cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.43cacc7422cc48d89600bffa0d404c5d
title: Data for On-Road Tailpipe Characterization of Exhaust Ammonia Emissions from in-use Light-Duty Gasoline Motor Vehicles

description: Ammonia exhaust tailpipe mixing ratios (ppm) from 47 light-duty gasoline motor vehicles were quantified using a portable ECM miniPEMS over on-road Real Driving Emissions (RDE) tests. The ECM miniPEMS was also used to retrieve various parameters data from the vehicle’s OBDII port such as vehicle speed, the revolution per minute (RPM) readings, engine load percentages, air-fuel ratio, and the temperature of the three-way catalyst converters. The vehicle exhaust temperature was also measured by the ECM miniPEMS using Type K thermocouples. The RDE tests were conducted on a 5.3-mile predefined urban testing route designed using the local road network in the City of Logan, Utah. The urban testing route included residential and highway roads, uphill and downhill road segments, stop signs, traffic lights, and a school zone with a reduced speed limit. The test cycle was coded as UWRL-UDTC (The Utah Water Research Laboratory Urban Driving Test Cycle). The portable Applus Autologic 5-Gas Portable Vehicle Gas Analyzer (model 310-0220) was also used to measure tailpipe mixing ratios (ppm) of post-catalyst carbon monoxide. Both instruments were carried onboard the tested vehicles during the test, while their sensors were mounted in the tested vehicle’s engine exhaust. The vehicle test sample of 47 light-duty gasoline motor vehicles was chosen to represent the same tier-level distribution as the on-road gasoline vehicle fleet along the Wasatch Front and the Cache County located in the U.S. State of Utah. Vehicle specifications including type, make, model, model year, mileage reading, engine displacement, number of cylinders, gross vehicle weight rating (GVWR), and tailpipe diameter were also collected for all tested vehicles. Atmospheric temperature and pressure at the time of testing were also measured. All the data collected throughout the project are included in the "Content" section of this resource. The "Content" section also includes an R Jupyter notebook used to analyze collected data. The mixing ratios of exhaust gases were first converted into emission rates (mg per mile), then, many descriptive and inferential statistical analyses and correlation analyses were performed. Many plots were also generated using the R script included in the Jupyter notebook. The main outcomes of this study can be found in the article included in the "Related Resources" section of this resource.
DOI: 10.4211/hs.6f1043f3468c44f3b21d2261c37c5c3f
title: Aragvi River Basin Model

description: This resources shares the water resources planning model  for the Aragvi River Basin, located in the country of Georgia. It provides: the model file that uses the WEAP platform, the documentation of the model, the input data used for the model, and an interface built in excel. This model evaluates the water supply of Tbilisi (Georgia's capitol) under different water demand conditions. This resources is the water planning model used in the following peer-reviewed publication:
Sandoval-Solis, S., Ortiz-Partida, J.P. and Floyd, L. E. (2022). Multi-objective water planning in a poor water data region: Aragvi River Basin. J. Sustainability. (14) 6: 3649 https://doi.org/10.3390/su14063649
DOI: 10.15468/dl.z4bhba
title: Occurrence Download

description: A dataset containing 131 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Russian Federation, Korea, Republic of, Japan)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Populus koreana"
 ]
}

 The dataset includes 131 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049969-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.658c359b8c83494aac0f58145b1b04e6
title: CAMELS dataset in NetCDF/Feather formats

description: The CAMELS datasets are not provided in an ideal format and takes a bit of data processing to convert them to useful and convenient forms for geospatial analyses. So, I decided to use the beloved netcdf and feather formats to make the dataset more accessible while taking care of some small annoyances! Three data sources are available from the CAMELS dataset:
1. Observed Flow: Streamflow observations for all 671 stations.
2. Basin Geometries: Polygons representing basins' boundaries for all 671 stations.
3. Basin Attributes: 60 Basin-level attributes for all 671 stations.

Two files are available:
1. camels_attributes_v2.0.feather: Includes basin geometries and 60 basin-level attributes that are available in CAMELS.
2. camels_attrs_v2_streamflow_v1p2.nc: Includes observed flows for all 671 stations, as well as the 60 basin-level attributes. It has two dimensions (station_id and time) and 60 data variables.

Additionally, some small annoyances in the original dataset are taken care of:
1. Station names didn't have a consistent format and there were some missing commas and extra periods! Now, the names have a consistent format (title) and there is comma before the states.
2. Station IDs and HUC 02 are strings with leading zeros if needed.

The code that was used to generate the dataset can be found at https://github.com/cheginit/camels_netcdf.
DOI: 10.4211/hs.79162a679f8a4273be582561a5504f68
title: 1-km soil moisture predictions in the United States with SOMOSPIE

description: Monthly and weekly soil moisture predictions in 2010 at 1-km spatial resolution using two different modeling methods integrated in the modular SOil Moisture SPatial Inference Engine (SOMOSPIE- Rorabaugh et al. 2019) (kernel-weighted k-nearest neighbors <KKNN>, Random Forests <RF>). Data were acquired from the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product version 6.1, 0.25-degrees spatial resolution. Modeled soil moisture layers are delivered for two regions in the conterminous United States. Each region encompasses a polygon of 7.5° x 3.75° (n = 450 pixels with 30 columns and 15 rows in the native resolution of the ESA CCI Soil moisture product). Region 1 <so called West Region> consists of an area of 275,516 km2. Region 2 <so called Midwest region> consists of an area of 283,499 km2. Predicted soil moisture values were validated by means of two approaches, cross-validation using the ESA CCI estimates and independent ground-truth records from the North American Soil Moisture Database (currently known as the National Soil Moisture Network). Detailed methods and results of this dataset are described in: Llamas, R.M; Valera, Leobardo; Olaya, Paula; Taufer, Michela; Vargas, Rodrigo "Downscaling Satellite Soil Moisture based on a modular SOil Moisture SPatial Inference Engine (SOMOSPIE)", Remote Sensing (submitted).
DOI: 10.4211/hs.3597a90867424381af252c119a331692
title: WEPPcloud hydrologic model runs for undisturbed forested watersheds in the Mica Creek Experimental Watershed, ID

description: This is a collection of model runs with the WEPPcloud interface. Each resource provides the complete model runs, including data input and output.
DOI: 10.15468/dl.9mja89
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Portugal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Asclepias curassavica",
 "Year 2024-2025"
 ]
}

 The dataset includes 10 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049971-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.2e1d064c765744db9d4b7855671e641d
title: Code and Data: Development of a New Pedotransfer Function Addressing Limitations in Soil Hydraulic Models and Observations

description: The attached files include all the data and code applied in the paper 

Notion: hr in the soil hydraulic model should be -1.0e4 cm, which was written mistakenly as -1.0e3 cm in Wang et al. (2022)

Wang, Y., Zhou, J., Ma, R., Zhu, G., & Zhang, Y. (2022). Development of a New Pedotransfer Function Addressing Limitations in Soil Hydraulic Models and Observations.
 Water Resources Research, 58, e2021WR031406. https://doi.org/10.1029/2021WR031406

The developed pedotranfer function codes for predicting soil hydraulic properties of the FXW and FXW-M1 model from soil texture information are provided in the form of Matlab, Python and R. Please refer to 
PTFs_FXW_Matlab.zip and PTF_FXW&SL_Python&R.zip for detail.

The code for improving the predictions of SHPs with the existing PTFs/ or only measurements in high water potential range is provided in code_for_FXW_SHP_EXT.zip.

The readme.txt was included in each zip file.
DOI: 10.4211/hs.038d87cecbd9430c890348466e2ec913
title: UWNSG Spouter Geyser

description: Here we share data collected at Spouter Geyser and Black Sand Basin, Yellowstone National Park, including near-surface geophysics (electrical resistivity tomography, transient electromagnetics, seismic refraction, nuclear magnetic resonance, ground-penetrating radar), time-lapse geophysical (electrical resistivity tomography, transient electromagnetics), and eruption duration (temperature transducer time-series). The subfolders and data files are detailed in the included "Overview_of_Data.docx" MS Word document. 

These data construct a comprehensive, active geophysical investigation into the subsurface structure and geyser eruption dynamics at Spouter Geyser, Yellowstone National Park. The data were collected to constrain the subsurface geometry of Spouter Geyser and to characterize the subsurface structure. Additional time-lapse data highlights how the geophysical parameters change throughout the eruption cycle, informing conclusions about the eruption dynamics. A temperature transducer time-series allows the geophysical data to be correlated to the eruption cycle and provides insight into the geyser cycle duration over the past two decades.
DOI: 10.4211/hs.f2c9289de92a41f5b5ca0590bfbe4ad1
title: Calculating streamwater age using StorAge Selection functions at Dry Creek, CA

description: Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2022).
DOI: 10.4211/hs.1fe097882d76414eafea6b533c36924c
title: Dissolved gas and nutrient data from flow-through sediment core incubations conducted at two temperatures

description: We collected intact sediment cores from the Sunflower River (near Ruleville, MS), and used flow-through incubations to test the interaction of nitrate concentration, carbon availability, and temperature on nitrous oxide production and yields (as % of total denitrification). We conducted the sediment core incubations at both 15 °C and 25 °C. Cores were dosed with a range of nitrate concentrations (1-6 mg L-1) and two carbon amendments: ambient (0.5 mg L-1) and +carbon (13.5 mg L-1). We used membrane inlet mass spectrometry (MIMS) to measure nitrous oxide, dinitrogen gas, and oxygen concentrations. We used the cadmium reduction method on a Lachat Quickchem Analyzer to measure nitrate concentrations and used the combustion catalytic oxidation method on a Shimadzu TOC-L to measure DOC concentrations.
DOI: 10.4211/hs.e46d4769a8a346fcaed7a27fcceb20ad
title: Supporting Dataset to accompany "Freshwater Unionid Mussels Threatened by Predation of Round Goby (Neogobius melanostomus)"

description: We provide supporting information to accompany the manuscript "Freshwater Unionid Mussels Threatened by Predation of Round Goby (Neogobius melanostomus)," published in Scientific Reports (see citation below). In the study, we consider the propensity of Round Goby to prey upon indigenous freshwater mussel species. First, we conducted lab experiments where Round Gobies were given the opportunity to feed on juvenile unionid mussels and macroinvertebrates, revealing rates and preferences of consumption. Here, we present information about the laboratory stream table setup and results.  Second, we investigated Round Gobies collected from their newly-invaded stream habitats in the French Creek watershed, using novel DNA metabarcoding methods to reveal the specific mussel species they consumed. Here, we provide datasets on the primers used for the amplification, samples included in the sequencing analysis, and metabarcoding gene sequences of 15 indigenous unionid mussel species. Further, we provide selected photos (e.g., of indigenous unionid mussels and invasive Round Goby) from the study. The sampling of aquatic life and experiments on animal subjects were carried out in accordance with standard research protocols. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
DOI: 10.4211/hs.773626e9b84248bc8d431da795ee1a16
title: Impacts of Alternative Hydrological and Demand Scenarios in the Upper Colorado River Basin on Lake Powell Storage

description: Existing models fail to represent future drought-like hydrologic inflow conditions in the Upper Colorado River Basin (UCRB) based on intensifying basin aridification. Hence, this study uses the Colorado River Simulation System (CRSS) model to investigate the effects of intensifying drought and changes in conservation and consumption of the UCRB on Lake Powell storage. The study also investigated the impact of linking Lake Powell’s outflow to UCRB’s hydrology using a new rule. The intensifying drought-like conditions in the URCB were simulated using the natural inflow data taken during the (2000 - 2018) drought period. The data was decreased by 20%, 35%, and 50%, respectively, portraying future intensifying drought responses to climate change. Changes in demand scenarios were simulated by changing the amounts of flow diverted from Lake Powell (increased consumption) and to Lake Powell (increased conservation). Model results were also utilized to predict the amount of time until Lake Powell storage levels reach the power pool elevation of 3490 feet. The results clearly show that under the 2016 Upper and Lower Basins Demands, the intensifying drought would greatly decrease Lake Powell storage and shorten the time until storage levels drop below the power pool elevation of 3490 feet. Additionally, having the outflow linked to the basin’s hydrology would save storage from reaching alarming levels. Saving some water as low as 5 % would stabilize the elevation. The CRSS outcomes also showed that increasing consumption in the UCRB would reduce the amount of storage in Lake Powell, whereas increasing conservation would increase the storage of Lake Powell.

See readme file for instructions on how to use this resource.
DOI: 10.4211/hs.e8efd71614214497998591661529f69c
title: Pipe dreams: the effects of stream bank soil pipes on hyporheic denitrification caused by a peak flow event

description: This is the data repository for the journal article entitled "Pipe dreams: the effects of stream bank soil pipes on hyporheic denitrification caused by a peak flow event" published in Water Resources Research in 2022 by W. Seth Lotts and Erich T. Hester. The repository contains all the data used to produce the figures of Lotts and Hester 2022, and input files necessary to reproduce the study. A detailed explanation of the repository's file organization, as well as detailed descriptions of file contents can be found in the master readme file in LottsHester2022_Data. Any questions regarding this repository can be directed to Erich Hester at ehester@vt.edu.
DOI: 10.4211/hs.aa38640675134062aa68df9a6fe92f28
title: Ground-penetrating radar, snowpit, probe and UAV-SfM observations from Cameron Pass, Colorado during NASA SnowEx21

description: This resource includes field observations collected during the NASA SnowEx21 campaign at Cameron Pass, Colorado.  The observations consist of ground-penetrating radar travel times, snow pit observations of snow density, temperature and permittivity, snow depth observations from a snow probe, and UAV Structure from Motion (SfM) derived surface elevation models. The analysis of these data is presented in McGrath et al. (2022), A time-series of snow density and snow water equivalent observations derived from the integration of GPR and UAV SfM observations, Frontiers in Remote Sensing, doi:10.3389/frsen.2022.886747.
DOI: 10.4211/hs.15c4ab0ebfe7447298b18af37caf4e0e
title: CONUS floodplain areas

description: This ArcGIS pro file geodatabase contains feature polygons for the continental United States agreement area floodplain, anthropogenically connected, and anthropogenically disconnected floodplains.
DOI: 10.4211/hs.b9b7bc7aa70e464a8563d4e9880c1601
title: Processed pre- and post-wildfire SNOTEL data with topographic and land cover variables

description: The median differences between pre- and post-wildfire snow measures (i.e., annual maximum SWE, date of annual maximum SWE, and melt-out date).  The individual fire information which burned each site is also included.  The difference between median pre- and post-wildfire for the unburned sites associated with the burned locations include "Com_" in the column heading.  The columns with heading titles that include "Diff_" are the wildfire only impacts at each of the burned locations.
DOI: 10.4211/hs.689986daebb04348a754e9b9d94f4871
title: SNOTEL Data for analysis of SWE impacts

description: The SNOTEL data used for analyzing the impacts of wildfire on snow phenology measures (i.e. annual maximum SWE, date of annual maximum SWE, and melt-out date).  The data were originally sourced from the Natural Resources Conservation (NRCS) Report Generator (https://wcc.sc.egov.usda.gov/reportGenerator/) and subsequently quality controlled for systematic data errors.  All errors were verified with the NRCS  staff before removal from the analysis dataset.  The data includes the period of record (through 2019) at SNOTEL sites which were identified by NRCS staff as being impacted from wildfire. The study area includes western United States and Alaska.  All burned SNOTEL sites include 'burned' in the data file name.  The comparison sites used to identify climate differences (which were subsequently used to isolate the wildfire only impacts at the  burned sites) are labeled 'comparison' in the file name.
DOI: 10.4211/hs.efb41192ddbd4785a2b0ef00bf5e7c62
title: Solute tracer timeseries, Stringer Creek, 2005 and 2006

description: Stream solute tracer data initially presented in:

Payn, R. A., Gooseff, M. N., McGlynn, B. L., Bencala, K. E., and Wondzell, S. M. (2009), Channel water balance and exchange with subsurface flow along a mountain headwater stream in Montana, United States, Water Resour. Res., 45, W11427, doi:10.1029/2008WR007644. 


A version of these data have been subsequently analyzed in several publications including:

Patil, S., T. P., Covino, A. I., Packman, B. L., McGlynn, J. D., Drummond, R. A., Payn, and R., Schumer (2013), Intrastream variability in solute transport: Hydrologic and geomorphic controls on solute retention, J. Geophys. Res. Earth Surf., 118, 413– 422, doi:10.1029/2012JF002455.

Kelleher, C., Wagener, T., McGlynn, B., Ward, A. S., Gooseff, M. N., and Payn, R. A. (2013), Identifiability of transient storage model parameters along a mountain stream, Water Resour. Res., 49, 5290– 5306, doi:10.1002/wrcr.20413. 

Ward, A. S., Payn, R. A., Gooseff, M. N., McGlynn, B. L., Bencala, K. E., Kelleher, C. A., Wondzell, S. M., and Wagener, T. (2013), Variations in surface water-ground water interactions along a headwater mountain stream: Comparisons between transient storage and water balance analyses, Water Resour. Res., 49, 3359– 3374, doi:10.1002/wrcr.20148. 

Ward, A.S., Kelleher, C.A., Mason, S.J.K., Wagener, T., McIntyre, N., McGlynn, B., Runkel, R.L., and Payn, R.A. (2017), A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations, Freshwater Science, 36(1), https://doi.org/10.1086/690444
DOI: 10.4211/hs.96eeb0d796a64b578f24e8154c166988
title: 1-km soil moisture predictions in the United States with SOMOSPIE framework

description: Monthly and weekly soil moisture predictions in 2010 at 1-km spatial resolution using two different modeling methods integrated in the modular SOil Moisture SPatial Inference Engine (SOMOSPIE- Rorabaugh et al. 2019) (kernel-weighted k-nearest neighbors <KKNN>, Random Forests <RF>). Data were acquired from the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product version 6.1, 0.25-degrees spatial resolution. Modeled soil moisture layers are delivered for two regions in the conterminous United States. Each region encompasses a polygon of 7.5° x 3.75° (n = 450 pixels with 30 columns and 15 rows in the native resolution of the ESA CCI Soil moisture product). Region 1 <so called West Region> consists of an area of 275,516 km2. Region 2 <so called Midwest region> consists of an area of 283,499 km2. Predicted soil moisture values were validated by means of two approaches, cross-validation using the ESA CCI estimates and independent ground-truth records from the North American Soil Moisture Database (currently known as the National Soil Moisture Network). Detailed methods and results of this dataset are described in: Llamas, R.M; Valera, Leobardo; Olaya, Paula; Taufer, Michela; Vargas, Rodrigo "Downscaling Satellite Soil Moisture based on a modular SOil Moisture SPatial Inference Engine (SOMOSPIE)", Remote Sensing (submitted).