road salt accumulation

description: Kampoosa Bog is a 465 ha wetland complex located in Stockbridge-Lee MA that is currently being impacted by road salt pollution. In an effort to understand the impacts of road salt on the water quality and vegetation in the wetland, the Mass DOT and Department of Fish and Wildlife have collaborated with engineers at the University of Massachusetts to monitor the water quality at gauge stations (KB100, KB150, KB300) within the Kampoosa watershed. At these gauge stations water flow, water temperature and specific conductance are recorded at a 15 minute interval as of Nov 2017.  Using road salt application, groundwater and surface water chemistry, and streamflow data collected between 2012 and 2019 we quantify the accumulation of road salt in the Kampoosa Bog subwatersheds.
DOI: 10.4211/hs.69c4b97915104c6d9a534ae99f8383fe
title: Water chemistry from the upper Provo River watershed, water year 2023

description: This dataset includes water chemistry from samples collected in the upper Provo River watershed, northern Utah, including river samples from Soapstone, Woodland, and Hailstone, soil water, snowpack, ephemeral streams, and groundwater. The samples were collected during the 2023 water year. The samples include concentrations on unfiltered, filtered (<0.45 micron), and filtered (<0.22 micron) fractions. The water chemistry includes field parameters, water isotopes, major ions, and trace elements. The field and lab methods are described in attached thesis (Caskey, 2024).
DOI: 10.4211/hs.41dac0a2caf24ce0924ec7fe35b27aa1
title: Hydrologic and meteorologic extremes and transitions for 4299 catchments in Europe

description: This dataset provides the data needed to reproduce the results by Brunner et al. 2025, ERL: 'Meteorological and hydrological dry-to-wet transition events are only weakly related over European catchments'. Namely, catchment shapefiles and attributes of 4299 catchments in Europe and extracted hydrologic (floods/droughts) and meteorologic extreme events (wet and dry spells) as well as their transitions.
It relies on a large-sample dataset of daily hydrological observations and catchment shapefiles compiled for 24 countries in Europe by collecting data from national agencies and existing large-sample datasets including the Global Runoff Database (GRDC, 2019), EStreams (Nascimento et al. 2024), and two datasets from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS; Addor et al. 2017) suite, namely, CAMELS-CH (Höge et al. 2023) and CAMELS-DE (Loritz et al. 2024) (see Table 1 in the Supplementary Information for an overview and data sources).
DOI: 10.4211/hs.0433d48779f94704b4e1b983f5e41da6
title: Graph-based flow and transport model and dataset for fractured granite core

description: Fractures are a primary feature controlling flow, transport, and coupled processes in geologic systems. To date, experimental image-based observations of these processes have been challenging. Here, we successfully demonstrate the use of a graph-based, laboratory-validated flow and transport model for conservative solute transport in a natural fracture. Pulse-tracer experiments with a conservative radiotracer ([18F]-FDG) spanning multiple flow regimes with simultaneous positron emission tomography (PET) imaging are used to characterize transport in a two-inch fractured Sierra granite core. Model network complexity, determined by the number of nodes and edges, significantly impacts model fit to observed data. Large graphs over-describe a fracture plane and act similarly to a porous medium while small graphs oversimplify the solute transport behavior. This work provides the first validation of graph-based flow and transport models across a range of experimental conditions and sets the groundwork for upscaling to more complex and computationally efficient fracture network models.
DOI: 10.5281/zenodo.17916008
title: fMRI ICA V1-100 Partial Corr

description: GWAS summary statistics for fMRI ICA partial corr
DOI: 10.5281/zenodo.17916009
title: fMRI ICA V1-100 Partial Corr

description: GWAS summary statistics for fMRI ICA partial corr
DOI: 10.4211/hs.a447dc8a74f44736bf3fe217c9228005
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.
DOI: 10.4211/hs.36fa9e69ff0849bb941b9ab2835b8a6e
title: Data and Code release: Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales (published in Agricultural Water Management).

description: This file releases data and code for the manuscript:

Zipper, S., Kastens, J., Foster, T., Wilson, B.B., Melton, F., Grinstead, A., Deines, J., Butler, J., Marston, L., 2024. Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales. Agricultural Water Management 303:109036. https://doi.org/10.1016/j.agwat.2024.109036

Please cite this manuscript if you use these data/code.

Manuscript abstract: 
Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6% to 4.9%, R2 = 0.53 to 0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.