cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.1c62269aa802467688d25540caf2467e
title: MODIS MOD10A2 derived snow persistence and no data index for the western U.S.

description: Snow persistence (SP) or the snow cover index (SCI), is the fraction of time that snow is present on the ground for a defined period. No data index (NDI) is the fraction of time that there is no data, cloud, or sensor saturation for the same period. SP and NDI were calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 5 obtained from the National Snow and Ice Data Center (NSIDC). We computed the 1 January – 3 July SP for each year as the fraction of 8-day MODIS images with snow present. The selected period brackets the temporal extent of peak snow accumulation to complete snow ablation in most parts of the western United States. Spatial coverage is for MODIS tiles h08v04, h08v05, h09v04, h09v05, and h10v04. The 3 July date is used because the 8-day MODIS image does not fall on the first of the month in this case. Files are provided in the "USA Contiguous Albers Equal Area Conic USGS" projection. File nomenclature follows the following structure: "MOD10A2_(SCI or NDI)_(Water year the values correspond to, ex. 2001)_eq_alb.tif." Funding provided by NSF grant EAR-1446870.
DOI: 10.4211/hs.28639de860f74340a3549735abb7b0c6
title: Water chemistry for La Jara Creek and nearby springs in the Jemez River Basin Critical Zone Observatory, Valles Caldera Preserve, New Mexico (2010-2013)

description: The data file includes discharge and associated hydrochemical data for La Jara stream water and springs around Redondo Peak in the Valles Caldera Preserve, New Mexico, within the Jemez River Basin Critical Zone Observatory, collected from March 2010 to May 2013.  Solute chemistry includes pH, temperature, dissolved inorganic and organic carbon, major cations and anions, and Ge for select samples.
DOI: 10.4211/hs.90487bcf16e44c62a677ae33ef95e968
title: Landslides Kosñipata Valley, Peru (Clark et al. 2016)

description: Please cite: Clark, K. E., West, A. J., Hilton, R. G., Asner, G. P., Quesada, C. A., Silman, M. R., Saatchi, S. S., Farfan Rios, W., Martin, R. E., Horwath, A. B., Halladay, K., New, M., and Malhi, Y. (2016), Storm-triggered landslides in the Peruvian Andes and implications for topography, carbon cycles, and biodiversity, Earth Surface Dynamics, 4, 47-70, doi: 10.5194/esurf-4-47-2016.

Landslides within the Kosñipata Valley in Peru were manually mapped over a 25-year period from 1988 to 2012 using Landsat 5 (Landsat Thematic Mapper) and Landsat 7 (Landsat Enhanced Thematic Mapper Plus) satellite images. The landslide inventory was produced by manually mapping landslide scars and their deposits in ArcGIS and by verifying via ground truthing of scars in the field. Mapping involved visually comparing images from one year to the next, specifically evaluating contrasting colour changes that suggest a landslide had occurred. The landslide areas visible via spectral contrast in the Landsat images include regions of failure, run-out areas, and deposits. Pan-sharpened high-resolution Quickbird and Worldview images were used to define the landslide boundaries.

Topographic shadow produced by hillslopes covered a minimum of 21% of the study area (35 km2 out of 185 km2), predominantly on southwest-facing slopes was consistently present between images. Landslides that fell within these shadow areas were not visible. Any landslides that were partially mapped underneath the Landsat topographic shadow were removed (see Figure 2a in Clark et al. 2016).

This product was created by Kathryn Clark (kathryn.clark23@gmail.com).  

Other spatial datasets from Clark et al. (2016):
Clark, K., J. West, R. Hilton (2017). Landsat topographic shadow, Kosñipata Valley, Peru (Clark et al. 2016), HydroShare, http://www.hydroshare.org/resource/bdb9c4b4788d4141845947c81e5cceba
Clark, K., J. West, R. Hilton (2017). Region of landslide mapping, Kosñipata Valley, Peru (Clark et al. 2016), HydroShare, http://www.hydroshare.org/resource/c08742b733274f7dbf75891a7c185626
Clark, K., J. West, R. Hilton (2017). Landslide rates and hillslope turnover, Kosñipata Valley, Peru (Clark et al. 2016), HydroShare, http://www.hydroshare.org/resource/147e9ebecde442ed97738de7f404c057
DOI: 10.4211/hs.5f432a520370472683b543d493f136d1
title: Kosñipata River discharge at San Pedro and Wayqecha, Peru (Clark et al. 2014)

description: Please cite: Clark, K. E., Torres, M. A., West, A. J., Hilton, R. G., New, M., Horwath, A. B., Fisher, J. B., Rapp, J. M., Robles Caceres, A., and Malhi, Y. (2014), The hydrological regime of a forested tropical Andean catchment, Hydrology and Earth System Sciences, 18, 5377-5397, doi: 10.5194/hess-18-5377-2014.

Sheet 1: Discharge measurements at the San Pedro gauging station (1360 m.a.s.l.), along the Kosñipata River, in the Andes of Peru. The Kosñipata River at the San Pedro gauging station drains an area of 164.4 km2. Field measurements consisted of river height, flow velocity, and cross-sectional area, which together allowed us to estimate discharge and runoff over the study period. River stage height was measured from January 2010 to February 2011 using a river logger (GlobalWater WL16 Data Logger, range 0–9 m), recording river level every 15 min. The instantaneous discharge associated with each height measurement was calculated based on calibrated stage–discharge relationships. The Kosñipata River discharge at San Pedro was measured through a complete water year,  with a 31-day gap partly in July and August (during low flow) that was covered by three manual measurements and the gap was filled using linear interpolation.

Sheet 2: Weekly to monthly discharge measurements at the Wayqecha gauging station (2250 m.a.s.l), along the Kosñipata River, in the Andes of Peru. The Wayqecha sub-catchment a nested catchment upstream of the San Pedro gauging station. It  encompasses the headwaters of the Kosñipata River, draining an area of 48.5 km2 (See the supplementary information in Clark et al. 2014). Locations of the San Pedro and Wayqecha gauging stations are provided as GIS coverages in a companion dataset. 

This product was created by Kathryn Clark (kathryn.clark23@gmail.com). 

Other related datasets from Clark et al. (2014):

Clark, K., J. West, R. Hilton (2017). Andes-Amazon gauging stations (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/b541f44606a44a4a911e0e09d1b88d74
Clark, K., J. West, R. Hilton (2017). Kosñipata River at San Pedro, Peru (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/b54b1cc138c54004a669f91a5351166e
Clark, K., J. West, R. Hilton (2017). Catchment boundary, Kosñipata River at San Pedro, Peru (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/0677a428cbd64d0ab62f7ab7a8e112f3
Clark, K., J. West, R. Hilton (2017). Catchment boundary, Kosñipata River at Wayqecha, Peru (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/8a21d07106564bcdb2d183c77a5de877
DOI: 10.4211/hs.2d061f70150c4e83b15977a74de9df0b
title: Blended Near Optimal Tools

description: Links to the repository (https://github.com/dzeke/Blended-Near-Optimal-Tools) that stores the Matlab 2013a source code and (1) Documentation for blended near-optimal tools that (2) generate alternatives, (3) visualize alternatives, and allow a user to interactively explore the near-optimal region from which alternatives are generated. Also contains the data and model files for a (4) linear programming example application to manage water quality for Echo Reservoir, Utah, (5) mixed-integer programming example application to manage water supply and demands in Amman, Jordan, and (6) multi-objective linear programming reservoir operations problem.

Near-optimal alternatives perform within a (near-optimal) tolerable deviation of the optimal objective function value and are of interest to managers and decision makers because they can address un-modelled objectives, preferences, limits, uncertainties, or issues that are not considered by the original optimization model or it's optimal solution. Mathematically, the region of near-optimal alternatives is defined by the constraints for the original optimization model as well as a constraint that limits alternatives to those with objective function values that are within a tolerable deviation of the optimal objective function value. The code and tools within this repository allow users to generate and visualize the structure and full extent of the near-optimal region to an optimization problem. The tools also allow users to interactively explore region features of most interest, streamline the process to elicit un-modelled issues, and update the model formulation with new information. The tools and their use are described here for generating, visualizing, and interactively exploring near-optimal alternatives to optimization problems, but the tools are general and can be used to generate and visualize points within any high-dimensional, closed, bounded region that can be defined by a system of constraints. The parallel coordinate visualization and several interaction tools can also be used for any high-dimensional data set.
DOI: 10.4211/hs.d5ba5d65348f4c4088bc0e4d1b9c8291
title: Measuring Water Use, Conservation, and Differences by Gender  Using an Inexpensive, High Frequency Metering System

description: This resource contains the final data files and R scripts used in our analysis of water use across two high-traffic, public restrooms on Utah State University's campus. We used an inexpensive, open source, water metering system that uses off-the-shelf electronic components and inexpensive analog meters to measure water use quantity and behavior at high temporal frequency (< 5 s). We demonstrated this technology in the two restrooms at Utah State University before and after installing high efficiency, automatic faucets and toilet flush valves. We also integrated an inexpensive sensor to count user traffic to the restrooms. Sensing and recording restroom visits and water use events at high frequency allowed us to monitor water use behavior and identify water fixture malfunctions, such as undesired leaks. Results also show average water use per person, variability in water use by different fixtures (faucets versus urinals and toilets), variability in water use by fixtures compared to manufacturer specifications, gender differences in water use, and the difference in water use related to retrofit of the restrooms with high efficiency fixtures. The inexpensive metering system can help institutions remotely measure and record water use trends and behavior, identify leaks and fixture malfunctions, and schedule fixture maintenance or upgrades based on their operation, all of which can ultimately help them meet goals for sustainable water use.
DOI: 10.15468/dl.y6453n
title: Occurrence Download

description: A dataset containing 144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-98.07691 19.33734,-98.05761 19.32388,-98.04966 19.33491,-98.06539 19.35113,-98.07691 19.33734))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 144 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049930-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.4008884050fd4196aae5e2be2e4bb2c6
title: Measurement of stony soil water retention curve using HYPROP

description: We used the HYPROP (HYdraulic PROPerty analyzer, Decagon Devices Inc) device for the simplified evaporation method (SEM), which measures the WRC with simultaneous measurements of matric potential using two miniature tensiometers and sample average water content using mass balance  to measure the stony soil water retention function. Mixtures of Millville silt loam with coarse sandstone and fine sandstone and mixtures of Wedron sand with pumice were evaluated considering six different volumetric stone contents (v =0, 0.05, 0.1, 0.2, 0.3, 0.4 ).
DOI: 10.4211/hs.88c4b48519d24ef3a9fbb2641ad2cafe
title: Numerical Simulation of Evaporation Process in the Stony Soil

description: The simplified evaporation experiment was simulated using HYDRUS 3D which numerically solves the Richard’s equation (Simunek et al., 2016). A three-dimensional evaporation process simulation (i.e., HYPROP apparatus) was performed for Millville silt loam with 40%  coarse sandstone by volume. The soil hydraulic parameters for the Millville silt loam and coarse sandstone were determined by fitting the van Genuchten model to their measured WRC data. The initial condition was set to saturated water content for both soil and stone inclusions. The upper boundary condition was set as the temporally variable evaporation rate measured during the evaporation experiment. The bottom boundary condition was set as zero flux.
DOI: 10.4211/hs.cad5dd0c4106489e87db2e8366dd66b1
title: HydroDS UEB model input setup Python scripts

description: The HydroDS tasks required to be executed to get complete UEB model inputs for an example watershed are given in the Python file “HydroDS_UEB_Setup”. This file calls functions from the other file, "hydrods_python_client" that has declarations for data service functions available from HydroDS. 

To run the workflow for a different watershed in the Western US, modify the coordinates of the watershed boundary, outlet location, the start and end time of model period, and the spatial reference (projection) information in the form of EPSG Code (http://spatialreference.org/ref/epsg/). The commands in the workflow script can also be called interactively from any Python command line, or from a user application that uses incorporates the Python Client Library. 

For watersheds outside of the Western US, but in the CONUS, you need to upload your own DEM. The services are currently limited to the US. 
You need to have a HydroDS account to use these services.

These scripts are for the following paper
Gichamo, T. Z., N. S. Sazib, D. G. Tarboton and P. Dash, (2020), "HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models," Environmental Modelling & Software, https://doi.org/10.1016/j.envsoft.2020.104623.
DOI: 10.4211/hs.72fa9b3835264b11b8661f2655997346
title: Numerical Simulation of Evaporation Process in Stony Soil

description: The simplified evaporation experiment was simulated using HYDRUS 3D which numerically solves the Richard’s equation (Simunek et al., 2016). A three-dimensional evaporation process simulation (i.e., HYPROP apparatus) was performed for Millville silt loam with 40%  coarse sandstone by volume. The soil hydraulic parameters for the Millville silt loam and coarse sandstone were determined by fitting the van Genuchten model to their measured WRC data. The initial condition was set to saturated water content for both soil and stone inclusions. The upper boundary condition was set as the temporally variable evaporation rate measured during the evaporation experiment. The bottom boundary condition was set as zero flux.
DOI: 10.4211/hs.306433fabc9740f0a3e5f91d248e3b40
title: European water sample H and O isotope ratios

description: Isotope ratio measurements for water samples collected in western Europe, fall 2005. Measurements were made by TC/EA-IRMS at the SIRFER lab, University of Utah.
DOI: 10.4211/hs.8379c2859e194c1eb7428dbf60b8e4c1
title: Community Key Informant

description: Forty-two water decision makers in cities in Utah were identified representing elected official positions as well as staff (e.g., public utilities, public works, etc.). Three valleys in the rapidly growing Northern Utah Wasatch Range Metropolitan Area (WRMA) are represented. In smaller cities where staff play multiple roles, those who performed some operations in water management were selected. Those selected for interviews were identified through city websites and, in a few cases, phone calls to city hall. Participants were contacted by email first and followed up telephone as needed. 
All of the interviews were conducted in-person between November 2015 and July 2016. During this time, city elections complicated contact and identifying key informants. When able, we interviewed the incumbents. Only one potential respondent who had initially agreed to an interview canceled without follow-up, for a response rate of 97.6%. Interviews were audio-recorded and tended to last between 20 and 90 minutes each. Each interview was transcribed with the help of two transcribers and deductively coded for themes by a team of three using NVIVO 11 Pro. The team started with an a priori coding matrix based on the interview guide and allowed for additional themes to emerge through the revision of categories and the coding agenda, reaching inter-coder reliability (<80% kappa coefficient). The database in NVIVO titled CKI_project_TEAM contains 40 transcribed interviews. One interview was not coded due to irrelevance and the pilot interview was not coded. Interview 013 does not exist because the respondent canceled. Overall,  coders maintained a range of kappa coefficients with % minimum agreement. The final agreement measurements were calculated on Interview 38 which was coded by all three coders. High dual-coder agreement was also attained on the following interviews: 001, 003, 004, and 011. Coders met weekly to retain alignment in nodes and definitions (qualitative agreement). Coders were instructed to code every respondent sentence to the period (quantitative agreement). If the respondent's answer was short (e.g., Yes/No), the coder coded the interview question along with the answer to retain context.  Respondents were asked the following: 1) the one key water issue facing their city today; 2) if their city had an adequate water supply to meet their city’s needs today, and 3) did they think their city had an adequate water supply to meet their city’s needs in the future.
DOI: 10.4211/hs.3f7680cf83dc426e858d5b48cb95a565
title: Green Infrastructure Designer  with RHESSys Workflow

description: Using the GI (Green Infrastructure) designer web site, users design green infrastructure via GIS maps and web services to create datasets for RHESSys workflows.

RHESSysWorkflows provides a series of Python tools for performing RHESSys data preparation workflows. These tools build on the workflow system defined by EcohydroLib and RHESSysWorkflows.

This version does not require user to edit files with text editor.
DOI: 10.4211/hs.5fc8d62937da439b85f61f57692a95c5
title: Data files and scripts to support analysis of water resources systems course sysllabi

description: This resource describes the data and script files used to mine text from 40 water resources systems analysis course syllabi and generate the results presented in Rosenberg et al (2017) "Towards More Integrated Formal Education and Practice in the Water Resources Systems Analysis." ASCE-Journal of Water Resources Planning and Management.  The original course syllabi are available on a repository of water resources systems analysis teaching materials at http://ecstatic.usu.edu. The ReadMe file below further describes each file. This work is part of a larger effort to review 40 WRSA course syllabi, interview 10 practitioners, and compare skills taught to the skills practitioners say they need. A preprint of the acticle is provided in the .docx file.
DOI: 10.4211/hs.1ae9c9f1262b4c8097340f04ea367159
title: Data files and scripts to support analysis of water resources systems course sysllabi

description: This resource describes the data and script files used to mine text from 40 water resources systems analysis course syllabi and generate the results presented in Rosenberg et al (2017) "Towards More Integrated Formal Education and Practice in the Water Resources Systems Analysis." ASCE-Journal of Water Resources Planning and Management.  The original course syllabi are available on a repository of water resources systems analysis teaching materials at http://ecstatic.usu.edu. The ReadMe file below further describes each file. This work is part of a larger effort to review 40 WRSA course syllabi, interview 10 practitioners, and compare skills taught to the skills practitioners say they need. A preprint of the acticle is provided in the .docx file.
DOI: 10.4211/hs.64e1c0f40d124b1c9d342ce421c2f7be
title: Carbon and oxygen isotope ratios of atmospheric carbon dioxide in the Salt Lake Valley, Utah, 2001-2011

description: Stable isotope ratio data are useful in understanding biosphere-atmosphere fluxes and the roles of different sources contributing to these fluxes. The carbon isotope ratio data are useful in understanding aspects of the carbon cycle, while oxygen isotope ratios provide information about the vegetation and the water cycle.

Presented here are long-term observations of the carbon and oxygen isotope ratios and concentrations of atmospheric carbon dioxide measured in an urban setting. These data These Salt Lake City observations were made on the roof of a 4-story building at the University of Utah campus (latitude 40.76348 degrees north, longitude -111.84834 degrees west, 1820 meters elevation). Current carbon dioxide concentration data streams can be obtained online at http://air.utah.edu.

These methods used in data collection, data analyses, and quality control can be found in the following publications:

Pataki, D. E., D. R. Bowling, and J. R. Ehleringer (2003), Seasonal cycle of carbon dioxide and its isotopic composition in an urban atmosphere: Anthropogenic and biogenic effects, Journal of Geophysical Research-Atmospheres, 108(D23).

Pataki, D. E., T. Xu, Y. Q. Luo, and J. R. Ehleringer (2007), Inferring biogenic and anthropogenic carbon dioxide sources across an urban to rural gradient, Oecologia, 152(2), 307–322.

These data are used in the following publication:
B. Raczka, S. C. Biraud, J. R. Ehleringer, C. Lai, J. B. Miller, D. E. Pataki, S. Saleska, M. S. Torn, B. H. Vaughn, R. Wehr, D. R. Bowling. 2017. Does vapor pressure deficit drive the seasonality of δ13C of the net land-atmosphere CO2 exchange across the United States? Journal of Geophysical Research Biogeosciences 

All stable isotope ratio analyses were conducted at Utah’s Stable Isotope Ratio Facility for Environmental Research (SIRFER, http://sirfer.utah.edu). 

The carbon dioxide concentration data are presented as parts per million (ppm). The carbon isotope ratio of atmospheric carbon dioxide are presented as per mil relative to the PDB international standard. The oxygen isotope ratio of atmospheric carbon dioxide are presented as per mil relative to the SMOW international standard.

Data flag information:

Flag1
-2, discard for 18O only
-1, discard for 13C only
0, good data
1, low pressure - suspect
2, bad fill suspected
3, low pressure - discard
4, bad fill - discard
5, discard other (use if both -1 and -2 apply)
6, suspect other (bad run on the mass spec)

Flag2
1, morning rush hour
2, evening rush hour
3, nighttime
4, diurnal
DOI: 10.4211/hs.11c23c95c4e9496190c1e3d2c74407a9
title: Household Water Supply and Coping Costs in Kathmandu Valley, 2001 and 2014 

description: This dataset is composed of surveys of 1500 randomly sampled households in Kathmandu, conducted in 2001 and 2014 to determine the costs people were incurring to cope with Kathmandu’s poor quality, unreliable piped water supply system and to estimate their willingness to pay for improved piped water services. This dataset accompanies the authors' 2017 paper, "The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal."