cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.c0f61754a85d41e18618bae5473f34d8
title: Spatially and temporally distributed, high-resolution dissolved oxygen data for the hyporheic zone of multiple dune-like bedforms.

description: Dissolved oxygen concentrations and consumption rates are a primary indicator of bioactivity levels in the hyporheic zone (HZ) of streams and rivers.  Conventional wisdom has held that bioactivity levels in the hyporheic zone were generally homogeneous and primarily controlled by nutrient (carbon) supplies.  In this view, variations in bioactivity levels are driven by spatial heterogeneity of nutrient resources.  Reeder et al. (2018) demonstrated that hyporheic hydraulics exert primary control over bioactivity levels in the HZ.  Variations in aerobic respiration rates are a linear function of the hyporheic flow velocity.  The data provided in this contribution includes: (1) bed surface and pressure profiles along with validation data for the bedforms used in a large-scale, long-term flume experiment, (2) spatially and temporally distributed hyporheic dissolved oxygen measurements, (3) calculated fluxes through the hyporheic, (4) calculated and measured residence times through the hyporheic and (5) calculated dissolved oxygen consumption rate constants (KDO). 

Reference: 
Reeder, W. J., A. M. Quick, T. B. Farrell, S. G. Benner, K. P. Feris, and D. Tonina Spatial and Temporal Dynamics of Dissolved Oxygen Concentrations and Bioactivity in the Hyporheic Zone, Water Resources Research,  doi: 10.1002/2017WR021388.
DOI: 10.4211/hs.31f30d14c88748d986842d278d125a5c
title: Quality Control Experiment

description: These are data resulting from and related to an effort to examine subjectivity in the process of performing quality control on  water quality data measured by in situ sensors. Participants (n=27) included novices unfamiliar with and technicians experienced in quality control. Each participant performed quality control post processing on the same datasets: one calendar year (2014) of water temperature, pH, and specific conductance. Participants were provided with a consistent set of guidelines, field notes, and tools. Participants used ODMTools (https://github.com/ODM2/ODMToolsPython/) to perform the quality control exercise. This resource consists of: 
1. Processed Results: Each file in this folder corresponds to one of the variables for which quality control was performed. Each row corresponds to a single time stamp and each column corresponds to the processed results generated by each participant. The first column corresponds to the original, raw data. 
2. Survey Data: The files in this folder are related to an exit survey administered to participants upon completion of the exercise. It includes the survey questions (pdf), the full Qualtrics output (QualityControlSurvey.pdf), data and metadata files organized and encoded for display in the Survey Data Viewer (http://data.iutahepscor.org/surveys/survey/QCEXP) (QCExperimentSurveyDataFile.csv, QCExperimentSurveyMetadata.csv), and a file used to organize data for plots for the associated paper. 
3. Field Record: Participants were provided this document, which gives information about the field maintenance activities relevant to performing QC. 
4. Scripts: Each file in this folder corresponds to a script automatically generated by ODMTools while performing quality control. The files are organized by user ID and by variable. 
5. Code and Analysis: Script used to generate the figures for this work in the associated paper. It is important to note that novice users correspond to IDs 1-22 and experienced users correspond to IDs 25-38. This folder also includes subsets of the data organized in supporting files used to generate Figure 6 (ExpGapVals.xlsx) and Table 5 (NoDataCount.xlsx).
DOI: 10.4211/hs.8ed47eb4baee45eeaaa60f85b75b5042
title: Spatially distributed, high-resolution nitrous oxide and residence time data for dune-like bedforms

description: Nitrous oxide (N2O) is a potent greenhouse gas that, over the past several decades , has been increasing its forcing potential for atmospheric warming.  An estimated 10% of all anthropogenically generated N2O is emitted from streams and rivers.  These emissions are strongly correlated to ammonium and nitrate runoff from agricultural and industrial processes.  However, not all impacted steams emit N2O.  Reeder et al. (2018) showed  that the  flow of surface water and its interaction with stream bed morphology exerts control over the biological processes that are the primary source of (N2O) emissions from rivers and streams. A mathematical model that predicts which flowlines have the correct properties to produce and emit N2O is presented.  The data provided in this contribution includes: (1) spatially distributed nitrous oxide measurements from a large-scale, long-term flume experiment, (2) KDO data used in the Tau_tilde transform, (3) data to calculate N2O fluxes through the hyporheic, (4) calculated  residence times through the hyporheic and (5) compiled data for the averaged N2O/Tau profiles.

Reference:
Reeder, W. J., Quick, A. M., Farrell, T. B., Benner, S. G., Feris, K. P., Marzadri, A., & Tonina, D. (2018). Hyporheic source and sink of nitrous oxide. Water Resources Research, 54. https://doi.org/10.1029/2018WR022564
DOI: 10.4211/hs.ae1ace69801f4e5bac368cd4a7d130c6
title: DMA testing

description: Reproduction of the OGI trench pumping test from march 2018.
See details on 
http://www.ogi.co.uk/dma/
DOI: 10.4211/hs.94c8f338845f48afaf3430b0e87cd6ca
title: Identifying and Correcting Step Losses in Single-Ended Fiber-Optic Distributed Temperature Sensing Data

description: Fiber-optic distributed temperature sensing (DTS) makes it possible to observe temperatures on spatial scales as fine as centimeters and at frequencies up to 1 Hz. Over the past decade, fiber-optic DTS instruments have increasingly been employed to monitor environmental temperatures, from oceans to atmospheric monitoring. Because of the nature of environmental deployments, optical fibers deployed for research purposes often encounter step losses in the Raman spectra signal. Whether these phenomena occur due to cable damage or impingements, sharp bends in the deployed cable, or connections and splices, the step losses are usually not adequately addressed by the calibration routines provided by instrument manufacturers and can be overlooked in postprocessing calibration routines as well. Here we provide a method to identify and correct for the effects of step losses in raw Raman spectra data. The utility of the correction is demonstrated with case studies, including synthetic and laboratory data sets.

Raw project data is available by contacting ctemps@unr.edu
DOI: 10.4211/hs.8c820686e27f4cc187fbd32fb6a6545f
title: Potential for Small Unmanned Aircraft Systems Applications for Identifying Groundwater‐Surface Water Exchange in a Meandering River Reach

description: The exchange of groundwater and surface water (GW‐SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here we describe the use of a suite of high spatial resolution remote sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW‐SW exchange. sUAS provided centimeter‐scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS‐based vegetation imagery, vegetation‐top elevation, and normalized difference vegetation index mapping indicated GW‐SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite‐based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.

Raw project data is available by contacting ctemps@unr.edu
DOI: 10.4211/hs.564b6d73040142579ad3236e1aeb4712
title: RAPID: Archiving and Enabling Community Access to Data from Recent US Hurricanes

description: NSF RAPID Proposal funded to create an archive of data from hurricanes Harvey and Irma that impacted the US in 2017.

Hurricane Harvey is the largest storm of up to 5 days duration ever recorded in the United States. Over 50 inches of rain fell in places, and flooding and associated damage in the greater Houston area was extensive, with the storm extending across Texas and neighboring states. Shortly after Harvey struck, Hurricane Irma cut a broad swath across the Caribbean, Florida, and into nearby states, also causing widespread devastation and flooding. During the first few days following these events, even the most elementary kinds of questions about flood inundation depths, extents, and impacts could not be answered because we currently lack the ability to collect important data and the ability to assimilate available data into decision relevant information. One of our team members, David Maidment, at the University of Texas (UT) at Austin was embedded in the Texas State Operations Center helping with the response to Harvey, and along with other colleagues from the UT Center for Water and Environment (CWE) helped the Texas Division of Emergency Management (TDEM) establish an internal geographic information system supporting emergency services. He thus has access to, and deep knowledge of, important data from this work and will now work with TDEM to determine what part of that information can be released for research. Making data from events such as Harvey and Irma accessible is important to fill gaps and improve our understanding of and capability to prepare for and respond to such extreme events. The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) provides a range of data services to the hydrologic research community, including HydroShare, which supports sharing and publication of a broad class of hydrologic data and models. This project will assemble, document, and archive data from hurricanes Harvey and Irma within the CUAHSI HydroShare community repository to make them easily accessible for research in broad hydrologic science community.
DOI: 10.4211/hs.2af40f0b7f64494a951412d90277dd72
title: Snow intermittence (snow to no snow counts) for the western U.S. for water years 2001 - 2015

description: This resource is useful for characterizing the intermittence of snow, or how continuously it covers an area (as opposed to snow persistence which quantifies the fraction of time that snow is present for any location for a defined time period: https://www.hydroshare.org/resource/1c62269aa802467688d25540caf2467e/, or the snow season, which provides the first day of snow occurrence, last day of snow occurrence, and the length of time between the first and last day of snow occurrence per water year: https://www.hydroshare.org/resource/197adcdc76b34591bd78a811bf1dfbfe/).Snow intermittence (snow to no snow counts) for the western U.S. for water years 2001 - 2015 contains annual and mean annual raster datasets  with the number of snow to no snow events. The events consist of any time that there was snow that was present and then followed by bare ground within 10 days of the original snow fall. Both  MOD10A1 and MOD10A2 binary snow products were used resulting in annual and mean annual rasters at the daily (MOD10A1) and 8-day (MOD10A2) temporal resolutions. This product is primarily intended for areas with sparse vegetation, as dense vegetation obscures the binary snow classification used for the MOD10A1 and MOD10A2 V5 products. These grids are available at the original 500 m MODIS resolution.
DOI: 10.4211/hs.889b9ccbb0c7407ea9a5a1b5d2bbb935
title: Optimizing Barrier Removal in Utah's Weber Basin

description: In-stream barriers, such as dams, culverts and diversions alter hydrologic processes and aquatic habitat. Removing uneconomical and aging in-stream barriers to improve stream habitat is increasingly used in river restoration. Previous barrier removal projects focused on score-and-rank techniques, ignoring cumulative change and spatial structure of barrier networks. Likewise, most water supply models prioritize either human water uses or aquatic habitat, failing to incorporate both human and environmental water use benefits. In this study, a dual objective optimization model prioritized removing in-stream barriers to maximize aquatic habitat connectivity for trout, using streamflow, temperature, channel gradient, and geomorphic condition as indicators of aquatic habitat suitability. Water scarcity costs are minimized using agricultural and urban economic penalty functions, and a budget constraint monetizes costs of removing small barriers like culverts and diversions. The optimization model is applied to a case study in Utah’s Weber River Basin to prioritize removing barriers most beneficial to aquatic habitat connectivity for Bonneville cutthroat trout, while maintaining human water uses. Solutions to the dual objective problem quantify and graphically show tradeoffs between connected quality-weighted habitat for Bonneville cutthroat trout and economic water uses. Removing 54 in-stream barriers reconnects about 160 km of quality-weighted habitat and costs approximately $10 M, after which point the cost effectiveness of removing barriers to connect river habitat decreases. The set of barriers prioritized for removal varied monthly depending on limiting habitat conditions for Bonneville cutthroat trout. This research helps prioritize barrier removals and future restoration project decisions within the Weber Basin. The modeling approach expands current barrier removal optimization methods by explicitly including both economic and environmental water uses and is generalizable to other basins.
DOI: 10.4211/hs.15e33695a9644eb2b08de7b02b6d1c6e
title: DR5 ParFlow.CLM model input files

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.4211/hs.7d4988a9a24c4f94a2d003771526f3da
title: Kevsway ParFlow.CLM model input files

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.4211/hs.c783bc38320e4f138efd775726a11630
title: Greengage ParFlow.CLM model input files

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.4211/hs.ec189864d4ba47acabb71b86f6fc5f4f
title: Redbrook ParFlow.CLM model input files

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.4211/hs.194e935cf1354563847a40f1b16a6192
title: Runnymeade ParFlow.CLM model input files

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.4211/hs.0b04046bcb2b42aabbf6804b04336e4f
title: Sunnydale ParFlow.CLM model input files

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.4211/hs.7aef846fbdaf40acb00642768a2e7bde
title: ParFlow.CLM model input files of six headwater catchments of the Gwynns Falls watershed, Baltimore County, Maryland, USA

description: Six headwater catchments of the Gwynns Falls watershed, located in Baltimore County, Maryland, USA, were simulated using ParFlow.CLM. We utilized a 10-m horizontal gridding, with catchment areas ranging on the order of 0.2 to 2 sq. km. Vertical discretization was variable ranging from 0.1 to 8m with the terrain-following grid feature of ParFlow.CLM activated. Model domains contained 69,120 to 505,440 finite difference cells. The simulation period was 1 January 2012 to 31 December 2015. 
Each compressed tar archive (.tar.gz) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for permeability, slope and geologic unit indicator field, CLM text files (.dat and .txt) for input paramaters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). A Bash shell script (.sh) containing model dimensions is also included.
DOI: 10.15468/dl.6dh9yq
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "TaxonKey is Ilex hanceana"
 ]
}

 The dataset includes 35 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049932-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.2836494ee75e43a9bfb647b37260e461
title: Hurricane Harvey 2017 Collection

description: Quick Start
This is a collection of flood datasets to support hydrologic research for Hurricane Harvey, August-September 2017. The best way to start exploring this collection is by opening the Hurricane Harvey 2017 Story Map [2]. It has separate sections for the different content categories, and links to the relevant HydroShare resources within this collection. 

More Details
This is the root collection resource for management of hydrologic and related data collected during Hurricane Harvey on the Texas-Louisiana Gulf coast. This collection holds numerous composite resources comprising streamflow forecasts, inundation polygons and depth grids, flooding impacts, elevation grids, high water marks, and numerous other related information sources. Texas address points are included to support estimating storm and flood impacts in terms of structures within an affected area.

The data providers for this collection are the Texas Division of Emergency Management, NOAA National Weather Service, NOAA National Hurricane Center, NOAA National Water Center, FEMA, 9-1-1 emergency communications agencies, and many others. Esri and Kisters also provided invaluable tools, data and geoprocessing services to support the initial data production, and these are included or referenced.

User-contributed resources from 2017 US Hurricanes may also be shared with The CUAHSI 2017 Hurricane Data Community group [1] to make them accessible to interested researchers, Anyone may join this group. 

An ArcGIS Story Map [2] has been created which provides example data views and interactive access to this collection.   

This collection has been produced by work on a US National Science Foundation RAPID Award "Archiving and Enabling Community Access to Data from Recent US Hurricanes" [3]. 

References
[1] CUAHSI 2017 Hurricane Data Community group  [https://www.hydroshare.org/group/41]
[2] Hurricane Harvey 2017 Archive Story Map [https://arcg.is/1rWLzL0]
[3] NSF RAPID Grant  [https://nsf.gov/awardsearch/showAward?AWD_ID=1761673]