ABSTRACT:

Specific Catchment area defined as contributing area per unit contour length for the Logan River Basin.

ABSTRACT:

Digital Elevation Model for the watershed draining the Logan River Basin near Logan Utah.

ABSTRACT:

This is the model simulation of snow water equivalent in Logan River watershed from 2008 to 2009. The model used is the Utah Energy Balance model which is a snowmelt model. The simulation result is used as the input data for SAC-SMA model to simulate the stream flow of the watershed.

ABSTRACT:

How do you manage, track, and share hydrologic data and models within your research group? Do you find it difficult to keep track of who has access to which data and who has the most recent version of a dataset or research product? Do you sometimes find it difficult to share data and models and collaborate with colleagues outside your home institution? Would it be easier if you had a simple way to share and collaborate around hydrologic datasets and models? HydroShare is a new, web-based system for sharing hydrologic data and models with specific functionality aimed at making collaboration easier. Within HydroShare, we have developed new functionality for creating datasets, describing them with metadata, and sharing them with collaborators. In HydroShare we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. In this presentation, we will discuss and demonstrate the collaborative and social features of HydroShare and how it can enable new, collaborative workflows for you, your research group, and your collaborators across institutions. HydroShare’s access control and sharing functionality enable both public and private sharing with individual users and collaborative user groups, giving you flexibility over who can access data and at what point in the research process. HydroShare can make it easier for collaborators to iterate on shared datasets and models, creating multiple versions along the way, and publishing them with a permanent landing page, metadata description, and citable Digital Object Identifier (DOI). Functionality for creating and sharing resources within collaborative groups can also make it easier to overcome barriers such as institutional firewalls that can make collaboration around large datasets difficult. Functionality for commenting on and rating resources supports community collaboration and quality evaluation of resources in HydroShare.

This presentation was delivered as part of a Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Cyberseminar in June 2016. Cyberseminars are recorded, and archived recordings are available via the CUAHSI website at http://www.cuahsi.org.

ABSTRACT:

This dataset contains raw data, scripts, and plots used to analyze responses to the iUTAH Research Focus Area (RFA) 3 model inventory. The inventory was conducted via a Google Survey Form among RFA3 researchers on the RFA email list from August 2015 to October 2015. The purpose of the survey/inventory was to overview iUTAH RFA3 team's modeling efforts, map current efforts onto iSAW conceptual model (doi:10.1002/2014EF000295), and identify further opportunities to couple models as part of the RFA3 team's mission. Results herein are intended to help visualize results from the survey and productively encourage further discussion + coupling work.

ABSTRACT:

This dataset includes measurements of soil nitrogen pools and fluxes from two vegetation types (forest and herbaceous) and two landscape positions (upper and lower slopes) in the Knowlton Fork sub-catchment of Red Butte Creek watershed. Sites are located near the iUtah Knowlton Fork Climate Station, and measurements were made during June, August, and October of 2015. The dataset includes concentrations of inorganic nitrogen, soil nitrate isotope values, bulk concentrations and stable isotope values of soil organic carbon and nitrogen, concentrations of soil microbial biomass carbon and nitrogen, and nitrate leachate from below the rooting zone. Also included are carbon and nitrogen concentrations and isotope values from leaves.

ABSTRACT:

This dataset includes chemistry data from snowpack samples collected across the iUTAH watersheds during spring 2014 and 2015. The field sampling was a collaborative effort by the iUTAH Snow Sampling Team. The chemistry data include stable water isotope ratios (d18O and dD), trace and major element concentrations, and 87Sr/86Sr ratios for selected samples.

ABSTRACT:

Ion concentrations and precipitation amount were measured at 14 sites in the Salt Lake and Cache Valleys from December 2013 to February 2014. Sample collection was sporadic at several sites. The goal of this study was to identify land use impacts on nitrogen deposition to the iUTAH watersheds.

A subset of samples was analyzed for 15N and 18O of NO3 and 15N of NH4.

Methods and findings are described in the associated JGR-B manuscript

ABSTRACT:

This dataset contains water chemistry data from samples collected at GAMUT sites and other locations in Logan, Red Butte, and Provo River watersheds during 2014-2015. Chemistry includes field parameters, stable water isotopes, 87Sr/86Sr ratios, major ions, and trace element concentrations.

ABSTRACT:

In collaboration with the Salt Lake City Parks and Public Lands Department, researchers at Utah State University created a tablet-based survey instrument to gather feedback from community members about a proposed green infrastructure project in the Glendale neighborhood at the "Three Creeks Confluence". The Confluence is where three urban creeks, Red Butte, Emigration, and Parleys, empty in to the Jordan River in pipes underground of the city. In addition to information about that specific project, this survey also gathered some broader community opinions regarding local parks along the Jordan River corridor. The survey was designed specifically for residents in the neighborhood surrounding the Jordan River and was implemented using iPads and a public-intercept convenience sampling methodology in publicly accessible spaces and public events including local parks, shopping areas, libraries, and community festivals. The Survey results are accessible for visualization at http://data.iutahepscor.org/surveys/survey/3Creeks.

ABSTRACT:

Overview:
The availability of updated climate data, streamflow data, updated water use estimates and the incorporation of the Topnet Water Management (Topnet-WM) components provides the opportunity to build watershed knowledge by better understanding the climate, watershed hydrology and water budget.

Purpose:
The Lower Nooksack Water Budget project analysis focused on the 16 drainages of the Lower Nooksack Subbasin and for each drainage considers precipitation, evapotranspiration, storage, streamflow and user withdrawals. Storage includes canopy storage, unsaturated soil storage, and subsurface storage. Total streamflow includes baseflow, surface runoff, and artificial drainage. User withdrawals include irrigation, dairy, municipal/industrial water supply, and residential and commercial water use served by small public water systems or private wells. The sum of user withdrawals for each drainage is partitioned into groundwater and surface water withdrawals. In addition to streamflow prediction at each drainage, streamflow is calculated at multiple locations of interest (nodes) within each drainage. Total streamflow at nodes is partitioned into surface runoff and baseflow.

Model calculations are conducted on a daily timestep from 1952-2011. In future work, further summaries of daily information and other various components can be done at multiple time scales (daily, monthly, seasonal, annual) and multiple spatial scales (WRIA 1, Upper or Lower Nooksack, individual drainage).

This resource is a subset of the LNWB Ch12-13 Existing and Historic Model Outputs Collection Resource.

ABSTRACT:

iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) is a collaborative research and training program in Utah. As part of project requirements, iUTAH developed a data policy that seeks to maximize the impact and broad use of datasets collected within iUTAH facilities and by iUTAH research teams. This policy document focuses on assisting iUTAH investigators in creating and sharing high-quality data. The policy defines the data types generated as part of iUTAH and clarifies timelines for associated data publication. It specifies the requirements for submittal of a data collection plan, the creation of metadata, and the publication of datasets. It clarifies requirements for cases involving human subjects as well as raw data and analytical products. The Policy includes guidelines for data and metadata standards, storage and archival, curation, and data use and citation. Agreements for data publishers and data use are also included as appendices.

ABSTRACT:

This is a cumulative rainfall map for Iowa for the period between August 24 and September 6, 2016.

ABSTRACT:

This dataset contains chemistry and mineralogy data for dust samples collected across northern Utah and Great Basin National Park (Nevada) as part of Dylan Dastrup's thesis project.

ABSTRACT:

Digital elevation model and related files for a height above the nearest drainage analysis in Onion Creek

ABSTRACT:

This resource contains lidar data, collected at the TW Daniels Experimental Forest (TWDEF) on six separate flights in 2008 and 2009 measuring surface and canopy properties during snow-on and snow-off conditions. It was collected for the purposes of obtaining a digital elevation model (DEM) to characterize the area for snowmelt modeling, and by differencing between snow-on and snow-off observations to characterize the spatial distribution of snow depth. Canopy lidar returns also characterize the vegetation. The data was collected by the Utah State University (USU) Lidar-Assisted Stereo Imaging (LASSI) laboratory. The data was initially processed at USU shortly after collection and additionally processed by the Space Dynamics Laboratory (SDL) in support of iUtah lidar efforts in 2016.

The metadata report (sdl16-1363-.pdf) gives details about the hardware used for data collection, the flight plans and resulting data, the data processing steps, and a brief error analysis.

Zip files are named by the collection date and contain:
- Terra Scan Binary Files
- LAS Files (one for each flight line and the combined file)
- KML Files (one for each flight line)
- ASC DEM file (1 m resolution)
- PNG Hillshade file
A complete list can be found on pp. 17-22 of the metadata report.

ABSTRACT:

HydroTerre ETV Data Bundle for Level-12 HUC 060102020105

Further information about how ETV data bundle was generated here:

Leonard L., Duffy C., 2013, “Essential Terrestrial Variable Data Workflows for Distributed Water Resources Modeling”, International Environmental Modelling & Software, Vol 50, pp 85-96
http://dx.doi.org/10.1016/j.envsoft.2013.09.003

ABSTRACT:

This is the HydroTerre ETV data bundle required for the sample RHESSys workflow notebook tutorial.

ABSTRACT:

This is the sample observation file required for the sample RHESSys workflow notebook tutorial.

ABSTRACT:

This dataset contains positions of RFID-equipped coarse sediment tracer particles before and after floods at the single flood to annual timescales in the Mameyes River, PR. Please see accompanying metadata file "RFID Tracer Documentation" (.txt). Tracer particle locations are available in both a cartesian coordinate system (UTM), as well as a streamwise normal coordinate system.

ABSTRACT:

The data set contains the monthly statistics for the SOILM0-100cm variable (0-100 cm top 1 meter soil moisture content) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar month of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted normal distribution

ABSTRACT:

The data set contains the monthly statistics for the EVPsfc variable (total evapotranspiration) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar month of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted gamma distribution

ABSTRACT:

The data set contains the monthly statistics for the APCPsfc variable (precipitation total) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar month of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted gamma distribution

ABSTRACT:

The data set contains the monthly statistics for the TMP2m variable (2-m above ground temperature) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar month of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted normal distribution

ABSTRACT:

The data set contains the monthly statistics for the SSRUNsfc variable (surface runoff - non-infiltrating) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar month of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted gamma distribution

ABSTRACT:

The data set contains the daily statistics for the SOILM0-100cm variable (0-100 cm top 1 meter soil moisture content) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar day of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted normal distribution

ABSTRACT:

The data set contains the daily statistics for the EVPsfc variable (total evapotranspiration) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar day of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted gamma distribution

ABSTRACT:

The data set contains the daily statistics for the APCPsfc variable (precipitation total) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, probability of event, and percentiles in 0.05 interval. The data set also includes a p-value per calendar day of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted gamma distribution given that there was a precipitation event.

ABSTRACT:

The data set contains the daily statistics for the TMP2m variable (2-m above ground temperature) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar day of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted normal distribution

ABSTRACT:

The data set contains the daily statistics for the SSRUNsfc variable (surface runoff - non-infiltrating) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, probability of event, and percentiles in 0.05 interval. The data set also includes a p-value per calendar day of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted gamma distribution given that there was a runoff event.

ABSTRACT:

iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) is a collaborative research and training program in Utah. As part of project requirements, iUTAH developed a data policy that seeks to maximize the impact and broad use of datasets collected within iUTAH facilities and by iUTAH research teams. This policy document focuses on assisting iUTAH investigators in creating and sharing high-quality data. The policy defines the data types generated as part of iUTAH and clarifies timelines for associated data publication. It specifies the requirements for submittal of a data collection plan, the creation of metadata, and the publication of datasets. It clarifies requirements for cases involving human subjects as well as raw data and analytical products. The Policy includes guidelines for data and metadata standards, storage and archival, curation, and data use and citation. Agreements for data publishers and data use are also included as appendices.

ABSTRACT:

This data includes cumulative measurements of CH4 flux, average soil water content, and average soil temperature for 32 sites distributed across the Tenderfoot Creek Experimental Forest, MT in the 2013 growing season (May 29th- September 12th). It also includes the associated terrain metrics derived from 3m and 10m DEMs and soil characteristics measured from soil samples. More details and the associated analysis can be found in Kaiser, K.E., B.L. McGlynn, and J.E. Dore (2018), Landscape analysis of methane across complex terrain, Biogeosciences. Please contact kendra.kaiser@gmail.com for additional information or to use data.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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 ).

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

This NOCA landslide data repository host the driver code and data files needed to run Landlab's LandslideProbability component, which models annual shallow landslide probability in a steep mountainous region in northern Washington, U.S.A. The model application covers North Cascade National Park Complex (NOCA), using 30-m grid resolution over 2,700 km2. The model use the classic infinite slope, limited equilibrium model driven by contemporary climatology from the Variable Infiltration Capacity (VIC) macroscale hydrology model. Readily available topographic, geophysical, and land cover data are provided to calculate the factor-of-safety stability index in a Monte Carlo simulation, which explicitly accounts for parameter uncertainty.

Data used for this analysis are spatial data on landscape characteristics for NOCA. They include soil, geology, vegetation, topography, and landform data that can be used for quantitative landslides hazard assessment. Elevation was acquired from National Elevation Dataset (NED) at 30 m grid scale; other datasets are matched to scale and location. Slope was derived from the elevation file as "tan theta". Specific contributing area represents the 'upstream' area draining to each cell divided by the cell's width (so minimum value is 30 m). Landform data was developed by Jon Riedel of National Park Service. Landslides were extracted from these data identified as "mass wasting" events. Land use and land cover (LULC) data were acquired from USGS National land Cover Data (NLCD) based on 2011 Landsat satellite data and grouped into eight general categories. Cohesion represent total cohesion, which is equivalent to root cohesion in this application; soils are assumed to be primarily cohesionless, lacking “true cohesion” because of their low clay content in this mountain terrain. Root cohesion is based on the LULC referenced to a look-up table within this resource: (https://www.hydroshare.org/resource/a771ba9bbae24ed8b4673c945fc321a3/). Soil depth comes from Soil Survey Geographic Database (SSURGO) maintained by NRCS processed as soil survey depth-to-restricted layer (weighted-average aggregation) within each soil map unit. An alternative modeled soil depth (SD) described in the accompany paper is also provided, but revisions in the driver notebook would be required to reference this file to see adjusted results. Transmissivity was derived from the soil survey saturated hydraulic conductivity (depth averaged) multiplied by depth-to-restricted layer for each soil map unit; another T file based on the model soil depth is also provided. However, the model can be run using hydraulic conductivity using data file provided to calculate T. All soils within this watershed are sandy loam or loamy sand; therefore, soil surface texture was used as an indicator of internal angle of friction (phi). A header file is provided to understand the spatial details of the ASCII files and to facilitate capability with GIS. Spatial reference for raster mapping is NAD_1983, Albers conical equal area projection.

The model run archived in this resource runs with Landlab version 1.1.0 . The component code (landslide_probability9Jun17.py) is provided as an archive to run a notebook that replicates results in Strauch et al., (in review) . As Landlab is developed with newer versions, the notebook and/or provided component code may need updating to run properly. To run the notebook to replicate results, use the resource "Regional Landslide Hazard Using Landlab - NOCA Observatory", HydroShare resource: https://www.hydroshare.org/resource/07a4ed3b9a984a2fa98901dcb6751954/

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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

ABSTRACT:

This is a poster developed for the EarthCube All Hands Meeting: https://www.earthcube.org/2017-all-hands-meeting; Seattle WA, USA June 7-9, 2017 “Making Connections & Moving Forward”.

Authors:
Christina J. Bandaragoda1, Anthony Castronova2, Jimmy Phuong3, Ronda Strauch1, Erkan Istanbulluoglu1, Sai Siddhartha Nudurupati1, David Tarboton4, Dandong Yin5, Shaowen Wang5, Katherine Barnhart6, Gregory E. Tucker6, Eric W. H. Hutton7, Daniel E. J. Hobley8, Nicole M. Gasparini9, Jordan M. Adams9

Affiliations:
1 Department of Civil and Environmental Engineering, University of Washington, Seattle, USA; 2 Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), USA;
3 Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, USA; 4 Department of Civil & Environmental Engineering, Utah State University, Logan, USA; 5 National Center for Supercomputing Applications (NCSA), University of Illinois, Urbana-Champagne, USA;
6 Department of Geological Sciences, University of Colorado, Boulder, USA; 7 Community Surface Dynamics Modeling System (CSDMS), University of Colorado, Boulder, USA; 8 Cardiff University, Cardiff, UK; 9 Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA.

Abstract:
The ability to test hypotheses about surface processes coupled to both subsurface and atmospheric regimes is invaluable to research in the Earth and planetary sciences; ,to swiftly develop experiments using community resources is extraordinary. However, creating a new model can demand a large investment of time, expert software skills, and can be constrained to adapting existing models with limited flexibility to address new questions. Advancing the state of knowledge includes not only experimentation and publication, but also communication and distribution of large, and complex models and datasets. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for sharing data and models. Together, Landlab on HydroShare accelerates the development of new process models by providing (1) a set of tools for regular and irregular grid structures, data manipulation and visualization to make it faster and easier to develop new physical process components, (2) a suite of modular and interoperable process components that can be combined to create an integrated model; (3) cyber infrastructure that provides collaboration functions with multiple levels of sharing and privacy settings, Creative Commons license options, and DOI publishing, and 4) cloud access with high-speed processing from the CyberGIS HydroShare JupyterHub server at the National Center for Supercomputing Applications. New users can run models from a web browser, while advanced users can execute and develop models from command line terminals. Landlab on HydroShare supports the modeling continuum from fully developed modelling applications, prototyping new science tools, hands on research demonstrations, and classroom applications. The HydroShare-Landlab building block in EarthCube is a model of technology collaboration and tool exchange in the geoscience modeling community.

ABSTRACT:

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."

ABSTRACT:

Here I provide water year discharge (Q) and precipitation (P) in units of mm as well as the water year runoff ratio (Q/P) for all reference USGS watersheds as defined by the GAGES-II project (Falcone, 2011) for water years 1981 to 2016. Precipitation values were extracted from PRISM monthly totals for the "Recent years" 4 km gridded dataset, and discharge values come from summations of USGS daily mean streamflow values. The dataset contains Q, P, and Q/P data by watershed for 1,594 reference USGS watersheds.

ABSTRACT:

This data set contains measurements for discharge in cfs and cms, stream temperature in °C , dissolved oxygen (DO) in mg/L and %/L, total dissolved solids (TDS) in µs/cm, pebble count, and geomorphic condition, at sites in the Weber River Basin and Bear River Basin. Discharge was measured using a Marsh McBurney hand-held flowmeter. DO, TDS, and stream temperature were measured using a YSI Pro 2030 water quality probe. Pebble count was conducted using a modified Wolman procedure where a random pebble is picked up every step diagonally across a stream in a zig-zag pattern until at least 100 pebbles are measured. The pebble is then measured to obtain size and recorded. Geomorphic condition was assessed visually by taking note of conditions such as stream complexity (presence or lack of pools, riffles, meandering thalweg etc.), percent shade on stream, flow and depth variability, bank stability, access to floodplain, wood recruitment, unnatural barriers and condition and quantity of riparian vegetation. Based on the these conditions, a classification of excellent, good, moderate, or poor was assigned. Atmospheric pressure, wind speed and air temperature were measured with a Kestrel handheld weather meter, cloud cover was assessed visually. A site key in addition to the date, time and location (latitude/longitude and UTM) is included. Not all sites have values for discharge and pebble count due to hazardous conditions.

ABSTRACT:

This is raw environmental time series data stored in a sqlite database with a data schema loosely based off of ODM1.1. This scheme is shown in the data model figure included in the resource. The geographical location of these data is in the Hampton Roads region in South East Virginia. The variables of the time series are rainfall, tide, wind, and water table elevations. These data were processed and used as input for data-driven modeling for street flood severity prediction. The processing and modeling are described in this Journal of Hydrology Paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

The introduction of pollutants into storm water runoff can be concerning since it can act as a vector for environmental pollution or even household contamination. This study will assess the microbial contamination of storm water runoff from various sources including a metal roof, a photocell, and dry wells collecting roof and parking lot runoff on the USU campus. The microbes being tested for are total coliforms, E. coli, and enterococcus. ISCO auto samplers and grab samples were used to gather storm water samples. Simulations were ran using off-gassed tap water on the metal roof a photovoltaic cell. The concentrations of the three microbes in the samples were determined using the IDEXX Quanti-tray 2000 system. It was found that samples taken from the dry wells had greater concentrations of total coliform and E. coli than surface samples. It was also found that the metal roof on the pump house had greater concentrations of all three indicator bacteria than the photovoltaic cell atop the roof.

ABSTRACT:

Soil properties are important for understanding soil and modeling process taking place throughout the soil profile. This dataset provides a detailed description of the soil profile including the soil texture, color, structure and root density within the soil pit excavated at each GAMUT weather station within the iUTAH network. In addition to soil information, the slope, aspect, vegetation, surface stone and rock content etc. for each station is also presented.​

ABSTRACT:

This dataset contains daily sapflux and transpiration data from greater Los Angeles area. Sapflux values are daily sums of data collected every 30 seconds by sapflux sensors installed in sapwood and stored as 30-minute averages by Campbell Scientific dataloggers. The data are quality controlled and processed to estimate average tree-level stand transpiration.

ABSTRACT:

This data was collected as part of a study to understand the connectivity and diversity of stream bacteria communities in streams flowing from high to low elevation through different types of urbanization and in different seasons. The dataset contains OTU (operational taxonomic units, the bacterial surrogate for species) abundance for bacteria at iUTAH aquatic GAMUT sites (http://data.iutahepscor.org/mdf/Data/Gamut_Network/) in three watersheds at 3 time points (November 2014, February 2015, and May 2015). Briefly, we filtered water column samples onto 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits (alternate low protocol yield using phenol chloroform). We sequenced bacterial DNA (PCR-amplified using V4 region specific primers) using Illumina Hi-Seq. We cleaned, clustered (using 97% OTU similarity cut-off) and aligned sequences using the Kozich et al. 2013 protocol, and classified OTUs to taxonomy based on the SILVA bacteria database. Code used for processing is available at https://github.com/erinfjones/mothurcode.

There are three files; site and sample metadata (e.g. date sampled) is included in the file stream_design.txt, observed OTU counts by sample are in the .shared file, and the taxonomic classification of OTUs is in the .taxonomy file.

Also included are zipped folders containing raw fastq files.

Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. (2013): Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Applied and Environmental Microbiology. 79(17):5112-20.

ABSTRACT:

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.

ABSTRACT:

This resource is a repository of the map products for the Annual Irrigation Maps - Republican River Basin (AIM-RRB) dataset produced in Deines et al. 2017. It also provides the training and test point datasets used in the development and evaluation of the classifier algorithm. The maps cover a 141,603 km2 area in the northern High Plains Aquifer in the United States centered on the Republican River Basin, which overlies portions of Colorado, Kansas, and Nebraska. AIM-RRB provides annual irrigation maps for 18 years (1999-2016). Please see Deines et al. 2017 for full details.

Preferred citation:
Deines, J.M., A.D. Kendall, and D.W. Hyndman. 2017. Annual irrigation dynamics in the US Northern High Plains derived from Landsat satellite data. Geophysical Research Letters. DOI: 10.1002/2017GL074071

Map Metadata
Map products are projected in EPSG:5070 - CONUS Albers NAD83
Raster value key:
0 = Not irrigated
1 = Irrigated
254 = NoData, masked by urban, water, forest, or wetland land used based on the National Land Cover Dataset (NLCD)
255 = NoData, outside of study boundary

Training and test point data sets supply coordinates in latitude/longitude (WGS84). Column descriptions for each file can be found below in the "File Metadata" tab when the respective file is selected in the content window.

Corresponding author: Jillian Deines, jillian.deines@gmail.com

ABSTRACT:

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.

ABSTRACT:

Introduction: Stable isotopes of water have extensively been used to understand hydrological cycle in natural environment, however their application in highly managed urban water systems have been limited. Recent research have shown that water isotopes reflect urban water management practices and have potential application in understanding urban water supply network dynamics, evaluating effect of climate variability on water resources, geolocation and water monitoring and regulation.

Jameel and colleagues ( WRR, 2016) attributed the strong and structured spatiotemporal variation in tap water isotope ratios of Salt Lake Valley (SLV) to complex distribution systems, varying water management practices and multiple sources used across the valley. Building on their result, we collaborated with the largest water supply company in SLV, Jordan Valley Water Conservancy District (JVWCD) and expanded our project which now includes predicting the source (or sources) contributing to a given supply area. The different sources supplying JVWCD (such as Provo River system, Wasatch Creeks and groundwater wells) have similar yet distinct isotope ratios, providing an excellent opportunity to test the robustness of water isotopes in monitoring distribution pattern of the sources in the supply system. For this project, we collected more than 100 samples/month (between April 2015-May 2016), from different water sources (creeks, streams and groundwater wells), water treatment plants (WTP), storage reservoirs and delivery locations along the supply lines across the water distribution area , measured their isotopic ratio and developed isotopic mixing models using Hierarchical Bayesian (HB) framework to understand the flow of water in an urban supply system and connect tap water at a specific location to its respective sources.

Data Collection Methods: Water samples collected from source, reservoirs, and different locations within the JVWCD service area.

Location of Data Collection: we collect approximately 100 samples per month. From May 2015 to April 2016, for each month, we sampled different sources supplying water to the JVWCD service area and at numerous locations on the JVWCD distribution line (subsequently referred to as supply sites). Source water samples were collected as effluent from the WTPs and from groundwater wells and supply sites samples were collected from monitoring taps positioned on the distribution line. Source and supply sites were sampled 1-3 times per month.

Data Analysis: For each site, samples were obtained by running the tap water for ~15 seconds before filling, capping and sealing (with parafilm) a clean 4 ml glass vial. Samples were analyzed for their isotopic composition within a few weeks of their collection at the Stable Isotope Ratios for Environmental Research (SIRFER), University of Utah, on Picarro L2130-i Cavity Ring Down Spectroscopy (CRDS) analyzer. All the sample values are reported using the δ notation, where δ=Rsample/Rstandard -1, R= 2H/1H and 18O/16O. Four injections of each sample were measured and corrected for memory effects, through-run drift, and calibrated to the VSMOW-VSLAP scale, using a suite of three laboratory reference waters (PZ: 16.9‰, 1.65‰; PT: -45.6‰, -7.23‰; UT: -123.1‰, -16.52‰; for δ2H and δ18O, respectively).

ABSTRACT:

Preferred citation:
Hyndman, DW, T Xu, JM Deines, G Cao, R Nagelkirk, A Vina, W McConnell, B Basso, A Kendall, S Li, L Luo, F Lupi, D Ma, JA Winkler, W Yang, C Zheng, and J Liu. 2017. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters, 44. DOI: 10.1002/2017GL074429

We developed a new systems modeling framework to quantify the influence of changes in land use, crop growth, and urbanization on groundwater storage for Beijing, China. This framework was then used to understand and quantify causes of observed decreases in groundwater storage from 1993 to 2006, revealing that the expansion of Beijing'’s urban areas at the expense of croplands has enhanced recharge while reducing water lost to evapotranspiration, partially ameliorating groundwater declines. Please see Hyndman et al. 2017 for full details.

This repository contains assembled model input data not easily acquired through cited sources, model-subcomponent output such as annual land use rasters, and the MODFLOW groundwater model files which integrates these subcomponents.

Groundwater Model and Data
The MODFLOW groundwater model files and associated data can be found in the "Groundwater Model Files" folder. This includes well observation data, input recharge data, as well as data stored within the groundwater model such as pumping data and aquifer top and bottom. See the readme.txt within the folder and Hyndman et al. 2017 for additional detail.

Annual Land Use Rasters
The "Annual land use rasters" folder contains annually modeled land use. The key for land use codes is in LandUse_codeKey.csv. For methods, see Hyndman et al. 2017.

Contact: David Hyndman, hyndman@msu.edu

ABSTRACT:

These data are from this publication: Oerter, E. J., Perelet, A., Pardyjak, E., & Bowen, G. (2017). Membrane inlet laser spectroscopy to measure H and O stable isotope compositions of soil and sediment pore water with high sample throughput. Rapid Communications in Mass Spectrometry, 31(1), 75-84.

Abstract:
RATIONALE: The fast and accurate measurement of H and O stable isotope compositions (δ2H and δ18O values) of soil and sediment pore water remains an impediment to scaling-up the application of these isotopes in soil and vadose hydrology. Here we describe a method and its calibration to measuring soil and sediment pore water δ2H and δ18O values using a water vapor-permeable probe coupled to an isotope ratio infrared spectroscopy analyzer.
METHODS: We compare the water vapor probe method with a vapor direct equilibration method, and vacuum extraction with liquid water analysis. At a series of four study sites in a managed desert agroecosystem in the eastern Great Basin of North America, we use the water vapor probe to measure soil depth profiles of δ2H and δ18O values.
RESULTS: We demonstrate the accuracy of the method to be equivalent to direct headspace equilibration and vacuum extraction techniques, with increased ease of use in its application, and with analysis throughput rates greater than 7h1. The soil depth H and O stable isotope profiles show that soil properties such as contrasting soil texture and pedogenic soil horizons control the shape of the isotope profiles, which are reflective of local evaporation conditions within the soils.
CONCLUSIONS: We conclude that this water vapor probe method has potential to yield large numbers of H and O stable isotope analyses of soil and sediment waters within shorter timeframes and with increased ease than with currently existing methods.

ABSTRACT:

These data are from the following publication:
Oerter, E. J., & Bowen, G. (2017). In situ monitoring of H and O stable isotopes in soil water reveals ecohydrologic dynamics in managed soil systems. Ecohydrology, 10(4).

Abstract:

The water cycle in urban and hydrologically-managed settings is subject to perturbations that are dynamic on small spatial and temporal scales, the effects of which may be especially profound in soils. We deploy a membrane inlet-based laser spectroscopy system in conjunction with soil moisture sensors to monitor soil water dynamics and H and O stable isotope ratios (δ H and δ18O values) in a seasonally irrigated urban landscaped garden soil over the course of 9 months between the cessation of irrigation in the autumn and the onset of irrigation through the summer. We find that soil water δ2H and δ18O values predominately reflect seasonal precipitation and irrigation inputs. A comparison of total soil water by cryogenic extraction and mobile soil water measured by in situ water vapor probes, reveals that initial infiltration events after long periods of soil drying (the autumn season in this case) emplace water into the soil matrix that is not easily replaced by, or mixed with, successive pulses of infiltrating soil water. Tree stem xylem water H and O stable isotope composition did not match that of available water sources. These findings suggest that partitioning of soil water into mobile and immobile “pools” and resulting ecohydrologic separation may occur in engineered and hydrologically-managed soils and not be limited to natural settings. The laser spectroscopy method detailed here has potential to yield insights in a variety of Critical Zone and vadose zone studies, potential that is heightened by the simplicity and portability of the system.

ABSTRACT:

The effects of climate change on corals are not uniform. Some corals tolerate greater rises in temperature, even across an individual reef and others thrive in naturally acidified waters. This phenomenon is present on Ofu Island, in American Samoa, where we conducted a field experiment. Identifying these resilient corals and prioritizing their protection may be the best strategy for long-term conservation of coral ecosystems. Although it is not fully understood what makes certain reefs more resilient to coral bleaching than others, emerging evidence suggests that reefs living in areas with naturally variable thermal environments may have higher temperature tolerance. By deploying DTS technology in the back-reef of Ofu Island, we can produce maps of environmental heterogeneity of unprecedented spatiotemporal resolution.

Raw project data is available by contacting ctemps@unr.edu

ABSTRACT:

In collaboration with Christa Kelleher (Syracuse University) and Julianne Davis (Syracuse University), AirCTEMPs is examining the impacts of beaver dam analogues (fake beaver dams) on deposition and erosion, vegetation greenness, and groundwater-surface water interactions. Ongoing data collection of stitched visual RGB imagery (left) and NDVI (right) is helping to reveal how these restoration structures impact the exchange of sediment, energy, and water across this landscape. Annual imagery collection (since 2017) will continue to benchmark how Red Canyon Creek and the surrounding floodplain are transformed by beaver dam analogue structures.

Davis, J., Lautz, L., Kelleher, C., Russoniello, C., and Vidon, P., 2019, Assessing the effects of beaver dam analogues on channel morphology using high-resolution imagery from unoccupied aerial vehicles (UAVs): Abstract H53M-1962, presented at 2019 AGU Fall Meeting, San Francisco, California, 9-13 December.

Raw project data is available by contacting ctemps@unr.edu

ABSTRACT:

Groundwater-surface water (GW-SW) flux measurement techniques, such as reach mass-balance, seepage meters, Darcian flux and temperature sensing can be applied simultaneously to provide multiple lines of evidence (e.g., Gonzalez et al. 2015, Schmadel et al. 2014, Kennedy et al. 2009, Gilmore et al. 2016b), but challenges remain for directly linking results from different spatial and temporal scales of measurement. For smaller streams where groundwater discharge is a significant percentage of stream discharge into the reach (typically ≥10%), the integrated groundwater flux from point measurements can be compared to a larger-scale (i.e. 10^2-10^3 m reach length) approach to confirm results. But for reaches in larger stream (river) systems, the stream-groundwater discharge ratio is usually much too large to use reach mass balance as a direct point of comparison (Gilmore et al. 2016b, Schmadel et al. 2010, Jain, 2000). A promising approach for linking point measurements and testing interpolation techniques in large river systems is fiber-optic distributed temperature sensing (FO-DTS) (Briggs et al. 2012a, Briggs et al. 2012b, Tyler et al. 2009). FO-DTS uses a fiber-optic cable to detect groundwater discharge through the streambed along the length of the cable (typically ≤1km). This may be an effective way to “connect the dots” between point measurements of groundwater discharge in large systems (Krause et al. 2012), when other techniques like reach mass balance, are not feasible. The overall goal of this research is to develop an optimal approach to link point measurements of groundwater-surface water fluxes in large river systems. The specific objectives are to: (1) test the combined DTS and point-measurement approach in a small stream, where interpolated results can be confirmed using a reach mass-balance approach, and (2) apply the technique in larger river systems to characterize spatial distributions and temporal variability of groundwater fluxes at existing groundwater-surface water monitoring stations on larger rivers. This project will improve techniques for multi-scale measurement of groundwater-surface water interactions, give critical insight into temporal and spatial variability of water fluxes in larger river systems, and improve our understanding of the value of existing groundwater-surface water monitoring stations.

Raw project data is available by contacting ctemps@unr.edu

ABSTRACT:

Utah Lake, the largest freshwater lake in the United States west of the Mississippi River, has received
heavy loading of various contaminants, such as high concentrations of phosphorus (P) and nitrogen (N) wastes
from raw sewage, effluent from sewage treatment plants, runoff from surrounding agricultural and farming land,
and metals from mining and industrial activities since European settlement. However, the rate of loading of N, P,
and trace metals to Utah Lake varies both in space and time. Therefore, a good understanding of such spatial and
temporal variability is critical for developing integrated approaches to managing lake water quality. In this project,
we took water and floc layer sediment samples from the American Fork River, Provo River, Hobble Creek,
Spanish Fork River, Jordan River and Utah Lake to investigate the temporal and spatial variations in nutrient (P,
N) load and trace metal (mercury/methylmercury, arsenic, lead, cadmium, etc.) concentrations. In addition, water
samples were analyzed for H and O stable isotopes to establish a water budget for Utah Lake, and floc layer
sediment samples were analyzed for C and N stable isotopes to differentiate organic matter sources to Utah
Lake. Upon completion of this project, we were able to quantify spatial and temporal variations in nutrient and
metal loading to Utah Lake and to examine how this variability affected water quality. Furthermore, we were
able to trace the origins of organic matter sources to the lake and establish nutrient, metal, and water budget for
Utah Lake. The knowledge from this project can guide actions that are increasingly required to safeguard the
services provided by Utah Lake ecosystem in a future with increasing pressure on freshwater resources. The water,
nutrient, and trace metal budgets developed in this project provide important information for determining
which inflows are contributing the largest contaminant loads to Utah Lake. Consequently, the data derived from
this project can help state agencies to address significant questions in water quality, hydrologic, environmental,
and biogeochemical sciences and management related to human-environment interactions.

ABSTRACT:

This hydrological dataset was collected as part of a project titled "Water harvesting and better cropping systems for smallholders of the East India Plateau" (project LWR/2002/100). The dataset includes a set of climatic variables (air temperature, relative humidity, wind speed and solar radiation), streamflow at 2 culverts, as well as water levels at a collection of ponds, wells and piezometers across a 250 ha study area in the Purulia district of West Bengal, India. The project was funded by the Government of Australia, the research component through the Australian Centre for International Agricultural Research and its application through AusAID. In-kind support was also provided by the Government of India through the Indian Council for Agricultural Research. Each of the Institutions engaged in the project also provided in-kind support. We particularly acknowledge the farmers and their families who put their trust in us, shared their thoughts, time and labour, and gave us access to their land and water, without which this project would not have been possible.

The final report for the project can be accessed from <a href="http://aciar.gov.au/publication/FR2013-25" rel="nofollow">http://aciar.gov.au/publication/FR2013-25</a>

ABSTRACT:

We surveyed more than 2,800 individual trees over an approximately 10 hectare area May 2-5, 2016 at the Eel River Critical Zone Observatory Sagehorn site in Mendocino County, California, USA. Trees species, size (height for juvenile and diameter for mature individuals), canopy position (for mature individuals), and location (meter-scale resolution GPS) were recorded. The survey area is a small portion of a 5,000 acre private cattle ranch that experienced a large fire in 1950 and some logging of Douglas fir in the first half of the twentieth century. The hilly landscape is part of the Central Belt Melange of the Franciscan Formation complex, and surveyed areas were underlain by sheared clay-rich melange matrix (dominant vegetation: annual herbaceous groundcover and Quercus garryana (Oregon White Oak)), as well as poorly sorted, immature sandstone blocks (greywacke; dominant vegetation: Arctostaphylos spp (Manzanita - not surveyed), Pseudotsuga menziesii (Douglas Fir) and Arbutus menziesii (Pacific Madrone)). Trees with diameters at breast height (1.4 m; DBH) greater than 5 cm (n = 313) were tagged for future surveys.

ABSTRACT:

Data to accompany Kelly SA, Takbiri Z, Belmont P, and Foufoula-Georgiou E. 2017. Human amplified changes in precipitation–runoff patterns in large river basins of the Midwestern United States. Hydrology and Earth System Sciences.

ABSTRACT:

River bathymetry data were collected between 2013 and 2016 at 10 Minnesota sites along the Minnesota River between Granite Falls and Shakopee. In total, 234.2 km were surveyed, with the most coverage between Mankato and Shakopee. We surveyed the Minnesota River using a cataraft with an outboard motor, an acoustic doppler current profiler, and a GPS to collect depth and positioning information. ADCP and GPS data were post processed using python scripts to take advantage of the four beam depths and correct for orthometric elevations. Channel bed elevations were calculated from the water surface elevation and measured depth. Bed elevation points were interpolated into a tin, then 1m x 1m rasters. For each year we have included rasters for each site, a shapefile of survey points, survey polygons, and raw measurement files from the ADCP software WinRiver II.

ABSTRACT:

Although usage of the inverse solution of the one-dimensional advection-diffusion equation for estimating streambed fluid fluxes, bed elevation changes, or bed thermal diffusivity have historically assumed the need for sinusoidal or at least nominally sinusoidal surface boundary temperature fluctuations, it is technically not a requirement. It has similarly been assumed that gradients in temperature in the bed would cause errors in parameter estimates. A published mathematical derivation (Luce et al., 2017) demonstrates that this is not true, but laboratory experiments and numerical modeling were done to support the illustration of the consequences of the derivation. These data are supplied here.

Reference:
Luce, C. H., Tonina, D., Applebee, R., & DeWeese, T. (2017). Was that assumption necessary? Reconsidering boundary conditions for analytical solutions to estimate streambed fluxes. Water Resources Research. doi: 10.1002/2017WR020618

ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services (https://github.com/CI-WATER/Hydro-DS). The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, tmin0.nc, tmax0.nc, srad0.nc, prcp0.nc, ueb_setup.py, hydrogate.py. The model parameter files include: control.dat, param.dat, inputcontrol.dat, outputcontrol.dat, siteinitial.dat. This model instance resource is complete for model simulation and the corresponding model output files are also included.

ABSTRACT:

This dataset includes data collected using a mobile sensing platform during baseflow and stormflow conditions in the Northwest Field Canal, located in Logan, UT. Data were collected by floating a payload of sensors in a longitudinal transect down the length of the canal and recording latitude, longitude, and several water quality variables, including fluorescent dissolved organic matter (FDOM), observations from custom fluorometers designed for calculating the fluorescence index (FI), dissolved oxygen, temperature, pH, specific conductance, and turbidity. The methods used in collection and processing of these data are described in detail in the methods document included within this resource.

ABSTRACT:

This dataset includes grab sample data collected during baseflow and stormflow conditions in the Northwest Field Canal (NWFC), located in Logan, UT. Grab sample data includes results from samples that were analyzed using dissolved organic carbon concentration analysis and excitation emission matrix spectroscopy to determine organic matter concentration and characteristics. Methods used in sample collection and analysis are described in detail within the methods document included as part of this resource.

ABSTRACT:

This dataset includes time series data collected during baseflow and stormflow conditions in the Northwest Field Canal, located in Logan, UT. Time series data includes fluorescent dissolved organic matter (FDOM), observations from custom fluorometers used to calculate the fluorescence index in situ, turbidity, and rainfall. Methods used for deploying sensors, collecting data, and processing for quality control are described in the methods document contained within this resource.

ABSTRACT:

The National Water Economy Database (NWED) quantifies and maps a spatially detailed and economically complete blue water footprint for the United States. NWED utilizes multiple mesoscale federal data resources from the United States Geological Survey (USGS), the United States Department of Agriculture (USDA), the U.S. Energy Information Administration (EIA), the U.S. Department of Transportation (USDOT), the U.S. Department of Energy (USDOE), and the U.S. Bureau of Labor Statistics (BLS) to quantify water use, economic trade, and commodity flows to construct this water footprint. Results corroborate previous studies in both the magnitude of the U.S. water footprint (F) and in the observed pattern of virtual water flows.

ABSTRACT:

Steady State and Transient models of Juab Valley converted to MODFLOW 2000 models.
<a href="https://pubs.er.usgs.gov/publication/70179114" rel="nofollow">https://pubs.er.usgs.gov/publication/70179114</a>

Plans to import water to Juab Valley, Utah, primarily for irrigation, are part of the Central Utah Project. A better understanding of the hydrology of the valley is needed to help manage the water resources and to develop conjunctive-use plans.

The saturated unconsolidated basin-fill deposits form the ground-water system in Juab Valley. Recharge is by seepage from streams, unconsumed irrigation water, and distribution systems; infiltration of precipitation; and subsurface inflow from consolidated rocks that surround the valley. Discharge is by wells, springs, seeps, evapotranspiration, and subsurface outflow to consolidated rocks. Ground-water pumpage is used to supplement surface water for irrigation in most of the valley and has altered the direction of groundwater flow from that of pre-ground-water development time in areas near and in Nephi and Levan.

Greater-than-average precipitation during 1980-87 corresponds with a rise in water levels measured in most wells in the valley and the highest water level measured in some wells. Less-than average precipitation during 1988-91 corresponds with a decline in water levels measured during 1988-93 in most wells. Geochemical analyses indicate that the sources of dissolved ions in water sampled from the southern part of the valley are the Arapien Shale, evaporite deposits that occur in the unconsolidated basin-fill deposits, and possibly residual sea water that has undergone evaporation in unconsolidated basin-fill deposits in selected areas. Water discharging from a spring at Burriston Ponds is a mixture of about 70 percent ground water from a hypothesized flow path that extends downgradient from where Salt Creek enters Juab Valley and 30 percent from a hypothesized flow path from the base of the southern Wasatch Range.

The ground-water system of Juab Valley was simulated by using the U.S. Geological Survey modular, three-dimensional, finite-difference, ground-water flow model. The numerical model was calibrated to simulate the steady-state conditions of 1949, multi-year transient-state conditions during 1949-92, and seasonal transient-state conditions during 1992-94. Calibration parameters were adjusted until model-computed water levels reasonably matched measured water levels. Parameters important to the calibration process include horizontal hydraulic conductivity, transmissivity, and the spatial distribution and amount of recharge from subsurface inflow and seepage from ephemeral streams to the east side of Juab Valley.

ABSTRACT:

HYMOD2 is an improved version of the widely used hydrologic model HYMOD. The improvements are made based on simple but realistic evaporation process multi-parameterization. There are two files in this resource, (1) hymod.m and (2) Inputs.mat. The first file is the model code which is well commented so that it is easy to follow. The second file is an example input file which consists of time series of three different variables, precipitation (column-1), potential evapotranspiration (column-2) and discharge (column-3), all in mm.
​
HYMOD2 Citation: ​
Roy, T., H. V. Gupta, A. Serrat-Capdevila, and J. B. Valdes (2017), Using Satellite-Based Evapotranspiration Estimates to Improve the Structure of a Simple Conceptual Rainfall-Runoff Model, Hydrology and Earth System Sciences, 21(2), 879–896, doi:10.5194/hess-21-879-2017.

HYMOD Citation:
​Boyle, D. P., H. V Gupta, and S. Sorooshian (2000), Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resources Research, 36, 3663– 3674, doi:10.1029/2000WR900207.

ABSTRACT:

We created a shapefile of Utah's Cache Valley street water conveyance system using ArcGIS. This included gutters, canals, and discontinued canals that transport secondary water to customers. This data collection and research supports coupled human-natural systems research because it connects human and environmental water systems. The purpose of our data collection and mapping is to support future analysis of street gutters and canals as unique secondary water delivery systems. We georeferenced the network of street water conveyance in summer 2016 that delivers secondary water. We drove, cycled, and walked Logan streets and marked those with observed water conveyance through gutters and canals on a printed map that was then transferred into an ArcGIS shapefile. To accurately determine which street gutters are part of the irrigation water delivery system, we contacted Cache County irrigation companies to receive guidance and feedback.

ABSTRACT:

These data are from the following publication:
Oerter, E. J., & Bowen, G. (2017). In situ monitoring of H and O stable isotopes in soil water reveals ecohydrologic dynamics in managed soil systems. Ecohydrology, 10(4).

Abstract:

The water cycle in urban and hydrologically-managed settings is subject to perturbations that are dynamic on small spatial and temporal scales, the effects of which may be especially profound in soils. We deploy a membrane inlet-based laser spectroscopy system in conjunction with soil moisture sensors to monitor soil water dynamics and H and O stable isotope ratios (δ H and δ18O values) in a seasonally irrigated urban landscaped garden soil over the course of 9 months between the cessation of irrigation in the autumn and the onset of irrigation through the summer. We find that soil water δ2H and δ18O values predominately reflect seasonal precipitation and irrigation inputs. A comparison of total soil water by cryogenic extraction and mobile soil water measured by in situ water vapor probes, reveals that initial infiltration events after long periods of soil drying (the autumn season in this case) emplace water into the soil matrix that is not easily replaced by, or mixed with, successive pulses of infiltrating soil water. Tree stem xylem water H and O stable isotope composition did not match that of available water sources. These findings suggest that partitioning of soil water into mobile and immobile “pools” and resulting ecohydrologic separation may occur in engineered and hydrologically-managed soils and not be limited to natural settings. The laser spectroscopy method detailed here has potential to yield insights in a variety of Critical Zone and vadose zone studies, potential that is heightened by the simplicity and portability of the system.

ABSTRACT:

Monitoring streambed elevation changes is important for many engineering and ecological applications. This contribution contains the data and the numerical code written in R used in the publication of DeWeese et al, (2017), who tested a new methodology based on stream water temperature as a signal to monitor local streambed elevation changes at the daily time scale. This contribution contains: (1) laboratory experiment time series of water temperature in the surface and within the sediment, (2) times series of sediment surface elevation changes in the laboratory, (3) field experiment time series of sediment elevation and (4) field experiment time series of surface and pore waters temperatures and (5) R code of the model to analyze the temperature data to extract streambed elevation changes and interstitial flows.

Reference:Timothy DeWeese, Daniele Tonina, Charles Luce, Monitoring streambed scour/deposition under non-ideal temperature signal and flood conditions, Water Resources Research, doi: 10.1002/2017WR020632

ABSTRACT:

Air temperature, ground temperature and relative humidity data was collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 - 1800 m above sea level. Data was collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Selected sites included relative humidity sensors to indicate whether precipitation was occuring. Data was collected every 3-4 hours from May 2015 to Sept 2018 (with ongoing collection). Code for processing daily mean, minimum, maximum, and rates of temperature changes with elevation is available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org.

ABSTRACT:

Continuous streamflow data collected by the Stroud Water Research Center within the 3rd-order research watershed, White Clay Creek above McCue Road.

Variables: Gage height, Discharge

Date Range: (1968-2014)

Dataset Creators/Authors: Stroud Water Research Center

Contact: Sara G. Damiano, Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, <sdamiano@stroudcenter.org>
Denis Newbold, Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311. <newbold@stroudcenter.org>
Anthony Aufdenkampe, Stroud Water Research Center, 970 Spencer Road, Avondale, PA 1931.1 <aufdenkampe@stroudcenter.org>

Field Area: White Clay Creek @ SWRC | Christina River Basin

Copied from:
Stroud Water Research Center (2014). "CZO Dataset: White Clay Creek - Stage, Streamflow / Discharge (1968-2014)." Retrieved 09 Nov 2017, from http://criticalzone.org/christina/data/dataset/2464/.

NOTE: does not include data in this CZO Data listing that was from this site: WCC2154: White Clay Creek, west branch at Rt. 926, downstream side.

In addition, Aufdenkampe added an example Jupyter Notebook in Python (CZODisplaytoDataFrame_WCC-Flow.ipynb), to create a single concatenated data frame and export to a single CSV file (CRB_WCC_STAGEFLOW_from_df.csv). The full example can be found at https://github.com/aufdenkampe/EnviroDataScripts/tree/master/CZODisplayParsePlot.

ABSTRACT:

There are limited open source data available for determining water production/treatment and required energy for cities across the United States. This database represents the culmination of a two-year effort to obtain data from cities across the United States via open records requests in order to determine the state of the U.S. urban energy-water nexus. Data were requested at the daily or monthly scale when available for 127 cities across the United States, represented by 253 distinct water and sewer districts. Data were requested from cities larger than 100,000 people and from each state. In the case of states that did not have cities that met these criteria, the largest cities in those states were selected. The resulting database represents a drinking water service population of 81.4 million and a wastewater service population of 86.2 million people. Average daily demands for the United States were calculated to be 560 liters per capita for drinking water and 500 liters per capita of wastewater. The embedded energy within each of these resources is 340 kWh/1000 m3 and 430 kWh/1000 m3, respectively. Drinking water data at the annual scale are available for production volume (89 cities) and for embedded energy (73 cities). Annual wastewater data are available for treated volume (104 cities) and embedded energy (90 cities). Monthly data are available for drinking water volume and embedded energy (73 and 56 cities) and wastewater volume and embedded energy (88 and 70 cities). Please see the two related papers for this database under "Related Resources."

ABSTRACT:

The Arkavathy watershed in southern India has exhibited considerable changes in inflow to major reservoirs, but explanations for these changes have been hampered by limited hydrological records in the watershed. In order to understand long-term hydrological change in this heavily managed watershed, we developed a spatially distributed dataset of surface water by generating time series of water extent in nearly 1700 man-made lakes, or tanks, over a 40-year period. Using an automated classification approach with subpixel unmixing, we classified water extent in each of the tanks in 40 Landsat images from 1973 to 2010, including 16 end-of-monsoon-season (December or January) images which characterized tank status after the rainy season. An additional 8 images in 2013 and 2014 were classified to analyze dry-season behavior and for validation. The classification results compared well with a reference dataset of water extent of tanks (R-squared=0.95). We also evaluated hydrological change in the watershed in 42 clusters of tanks by modeling water extent in the cluster on hydrological covariates and time. Based on a water balance argument, we inferred that any statistically significant results for the coefficient on the time covariate were indicative of long-term hydrological change. The results of this remote sensing classification for each of the tanks in the Arkavathy watershed are contained in this resource. The covariates and results from the statistical model are also included.

ABSTRACT:

Changes in streamflow and water table elevation influence oxidation–reduction (redox) conditions near river–aquifer interfaces, with potentially important consequences for solute fluxes and biogeochemical reaction rates. Although continuous measurements of groundwater chemistry can be arduous, in situ sensors reveal chemistry dynamics across a wide range of timescales. We monitored redox potential in an aquifer adjacent to a tidal river and used spectral and wavelet analyses to link redox responses to hydrologic perturbations within the bed and banks. Storms perturb redox potential within both the bed and banks over timescales of days to weeks. Tides drive semidiurnal oscillations in redox potential within the streambed that are absent in the banks. Wavelet analysis shows that tidal redox oscillations in the bed are greatest during late summer (wavelet magnitude of 5.62 mV) when river stage fluctuations are on the order of 70 cm and microbial activity is relatively high. Tidal redox oscillations diminish during the winter (wavelet magnitude of 2.73 mV) when river stage fluctuations are smaller (on the order of 50 cm) and microbial activity is presumably low. Although traditional geochemical observations are often limited to summer baseflow conditions, in situ redox sensing provides continuous, high-resolution chemical characterization of the subsurface, revealing transport and reaction processes across spatial and temporal scales in aquifers.

ABSTRACT:

This is tabular output data from two data-driven models used to predict flood severity, Poisson regression and Random Forest regression. Both outputs from the training and testing phases of the modeling are included in the resource. Additionally, results indicating the relative importance of each predictor variable in the Random Forest model are provided in the "rf_impo_out.csv" file. This work is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

This is tabular input data originally used in two data-driven models (Poisson regression and Random Forest) for predicting flood severity. The inputs to the model (or predictor variables) are environmental conditions such as cumulative rainfall, high and low tides, etc. The outputs (or target variable) of the model is the number of flood reports per storm event. This data was used in work that is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

This is a script written in the R programming language. The script is used to train and apply two data-driven models, Random Forest and Poisson regression. The target variable is the number of flood reports per storm event in Norfolk, VA USA. The input variables for the models are environmental conditions on an event time scale (or daily if no flood reports were made for an event). This script was used to produce results published in a paper in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
---
Original run configurations:
R version = 3.3.3
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Packages used:
'randomForest' (version 4.6-12)
'caret' (version 6.0-73)

ABSTRACT:

Introduction
This document provides an overview of the accompanying data files used in the production the accompanying manuscript. 
Text S1.
For each virtual gauging station, we have included:
1. NIR orthorectified mosaics for each flight
2. Low flow RGB orthorectified mosaic and DSMs
3. Shapefiles of the hydraulic model domains
4. HEC-RAS models
5. Total station channel surveys
6. In situ observations of wetted width and wetted widths extracted from imagery

WRR_GIS:
All of the imagery, digital surface models, and shapefiles for the hydraulic model domains (items 1-3 above) are included in the WRR_GIS folder. To view these data, launch the included ArcMap Map Document: KingEtAl_WRR_2017.mxd. Data reference paths are relative for inter computer fidelity. This map document was produced with ArcMap 10.3. The hydraulic domain shapefile data were used to produce the geometry files used in HEC-RAS with the HEC-GeoRAS toolkit.

WRR_HEC-RAS:
The HEC-RAS models (item 4 above), produced in HEC-RAS 5.0.3 are provided in the folder WRR_HEC-RAS. The project files for each virtual gauging station are located in WRR_HEC-RAS \ VGS# \ Projects where VGS# is virtual gauging station number of interest. These project files contain the information to run the open channel hydraulic models. Opening these files will prompt a warning message that some files were not found. These files were test runs that are not germane to the final results and were therefore not included in an attempt to minimize confusion. Note that HEC-RAS models follow our local naming convention and map onto the virtual gauging station naming convention as follows: VGS1 = Kup8US, VGS2 = Kup7DS, VGS3 = Kup5uus.

WRR_GroundTruthing:
The ground truthing data (items 5 and 6 above) are included in csv files within the WRR_GroundTruthing folder. Total station surveys are provided along with corresponding elevations extracted from the Digital Surface Models in csv files located at WRR_GroundTruthing \ ChannelSurveys \ VGS#TransectCompare.csv where VGS# is the virtual gauging station of interest. Observed wetted width and the corresponding wetted widths extracted from the NIR mosaics are provided in the csv file: WRR_GroundTruthing \ WettedWidths \ WettedWidthComparison.csv.

ABSTRACT:

Street flooding reports made by mostly City of Norfolk staff from 2010-2016. The coordinate system used for the X and Y coordinates is "Virginia state plane, south zone, feet (NAD83)." These data were processed and used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

Script and accompanying notebook written in Python 2.7 for processing street flood reports made by City of Norfolk staff. The output data from this script were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

Processed street flooding data from street flood reports made by City of Norfolk, VA staff 2010-2016. These data were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

Script and accompanying notebook written in Python 2.7 for combining flood report data (output) and environmental data (input) into a format suitable for a data-driven model. These data used as target values for street data-driven flood prediction severity modeling for Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

Daily observations data for rainfall, wind, tide, and water table levels. These variables are more fully defined in the raw source data. These data are used as input for data-driven prediction of street flood severity in Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums. The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

NOAA publishes advisory bulletins with named storm conditions and expectations, see [http://www.nhc.noaa.gov/archive/2017/IRMA.shtml]. Information from these bulletins was extracted using an R Code in "Generate Hurricane Track.ipynb" to produce the file Irma.csv. This was then converted to a vector shapefile using ArcGIS.

See also "NOAA NHC Irma 2017 Storm Track" page for other related data [https://www.hydroshare.org/resource/aa5c9982a4694a19be2fa9299b78e5ca/]

ABSTRACT:

Continuous data is collected at 9 sites throughout New Hampshire. At each site data is collected every 15 minutes by the datalogger from a HOBO Stage logger (or site is paired with USGS site), Satlantic SUNA and YSI EXO2. Data is collected and transmitted to UNH by cell telemetry once a day where it is stored on the NH EPSCoR data server. The data that is collected from the SUNA are Nitrate in mg/L and is corrected by grab sample NO3 analyzed by IC. Data that is collected by the YSI are Stream Temperature, Specific Conductance, and fDOM in QSU. The fDOM is corrected by temperature, turbidity (not included), and absorbance.

This data set was used to analyze the high-frequency time series of stream solutes to characterize the timing and magnitude of nutrient and organic matter transport over event, seasonal, and annual timescales as well as to assess to whether nitrate (NO3-) and dissolved organic carbon (DOC) transport are coupled in watersheds. Our dataset includes in situ observations spanning 2 – 4 years in 10 streams and rivers across New Hampshire, including observations of nearly 700 individual hydrologic events. These events are identified in the files.

Methods and findings are described in the associated WRR manuscript.

ABSTRACT:

A state-of-the-art network of water quality sensors was established in 2012 to gather year-round high temporal frequency hydrochemical data in streams and rivers throughout the state of New Hampshire through the NH EPSCoR project. This spatially-extensive network includes eight headwater stream and two main-stem river monitoring sites, spanning a variety of stream orders and land uses. We evaluated the performance of nitrate, fluorescent dissolved organic matter (fDOM), and turbidity sensors included in the sensor network and the data is shared here.

Nitrate sensors were first evaluated in the laboratory for interference by different forms of dissolved organic carbon (DOC), and then for accuracy in the field across a range of hydrochemical conditions. Turbidity sensors were assessed for their effectiveness as a proxy for concentrations of total suspended solids (TSS) and total particulate C and N, and fDOM as a proxy for concentrations of dissolved organic matter. Overall sensor platform performance was also examined by estimating percentage of data loss due to sensor failures or related malfunctions. Although laboratory sensor trials show that DOC can affect optical nitrate measurements, our validations with grab samples showed that the optical nitrate sensors provide a reliable measurement of NO3 concentrations across a wide range of conditions. Results showed that fDOM is a good proxy for DOC concentration (r2=0.82) but is a less effective proxy for dissolved organic nitrogen (r2=0.41). Turbidity measurements from sensors correlated well with TSS (r2=0.78), PC (r2=0.53) and PN (r2=0.51).

ABSTRACT:

Total suspended solids (TSS) and Volatile Suspended Solids (VSS) from White Clay Creek near the Stroud Water Research Center, Avondale, PA, USA. The purpose is to quantify export of inorganic and organic particulate matter from the 725-hectare watershed. Samples consist of those taken at monthly intervals, normally the first Wednesday of each month regardless of weather or flow conditions and those taken after precipitation events. The monthly samples are manual grab samples collected in 5-L polyethylene “space saver” bottles from a few centimeters below the surface and without disturbance to the stream bed. The event samples were collected in response to precipitation of 20 mm or more using an ISCO automated sampler which collected 1-L samples s in polyethylene bottles at hourly intervals through an intake approximately 20 cm above the bed. Each of approximately four events per year are represented by approximately 10 samples selected from the hourly series to characterize the rise, peak, and falling limb of the hydrograph. Additional events are represented by the three samples nearest peak flow.

Variables: Solids_ total suspended, carbon to nitrogen molar ratio, carbon_ particulate organic, nitrogen_ particulate organic, nitrogen-15 stable isotope ratio delta

Date Range: (1993-2012)

Dataset Creators/Authors: Aufdenkampe, A.K.; Newbold, J.D.; Anderson, B. A.; Richardson, D.; Damiano, S.G.

Contact: Sara G. Damiano, Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, sdamiano@stroudcenter.org

Field Area: Boulton Run | Christina River Basin | Forest Endmember: Spring Brook | White Clay Creek @ SWRC | Construction Endmember: White Clay Creek below landfill | Lower White Clay Creek | Agricultural Endmember: South Branch Doe Run

Copied from:
Aufdenkampe, A.K.; Newbold, J.D.; Anderson, B. A.; Richardson, D.; Damiano, S.G. (2012). "CZO Dataset: Christina River Basin - Stream Suspended Sediment (1993-2012)." Retrieved 21 Jan 2018, from http://criticalzone.org/christina/data/dataset/2474/.

For guidance on how to parse these CZODisplayFiles, see example Jupyter Notebooks in Python at https://github.com/aufdenkampe/EnviroDataScripts/tree/master/CZODisplayParsePlot.

ABSTRACT:

Deuterium and Oxygen-18 measured on stream water samples collected during baseflow and stormflow conditions.

Variables: oxygen-18 (d18O), deuterium (dD)

Date Range: (2011-2012)

Dataset Creators/Authors: Diana L. Karwan

Contact: Diana L. Karwan, Department of Forest Resources, University of Minnesota, St. Paul, MN 55108

Field Area: White Clay Creek @ SWRC

Copied from: Diana L. Karwan (2012). "CZO Dataset: Third-order White Clay Creek and Boulton Run - Climate, Stable Isotopes, Stream Water Chemistry (2011-2012)." Retrieved 21 Jan 2018, from http://criticalzone.org/christina/data/dataset/3365/

For guidance on how to parse these CZODisplayFiles, see example Jupyter Notebooks in Python at https://github.com/aufdenkampe/EnviroDataScripts/tree/master/CZODisplayParsePlot.

ABSTRACT:

This resource supports the work published in Strauch et al., (2018) "A hydroclimatological approach to predicting regional landslide probability using Landlab", Earth Surf. Dynam., 6, 1-26 . It demonstrates a hydroclimatological approach to modeling of regional shallow landslide initiation based on the infinite slope stability model coupled with a steady-state subsurface flow representation. The model component is available as the LandslideProbability component in Landlab, an open-source, Python-based landscape earth systems modeling environment described in Hobley et al. (2017, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017). The model operates on a digital elevation model (DEM) grid to which local field parameters, such as cohesion and soil depth, are attached. A Monte Carlo approach is used to account for parameter uncertainty and calculate probability of shallow landsliding as well as the probability of soil saturation based on annual maximum recharge. The model is demonstrated in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2.

This resource contains a 1) User Manual that describes the Landlab LandslideProbability Component design, parameters, and step-by-step guidance on using the component in a model, and 2) two Landlab driver codes (notebooks) and customized component code to run Landlab's LandslideProbability component for 2a) synthetic recharge and 2b) modeled recharge published in Strauch et al., (2018). The Jupyter Notebooks use HydroShare code libraries to import data located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/.

The Synthetic Recharge Jupyter Notebook <Synthetic_recharge_LandlabLandslide.ipynb> demonstrates the use of the Landlab LandslideProbability Component on a synthetic grid with synthetic data with four options for parameterizing recharge. This notebook was used to verify and validated the theoretical application and digital representation of Landslide processes.

The Modeled Recharge Jupyter Notebook <NOCA_runPaper_LandlabLandslide.ipynb> models annual landslide probability in the North Cascades National Park Complex, and was used to verify that model results in Strauch et al., (2018) could be reproduced online.

ABSTRACT:

Research data for Y. Olshansky et al.:Wet–dry cycles impact DOM retention in subsurface soils. Biogeosciences

ABSTRACT:

Diagram depicting the relationship between 10 different HydroShare resources used to produce results for data-driven street flood severity modeling done for Norfolk, VA for 2010-2016. The analysis is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

ABSTRACT:

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.

ABSTRACT:

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).

ABSTRACT:

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

ABSTRACT:

Reproduction of the OGI trench pumping test from march 2018.
See details on
http://www.ogi.co.uk/dma/

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

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.

ABSTRACT:

The Value Landscape Engineering (VLE) spreadsheet program identifies the costs, labor, water, fertilizers, pesticides, energy, and fuel required to install and maintain a residential or commercial landscape in Utah. The program also identifies the carbon footprint and particulates generated from landscape installation and maintenance activities. VLE considers all activities associated with a particular landscape over its life with the goal to maximize value and reduce required inputs. The VLE spreadsheet program tabulates all onsite costs, inputs, and impacts over the life of the landscape including preparing the site, purchasing and installing materials, annual maintenance and operations, and replacing landscape features and components that wear out or die. A variety of program options allow the user to select the planting and mulch materials and coverage, structures, irrigation systems, equipment, and to tailor the analysis to site-specific conditions. Users can simultaneously compare up to three different landscapes.
Data to support the spreadsheet program was gathered from the scientific literature, nurseries, websites of manufacturers and home building supply stores, extension publications, and landscape cost estimate reports. Cache Valley and Wasatch Front arborists, landscapers, and Cooperative Extension professionals also provided information specific to their expertise. Because urban landscapes are complex systems, the spreadsheet program makes several simplifying assumptions. Thus, spreadsheet program estimates of required inputs and impacts are accurate to within 30%. Users should verify cost estimates with bids from landscape companies. Given these estimation levels, use the spreadsheet program to compare the relative advantages and tradeoffs among different landscapes. We demonstrate use of the spreadsheet program for three landscapes at the Jordan Valley Water Conservancy District (JVWCD) conservation garden. These landscapes are the Traditional Landscape that has a large area of cool-season turfgrass, shrubs, perennials, ground cover, and common shade trees; the Perennial Landscape that has mostly drought-tolerant perennials and annuals; and the Woodland Landscape that consists largely of drought-tolerant shrubs and trees. To verify spreadsheet program results, we compare spreadsheet program estimates of water, labor, fertilizer, and fuel use in each landscape to observations made over 8 years by JVWCD garden staff. Generally, spreadsheet program estimates and JVWCD staff observations agree within the 30% estimation level for the spreadsheet program. Homeowners, commercial property owners, and landscapers can use the spreadsheet program to identify the total costs, water use, and other required inputs for their landscape choices. The program can identify tradeoffs in costs, inputs, and impacts among an existing (or planned) landscape and modifications to it. By examining results and changing the landscape design, the user can develop a landscape plan that should cost less and require less water, labor, fertilizers, and other inputs.

The published version of the work is available at: Rosenberg, D. E., Kopp, K., Kratsch, H. A., Rupp, L., Johnson, P., and Kjelgren, R. (2011). "Value Landscape Engineering: Identifying Costs, Water Use, Labor, and Impacts to Support Landscape Choice." JAWRA Journal of the American Water Resources Association, 47(3), 635-649. http://dx.doi.org/10.1111/j.1752-1688.2011.00530.x.

Description of file contents:
1) VLE_Manual_Sept2011.pdf: Model manual including quick start guide and directions to use the spreadsheet model
2) ModelDataFiles.zip: Zip folder with files for the different versions of the model.
3) FileDescriptions.txt: Explanation of files in ModelDataFiles.zip and list of model versions

ABSTRACT:

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]

ABSTRACT:

This collection is for datasets of flood depths, flood extents, high water marks, streamflow, damages recorded, aerial oblique photos, and related subjects. This includes both forecast and observed data. These were primarily obtained from national agencies such as NOAA (weather related), USGS (surface water related), FEMA (surface water and damage related), and Civil Air Patrol (aerial photos).

ABSTRACT:

This resource provides datasets for stream discharge (flow rate) in cubic feet per second, and gage height (stream depth) from 924 active USGS gages in the Hurricane Harvey impact zone across Texas, Louisiana, Mississippi and Arkansas (see shapefile for all gages).

These data were obtained from the USGS National Water Information System (NWIS) [1] using R scripts provided here. When running these R scripts, 745 of the 924 gages had gage height values, and 577 of the 924 had discharge values. For help in using these R scripts, USGS used to provide support tools but the links for those no longer work. I used R Studio on Windows for these retrievals.

Formats provided:
- Shapefile and csv for gage locations, including link to USGS gage details [1]
- Tabular (csv) datasets for timeseries of water discharge (flow rate) in cubic ft/sec, and timeseries of gage height in ft.
- R scripts to download timeseries data from NWIS

References
[1] USGS NWIS - interactive portal for stream gage site info [https://waterdata.usgs.gov/nwis]

ABSTRACT:

The NOAA National Hurricane Center (NHC) publishes advisory bulletins with named storm conditions and expectations, see [1]. We have also downloaded shapefiles for forty-three 5-day forecasts (published from August 17 to August 30) of track line, predicted points, ensemble forecasts envelope, and affected shoreline where applicable. NOAA also publishes the best track for major storms [3]. The "best track" is a smoothed version of the advisories track. Web services are also provided by NHC for the advisory points and lines [4] [5].

References
[1] NOAA NHC - Harvey storm advisories [http://www.nhc.noaa.gov/archive/2017/HARVEY.shtml]
[2] NOAA NHC - Harvey 5-day forecasts [https://www.nhc.noaa.gov/gis/archive_forecast_results.php?id=al09&year=2017&name=Hurricane%20HARVEY]
[3] NOAA NHC - best tracks for 2017 storms [https://www.nhc.noaa.gov/data/tcr/index.php?season=2017&basin=atl]
[4] NOAA NHC - Harvey advisory points web service [https://services.arcgis.com/XSeYKQzfXnEgju9o/ArcGIS/rest/services/The_2017_Atlantic_Hurricane_season_(to_October_16th)/FeatureServer/0]
[5] NOAA NHC - Harvey advisory lines web service [https://services.arcgis.com/XSeYKQzfXnEgju9o/ArcGIS/rest/services/The_2017_Atlantic_Hurricane_season_(to_October_16th)/FeatureServer/5]

ABSTRACT:

These datasets were obtained from ECMWF/GloFAS on November 13, 2017, to include the flood forecast (area grid) for Hurricanes Harvey and Irma in the USA from August 15 - September 15, 2017. These are contained in netCDF files, one per day.

Note that while folders and files may have the words "areagrid_for_Harvey" in the name, all the data here are for the southeast USA, encompassing both Harvey and Irma impact areas.

Dataset variables:
- dis = forecasted discharge (for all forecast step 1+30 as initial value and 30 daily average values, with ensemble members as 1+50 where the first is the so-called control member and the 50 perturbed members)
- ldd = local drainage direction within routing model
- ups = upstream area of each point within routing model
- rl2,rl5,rl20 = forecast exceedance thresholds for 2-, 5- and 20-year return period flows, based on gumbel distribution from ERA-interim land reanalysis driven through the lisflood routing.

Models used (see [2] for further details):
- Hydrology: River discharge is simulated by the Lisflood hydrological model (van der Knijff et al., 2010) for the flow routing in the river network and the groundwater mass balance. The model is set up on global coverage with horizontal grid resolution of 0.1° (about 10 km in mid-latitude regions) and daily time step for input/output data.
- Meteorology: To set up a forecasting and warning system that runs on a daily basis with global coverage, initial conditions and input forcing data must be provided seamlessly to every point within the domain. To this end, two products are used. The first consists of operational ensemble forecasts of near-surface meteorological parameters. The second is a long-term dataset consistent with daily forecasts, used to derive a reference climatology.

Suggestions for usage:
- Selected software: ArcGIS or QGIS
- Select dis for example, then any of the bands (51*31 in total), then set the range manually to 0-1000 or something like that.

Agency:
GloFAS [1]
From its public website: "The Global Flood Awareness System (GloFAS), jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF), is independent of administrative and political boundaries. It couples state-of-the art weather forecasts with a hydrological model and with its continental scale set-up it provides downstream countries with information on upstream river conditions as well as continental and global overviews. GloFAS produces daily flood forecasts in a pre-operational manner since June 2011."

References
[1] GloFAS home page [http://www.globalfloods.eu/]
[2] Data and methods [http://www.globalfloods.eu/user-information/data-and-methods]

ABSTRACT:

During and after Hurricane Harvey, the US Geological Survey recorded high water marks across southeast Texas, as they do for every major storm. The files in this dataset provide 2123 high water marks for Hurricane Harvey flooding, among 1258 sites. These files were downloaded following the steps below. If you'd like to check the original sources again, or search for HWM for a different storm, you may find these directions helpful.

Finding, Downloading and Filtering USGS High Water Marks (HWM)
1. Visit USGS website: https://water.usgs.gov/floods/history.html, which lets you…
2. Click on Hurricane Harvey: https://www.usgs.gov/harvey, which lets you…
3. Click on green button Get Data: https://stn.wim.usgs.gov/fev/#HarveyAug2017
4. In left margin menu of resulting page, click a second Get Data link. This will open up the remaining options below.
5. Click each data type you want, such as High-Water Mark, Peak Summary, or Sensor Data. It’s only csv or REST (json or xml).

* I downloaded the HWM as csv, opened in Excel, clicked the Sort & Filter tool in Excel toolbar, clicked Filter, then filtered on "Harvey Aug 2017” in the popup list for column E (Event Name). I saved the result to a new spreadsheet which now has 2123 records, plus column labels in row 1.

* To understand the fields or columns of this table, see HWM_Peaks_Sensors_Data_Dictionary_20180329.xslx in the contents below.

ABSTRACT:

The National Water Model (NWM) is a water forecasting model operated by the National Water Center (NWC) of the NOAA National Weather Service. The NWM continually forecasts flows on 2.7 million stream reaches covering 3.2 million miles of streams and rivers in the continental United States [1]. It operates as part of the national weather forecasting system, with inputs from NOAA numerical weather prediction models, and from weather and water conditions observed through the US Geological Survey's National Water Information System. Reference materials for the computational framework behind NWM is published by NCAR [9] [10].

The NWC generates NWM streamflow forecasts for the continental US (CONUS) with multiple forecast horizons and time steps. Due to the output file sizes, these are normally not available for download more than a couple days at a time [2]. However, a 40-day rolling window of these forecasts is maintained by HydroShare at RENCI [3], and a complete retrospective (August 2016 to the present) of the NWM Analysis & Assimilation outputs is maintained as well (contact help@cuahsi.org for access).

An archive of all NWM forecasts for the period Aug 18 to Sept 10, 2017 has been compiled at RENCI [4] [5], available as netCDF (.nc) files totaling 8TB. These can be browsed, subsetted, visualized, and downloaded (see [6] [7] [8]). In addition to these output files, we have uploaded to this HydroShare resource the input parameter files needed to re-run the NWM for the Harvey period, or for any time period covered by NWM v1.1 and 1.2 (August 2016 to this publication date in August 2018). These parameter files are also made available at [1].

See README for further details and usage guidance. Please see NOAA contacts listed on [1] for questions about the NWM data contents, structure and formats. Contact help@cuahsi.org if any questions about HydroShare-based tools and data access.

References
[1] Overview of the NWM framework and output files [http://water.noaa.gov/about/nwm]
[2] Free access to all National Water Model output for the most recent two days [ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/nwm]
[3] NWM outputs for rolling 40-day window, maintained by HydroShare [http://thredds.hydroshare.org/thredds/catalog/nwm/catalog.html]
[4] Archived Harvey NWM outputs via RENCI THREDDS server [http://thredds.hydroshare.org/thredds/catalog/nwm/harvey/catalog.html]
[5] RENCI is an Institute at the University of North Carolina at Chapel Hill
[6] Live map for National Water Model forecasts [http://water.noaa.gov/map]
[7] NWM Forecast Viewer app [https://hs-apps.hydroshare.org/apps/nwm-forecasts]
[8] CUAHSI JupyterHub example scripts for subsetting NWM output files [https://hydroshare.org/resource/3db192783bcb4599bab36d43fc3413db/]
[9] WRF-Hydro Overview [https://ral.ucar.edu/projects/wrf_hydro/overview]
[10] WRF-Hydro User Guide 2015 [https://ral.ucar.edu/sites/default/files/public/images/project/WRF_Hydro_User_Guide_v3.0.pdf]

ABSTRACT:

This collection contains map data often used as base layers for hydrologic and geographic analysis, organized by these categories:
- Addresses and Boundaries (Texas address points, counties, Councils of Government boundaries, Texas Dept of Public Safety districts and regions)
- Hydrology (streams, gages, dams, catchments, watersheds)
- Transportation (Texas roads, railways, bridges, low water crossings)

The Addresses, Transportation and Dams datasets are for Texas only, but the remaining Hydrology data covers an area of 39 HUC6 basins around the Harvey zone across southeast Texas, Louisiana, Mississippi and Arkansas.

These data layers generally date to 2015-2016, so could be considered reasonably representative of the base layers at the time of Hurricane Harvey.

The Texas Address and Base Layers Story Map referenced here [1] is an interactive web app supported by Esri ArcGIS Online, that provides visualization and access to specific data layers for Texas only.

One other base layer is the Social Vulnerability Index (SVI) developed by the U.S. Centers for Disease Control (CDC). This is used by the emergency response community to anticipate areas where social support systems are weaker, and residents may be more likely to need help.

References
[1] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/]

ABSTRACT:

This site provides access to download an ArcGIS geodatabase or shapefiles for the 2017 Texas Address Database, compiled by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide. This is part of the Texas Water Model (TWM), a project to adapt the NOAA National Water Model [1] for use in Texas public safety. This database was compiled over the period from June 2016 to December 2017. A number of gaps remain (towns and cities missing address points), see Address Database Gaps spreadsheet below [4]. Additional datasets include administrative boundaries for Texas counties (including Federal and State disaster-declarations), Councils of Government, and Texas Dept of Public Safety Regions. An Esri ArcGIS Story Map [5] web app provides an interactive map-based portal to explore and access these data layers for download.

The address points in this database include their "height above nearest drainage" (HAND) as attributes in meters and feet. HAND is an elevation model developed through processing by the TauDEM method [2], built on USGS National Elevation Data (NED) with 10m horizontal resolution. The HAND elevation data and 10m NED for the continental United States are available for download from the Texas Advanced Computational Center (TACC) [3].

The complete statewide dataset contains about 9.28 million address points representing a population of about 28 million. The total file size is about 5GB in shapefile format. For better download performance, the shapefile version of this data is divided into 5 regions, based on groupings of major watersheds identified by their hydrologic unit codes (HUC). These are zipped by region, with no zipfile greater than 120mb:
- North Tx: HUC1108-1114 (0.52 million address points)
- DFW-East Tx: HUC1201-1203 (3.06 million address points)
- Houston-SE Tx: HUC1204 (1.84 million address points)
- Central Tx: HUC1205-1210 (2.96 million address points)
- Rio Grande-SW Tx: HUC2111-1309 (2.96 million address points)

Additional state and county boundaries are included (Louisiana, Mississippi, Arkansas), as well as disaster-declaration status, for use with the Hurricane Harvey 2017 Data Archive at HydroShare [7].

Compilation notes: The Texas Commission for State Emergency Communications (CSEC) provided the first 3 million address points received, in a single batch representing 213 of Texas' 254 counties. The remaining 41 counties were primarily urban areas comprising about 6.28 million addresses (totaling about 9.28 million addresses statewide). We reached the GIS data providers for these areas (see Contributors list below) through these emergency communications networks: Texas 9-1-1 Alliance, the Texas Emergency GIS Response Team (EGRT), and the Texas GIS 9-1-1 User Group. The address data was typically organized in groupings of counties called Councils of Governments (COG) or Regional Planning Commissions (RPC) or Development Councils (DC). Every county in Texas belongs to a COG, RPC or DC. We reconciled all counties' addresses to a common, very simple schema, and merged into a single geodatabase.

November 2023 updates: In 2019, TNRIS took over maintenance of the Texas Address Database, which is now a StratMap program updated annually [6]. In 2023, TNRIS also changed its name to the Texas Geographic Information Office (TxGIO). The datasets available for download below are not being updated, but are current as of the time of Hurricane Harvey.

References:
[1] NOAA National Water Model [https://water.noaa.gov/map]
[2] TauDEM Downloads [https://hydrology.usu.edu/taudem/taudem5/downloads.html]
[3] NFIE Continental Flood Inundation Mapping - Data Repository [https://web.corral.tacc.utexas.edu/nfiedata/]
[4] Address Database Gaps, Dec 2017 (download spreadsheet below)
[5] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/]
[6] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]
[7] Hurricane Harvey 2017 Data Archive Story Map [https://arcg.is/1rWLzL0]

ABSTRACT:

This resource contains Texas statewide hydrologic map data for about 100,000 NHD (National Hydrography Dataset) stream reaches and associated catchments & subwatersheds [1], covering 190,000 stream miles in Texas. Additional map layers include dams, FEMA floodplains and warning zones [2], stream gages [3], and National Weather Service River Forecast Points [4].

The USGS stream gages and NWS AHPS forecast points both have a URL field, which takes you to the authoritative webpage for each selected gage or forecast point.

References
[1] NHDPlus Version 2 [http://www.horizon-systems.com/NHDPlus/V2NationalData.php]
[2] Esri Living Atlas [https://livingatlas.arcgis.com]
[3] USGS NWIS [https://waterdata.usgs.gov/nwis]
[4] NOAA AHPS [https://water.weather.gov/ahps/forecasts.php]

ABSTRACT:

This resource contains statewide networks of roadways, railroads, bridges, and low-water crossings, for Texas only.

Roadways detail: The Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) maintains a spatial dataset of roadway polylines for planning and asset inventory purposes, as well as for visualization and general mapping. M values are stored in the lines as DFOs (Distance From Origin), and provide the framework for managing roadway assets using linear referencing. This dataset covers the state of Texas and includes on-systems routes (those that TxDOT maintains), such as interstate highways, U.S. highways, state highways, and farm and ranch roads, as well as off-system routes, such as county roads and local streets. Date valid as of: 12/31/2014. Publish Date: 05/01/2015. Update Frequency: Quarterly.

Bridges detail: As with the roadways, both on-system and off-system bridges are maintained in separate datasets (54,844 total bridges, 36,007 on-system and 18,837 off-system). Bridges have numerous useful attributes, see coding guide [1] for documentation. One such attribute identifies structures that cross water: the second digit of Item 42 “Type of Service”. If the second digit is between 5 and 9 (inclusive) then the structure is over water. The bridges datasets are valid as of December 2016.

The roadways and bridges datasets contained here were obtained directly from TxDOT through personal correspondence. An additional transportation data resource is the Texas Natural Resources Information System (TNRIS) [3]. The railroads and low-water crossings were obtained through TNRIS.

November 2023 updates: in the years since this data archive was first published, TxDOT has developed an open data portal for downloading their roadway inventory and other datasets. Also, in 2023 TNRIS was renamed as the Texas Geographic Information Office (TxGIO). Their datahub [3] is continually evolving, but still has the tnris.org domain for now. We are not updating any of the basemap data in the contents list below, which was current at the time of Hurricane Harvey.

References
[1] TxDOT Bridges Coding Guide (download below)
[2] TxDOT Open Data Portal [https://gis-txdot.opendata.arcgis.com/]
[3] TNRIS/TxGIO data downloads [https://data.tnris.org/]

ABSTRACT:

This is a step-by-step demonstration of how to browse NASA data services for land surface maps and time series data using the Data Rods Explorer (DRE) App [1]; followed by a step by step demonstration of how to compare a single model variable for a single location over multiple years. See the DRE User Guide [2] for complete description of this application.

References
[1] Data Rods Explorer App [https://apps.hydroshare.org/apps/data-rods-explorer/]
[2] DRE User Guide [https://github.com/gespinoza/datarodsexplorer/blob/master/docs/DREUserGuide.md]

ABSTRACT:

This dataset includes concentration and redox speciation of analytes (iron, sulfur, nitrogen) from targeted chemical extraction of surface soil samples collected from the Luquillo CZO in September 2011 as well as greenhouse gas flux rates (carbon dioxide, methane, and nitrous oxide) for incubated subsamples of these surface soils under experimental manipulations of anaerobiosis (oxygen removal) and flooding.

The metadata file provides treatment key.

Statistical summaries and methodological details for these data have been published at doi:10.1002/2013JG002433.

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

This version updated for the sopron workshop in Hungary(15~18 April, 2018)

ABSTRACT:

Height Above Nearest Drainage (HAND) is an approach for estimating the vertical height of any point on the landscape from the nearest stream surface or bed. This dataset is based on the U.S. Geological Survey's National Elevation Dataset (NED) with 10-meter horizontal resolution, comprising raster data for the 331 HUC-6 units in conterminous U.S. (CONUS), excluding the five units of the great lakes. This was developed at the UIUC CyberGIS supercomputing facility, and is now archived at the UT Austin TACC (Texas Advanced Computing Center) for download.

To interactively select HAND data by HUC6 basin in either the Harvey or Irma hydrologic study area, use the Harvey Archive Story Map [http://arcg.is/001jje] or the Irma Archive Story Map [http://arcg.is/PSOKH] and click on the HAND tab. To directly browse this data for anywhere in CONUS, visit [https://web.corral.tacc.utexas.edu/nfiedata/].

For an explanation of the contents of the nfiedata folder at TACC, see README file for this resource.

ABSTRACT:

This resource describes a dataset of gridded depth at horizontal resolution of 3 meters, published November 15, 2017, downloaded from FEMA [1] and hosted in this archive at the University of Texas Advanced Computing Center (TACC) [2].. The raster dataset is contained within an Esri ArcGIS geodatabase. This product utilized Triangulated Irregular Network (TIN) interpolation, four quality assurance measures (identifying dips, spikes, duplication, and inaccurate/unrealistic measurements). High Water Marks were obtained from the Harris County Flood Control District (HCFCD), US Geological Survey (USGS), and other inspection data. Elevation data comprised a mosaic of 3 meter resampled elevations from 1M & 3M LiDAR, and IFSAR data. One section of the IfSAR data was found to be erroneous, and replaced with a blended 10 meter section.
[This description was in correspondence January 22, 2018, from Mark English, GeoSpatial Risk Analyst, FEMA Region VIII, Mitigation Division.]

A preliminary version of these depths dated September 10, 2017 can be viewed in a FEMA web map [3]. This web map shows a forecasted depth grid, based on National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasted water levels.

See FEMA's Natural Hazard Risk Assessment Program (NHRAP) ftp site [4] for additional HWM-based depth grids and inundation polygons:
- Harris County AOIs and Inundation Boundaries [5]
- Harris County Depth Grids [6]
- Aransas, Nueces, and San Patricio Coastal Depth Grids and Boundaries [7]
FEMA notes on these Modeled Preliminary Observations:
o Based on observed Water Levels at stream gauges interpolated along rivers, downsampled to 5m resolution DEM
o Depth grids updated with new observed peak crest as they become available
o Will include High Water Mark information as it becomes available
o Extents validated with remote sensing
o Use for determining damage levels on specific structures

See also FEMA's journal of mitigation planning and actions related to Harvey [8].

References and related links:
[1] FEMA_Depths_3m_v3.zip (39 gb ftp download) [https://data.femadata.com/Region8/Mitigation/Data_Share/]
[2] TACC 39gb wget or ftp download [https://web.corral.tacc.utexas.edu/nfiedata/Harvey/flood_data/FEMA_Harvey_Depths_3m.gdb.zip]
[3] FEMA map viewer for Hurricane Harvey resources (flood depths is bottom selection in layers list) [https://fema.maps.arcgis.com/apps/webappviewer/index.html?id=50f21538c7bf4e08b9faab430bc237c9]
[4] FEMA NHRAP ftp [https://data.femadata.com/FIMA/NHRAP/Harvey/]
[5] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harris_AOIandBoundaries.zip]
[6] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harris_Mosaic_dgft.zip]
[7] [https://data.femadata.com/FIMA/NHRAP/Harvey/Rockport_DG_unclipped.zip]
[8] Hurricane Harvey Mitigation Portfolio - FEMA map journal [https://fema.maps.arcgis.com/apps/MapJournal/index.html?appid=70204cf2762d45409553fd9642700b7f]

ABSTRACT:

The 2017 Atlantic hurricane season was among the busiest on record, producing 18 tropical depressions, 18 tropical storms, 10 hurricanes that occurred in succession, and 23 separate landfalls by Atlantic named storms. Six of the ten hurricanes further  strengthened into major hurricanes. Three of these were Harvey (TX-LA-MS), Irma (FL-GA-NC), and Maria (Puerto Rico and other Caribbean islands). These three were so devastating, and so close to each other in time, that the National Science Foundation (NSF) urged the research community to apply for NSF RAPID grants to study these hurricanes and their impacts, to better understand how these occurred. A team led by David Tarboton, David Maidment, Jerad Bales, and Ray Idaszak received funds [1] to build a storm and flood data archive for Harvey and Irma, to be housed on HydroShare, and managed by CUAHSI. The project period is from October 2017 to September 2018. This presentation summarizes work to date (April 2018) on the Harvey part of the collection.

The downloadable datasets are accessed via HydroShare. Some large datasets are hosted on RENCI iRODS and the Univ of Texas Advanced Computing Center (TACC) facilities.
- Use the Hurricane Harvey 2017 Story Map [2] for contextual access to the data, via links in the side panel on each tab. 
- For file browser access to the archive, visit the Hurricanes data collection on HydroShare [3].
- Visit the CUAHSI project page [4] to learn more about the project, and for access to Irma and Maria data.

[1] NSF RAPID Grant [https://nsf.gov/awardsearch/showAward?AWD_ID=1761673]
[2] Hurricane Harvey 2017 Story Map [http://arcg.is/001jje]
[3 Hurricane collection root on HydroShare [https://www.hydroshare.org/resource/2836494ee75e43a9bfb647b37260e461/]
[4] CUAHSI project landing page [https://www.cuahsi.org/projects/hurricanes-2017-data-archive]

ABSTRACT:

This dataset holds all the data measured for the MSc Hydrology fieldwork from 15 till 21st of April 2018. It includes measurements of EC, pH, Alkalinity, Hardness, Phosphorus, Nitrate and the coordinates. It is the collection of data of 5 groups on 5 days.

ABSTRACT:

The Florida Department of Environmental Protection (FDEP) conducts annual probabilistic surveys of Florida’s freshwater resources (flowing waters consisting of rivers, streams, and canals, lakes, and unconfined aquifers). FDEP incorporated organo-nitrogen and organo-phosphorous pesticides, their degradants, and the neonicotinoid imidacloprid into their probabilistic sample surveys for unconfined aquifers in 2015, flowing waters in 2016, and lakes in 2017. Pesticide use intensities (kilograms per hectare) were derived for three classes of pesticides (herbicides, fungicides & insecticides) by creating chloropleth maps of estimated usage versus total agricultural area within 29 drainage basins. Total fresh water concentrations of the most frequently detected pesticides were derived by dissolving data at the sampling locations and aggregating their values. Two graduated symbol maps present these results graphically, one map depicts total number of detections for each sampling location and the other map shows total concentrations of the most frequently detected compounds. The investigation shows the occurrence of these pesticides and their degradants within and among Florida’s freshwater resources and its relationship to pesticide use.

ABSTRACT:

The CUAHSI-SCOPE team conducted user-based research to evaluate and design an improved user experience for HydroShare. The user-oriented project focused on identifying key users and workflows, defining current limitations of the system, and developing a comprehensive document of design recommendations.

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

This version updated for the sopron workshop in Hungary(15~18 April, 2018)

ABSTRACT:

Snow season length (SS), reported in days, is the length of time that snow is present on the ground on an annual basis. It is determined by finding the first and last snow occurrence for any given water year (Oct 1 - Sep 30 NH, Jan 1 - Dec 31 SH), and then finding the difference between these two dates. SS was calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC) for global calculation and MOD10A1 for US calculation. Spatial coverage is for MODIS tiles h08v04, h08v05, h09v04, h09v05, and h10v04 for water years 2001 - 2015. Files are provided in the "USA Contiguous Albers Equal Area Conic USGS" projection. Funding provided by NSF grant EAR-1446870.

ABSTRACT:

Flooding analysis on Nakhon si thammarat area in THAILAND

ABSTRACT:

This document provides an overview of the accompanying data files used in the production of the manuscript entitled "Stream-centric methods for establishing groundwater contributions in karst mountain watersheds".

LR_site_Locations.xlsx:
Latitude and Longitude of sites gaged during the 2014, 2015, and 2016 sampling events.

LR_Field_Data_Summary.xlsx:
Flow and water quality data for the 2014, 2015, and 2016 sampling events.

LR_Chemistry_Summary.xlsx:
Latitude and Longitude of all springs sampled and the accompanying measured ion concentrations.

LR_Stream_Flow_Data.xlsx:
Daily averaged stream flow measurements used in the flow balance for R1, R2, R2a, and R2b.

ABSTRACT:

This resource contains medium-resolution (1:100k) National Hydrography Dataset (NHDPlus) [1] map data for a region of 39 Hydrologic Unit Code (HUC) 6-digit (HUC6) basins around the Hurricane Harvey impact zone across Texas, Louisiana, Mississippi and Arkansas. This includes 5978 subwatersheds, 190,192 catchments, and 192,267 flowlines.

USGS active stream gages (924) were downloaded from the USGS National Water Information System (NWIS) [2] and augmented with each gage's HUC2, HUC4, HUC6, HUC8, HUC10 & HUC12 basin identifiers, and COMID of the NHD stream reach for the containing catchment. This allows the user to easily aggregate gages by various watershed boundaries.

NOAA Advanced Hydrologic Prediction System (AHPS) [3] has 362 river forecast points in the Harvey study area. Many of these are co-located with USGS NWIS gages to leverage authoritative observation data.

A shapefile of Texas dams (7290) was directly received from the Texas Commission for Environmental Quality (TCEQ) [4]. They suggest if you have any questions about data, to make an Open Records Request [5].

References
[1] NHDPlus Version 2 [http://www.horizon-systems.com/NHDPlus/V2NationalData.php]
[2] USGS NWIS [https://waterdata.usgs.gov/nwis]
[3] NOAA AHPS [https://water.weather.gov/ahps/forecasts.php]
[4] TCEQ Data and Records [https://www.tceq.texas.gov/agency/data]
[5] TCEQ Open Records Request [https://www.tceq.texas.gov/agency/data/records-services/reqinfo.html]

ABSTRACT:

This resource contains Lidar-DEM collection status shapefiles from the Texas Natural Resources Information System (TNRIS) [http://tnris.org].
November 2023 updates: this year, TNRIS changed its name to Texas Geographic Information Office (TxGIO). The domain name hasn't changed yet, but the data hub is continually evolving. See [1], [2] for current downloadable data.

For purposes of Hurricane Harvey studies, the 1-m DEM for Harris County (2008) has also been uploaded here as a set of 4 zipfiles containing the DEM in tiff files. See [1] for a link to the current elevation status map and downloadable DEMs.
Project name: H-GAC 2008 1m
Datasets: 1m Point Cloud, 1M Hydro-Enforced DEM, 3D Breaklines, 1ft and 5ft Contours
Points per sq meter: 1
Total area: 3678.56 sq miles
Source: Houston-Galveston Area Council (H-GAC)
Acquired by: Merrick, QA/QC: Merrick
Catalog: houston-galveston-area-council-h-gac-2008-lidar

References:
[1] TNRIS/TxGIO StratMap elevation data [https://tnris.org/stratmap/elevation-lidar/]
[2] TNRIS/TxGIO DataHub [https://data.tnris.org/]

ABSTRACT:

This resource contains shapefiles for FEMA Damage Assessments, Auto Claims, and Property Claims, publicly available here [1].
Damage assessments are organized in daily map layers. These appear to be cumulative, but some days' records do not include all the previous days' records.
- Aug 27, 2017: Coastal damage assessments (26,027 records)
- Aug 28: Damage assessments (78,218)
- Aug 29: Damage assessments (115,412)
- Aug 30: Damage assessments (137,754)
- Aug 31: Damage assessments (161,366)
- Sep 02: Damage assessments (156,099)
A document is provided that explains the damage assessment methodology.

Auto and Property Claims are each in a single shapefile, containing all records from Aug 25-Sep 08:
- Auto claims, Aug 25-Sep 08 (203, 312 records)
- Property claims, Aug 25-Sep 08 (226,167)
These identify location, date and type of loss. These are all claims submitted during this period, which may include damages not caused by Hurricane Harvey.

Other damage assessments and inundation depth grids are available at the FEMA Natural Hazard Risk Assessment Program (NHRAP) ftp site [2]. These include:
- Windfield contours [3]
- PDC Hazus Wind Adv26 (Hurrevac) [4]

References
[1] FEMA Damage Assessments ftp [https://data.femadata.com/NationalDisasters/HurricaneHarvey/Data/DamageAssessments/]
[2] FEMA Natural Hazard Risk Assessment Program (NHRAP) ftp [https://data.femadata.com/FIMA/NHRAP/Harvey/]
[3] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harvey_WindSpeedContours.zip]
[4] [https://data.femadata.com/FIMA/NHRAP/Harvey/PDC_HAZUS_Damage_Loss_Assessment_ADV26_26AUG17_2100UTC.PDF]

ABSTRACT:

This collection contains Texas statewide map data often used as base layers for hydrologic and geographic analysis, organized by these categories:
- Addresses and Boundaries (Texas address points, counties, Councils of Government boundaries, Texas Dept of Public Safety districts and regions)
- Hydrology (streams, gages, dams, catchments, watersheds)
- Transportation (Texas roads, railways, bridges, low water crossings)

These data layers generally date to 2015-2016.

The Texas Address and Base Layers Story Map referenced here [1] is an interactive web app supported by Esri ArcGIS Online, that provides visualization and access to specific data layers for Texas only.

References
[1] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/]

ABSTRACT:

Datasets generated during Jabari Jones' Master's thesis at Utah State University, focused on channel change of Sixth Water Creek and Diamond Fork River, Utah, USA (Jones, J.C., 2018. Historical channel change caused by a century of flow alteration on Sixth Water Creek and Diamond Fork River, UT. Master's thesis, Utah State University). This resource includes data collected in the field as well as data generated in GIS. Field data include cross-section surveys, RTK GPS surveys, sediment transport measurements, bed grain size analysis, and unmanned aerial vehicle (drone) photography. GIS data include shapefiles generated from aerial imagery and digital elevation models. Data were collected and generated between July 2016 and May 2018 All data, metadata and related materials meet the quality standards relative to the purpose for which they were collected and generated.

ABSTRACT:

Advances in many domains of earth science increasingly require integration of information from multiple sources, reuse and repurposing of data, and collaboration. HydroShare is a web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare includes a repository for users to share and publish data and models in a variety of formats, and to make this information available in a citable, shareable, and discoverable manner. HydroShare also includes tools (web apps) that can act on content in HydroShare, providing users with a gateway to high performance computing and computing in the cloud. Jupyter notebooks, and associated code and data are an effective way to document and make a research analysis or modeling procedure reproducible. This presentation will describe how a Jupyter notebook in a HydroShare resource can be opened from a JupyterHub app using the HydroShare web app resource and API capabilities that enable linking a web app to HydroShare, reading of data from HydroShare and writing of results back to the HydroShare repository in a way that results can be shared among HydroShare users and groups to support research collaboration. This interoperability between HydroShare and other cyberinfrastructure elements serves as an example for how EarthCube cyberinfrastructure may integrate. Base functionality within JupyterHub supports data organization, simple scripting and visualization, while Docker containers are used to encapsulate models that have specific dependency requirements. This presentation will describe the strategy for, and challenges of using models in Docker containers, as well as using Geotrust software to package computational experiments as 'geounits', which are reproducible research objects that describe and package computational experiments.

Presentation at EarthCube all hands meeting, June 6-8, 2018, Washington, DC https://www.earthcube.org/ECAHM2018

ABSTRACT:

These date files provide observations, model calibration, and model testing results used in King and Neilson 2019, "Quantifying Reach-Average Effects of Hyporheic Exchange on Arctic River Temperatures in an Area of Continuous Permafrost" published in Water Resources Research.

Note on Units:
All temperature values are in degrees Celsius.
All discharge values are in cubic meters per second
All solute concentrations are in milligrams sodium-chloride per L

Files Description:
ParetoOptimal_Solute_Temperature.dat:
Time series of simulated main channel solute breakthrough curve and temperature at 1500 m downstream of the injection location using the Pareto optimal model calibration.

Solute_EndMember_Solute_Temperature.dat:
Time series of simulated main channel solute breakthrough curve and temperature at 1500 m downstream of the injection location using the solute end member model calibration.

Temperauture_EndMember_Solute_Temperature.dat:
Time series of simulated main channel solute breakthrough curve and temperature at 1500 m downstream of the injection location using the temperature end member model calibration.

ParetoOptimal_HTS_Solute.dat:
Time series of simulated solute breakthrough curve in sediment at 1500 m downstream of the injection location using the Pareto optimal model calibration.

ParetoOptimal_HTS_Temp.dat:
Time series of simulated sediment temperature at 1500 m downstream of the injection location using the Pareto optimal model calibration.

YYYY-Site9-Site8_HeatFluxes-withHTS.DAT:
Time series of simulated heat fluxes for the test reach for the simulation period in year “YYYY” using the Pareto optimal model calibration.

YYYY-Site9-Site8_ModelOutput-withHTS.DAT:
Time series of simulated temperature at Site8 for the test reach for the simulation period in year “YYYY” using the Pareto optimal model calibration.

YYYY-Site9-Site8_ModelOutput-noHTS.DAT:
Time series of simulated temperature at Site8 for the test reach for the simulation period in year “YYYY” using the Pareto optimal model calibration, but setting QHTS to zero.

2015-Site8_HeatFlux_SedWarm+X.DAT:
Time series of simulated heat fluxes for the test reach in the simulation period in 2015 using Pareto optimal model calibration and changing observed ground temperatures by X degrees C.

2015-Site8_ModelTemp_SedWarm.DAT:
Time series of simulated main channel temperatures at Site8 for the test reach in the simulation period in 2015 using Pareto optimal model calibration and changing observed ground temperatures by plus or minus 1, 2 or 4 degrees C.

Site8_MCTemp_2013-2017_DegC.csv:
Time series of observed main channel temperature at Site 8.

Site8Discharge_cms.csv:
Time series of observed discharge at Site 8.

Site9_MCTemp_2013-2017_DegC.csv:
Time series of observed main channel temperature at Site 9.

Site9_Discharge_cms.csv:
Time series of observed discharge at Site 9.

Site9_Sediment_Temperatures_DegC.csv:
Time series of sediment/ground temperatures at Site 9 from 2017 at depths of 10, 20, 30, 40, 50, and 60 cm below the river bed.

201707DDTS.csv:
Time series of main channel and piezometer solute concentrations and temperatures during tracer studies conducted on the “DD” day of July 2017.

Model_Cell_Extracted_Wetted_Widths_m_And_Interpolated_Discharge_cms.csv:
Wetted widths extracted from aerial imagery and associated spatially interpolated discharge for each 10 m model cell. These data were used to estimate reach average wetted widths for the calibration and test simulations.

KupSolarRad_2014-2016.csv:
Observed incoming and outgoing shortwave radiation at Site 9 in the summers (June – August) of 2014, 2015, and 2016. These observations were used to estimate time varying albedo.

ABSTRACT:

The Civil Air Patrol is routinely tasked by FEMA and local public safety officials with taking aerial photographs. This collection comprises nearly 30,000 photos taken over the Hurricane Harvey study area, between August 19, 2017 and June 2, 2018. The majority of this collection were taken over southeast Texas from August 10 to September 2, 2017. These were originally uploaded to the web using the GeoPlatform.gov imageUploader capability, and hosted as a web map layer [1]. For this Harvey collection, I exported the dataset of photo location points to a local computer, subset it to the Harvey event, and created a shapefile, which is downloadable below. The photos and thumbnails were not included in this archive, but are attribute-linked to the FEMA-Civil Air Patrol image library on Amazon cloud [2].

The primary resource for these photos is the University of Texas at Austin Center for Space Research (UT CSR), hosted at the Texas Advanced Computational Center (TACC) [3]. These photos are organized by collection date, and each date folder has photo metadata in Javascript (js) and json format files. UT CSR has published a separate web app for browsing these photos [4], as well as several other flood imagery sources.

Note: The cameras used by the Civil Air Patrol do not have an electronic compass with their GPS to record the viewing direction. The easiest way to determine the general angle is to look at consecutive frame counterpoints to establish the flightpath direction at nadir and adjust for the photographer's position behind the pilot looking out the window hatch on the port (left) side of the aircraft. The altitude above ground level is typically between 1000-1500 feet, so it's easy to locate features in reference orthoimages.

Another source of aerial imagery is from the NOAA National Geodetic Survey (NGS) [5]. This imagery was acquired by the NOAA Remote Sensing Division to support NOAA homeland security and emergency response requirements.

References
[1] US federal GeoPlatform.gov Image Uploader map service (ArcGIS Server) [https://imageryuploader.geoplatform.gov/arcgis/rest/services/ImageEvents/MapServer]
[2] FEMA-Civil Air Patrol image library on Amazon cloud [https://fema-cap-imagery.s3.amazonaws.com]
[3] UT CSR primary archive for Harvey photos on TACC [https://web.corral.tacc.utexas.edu/CSR/Public/17harvey/TxCAP/]
[4] UT CSR web app for browsing CAP photos [http://magic.csr.utexas.edu/hurricaneharvey/public/]
[5] NOAA NGS Hurricane Harvey Imagery [https://storms.ngs.noaa.gov/storms/harvey/index.html#7/28.400/-96.690]

ABSTRACT:

Representing urban water demands economically is useful to understand how anticipated changes like population growth, conservation, water development, climate change, and environmental water demands may affect water deliveries and scarcity. Utah is the second driest state in the nation, while per capita water use is near the highest in the nation, averaging 167 gallons per person per day. This implies that creative water management will be ongoing in Utah’s future. Urban economic loss functions are estimated using residential demand functions for Utah’s Wasatch Front Metropolitan Area, which includes Logan, Salt Lake City, Ogden, Layton, Provo, and Orem urban regions. Water price, volume of water applied at that price, urban population, and price elasticity data are presented. Results show seasonal residential water demand functions and seasonal urban (residential, industrial, institutional, and commercial) economic loss functions for Logan, Ogden, Salt Lake City, and Provo metropolitan areas. Limitations to this method are outlined and discussion focuses on estimating urban water demand functions and potential economic losses input into hydro-economic models and ecological-economic models to evaluate promising solutions to Utah’s persistent water problems.

ABSTRACT:

This is the Social Vulnerability Index (SVI) developed by the U.S. Centers for Disease Control (CDC) [1]. This is often used by the emergency response community to anticipate areas where social support systems are weaker, and residents may be more likely to need help. A map viewer for the national database can be found here [2].

November 2023 updates: at the time of Hurricane Harvey, the latest SVI was based on 2014 census data. The CDC SVI website and feature services have since changed. See the current (updated) links for more details.

Subsets of CDC's 2014 SVI for the Hurricane Harvey and Hurricane Irma hydrologic study areas can be downloaded from the contents list below.

[1] SVI web site [https://www.atsdr.cdc.gov/placeandhealth/svi/index.html
[2] SVI interactive map [https://www.atsdr.cdc.gov/placeandhealth/svi/interactive_map.html]]

ABSTRACT:

The Central Siberia Plateau (CSP) is undergoing rapid climate change resulting in increasing frequency of forest fires, which have uncertain effects on organic matter and nutrient delivery from headwater streams to downstream ecosystems. Across a fire chronosequence (3 to >100 years) underlain by continuous permafrost, we quantified the effects of wildfire on quantity and quality of dissolved organic matter (DOM) and inorganic nutrients in streams. Wildfire decreased DOM concentrations for about 50 years, but elevated nitrate (NO3-) concentrations lasted only 10 years; ammonium and phosphate concentrations were unchanged. This increase in NO3- and decrease in dissolved organic carbon (DOC) results in a wide range of DOC:NO3-, a ratio that is known to regulate NO3- uptake and denitrification in streams. Ultrahigh-resolution mass spectrometry and DOM optical properties showed that the composition of stream DOM changes after fire, with decreased abundance of polyphenols and aliphatic forms of DOM that are typically more biolabile than other forms of OM. Increasing wildfire frequency is thus likely to have major shifts in the metabolism, carbon flux, and nutrient balance of Arctic fluvial systems.

ABSTRACT:

HydroShare is a web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). Within HydroShare, users can create and share data and models using a variety of file formats and flexible metadata. HydroShare enables users to formally publish these resources as well as create linkages between published data and model resources and peer reviewed journal publications that describe them. Ability to link published data and models with the papers that describe them is a great step in the direction of scientific reproducibility, but is only a first step. HydroShare supports further transparency in the scientific process by enabling scripting of analytical steps via a RESTful application programming interface (API). Using this API, HydroShare users can develop scripts to read data from HydroShare, perform an analytical step (e.g., data processing or visualization), and then write results back to HydroShare. The script itself can then be shared as part of the published dataset in HydroShare, or it can be shared as a Jupyter Notebook that can be executed within the HydroShare environment. Scripts or Jupyter Notebooks can then be executed by others to reproduce the analysis used by the original authors. In this presentation, we discuss how HydroShare can enable best practices for linking publications with data and models and for promoting reproducibility in environmental analyses through sharing of data, models, and scripts that encode the scientific workflow. The HydroShare system is available at http://www.hydroshare.org. Source code for HydroShare is available at https://github.com/hydroshare.

ABSTRACT:

Data collection on water the potential for water markets to address Great Salt Lake water conservation needs.

To create cost estimates we build on the approach of Edwards et al (2017) to create conservation cost curves estimates for each of the Bear River, Weber River, and Jordan River watersheds. We designate potential conservation measures as occurring in the agricultural or urban sectors. Estimates come from other sources and are then applied to the case at hand. Overall we estimate the conservation potential and cost savings of 15 measures, shown in the table. While the list is not comprehensive, efforts were made to include the measures likely to be implemented.

Conservation measures by category.
Urban Residential: low-flow toilets
Residential: low-flow showers
Residential: high-efficiency clothes washers
Residential irrigation: rainwater harvesting
Residential irrigation: watering at night
Residential irrigation: scheduling
Residential irrigation: partial turf conversion
Institutional irrigation: watering at night
Institutional irrigation: scheduling
Commercial irrigation: watering at night
Commercial irrigation: scheduling
Secondary wastewater reuse
Agriculture Conversion to sprinkler irrigation
Improved irrigation efficiency
Canal piping

For each conservation we estimate the quantity of water that could be conserved as well as the cost of conserving that water. We create a low, baseline, and high estimate of costs for each measure. We also create a high, baseline, and low estimate of the amount of water made available by each measure. The low cost estimate is combined with the high water availability estimate to arrive at an upper bound of each water supply curve; similarly the high-cost and low water-availability estimates are combined to create a lower-bound.

ABSTRACT:

Snow chemistry data from the Uinta Mountains in the upper Provo River Watershed. Data set includes sampling location, water isotopes, disolved organic carbon(DOC), flitered major and trace elements, and 87Sr/86Sr ratios.

ABSTRACT:

Water chemistry data for the upper Provo River at three aquatic sites, Soapstone, Woodland and Hailstone. Data set includes, water isotopes, DOC, trace elements, major elements, and 87Sr/86Sr ratios.

ABSTRACT:

Soil chemistry for two locations in the upper Provo River watershed of the Uinta Mountains. Data set includes sequential leaches of soil pits in 10 cm increments for trace elements, major elements and 87Sr/86Sr ratios.

ABSTRACT:

Water chemistry data for springs, soil water, and ephemeral streams in the upper Provo river within the Uinta Mountains. Data set includes water isotopes, trace elements, major elements, 87Sr/86Sr, DOC, and various other water quality measurements.

ABSTRACT:

This resource contains a model that enables to obtain the flood hydrographs or the peak discharges for a real basin in Sicily and a set of return times.

ABSTRACT:

A permeable pavement green infrastructure (GI) site, located in Philadelphia, Pennsylvania, USA, was simulated using ParFlow.CLM. We utilized 1-m horizontal gridding, representing a site area of 400 square meters. Vertical discretization was variable ranging from 0.01 to 5m with the terrain-following grid feature of ParFlow.CLM activated. The model domain contains 8,000 finite difference cells. The simulation period was 1 January 2016 to 31 December 2016.
The zip archive (.zip) contains the inputs required to execute the model run. These include: ParFlow binary files (.pfb) for the geologic unit indicator field and pressure initial condition, CLM text files (.dat and .txt) for input parameters, 1-D NLDAS2 forcing, vegetation matrix and vegetation parameters, and a ParFlow input tcl file to generate model input database (.tcl). CLM restart files (.rst) contain the CLM initial condition. A Bash shell script (.sh) containing model dimensions is also included.

ABSTRACT:

HYMOD2 is an improved version of the widely used hydrological model HYMOD. The improvements are made based on simple but realistic evaporation process parameterizations. The file "hymod2.m" is the model code, which is also well commented so that it is easy to follow.

The input file needs to be in MATLAB data format (.mat). Rows should include daily sequence and columns are for the following variables: Precipitation (column-1), Potential Evaporation (column-2) and Discharge (column-3). Check "LoadData.m" function for input information and make changes as necessary. Parameter values are assigned in "PriorHyModV2.m" function. Results are stored in "Results.mat" file.

Disclaimer:
This program comes "as is" and the authors cannot be held responsible for any issues resulting from the improper functioning of the program. If found, please report the bugs to royt@email.arizona.edu. Prompt action will be taken to fix reported bugs.

HYMOD2 Citation: ​
Roy, T., H. V. Gupta, A. Serrat-Capdevila, and J. B. Valdes (2017), Using Satellite-Based Evapotranspiration Estimates to Improve the Structure of a Simple Conceptual Rainfall-Runoff Model, Hydrology and Earth System Sciences, 21(2), 879–896, doi:10.5194/hess-21-879-2017.

HYMOD Citation:
​Boyle, D. P., H. V Gupta, and S. Sorooshian (2000), Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resources Research, 36, 3663– 3674, doi:10.1029/2000WR900207.

ABSTRACT:

Quick Start
This is a collection of flood datasets to support hydrologic research for Hurricane Irma in Florida-Georgia, August-September 2017. The best way to start exploring this collection is by opening the Hurricane Irma 2017 Story Map [http://arcg.is/PSOKH]. It has separate tabs for the different content categories, and links to the relevant HydroShare resources within this collection.
For more information on this hurricane archive project, as well as links to Hurricanes Harvey and Maria data archives, see the CUAHSI public page on the Hurricane 2017 Archives. [1]

More Details
This is the root collection resource for management of hydrologic and related data collected during Hurricane Irma, primarily in Florida, Georgia, and neighboring states within the storm's wind swath. 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. Building outlines (polygons) for the affected states are also provided, to help understand storm impacts.

The data providers for this collection are the NOAA National Weather Service, NOAA National Hurricane Center, NOAA National Water Center, FEMA, 9-1-1 emergency communications agencies, and many others.

User-contributed resources from 2017 US Hurricanes may also be shared with The CUAHSI 2017 Hurricane Data Community group [2] to make them accessible to interested researchers, Anyone may join this group.

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" [4].

References
[1] CUAHSI Projects > Hurricane 2017 Archives [https://www.cuahsi.org/projects/hurricanes-2017-data-archive ]
[2] CUAHSI 2017 Hurricane Data Community group [https://www.hydroshare.org/group/41]
[3] Hurricane Irma 2017 Archive Story Map [http://arcg.is/PSOKH]
[4] NSF RAPID Grant [https://nsf.gov/awardsearch/showAward?AWD_ID=1761673]

ABSTRACT:

This collection is for datasets of flood depths, flood extents, high water marks, streamflow, damages recorded, aerial oblique photos, and related subjects. This includes both forecast and observed data. These were primarily obtained from national agencies such as NOAA (weather related), USGS (surface water related), FEMA (surface water and damage related), the Civil Air Patrol (aerial photos), and ECMWF (European Centre for Medium-Range Weather Forecasting, for flood area grids).

ABSTRACT:

The NOAA National Hurricane Center (NHC) publishes advisory bulletins with named storm conditions and expectations, see [1]. We have also downloaded shapefiles for eighty-four 5-day forecasts (published from August 30 to September 11) of track line, predicted points, ensemble forecasts envelope, and affected shoreline where applicable [2]. NOAA also publishes the best track for major storms [3]. The "best track" is a smoothed version of the advisories track. Web services are also provided by NHC for the advisory points and lines [4] [5]. Another user has constructed the Irma track (shapefile) from the NHC advisory bulletins [6].

FEMA also posts windfield data, including peak wind gust and contours [7].
See FEMA disaster webpage [8] for map and list of counties receiving disaster declarations (map pdf available for download from this page)

References
[1] NOAA NHC - Irma storm advisories [http://www.nhc.noaa.gov/archive/2017/IRMA.shtml]
[2] NOAA NHC - Irma 5-day forecasts [https://www.nhc.noaa.gov/gis/archive_forecast_results.php?id=al11&year=2017&name=Hurricane%20IRMA]
[3] NOAA NHC - best tracks for 2017 storms [https://www.nhc.noaa.gov/data/tcr/index.php?season=2017&basin=atl]
[4] NOAA NHC - Irma advisory points web service [https://services.arcgis.com/XSeYKQzfXnEgju9o/ArcGIS/rest/services/The_2017_Atlantic_Hurricane_season_(to_October_16th)/FeatureServer/1]
[5] NOAA NHC - Irma advisory lines web service [https://services.arcgis.com/XSeYKQzfXnEgju9o/ArcGIS/rest/services/The_2017_Atlantic_Hurricane_season_(to_October_16th)/FeatureServer/6]
[6] Irma Advisories Track, compiled by David Tarboton [https://www.hydroshare.org/resource/546fa3feeaf242fc8aabf9fe05ab454c/]
[7] FEMA public download site for Hurricane Irma 2017 [https://data.femadata.com/NationalDisasters/HurricaneIrma/]
[8] FEMA Disaster Declarations and related links [https://www.fema.gov/disaster/4337]

ABSTRACT:

During and after Hurricane Irma, the US Geological Survey recorded high water marks across the affected area, as they do for every major storm [https://www.usgs.gov/special-topic/hurricane-irma]. The files in this dataset provide 506 high water marks for Hurricane Irma flooding, and 202 peak sites. These files were downloaded following the steps below. If you'd like to check the original sources again, or search for HWM for a different storm, you may find these directions helpful.

The High Water Marks can also be visualized directly from the USGS Flood Event Viewer for Irma [https://stn.wim.usgs.gov/fev/#IrmaSeptember2017]

Finding, Downloading and Filtering USGS High Water Marks (HWM)
1. Visit USGS website: https://water.usgs.gov/floods/history.html, which lets you…
2. Click on Hurricane Irma: https://www.usgs.gov/irma, which lets you…
3. Click on green button Get Data: https://stn.wim.usgs.gov/fev/#IrmaSeptember2017
4. In left margin menu of resulting page, click a second Get Data link. This will open up the remaining options below.
5. Click each data type you want, such as High-Water Mark, Peak Summary, or Sensor Data. It’s only csv or REST (json or xml).

* To understand the fields or columns of this table, see HWM_Peaks_Sensors_Data_Dictionary_20180329.xslx in the contents below.

ABSTRACT:

The National Water Model (NWM) is a water forecasting model operated by the NOAA National Weather Service that continually forecasts flows on 2.7 million stream reaches covering 3.2 million miles of streams and rivers in the continental United States [1]. It operates as part of the national weather forecasting system, with inputs from NOAA numerical weather prediction models, and from weather and water conditions observed through the US Geological Survey's National Water Information System. Reference materials for the computational framework behind NWM is published by NCAR [9] [10].

The NWC generates NWM streamflow forecasts for the continental US (CONUS) with multiple forecast horizons and time steps. Due to the output file sizes, these are normally not available for download more than a couple days at a time [2]. A 40-day rolling window of these forecasts is maintained by HydroShare at RENCI [3], and a complete retrospective (August 2016 to the present) of the NWM Analysis & Assimilation outputs is maintained as well (contact help@cuahsi.org for access).

An archive of all NWM forecasts for the period Aug 29 to Sept 17, 2017 has been compiled at RENCI [4] [5], available as netCDF (.nc) files totaling 6.8 TB. These can be browsed, subsetted, visualized, and downloaded (see [6] [7] [8]). In addition to these output files, we have uploaded to this HydroShare resource the input parameter files needed to re-run the NWM for the Irma period, or for any time period covered by NWM v1.1 and 1.2 (August 2016 to this publication date in August 2018). These parameter files are also made available at [1].

See README for further details and usage guidance. Please see NOAA contacts listed on [1] for questions about the NWM data contents, structure and formats. Contact help@cuahsi.org if any questions about HydroShare-based tools and data access.

References
[1] Overview of the NWM framework and output files [http://water.noaa.gov/about/nwm]
[2] Free access to all National Water Model output for the most recent two days [ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/nwm]
[3] NWM outputs for rolling 40-day window, maintained by HydroShare [http://thredds.hydroshare.org/thredds/catalog/nwm/catalog.html]
[4] Archived Irma NWM outputs via RENCI THREDDS server [http://thredds.hydroshare.org/thredds/catalog/nwm/irma/catalog.html]
[5] RENCI is an Institute at the University of North Carolina at Chapel Hill
[6] Live map for National Water Model forecasts [http://water.noaa.gov/map]
[7] NWM Forecast Viewer app [https://hs-apps.hydroshare.org/apps/nwm-forecasts]
[8] CUAHSI JupyterHub example scripts for subsetting NWM output files [https://hydroshare.org/resource/3db192783bcb4599bab36d43fc3413db/]
[9] WRF-Hydro Overview [https://ral.ucar.edu/projects/wrf_hydro/overview]
[10] WRF-Hydro User Guide 2013 [https://ral.ucar.edu/sites/default/files/public/images/project/WRF_Hydro_User_Guide_v3.0.pdf]

ABSTRACT:

Scientists are faced with many data-centric challenges in their day-to-day research including, but not limited to, management, collaboration, archival, and publication. This is complicated by the disparate and diverse nature of earth surface data which typically makes a single repository less than ideal for all data used and created for a given study. In recent years, initiatives such as National Science Foundation data management plans and the American Geophysical Union findable, accessible, interoperable, and reusable (FAIR) principals have incentivized researchers to explore solutions for archiving data that will improve future research capabilities. In the area of Hydrologic Data Management, CUAHSI has been developing software tools to help researchers collaborate, share, manage and publish their research data, making it FAIR. This workshop will introduce these tools which include the hydrologic information system (HIS), observations data model version 2 (ODM2), ODM2Admin data management portal, and HydroShare. Attendees will be given an overview of CUAHSI's efforts to support research activities by participating in a series of interactive presentations that progress from (1) simple time series data, to (2) advanced earth observations, and finally to (3) complex data types. We welcome novice and advanced data creators, users, and managers to join us in this workshop.

ABSTRACT:

This is the meteorological data at Lysina, Czech Republic

ABSTRACT:

It includes all inputs, outputs and source code of PIHM simulation at Lysina

ABSTRACT:

Supporting data files for Neilson et al., 2018, Groundwater flow and exchange across the land surface explain carbon export patterns in continuous permafrost watersheds.

Flow and DOC data used in the manuscript can be found online at http://ine.uaf.edu/werc/projects/NorthSlope/imnavait/flume/flume.html and http://arclter.ecosystems.mbl.edu/data-catalog, respectively.

Permeability_Depth_Profile.xlsx
Figure S3a: Vertical permeability profile measured with KSAT or slug test methods and used in the vertically explicit groundwater model. KSAT done in lab, slug tests done in the field.

Porosity_Depth_Profile.xlsx
Figure S3b: Vertical porosity profile used in the vertically explicit groundwater model.

Fill_DEM_3m1.tif
Figures S1a and S4: Digital Elevation Model at 3 m resolution resampled from 20cm FodarDEM (http://fairbanksfodar.com/fodar-earth) and used in the vertically integrated groundwater model.

ALT_RawData_IncludeSmallGrid.xlsx
Figure S2: Top of casing elevation, ground surface elevation, water depth in well, total well length, and triplicate distance below land surface to frozen surface.

SurfaceTopography.xlsx
Figures 1, 2, S3, and S5: Land surface elevation profile used in the vertically explicit groundwater model.

Hydrozoid_DOC_to_WEB.xlsx
Figure S6: Soil dissolved organic carbon concentrations from Imnavait Creek.

ABSTRACT:

Includes daily USGS streamflow measurements for 454 gauges throughout the Mississippi River Basin for the period April 1, 2010 to May 1, 2016, a shapefile of the USGS gauges, and assuming a theoretical launch date of the Surface Water and Ocean Topography (SWOT) Mission being April 16, 2010, sampled SWOT-observed discharges with and without preliminary SWOT discharge uncertainties (based on Hagemann et al. 2017; DOI: 10.1002/2017WR021626) .

ABSTRACT:

This resource contains statewide networks of roadways, bridges, and railway crossings, for Florida only. These datasets were obtained from Florida Dept of Transportation (FDOT) in August 2018 via their website [1]. The FDOT GIS data is continually being updated, so if you wish to find the most complete and current data, please visit that site.

Contained in the zipfile:
- Roadways (28,992 instances) including the following:
- Interstate Highways (81)
- US Highways (643)
- Toll Roads (90)
- State Highways(1,917)
- Active on the State Highway System (1,450)
- Active off the State Highway System (9,452)
- Florida Roadways (15,359)
- Bridges (9,527)
- Railway Crossings (1,937)
- Numerous other GIS feature layers as well.
All feature layers in the FDOT geodatabase also have ArcMap (.lyr) files for efficient loading and symbolizing.

References:
[1] FDOT GIS data [http://www.fdot.gov/statistics/gis/]

ABSTRACT:

Modeling the coupled social and biophysical dynamics of water resource systems is increasingly important due to expanding population, fundamental transitions in the uses of water, and changes in global and regional water cycling driven by climate change. Models that explicitly represent the coupled dynamics of biophysical and social components of water resource systems are challenging to design and implement, particularly given the complicated and cross-scale nature of water governance. Agent based models (ABMs) have emerged as a tool that can capture human decision-making and nested social hierarchies. The transferability of many agent-based models of water resource systems, however, is made difficult by the location-specific details of these models. The often ad-hoc nature of the design and implementation of these models also complicates integration of high fidelity sub-models that capture biophysical dynamics like surface-groundwater exchange and the influence of global markets for commodities that drive water use. A consistent, transferable description of the individuals, groups, and/or agencies that make decisions about water resources would significantly advance the rate at which ABMs of water resource systems can be developed, enhance their applicability across ranges of spatiotemporal scales, and aid in the synthesis and comparison of models across different sites. We outline here a framework to systematically identify the primary agents that influence the storage, redistribution, and use of water within a given system.

This resource is the literature review that supports our proposed water resources agent types that capture the operational roles that modify the water balance (see Kaiser et al., 2020). This typology characterizes common actors in water management systems but can be modified to represent the particularities of specific systems when more detailed information about specific actors is available (e.g. social networks, demographics, learning and decision-making processes). Application of the proposed typologies will support the systematic design and development of transferable scaleable water resources ABMs and facilitate the dynamical coupling of social and biophysical process modeling.

ABSTRACT:

The data file includes water stables isotope data (throughfall, seepage, bulk soil water, and xylem water), soil volumetric water content, soil matric potential, leaf water potential, and ecosystem-level transpiration estimates, from the 10-month drought and rainfall labelling experiment at the Biosphere 2 Tropical Rainforest Biome (B2-TRF) in 2014-2015.
Data are organized in the csv files:
• evaristo_b22014_transpiration – ecosystem-level, meteorologically derived evapotranspiration
• evaristo_b22014_lwp – leaf water potential
• evaristo_b22014_swp – soil water potential
• evaristo_b22014_vwc – soil vol. water content
• evaristo_b22014_isotopes_throughfall – throughfall stable isotopes
• evaristo_b22014_isotopes_seepage – seepage stable isotopes
• evaristo_b22014_isotopes_soil – bulk soil water stable isotopes
• evaristo_b22014_isotopes_xylem – xylem water stable isotopes

ABSTRACT:

The data belong to a manuscript submitted to the Journal of Water Resources Planning and Management.
If the following line of codes doesn't work, try loading 'Zarrineh.rda' available here.
load(url("http://up2www.com/uploads/c7e0zarrineh.mp3"))

ABSTRACT:

Most catchment modelling software use a Graphical User Interface (GUI) to allow direct manipulation of the models and adapt to specific case studies. GUI is generally easy to use for novice users but opens sources of irreproducible research. We present a workflow (QSWAT Workflow) that promotes reproducible SWAT model studies while remaining user-friendly for both novice and expert users. We applied this environment to the Blue Nile catchment and show that it yields the same results as using the QSWAT GUI. The workflow includes benefits in rebuilding earlier model configurations and implementing changes to existing setups while saving time. The project can still be viewed and modified in the GUI. We conclude that workflows can help reduce cases of irreproducible catchment studies and offer benefits for researchers building upon existing models including opportunities for catchment model setup with cloud computing without losing interoperability with GUIs. This workflow is on GitHub: https://github.com/VUB-HYDR/QSWAT_Automated_Workflow)

ABSTRACT:

This folder contains the output files from:

L.A. Melsen and B. Güse (2019), Hydrological drought simulations: How climate and model structure control parameter sensitivity, Water Resources Research, doi: 10.1029/2019WR025230

The data contain hydrological drought indicators (e.g. median drought duration) for three different models (SAC, VIC, HBV), where these models were run with a sample of parameters for 605 basins in the US. Sensitivity analysis was applied to the indicators.

Study abstract:
Hydrological drought, defined as below average streamflow conditions, can be triggered by different mechanisms which are to a large extent dictated by the climate. Moreover, the simulation of hydrological droughts highly depends on the model structure and how drought triggering mechanisms are parameterized. In this large-sample hydrological study, we investigate how climate and model structure control hydrological drought simulations. We conducted sensitivity analysis on parameters of three frequently used hydrological models (HBV, SAC, and VIC) for the simulation of drought duration and drought deficit over 605 basins covering more than ten different K\"oppen-Geiger climates. The sensitivity analysis revealed that, as anticipated, different parameter are sensitive in different climates. However, not all expected drought mechanisms were reflected in the parameter sensitivity: especially the sensitivity of ET parameters does not align with the theory, and the role of snow parameters in snow-related droughts shows a distinction between degree-day based models and energy-balance models. Besides parameter sensitivity being different over climates, we also found that parameter sensitivity differed over the different models. Where HBV and SAC did display fairly similar behaviour, in VIC other model mechanisms were triggered. This implies that conclusions on driving mechanisms in hydrological drought cannot be based on hydrological models only, as different models would lead to different conclusions. Hydrological models can have heuristic value in drought research, to formulate new theories and identify research directions, but formulated theories on driving processes should always be backed up by observations.

ABSTRACT:

This resource groups data downloaded from FEMA public FTP site for Hurricane Irma [1] for depth grids, flood extents, windfield, and damage assessments.
See FEMA's Natural Hazard Risk Assessment Program (NHRAP) ftp site [2] for additional HWM-based depth grids, inundation polygons, and windfield.

References and related links:
[1] FEMA public download site for Hurricane Irma 2017 [https://data.femadata.com/NationalDisasters/HurricaneIrma/]
[2] FEMA NHRAP ftp [https://data.femadata.com/FIMA/NHRAP/Irma/]

ABSTRACT:

This resource contains the data that underlie figures presented in MS# 2018JF004861 , " Topography and Overmarsh Circulation in a Small Salt Marsh Basin". Specifically, there are two Excel spreadsheets that contain at-a-station residual velocities used to assess tidal current asymmetry and e-folding time, and raster datasets used to estimate tracer dispersal and spatial distribution.

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the impact of different stomatal resistance parameterizations on total evapotranspiration (ET) in the Reynolds Mountain East catchment in southwestern Idaho. This study applied three different stomatal resistance parameterizations: the simple soil resistance method, the Ball Berry method, and the Jarvis method.

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the sensitivity of different root distribution exponents (0.25, 0.5, 1.0). The sensitivity of evapotranspiration to the distribution of roots, which dictates the capability of plants to access water.

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the impact of the lateral flux of liquid water on total evapotranspiration (ET) using a SUMMA model for the Reynolds Mountain East catchment. This study looked at the sensitivity of the different model representation of the lateral flux of liquid water, which determines the availability of soil water.

ABSTRACT:

Abstract
This dataset includes measurements of volumetric water content (VWC %) at the plot (707 m2) and landscape scale (42 sites distributed across a 400 ha catchment). At the plot scale, 30 VWC measurements were made 4 times over the growing season, and at the landscape scale measurements were made weekly from May 29th to August 6th 2013 (11 time points). Relative coordinates of the 30 sampling points within the plots are provided as xy data for use in variogram or correlogram analysis. Values of terrain metrics for each site are included at 3m and 10m resolutions. More details and the associated analysis can be found in Kaiser, K.E. and B.L. McGlynn, (2018), Nested scales of spatial and temporal variability of soil water content across a semi-arid montane catchment. Water Resources Research. Please contact kendra.kaiser@gmail.com for additional information or to use data.

ABSTRACT:

This resource contains data and tools used in the paper “GeoFlood: large-scale flood inundation mapping based on high-resolution terrain analysis” submitted to Water Resources Research.
Simple and computationally efficient flood inundation mapping methods are needed to take advantage of increasingly available high-resolution topography data. In this work, we present a new approach, called GeoFlood, for flood inundation mapping using high-resolution topographic data. This approach combines GeoNet, an advanced method for high-resolution terrain data analysis, and the Height Above Nearest Drainage. GeoFlood can rapidly convert real-time forecasted river flow conditions to corresponding flood maps. A case study in central Texas demonstrated that the flood maps generated with our approach capture the majority of the inundated extent reported by detailed FEMA flood studies. Our results show that GeoFlood is a valuable solution for rapid inundation mapping.

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the Impact of the canopy shortwave radiation parameterizations on below canopy shortwave radiation using a SUMMA model for the Reynolds Mountain East catchment. This study looked at four different canopy shortwave radiation parameterizations: BeersLaw method(as implemented in VIC), NL_scatter method(Nijssen and Lettenmaier, JGR 1999:NL 1999), UEB_2stream method(Mahat and Tarboton, WRR 2011:MT 2012), CLM_2stream method(Dick 1983)

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the Impact of the canopy shortwave radiation parameterizations on below canopy shortwave radiation using a SUMMA model for the Reynolds Mountain East catchment. This study looked at four different canopy shortwave radiation parameterizations: BeersLaw method(as implemented in VIC), NL_scatter method(Nijssen and Lettenmaier, JGR 1999:NL 1999), UEB_2stream method(Mahat and Tarboton, WRR 2011:MT 2012), CLM_2stream method(Dick 1983)

ABSTRACT:

National Water Center Innovators Program Summer Institute Report 2018

ABSTRACT:

Global sensitivity analysis GSA is a useful tool for diagnosing and quantifying uncertainty within hydrologic models. Facilitating advanced model analyses such as GSA of parameters has the potential to help advance our fundamental understanding of hydrologic process representations. This document acts as a working template to apply a GSA method for parameters of the well-known Preceipitation-Runoff Modeling System (PRMS) hydrologic model maintained by the United States Geological Survey. Specifically, it documents a workflow for a moment-independent, GSA method based on empirical cumulative distribution functions named PAWN. The template is a Jupyter notebook that uses an open-source Python package called PRMS-Python; installation instructions for PRMS-Python and links to both PAWN and the Python software are included. PRMS-Python has a built in routine for Monte Carlo parameter resampling that this template demonstrates and uses to implement PAWN. The template is written so that it could be modified for an arbitrary set of PRMS parameters and is heavily commented for clarity. As such, this template along with the open-source Python package aim to encourage and facilitate the greater hydrologic modeling community to conduct advanced model analyses such as GSA. Similarly, the PRMS-Python framework has tools for self-generation of metadata files that track data provenance of large model ensembles- a useful tool for sharing model results on platforms such as HydroShare.

ABSTRACT:

The Civil Air Patrol is routinely tasked by FEMA and local public safety officials with taking aerial photographs. This collection comprises about 38,000 photos taken over Florida and Georgia during September 8-20, 2017. These were originally uploaded to the web using the GeoPlatform.gov imageUploader capability, and hosted as a web map layer [1]. For this Irma collection, I exported the dataset of photo location points to a local computer, subset it to the Irma event, and created a shapefile, which is downloadable below. The photos and thumbnails were not included in this archive, but are attribute-linked to the FEMA-Civil Air Patrol image library on Amazon cloud [2].

Note: The cameras used by the Civil Air Patrol generally do not have an electronic compass with their GPS to record the viewing direction. The easiest way to determine the general angle is to look at consecutive frame counterpoints to establish the flightpath direction at nadir and adjust for the photographer's position behind the pilot looking out the window hatch on the port (left) side of the aircraft. The altitude above ground level is typically between 1000-1500 feet, so it's easy to locate features in reference orthoimages.

References
[1] US federal GeoPlatform.gov Image Uploader map service (ArcGIS Server) [https://imageryuploader.geoplatform.gov/arcgis/rest/services/ImageEvents/MapServer]
[2] FEMA-Civil Air Patrol image library on Amazon cloud [https://fema-cap-imagery.s3.amazonaws.com]

ABSTRACT:

At the request of the US Department of Homeland Security (DHS), a team at the Oak Ridge National Laboratory (ORNL) has developed a method for capturing building outlines over a large area. For Hurricane Irma, ORNL assembled this collection [1] of building footprints for Florida (6.5 million), Georgia (3.5 million), Alabama (2.4 million), and the South Carolina coastal area (374k). This was intended as an overlay with predicted or observed flooding extent, to estimate the number of buildings that might be damaged. While not completely accurate, these building outlines are useful for estimating aggregate totals across large areas of interest.

References
[1] FEMA public FTP download site [https://data.femadata.com/NationalDisasters/HurricaneIrma/Data/Buildings/Outlines/OakRidgeNationalLaboratory/]

ABSTRACT:

This resource contains medium-resolution (1:100k) National Hydrography Dataset (NHDPlus) [1] map data for a region of 23 Hydrologic Unit Code (HUC) 6-digit (HUC6) basins around the Hurricane Irma impact zone across Florida, Georgia, and the Carolinas. This includes 5,236 subwatersheds, 217,308 catchments, and 220,418 flowlines.

State and county boundaries were obtained from the Esri Living Atlas [2].

USGS active stream gages can be downloaded from the USGS National Water Information System (NWIS) [3], or visualized at the USGS WaterWatch site [4].
NOAA Advanced Hydrologic Prediction System (AHPS) river forecast points can be downloaded as well [5]. Many of these are co-located with USGS NWIS gages to leverage authoritative observation data.

References
[1] NHDPlus Version 2 [http://www.horizon-systems.com/NHDPlus/V2NationalData.php]
[2] Esri Living Atlas [https://livingatlas.arcgis.com]
[3] USGS NWIS [https://waterdata.usgs.gov/nwis]
[4] USGS WaterWatch [https://waterwatch.usgs.gov]
[5] NOAA AHPS [https://water.weather.gov/ahps/forecasts.php]

ABSTRACT:

This is the data repository for the journal article entitled "Variability of Subsurface Structure and Infiltration Hydrology among Surface Coal Mine Valley Fills" published in Science of the Total Environment in 2018 by Erich T. Hester, Kathryn L. Little, Joseph D. Buckwalter, Carl E. Zipper, and Thomas J. Burbey. The data themselves, as well as information about the data, for example geographic location and date, can be found in several locations:

1) Many data are in the journal article or the associated supplementary information, which are available at the journal website or can be requested by emailing Erich Hester at ehester@vt.edu
2) Many data are available in the files associated with this Hydroshare resource, which are described in the readme.txt file
3) Any questions that are not answered by the above methods can be directed to Erich Hester at ehester@vt.edu

Note that the data included in this Hydroshare resource are for the electrical resistivity imaging (ERI). The other data collected for the published article (e.g., bore logs) are solely within the article itself and the associated supplementary information published with the article at the journal website.

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the Impact of the canopy wind parameter for the exponential wind profile on simulations of below canopy wind speed at the aspen site in the Reynolds Mountain East catchment. This study looked at the impact of the Canopy wind parameter[0.10, 0.28, 0.50, 0.750] as used in the parameterization described by the exponential wind profile

ABSTRACT:

R scripts presented as Jupyter Notebooks and data to generate load and concentration estimates produced for the journal publication:
McDowell, W. H., McDowell, W. G., Potter, J. D. and Ramírez, A. (2018), Nutrient export and elemental stoichiometry in an urban tropical river. Ecol Appl. Accepted Author Manuscript. doi:10.1002/eap.1839

Find the publication here: https://doi.org/10.1002/eap.1839

We recommend running the JupyterNotebooks on a local JupyterHub instead of the online CUAHSI JupterHub. You will need to run install.R in order to load the needed R packages for the R script.

A prerender version of the Quebrada Sonadora Jupyter Notebook is available here https://nbviewer.jupyter.org/github/miguelcleon/River-nutrient-exports-Puerto-Rico-/blob/master/Sonadora%20%28QS%29%20flux%20and%20concentrations%202009-2014.ipynb

An interactive version of the Jupyter Notebooks maybe available on mybinder, mybinder is in beta and has been functioning inconsistently https://beta.mybinder.org/v2/gh/miguelcleon/River-nutrient-exports-Puerto-Rico-/master

The script 'Sonadora (QS) flux and concentrations 2009-2014.ipynb' in the contents below contains nicely formatted tables that match the tables in the publication. We suggest running this script first if you are interested in how the results were generated. The other two scripts 'Mameyes- Puente Roto (MPR) flux and concentrations 2009-2014.ipynb' and 'Rio Piedras flux and concentrations 2009-2014.ipynb' are raw scripts without formatted output.

The journal publication abstract is presented here:

Nutrient inputs to surface waters are particularly varied in urban areas, due to multiple nutrient sources and complex hydrologic pathways. Because of their close proximity to coastal waters, nutrient delivery from many urban areas can have profound impacts on coastal ecology. Relatively little is known about the temporal and spatial variability in stoichiometry of inorganic nutrients such as dissolved silica, nitrogen, and phosphorus (Si, N, and P) and dissolved organic matter in tropical urban environments. We examined nutrient stoichiometry of both inorganic nutrients and organic matter in an urban watershed in Puerto Rico served by municipal sanitary sewers and compared it to two nearby forested catchments using samples collected weekly from each river for 6 years. Urbanization caused large increases in the concentration and flux of nitrogen and phosphorus (2- to 50-fold), but surprisingly little change in N:P ratio. Concentrations of almost all major ions and dissolved silica were also significantly higher in the urban river than the wildland rivers. Yield of dissolved organic carbon (DOC) was not increased dramatically by urbanization, but the composition of dissolved organic matter shifted toward N-rich material, with a larger increase in dissolved organic nitrogen (DON) than DOC. The molar ratio of DOC:DON was about 40 in rivers draining forested catchments but was only 10 in the urban river. Inclusion of Si in the assessment of urbanization’s impacts reveals a large shift in the stoichiometry (Si:N and Si:P) of nutrient inputs. Because both Si concentrations and watershed exports are high in streams and rivers from many humid tropical catchments with siliceous bedrock, even the large increases in N and P exported from urban catchments result in delivery of Si, N, and P to coastal waters in stoichiometric ratios that are well in excess of the Si requirements of marine diatoms. Our data suggest that dissolved Si, often neglected in watershed biogeochemistry, should be included in studies of urban as well as less developed watersheds due to its potential significance for marine and lacustrine productivity.

ABSTRACT:

The Center for Experimental Study of Subsurface Environmental Processes (CESEP) operates and conducts research at a large scale coupled wind tunnel-porous media test facility located at the Colorado School of Mines in Golden, Colorado. The facility consists of a closed-circuit, climate-controlled (i.e., relative humidity 5 to 95%, air temperature -2 to 45 deg. C, soil temperature -4 to 35 deg. C, grow lights), low wind speed (<10 m/s) wind tunnel that is interfaced along the centerline of it's test-section with a large soil tank (inner dimensions l x w x d = 7.15 x 0.11 x 1.1 m). Climate conditions are manually set and automatically maintained using a variety of climate controls. The soil tank and test section are outfitted with a variety of sensors for the continuous measurement of key atmospheric and subsurface state variables. This resource contains the raw data from a series of bare-soil evaporation experiments conducted under varying subsurface and surface conditions by the shown research team. The test-facility is described in detail and these experimental results are analyzed in a manuscript entitled "Experimental testing scale considerations for the investigation of bare-soil evaporation dynamics in the presence of sustained above-ground airflow" published in Water Resources Research. For questions regarding the test-facility or possible collaboration involving the facility, interested parties are referred to the CESEP website (www.cesep.mines.edu) and CESEP director, Dr. Tissa Illangasekare.

ABSTRACT:

This is the model data set for the steady-state Conduit Flow Process v2 (CFPv2) model of the Edwards Aquifer in the San Antonio, TX, USA region. The model simulates water flow through the aquifer matrix and karst conduits. The results of this model are part of the manuscript "Nitrate Transport in a Karst Aquifer: Numerical Model Development and Source Evaluation" submitted for publication in Water Resources Research.

ABSTRACT:

This is the model data set for the Conduit Mass Transport Three-Dimensional Model (CMT3D) model of the Edwards Aquifer in the San Antonio, TX, USA region. The model simulates nitrate transport through the aquifer matrix and karst conduits. The results of this model are part of the manuscript "Nitrate Transport in a Karst Aquifer: Numerical Model Development and Source Evaluation" submitted for publication in Water Resources Research.

ABSTRACT:

This is the model data set for the Conduit Flow Process version 2 (CFPv2) model of the Edwards Aquifer in the San Antonio, TX, USA region. The model simulates water movement through the aquifer matrix and karst conduits for calendar years 2001-2010. The results of this model are part of the manuscript "Nitrate Transport in a Karst Aquifer: Numerical Model Development and Source Evaluation" submitted for publication in Water Resources Research.

ABSTRACT:

This resource contains a survey and responses of flooding professionals within Tompkins County in 2018 to understand perceptions around flooding hazard and risk. Further details are available in Knighton et al. (2018) Challenges to Implementing Bottom-Up Flood Risk Decision Analysis Frameworks: How Strong are Social Networks of Flooding Professionals? Hydrology and Earth Systems Sciences. DOI: 10.5194/hess-22-1-2018

ABSTRACT:

This is the Soil & Water Assessment Tool (SWAT) model dataset for the Cibolo and Dry Comal Creek basins in the San Antonio, TX region. This SWAT model simulates streamflow and nitrate transport in two karstic watersheds for calendar years 1996-2010. The results of this model were published in Sullivan, T.P., Gao, Y., 2016. Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010. Environ Earth Sci, 75(9): 1-20. DOI:10.1007/s12665-016-5546-0. The model output was also utilized in the manuscript, "Nitrate Transport in a Karst Aquifer: Numerical Model Development and Source Evaluation" submitted for publication with Water Resources Research.

ABSTRACT:

The data set contained in this archive was developed and used to conduct a study titled 'Exploring Cooperative Transboundary River Management Strategies for the Eastern Nile Basin'. The data set includes three ensembles of hydrologic inputs, each developed from a simulated annealing technique. A baseline ensemble uses statistical properties similar to historical conditions from 1900-2002. A second ensemble increases the interannual standard deviation of all inputs by 15%. A third ensemble increases the long-term persistence of all inputs by increasing the Hurst coefficient by 20%. The data provides inputs for the Eastern Nile RiverWare Model (ENRMv3.3). The study explored cooperative management strategies between the countries of Ethiopia, Sudan and Egypt after the construction of the Grand Ethiopian Renaissance Dam using a multi-objective evolutionary algorithm. This results of this study have been published in Water Resources Research (https://doi.org/10.1029/2017WR022149).

ABSTRACT:

Includes daily USGS streamflow measurements for 454 gauges throughout the Mississippi River Basin for the period April 1, 2010 to May 1, 2016, a shapefile of the USGS gauges, and assuming a theoretical launch date of the Surface Water and Ocean Topography (SWOT) Mission being April 16, 2010, sampled SWOT-observed discharges with and without preliminary SWOT discharge uncertainties (based on Hagemann et al. 2017; DOI: 10.1002/2017WR021626) .

ABSTRACT:

This is the model simulation of snow water equivalent for the watershed of Dolores River above McPhee reservoir in the Colorado River Basin from 1988 to 2010. The model used is the Utah Energy Balance model which is a physically based snow melt model.

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the impact of the lateral flux of liquid water on Runoff using a SUMMA model for the Reynolds Mountain East catchment. This study looked at the sensitivity of the different model representation of the lateral flux of liquid water, which determines the availability of soil water.

ABSTRACT:

(update ESSD cross-ref)

ABSTRACT:

(update ESSD Cross-ref when published)

ABSTRACT:

These data accompany the publication:

Ward, A. S., Zarnetske, J. P., Baranov, V., Blaen, P. J., Brekenfeld, N., Chu, R., Derelle, R., Drummond, J., Fleckenstein, J., Garayburu-Caruso, V., Graham, E., Hannah, D., Harman, C., Hixson, J., Knapp, J. L. A., Krause, S., Kurz, M. J., Lewandowski, J., Li, A., Martí, E., Miller, M., Milner, A. M., Neil, K., Orsini, L., Packman, A. I., Plont, S., Renteria, L., Roche, K., Royer, T., Schmadel, N. M., Segura, C., Stegen, J., Toyoda, J., Wells, J., Wisnoski, N. I., and Wondzell, S. M.: Co-located contemporaneous mapping of morphological, hydrological, chemical, and biological conditions in a 5th order mountain stream network, Oregon, USA, Earth Syst. Sci. Data. https://doi.org/10.5194/essd-11-1-2019

ABSTRACT:

(update with ESSD cross-reference)

ABSTRACT:

(add ESSD cross-reference here)

ABSTRACT:

This resource links to the Hurricane Harvey 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Harvey.

References
[1] Hurricane Harvey Story Map [https://arcg.is/1GWyKi]

ABSTRACT:

This resource links to the Hurricane Irma 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Irma.

References
[1] Hurricane Irma Story Map [https://arcg.is/19z9jL]

Referenced external maps
Irma crowdsource photos story map (NAPSG) [https://arcg.is/1WOr4b]

ABSTRACT:

This resource links to the Hurricane Harvey 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Harvey.

November 2023 updates: Esri has deprecated the previous story map template, so a new story map has been generated. Most of the content is the same as before, with these exceptions:
- The Vulnerabilities and the Harvey Stories pages have been removed, due to nonfunctioning web links to other Harvey resources out of our control.
- Story map links to HydroShare resource pages have been updated to the most current HydroShare resource versions.

References
[1] Hurricane Harvey Story Map [https://arcg.is/1rWLzL0]

ABSTRACT:

This resource links to the Texas Address and Base Layers Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download Texas statewide address points, as well as contextual map layers including roads, rail, bridges, rivers, dams, low water crossings, stream gauges, and others. The addresses were compiled over the period from June 2016 to December 2017 by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide.

For detailed compilation notes, see [2]. Contextual map layers will be found at [3] and [4].

November 2023 update: in 2019, TNRIS took over maintenance of the Texas Address Database, which is now updated annually as part of the StratMap program [5]. Also, TNRIS changed its name this year to the Texas Geographic Information Office (TxGIO). The StratMap and DataHub download sites still use the tnris.org domain but that may change .

References
[1] Texas Address and Base Layers story map [https://arcg.is/19PWu1]
[2] Texas-Harvey Basemap - Addresses and Boundaries [https://www.hydroshare.org/resource/d2bab32e7c1d4d55b8cba7221e51b02d/]
[3] Texas Basemap - Hydrology Map Data [https://www.hydroshare.org/resource/5efdb83e96da49c5aafe5159791e0ecc/]
[4] Texas Basemap - Transportation Map Data [https://www.hydroshare.org/resource/106b38ab28b54f09a2c7a11b91269192/]
[5] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]

ABSTRACT:

The dataset presented in this resource are part of the hydrometeorological data measured in colombian páramos during the research project "Estudio ecohidrológicos de los páramos y los bosques alto andinos, naturales e intervenidos: Análisis de la vulnerabilidad y adaptabilidad al cambio climático" directed by the professor Conrado Tobón and financed by Colciencias through the the call for a bank of eligible projects in CT&i 569 - 2012.

ABSTRACT:

This is the supplement for: Anache, J. A. A., Wendland, E., Rosalem, L. M. P., Youlton, C., and Oliveira, P. T. S.: Hydrological trade-offs due to different land covers and land uses in the Brazilian Cerrado, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-415, in review, 2018.

Farmland expansion in the Brazilian Cerrado, considered one of the largest agricultural frontiers in the world, has the potential to alter water fluxes on different spatial scales. Despite some large-scale studies being developed, there are still few investigations in experimental sites in this region. Here, we investigate the water balance components in experimental plots and the groundwater table fluctuation in different land covers: wooded Cerrado, sugarcane, pasture and bare soil. Furthermore, we identify possible water balance trade-offs due to the different land covers. This study was developed between 2012 and 2016 in the central region of the state of São Paulo, Southern Brazil. Hydrometeorological variables, groundwater table, surface runoff and other water balance components were monitored inside experimental plots containing different land covers; the datasets were analyzed using statistical parameters; and the water balance components uncertainties were computed. Replacing wooded Cerrado by pastureland and sugarcane shifts the overland flow (up to 42 mm yr-1), and water balance residual (up to 504 mm yr-1). This fact suggests significant changes in the water partitioning in a transient land cover and land use (LCLU) system, as the evapotranspiration is lower (up to 719 mm yr-1) in agricultural land covers than in the undisturbed Cerrado. We recommend long-term observations considering multiple scales to continue the evaluations initiated in this study, mainly because tropical environments have few basic studies at the hillslope scale and more assessments are needed for a better understanding of the real field conditions. Such efforts should be made to reduce uncertainties, validate the water balance hypothesis and catch the variability of hydrological processes.

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This notebook demonstrates how to use the Python package "hydrofunctions" to download stream discharge data from the NWIS and plot a stream hydrograph and a flow duration chart.

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The US Critical Zone Observatories (CZOs) were created to advance understanding of the thin layer of the earth’s surface extending from unweathered bedrock to the top of the vegetation canopy. As part of this mission, each of the U.S CZOs is collecting and/or utilizing data for quantifying fluxes of water into and through the critical zone. This data resource collates common hydrological data from each of the U.S. CZOs, as a way of facilitating network-scale insight into the storage and transmission of water in the critical zone. Specifically, the data resource contains two “Levels” of data products. Level 1 data products are re-formatted versions of already existing data sets, such that variable units and the row-column orientation of each data set have been made standard to facilitate rapid utilization. Level 2 data products include basin-average estimates of precipitation, runoff, potential evapotranspiration, and transpiration which have been derived from Level 1 products. Specific methods for generating Level 2 data are outlined in the meta data, and detailed in a forthcoming publication by Wlostowski et al. (expected 2019).

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HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling individual researchers to more easily share products resulting from their research. The community platform supports, not just the scientific publication summarizing a study, but also the data, models and workflow scripts used to create the scientific publication and reproduce the results therein. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps) and that allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe the capabilities developed for HydroShare to support the full research data management life cycle. Data can be entered into HydroShare as soon as it is collected, and initially shared only with the team directly working on the data. As analysis proceeds, tools, scripts and models that act on the data to produce research results may be stored in HydroShare resources alongside the data. At the time of publication these resources may be permanently published and receive digital object identifiers and cited in research papers. Resources may themselves include citations to the research papers, thereby linking the publications to the supporting data, scripts and models. HydroShare design choices and capabilities for establishing relationships and versioning, based on simplicity, and ease of use, and some of the challenges encountered, will be discussed.

Poster IN53E-0656 presented at 2018 Fall Meeting, AGU, Washington, DC, 10-14 Dec, https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/424998.

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SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

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This is a SWMM5 model that used for demonstrating swmm_mpc, a Python package for simulating model predictive control using SWMM5 as the process model. swmm_mpc is on github: https://github.com/UVAdMIST/swmm_mpc. To run these, you will need to install the swmm_mpc python package or use the Docker image according to the instructions on the github repo readme. This model was used as a demonstration in the following manuscript:

Sadler, J. M., Goodall, J. L., Behl, M., Morsy, M. M., Culver, T. B., & Bowes, B. D. (2019). Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5. Environmental Modelling & Software, 120, 104484. https://doi.org/10.1016/j.envsoft.2019.07.009

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This presentation will describe the development of the HydroShare (www.hydroshare.org) web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI, www.cuahsi.org). HydroShare has been developed as a domain specific repository for the hydrologic science research community to share and publish data and models such that they are Findable, Accessible, Interoperable and Reusable (FAIR principles). As software, HydroShare was developed using an open development model with contributions from developers ranging from hydrology graduate students to seasoned developers. As infrastructure, HydroShare has been developed with interoperability in mind to serve as a component in an ecosystem of interacting cyberinfrastructure elements. HydroShare is a system for advancing hydrologic science by enabling individual researchers to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data, models, and workflow scripts used to create the scientific publication. It accepts data from anybody, and supports FAIR principles that help enable researchers meet the requirements of open data management plans. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web-based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps). HydroShare allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results.

NSF OAC Webinar August16, 2018 https://www.nsf.gov/events/event_summ.jsp?cntn_id=296301&org=NSF
Recording on Youtube https://youtu.be/EAVLqIIpDRg

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We share the maltab code of hydrologic model named vertically mixed model VMM, also it includes a Parameter optimization method sce-ua.

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The current scope involves three FO-DTS deployments, each 1km in length and for 5 days each. The site we are working on would like to evaluate groundwater discharge from the site into the river downgrade to facilitate discrete porewater sampling and evaluate potential plume migration. Previous GSI studies have been conducted here; however, due to recent landscape changes a comprehensive seepage survey is desired.

Raw project data is available by contacting ctemps@unr.edu

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We are performing an Aquifer Storage and Recovery injection test in a newly installed test well. A nearby well with a relatively long well screen interval will be used to sample recovered groundwater to assess water quality. The DTS cable will be installed in the nearby observation to determine the best depth to place a sampling pump for performing the water quality tests and to better evaluate potential vertical variations in relative aquifer transmissivity during the test. The DTS cable will be deployed in a locked environment at the City of Sonoma wellsite.

Raw project data is available by contacting ctemps@unr.edu

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Biogeochemical reactions within intertidal zones of coastal aquifers, induced by the mixing between fresh groundwater and saline seawater, have been shown to alter the concentrations of terrestrial solutes prior to their coastal discharge. In organic-poor sandy aquifers, the input of marine organic matter from infiltrating seawater has been attributed to active biogeochemical reactions within the sediments. However, while the seasonality of surface water organic carbon concentrations (primary production) and groundwater mixing patterns have been well-documented, there has been limited speculation on the contributions of particulate organic carbon pools within the sediments that arise from transient hydrologic conditions. To understand the relationship between physical movements of the circulation cell and the seasonal migration of geochemical patterns, beach porewater and sediment samples from six field sampling events spanning two years were analyzed. While oxygen saturation, oxygen consumption rates, and silica distributions closely followed the seasonally-dynamic salinity, other chemically-reactive parameters (pH, ORP) and nutrient characteristics (N distributions, denitrification rates, reactive organic carbon distributions) were unrelated to contemporaneous salinity patterns. Particulate organic matter was distributed in pools within the aquifer due to the filtration effect of sediments, contributing to the divergence of chemical patterns from salinity patterns via nutrient release and leaching. Together, the results present the asynchronous movement of chemical conditions to salinity patterns due to the divergent transport pathways between solutes and particles arising from transient hydrologic forcing.

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The use of tile drainage is documented as far back as 200 B. C. and continues to be used in poorly drained agricultural regions throughout the world. Recent increases in annual precipitation throughout the mid-western United States, the potential for future regulation of tile, and more efficient installation methods for plastic tile have accelerated tile installation across the region. While good for crop production, the eco-hydrologic impacts of this modification have been shown to adversely affect natural drainage networks. Knowing the location of tile drain networks is essential to developing groundwater and surface water models. The geometry of tile networks installed decades ago has often been lost with time or was never well documented in the first place. Previous work has recognized that tiles can be observed for certain soil types in visible remote sensing data due to changes in soil albedo. The soil surface directly above the tile appears to have a lower soil moisture content due to strong water table gradients adjacent to tiles, causing a detectable color contrast at the surface. In this work, small Unmanned Aerial Systems (sUAS) were used to collect high resolution visible and thermal data to map tile drain patterns. Within less than 96 hours of a 12 mm rain event, a total of approximately 60 hectares of sUAS thermal and RGB data were acquired at two different locations at the Intensively Managed Lands Critical Zone Observatory in Illinois. Selected thermal images were co-registered with RGB images at known tile locations. The thermal imagery showed limited evidence of thermal contrast related to the tile, however, it is possible that a contrast could have been detected sooner after the rain event when greater thermal contrasts due to lower soil moisture proximal to tile would be expected. The RGB data, however, elucidated the tile entirely at one site and provided traces of the tile at the other site. These results illustrate the importance of the timing of sUAS data collection with respect to the precipitation event. Ongoing related work focusing on laboratory and numerical experiments to better quantify feedbacks between albedo, soil moisture, and heat transfer will help predict the optimal timing of data collection for applications such as tile mapping.

Raw project data is available by contacting ctemps@unr.edu

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This resources contains the data from the manuscript Continental Hydrologic Intercomparison Project (CHIP), Phase 1: A Large-Scale Hydrologic Model Comparison over the Continental United States.

High-resolution, coupled, process-based hydrology models, in which subsurface, land-surface, and energy budget processes are represented, have been applied at the basin-scale to ask a wide range of water science questions. Recently, these models have been developed at continental scales with applications in operational flood forecasting, hydrologic prediction, and process representation. As use of large-scale model configurations increases, it is exceedingly important to have a common method for performance evaluation and validation, particularly given challenges associated with accurately representing large domains. Here we present phase 1 of a comparison project for continental-scale, high-resolution, processed-based hydrologic models entitled CHIP—the Continental Hydrologic Intercomparison Project. The first phase of CHIP is based on past Earth System Model intercomparisons and is comprised of a two-model proof of concept comparing the ParFlow-CONUS hydrologic model, version 1.0 and a NOAA US National Water Model configuration of WRF-Hydro, version 1.2. The objectives of CHIP phase 1 are: 1) describe model physics and components, 2) design an experiment to ensure a fair comparison, and 3) assess simulated streamflow with observations to better understand model bias. To our knowledge, this is the first comparison of continental-scale, high-resolution, physics-based models which incorporate lateral subsurface flow. This model intercomparison is an initial step toward a continued effort to unravel process, parameter, and formulation differences in current large-scale hydrologic models and to engage the hydrology community in improving hydrology model configuration and process representation.

Tijerina, D.T., Condon, L.E., FitzGerald, K., Dugger, A., O'Neill, M. M., Sampson, K., Gochis, D.J., and Maxwell, R.M. (2021). Continental Hydrologic Intercomparison Project (CHIP), Phase 1: A Large-Scale Hydrologic Model Comparison over the Continental United States. Water Resources Res. doi: 10.1029/2020WR028931.

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This is the data repository for the journal article entitled "Effect of Surface Water Stage Fluctuation on Mixing-Dependent Hyporheic Denitrification in Riverbed Dunes" published in Water Resources Research in 2019 by Erich T. Hester, Lauren A. Eastes, and Mark A. Widdowson. The data themselves, as well as information about the data, can be found in several locations:

1) Many data are in the journal article or the associated supplementary information, which are available at the journal website or can be requested by emailing Erich Hester at ehester@vt.edu
2) Many data are available in the files associated with this Hydroshare resource, which are described in the readme.txt file
3) Any questions that are not answered by the above methods can be directed to Erich Hester at ehester@vt.edu

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WEAP and WASH Bear River Systems Models

This resource includes an SQLite file for the Water Management Data Model (WaMDaM) that stores data for two models in the Bear River Watershed in Utah.

The first model is the Watershed Area of Suitable Habitat (WASH) optimization model that allocates water to maximize watershed habitat areas for the Lower Bear River Watershed (Utah portion) (Alafifi and Rosenberg, 2020). The WASH model uses the General Algebraic Modeling System (GAMS) engine which has no user interface.

The second model is a WEAP simulation model that allocates water by water right priority within the Bear River Watershed (Utah and Idaho portions). WEAP has a proprietary database and does not support data publication. Both the WASH and WEAP models were developed from a predecessor 2010 Utah Division of Water Resources model for the lower Bear River basin that had a plain text input file and Fortran computational engine which was depreciated. The WASH model disaggregated irrigation demands within Cache Valley, Utah while the WEAP model extended the model domain upstream to Idaho and Bear Lake.

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Monterrey Mexico model From OpenAgua

This resource includes an SQLite file for the Water Management Data Model (WaMDaM) that stores data for the water allocation model for the Monterrey metropolitan area, Mexico. The model is imported from OpenAgua using the WaMDaM Wizard. For more info, visit OpenAgua here https://www.openagua.org/home

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The contents of this resource include materials from the CUAHSI Data Services Workshop hosted at the University of Arizona. The contents include the presentation powerpoint, a HydroShare Data Discovery Exercise, and a Data Management Plan Evaluation Exercise. If you reuse these materials please contact the author.

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Quantitative characterization of the dynamics of water exchange fluxes between rivers and aquifers is necessary for water resources management, water quality, environment and ecology of the river-aquifer systems. The main uncertain factors for predicting river–aquifer exchange fluxes are aquifer and riverbed properties. In this study, we characterize the flux exchange dynamics between Brazos River Alluvium Aquifer and Brazos River, TX, USA, using alternative conceptual models. Six alternative conceptual models for the connection between the river and the aquifer, having varying aquifer lithology and river incision levels and incorporating processes such as river bed clogging and seepage face flow, are numerically modeled in HYDRUS 2D using small-scale, high-resolution transects across the river. Modeled results are tested against observed heads in three wells and finally a best-fit conceptual model is used to quantify river-aquifer flux exchange dynamics. Additionally we focused on how factors such as aquifer lithology, river channel incision, water table conditions, seepage face boundaries, and low-conductivity river-bed effect hydraulic head distribution and the corresponding flux exchange dynamics. Our results demonstrate that only a small portion of the aquifer close to the river channel is well-connected with the river and a major portion of the aquifer is disconnected. The proposed conceptual model predicts a) much frequent flux reversals (changes between gaining and losing conditions) and b) much smaller amount of recharge and discharges compared to that of the conceptual model which has been assumed by earlier studies; a reduction of 151% in recharge and 116% in discharges. These results suggest that the magnitude and dynamics of water flux exchange between the river and the aquifer are independent of the hydraulic gradients in the wider disconnected aquifer and are determined by the hydraulic gradients in the connected aquifer close to the river. The results also demonstrate that river-aquifer flux exchange is sensitive to aquifer lithology, river incision depth, and river-bed clogging. While different settings of aquifer lithology and river incision can produce very similar heads in the wider aquifer, the hydraulic head distribution close to the river and hence the river-aquifer flux exchange varies quite drastically from model to model. River-bed clogging decreases the magnitude of fluxes and effects hydraulic head in the aquifer, especially in the vicinity of the river channel, depending upon the gaining and losing river conditions. Furthermore, seepage face flow could be of the same order as that of flows through river-bed depending upon aquifer lithology and corresponding river incision depth.

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This is model files processing codes for offshore pumping simulations

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Preferred citation:
Xu, T., Deines, J., Kendall, A., Basso, B., and Hyndman, DW. 2019. Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data. Remote Sensing.

We developed annual, 30-m resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). Please see Xu et al. 2019 for full details. The rasters are in UINT 8 format, with 0 indicates rainfed, 1 indicates irrigated, and 3 indicates masked (not row crops according to NLCD before 2007 and not corn or soybeans according to CDL since 2007).

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The data belong to a manuscript submitted to the Journal of Water Resources Planning and Management.

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Title: Dataset: Temperatures and flow rates for some springs in New England, 2017-18

Authors: Dallas Abbott1, William Menke1, Juliette Lamoureux2, Dionne Hutson2 and Alyssa Marrero3

1Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York
2City College of New York, New York, New York
3Kingsborough Community College, Brooklyn, New York
Summary: In 2017-2018, we visited a suite of about 80 springs in New York and New England (USA). We measured water temperature with a Lascar EL-WIFI-TP digital temperature logger (0.1°C precision) at the closest accessible point to the source, which was usually the reservoir inside a spring house or the outflow pipe from a spring house. When both reservoir and outflow pipe were accessible, we found that temperatures agreed to within ±0.2°C. We also measured the flow rate of the spring with a bucket and a stopwatch, with a repeatability of about ±10%.

A temperature anomaly ∆T was determined for each spring by subtracting the annual average temperature at the spring site. Annually averaged temperatures are rarely available for spring sites but are available for airports via the National Oceanic and Atmospheric Administration’s (NOAA’s) National Center for Environmental Information. We therefore used the annually averaged temperature for the nearest airport (typically ~10-20 km away), corrected to the elevation of the spring using the dry adiabatic lapse rate of 9.8°C/km.

Data was used in the following paper:

Menke, W., Lamoureux, J., Abbott, D., Hopper, E., Hutson, D. and Marrero, A., 2018. Crustal heating and lithospheric alteration and erosion associated with asthenospheric upwelling beneath southern New England (USA). Journal of Geophysical Research: Solid Earth, 123(10), pp.8995-9008.

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The “active-passive surface water classification” (APWC) method leverages cloud-based computing resources and machine learning techniques to merge Sentinel 1 synthetic aperture radar and Landsat observations and generate monthly 10-meter resolution waterbody maps. Merging data from two sensor types reduces the impact of errors associated with the individual sensors. The skill of the APWC method is demonstrated by mapping surface water change over the Awash River basin in Ethiopia from October 2014 through March 2017. This period corresponds to the 2015 East African regional drought and 2016 localized flood events. Errors of omission and commission in the case study area are 7.16% and 1.91%, respectively. These data were generated using the APWC method on August 18, 2017.

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Globally changing temperature and precipitation patterns are causing rapid changes stream temperatures, which in turn drive changes in the life histories and distributions of aquatic biota. However, large-scale stream temperature datasets have not been developed, and observational data remains limited. In order to better understand how ongoing thermal regime changes impact aquatic species, managers and researchers need better methods of quantifying stream temperatures at large spatial scales. Here, a linear regression model is used to develop a relationship between air and stream temperature, then is used to predict stream temperatures across the state of Utah in the month of August. Model validity was assessed by examining goodness of fit to observation data using R², Nash-Sutcliffe Efficiency index, and root mean square error-observations standard deviation ratio (RSR). Impact of outliers were assessed by examining mean absolute error (MAE), root mean square error (RMSE), and residuals. The approach presented here contributes to the well-described linear air/stream temperature model by providing a study of its performance at large spatial scales.

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This dataset consists of InSAR-measured line-of-sight surface deformation over the Ara watershed (located in northern Benin) for two dry seasons (November 2015-June 2016 and November 2016-June 2017). Thirty-six single look complex (SLC) images acquired by the Sentinel 1 mission were obtained from the Alaska Satellite Facility (ASF) Distributed Active Archive Centers (DAAC; http://www.asf.alaska.edu/). 12-, 24-, and 36-day interferograms were generated using the open source (GNU General Public License) Generic Mapping Tools 5 Synthetic Aperture Radar (GMT5SAR) processing system (Sandwell et al 2016, Massonnet and Feigl 1998). GMT5SAR geometrically aligns Sentinel TOPSAR images to a single master image with centimeter accuracy, maps topography into phase, and forms a stack of complex interferograms (Sandwell et al 2016). The Generic Mapping Tools- (GMT-) (Wessel et al 2013) based GMT5SAR postprocesser filters the interferogram and generates phase, coherence, and phase gradient products. GMT5SAR unwraps the interferograms using the well-known snaphu algorithm (Chen and Zebker 2000). Filter and decimation parameters for the inSAR processing were chosen to produce relatively high resolution interferograms, considering the computational cost of phase unwrapping. Lighter filtering and decimation improves interferogram resolution, but increases the computational time for phase unwrapping. Pixels were decimated by a factor of 8 in the range and 2 in the azimuth directions, generating interferograms with a pixel size of approximately 18.4 x 28.2 meters (range x azimuth). A 100 meter Gaussian filter was selected for the Ara study area. Enhances spectral diversity was used to reduce phase mismatch at the burst boundary (Sandwell et al 2016). The new small baseline subset(NSBAS) technique (Doin et al 2011) was used was used to generate a time series analysis of deformation across the study area. The NSBAS algorithm was applied using the Generic InSAR Analysis Toolbox (GIAnT; Agram et al 2012, 2013). The GIAnT tool box stacked the geometrically-aligned phase-unwrapped interferograms, estimated and applied corrections for residual long‐wavelength errors due to imprecise orbits, and estimated line-of-sight displacements using the NSBAS technique.

Agram P S, Jolivet R, Riel B, Lin Y N, Simons M, Hetland E, Doin M-P and Lasserre C 2013 New Radar Interferometric Time Series Analysis Toolbox Released Eos Trans. Am. Geophys. Union 94 69–70
Chen C W and Zebker H A 2000 Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms J Opt Soc Am A 17 401–414
Doin M-P, Guillaso S, Jolivet R, Lasserre C, Lodge F, Ducret G and Grandin R 2011 Presentation of the small baseline NSBAS processing chain on a case example: the Etna deformation monitoring from 2003 to 2010 using Envisat data Proceedings of the Fringe Symposium (ES) pp 3434–3437
Massonnet D and Feigl K L 1998 Radar interferometry and its application to changes in the Earth’s surface Rev. Geophys. 36 441–500
Sandwell D, Mellors R, Tong X, Wei M and Wessel P 2016 Gmtsar: An insar processing system based on generic mapping tools (second edition)
Wessel P, Smith W H, Scharroo R, Luis J and Wobbe F 2013 Generic mapping tools: improved version released Eos Trans. Am. Geophys. Union 94 409–410

ABSTRACT:

One of the greatest threats to Great Salt Lake wetlands is the invasion of Phragmites australis. Recent research has highlighted effective control strategies for Phragmites, however natural recolonization of native plants needed to support wetland functions has been limited. Seeding is a feasible restoration option, however seedling mortality is often high. Understanding the mechanisms that drive early seedling outcomes by quantifying regeneration traits can improve our ability to manipulate and predict restoration actions. Additionally, managers involved in wetland restoration need to know how many seeds to sow, which sites should be prioritized for restoration, and when they should seed. I developed a simulation model to explore changes in native and invasive seed germination across initial seeding density, restoration site, and seasonal timing scenarios. Additionally, I incorporated the influence of seed mass on native species germination into my model. This approach represents a starting point for developing an important management tool that can be used to identify targeted, cost-effective wetland restoration strategies following Phragmites treatment.

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The files provided here are input files for the river temperature modeling components created for the calibration exercise presented in Buahin et al. 2019, "Parallel multi-objective calibration of a component-based river temperature model" in Environmental Modeling and Software (https://doi.org/10.1016/j.envsoft.2019.02.012). Input files are organized into different folders for the different components.

Folders are organized as follows:

Composition: Contains the coupled model composition files (i.e., *.hcp) used in the HydroCoupleComposer graphical user interface. It describes the components that have been coupled as well as the data exchange connections nodes between them.

CalibrationComponent: Contains the input files for the calibration component specifying the calibration objectives, decision variables, and optimization algorithm to use for calibration.

CSHComponent: Contains input files for the channel solute and heat transport component.

HTSComponent: Contains input files for the hyporheic transient storage component.

ObjectiveFunctionComponent: Contains input files used to specify the objectives to be minimized by the CalibrationComponent.

RHEComponent: Contains input files used in the radiative heat exchange component.

SWMMComponent: Contains input files used in the Stormwater Management Model (SWMM) component.

TimeSeriesProviderComponent: Contains input files used to prescribe time varying input data to other components.

ABSTRACT:

This dataset contains standardized drought indexes (SIs) over East Africa derived from total water storage anomaly (TWSA) observations from the Gravity Recovery and Climate Experiment (GRACE) mission. We obtained the RL05M.1 CRI Filtered Version 2 (Wiese et al., 2016, Watkins et al., 2015) monthly mass grids of the NASA JPL global mascon solution from the JPL’s GRACE TELLUS site. TWSAs for 40 mascons covering East Africa were extracted from the dataset for May, 2002 through August, 2016. Observations collected between the 10th and 20th day of the month were retained as the SI dataset. The resulting SI dataset did not contain data for several months due to missing observations in the global mascon solution dataset or because the observation for the month was not between the 10th and 20th day of the month. The missing values were interpolated and the final SI dataset contains a TWSA value for each mascon for each month from May, 2002 through August, 2016. 1-month, 3-month, and 6-month standardized drought indexes were calculated following the nonparametric approach described by Farahman and AghaKouchak (2015). This methodology derives the SI using the empirical Gringorten plotting position (Gringorten, 1963).

The SI dataset contains the following fields:
• ID: mascon ID assigned by NASA JPL
• Year: year of the SI
• Month: month of the SI
• SI1Mo: one-month SI
• SI3Mo: three-month SI
• SI6Mo: six-month SI

This dataset was created on April 20, 2017. Figures mapping the drought indices by year are presented with the dataset.

Farahmand, A., & AghaKouchak, A. (2015). A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140–145. https://doi.org/10.1016/j.advwatres.2014.11.012
Gringorten, I. I. (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research, 68(3), 813–814. https://doi.org/10.1029/JZ068i003p00813
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., & Landerer, F. W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons: Improved Gravity Observations from GRACE. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. https://doi.org/10.1002/2014JB011547
D. N. Wiese, D.-N. Yuan, C. Boening, F. W. Landerer, M. M. Watkins. 2016. JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2. Ver. 2. PO.DAAC, USA. Dataset accessed [2017-02-07] at http://dx.doi.org/10.5067/TEMSC-2LCR5.

ABSTRACT:

This dataset contains standardized drought indexes (SIs) over East Africa derived from total water storage anomaly (TWSA) observations from the Gravity Recovery and Climate Experiment (GRACE) mission. We obtained the RL05M.1 CRI Filtered Version 2 (Wiese et al., 2016, Watkins et al., 2015) monthly mass grids of the NASA JPL global mascon solution from the JPL’s GRACE TELLUS site. TWSAs for 40 mascons covering East Africa were extracted from the dataset for May, 2002 through August, 2016. Observations collected between the 10th and 20th day of the month were retained as the SI dataset. The resulting SI dataset did not contain data for several months due to missing observations in the global mascon solution dataset or because the observation for the month was not between the 10th and 20th day of the month. The missing values were interpolated and the final SI dataset contains a TWSA value for each mascon for each month from May, 2002 through August, 2016. 1-month, 3-month, and 6-month standardized drought indexes were calculated following the nonparametric approach described by Farahman and AghaKouchak (2015). This methodology derives the SI using the empirical Gringorten plotting position (Gringorten, 1963).

The SI dataset contains the following fields:
• ID: mascon ID assigned by NASA JPL
• Year: year of the SI
• Month: month of the SI
• SI1Mo: one-month SI
• SI3Mo: three-month SI
• SI6Mo: six-month SI

This dataset was created on April 20, 2017. Figures mapping the drought indices by year are presented with the dataset.

Farahmand, A., & AghaKouchak, A. (2015). A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140–145. https://doi.org/10.1016/j.advwatres.2014.11.012
Gringorten, I. I. (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research, 68(3), 813–814. https://doi.org/10.1029/JZ068i003p00813
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., & Landerer, F. W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons: Improved Gravity Observations from GRACE. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. https://doi.org/10.1002/2014JB011547
D. N. Wiese, D.-N. Yuan, C. Boening, F. W. Landerer, M. M. Watkins. 2016. JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2. Ver. 2. PO.DAAC, USA. Dataset accessed [2017-02-07] at http://dx.doi.org/10.5067/TEMSC-2LCR5.

ABSTRACT:

This dataset contains total water storage anomalies (TWSAs) over East Africa predicted from observations from the Gravity Recovery and Climate Experiment (GRACE) mission using a Bayesian spatiotemporal mixed effects model. The model was also used to estimate missing observations from the GRACE mission. We obtained the RL05M.1 CRI Filtered Version 2 (Wiese et al., 2016, Watkins et al., 2015) monthly mass grids of the NASA JPL global mascon solution from the JPL’s GRACE TELLUS site. TWSAs for 40 mascons covering East Africa were extracted from the dataset for May, 2002 through August, 2016. This dataset did not contain data for several months due to missing observations in the global mascon solution dataset. The missing values were predicted by the model.

The following Bayesian spatiotemporal mixed effects model was used to generate the modeled dataset. The GRACE TWSA data can be represented by the spatiotemporal mixed effects model: Zt = Xtβ + Yt + εt; where Zt is a vector of TWSAs observations at time t, Xt is a matrix of fixed seasonal affects, β is a vector of fixed covariate values for the seasonal affects, Yt is a vector of the true, underlying process, and εt is a vector of errors error terms.

The true TWSAs at time t can be modeled by the autoregressive process: Yt = ΦYt-1 +ηt; where Φ defines the spatial-temporal structure of the GRACE TWSA and ηt is a vector of errors error terms. However, estimating Φ is computationally difficult because of its high dimensionality.

Empirical orthogonal function (EOF) analysis (Cressie & Wikle, 2011) can be used to identify the principal spatial structures in the GRACE TWSA data. The dimensionality of the model is reduced by modeling the spatial structure using EOFs: Yt = Mut +ηt; ut = Ξut-1 + ζt; where M is a matrix of fixed, time-invariant basis functions defined as the first p empirical orthogonal functions (EOFs) of the data, ut is a vector representing a rank reduced process at time t, Ξ is a diagonal matrix defined as diag(ξ1.. ξp), representing the eigenvalues corresponding to the EOFs, and ζt is a vector of random errors error terms. EOF analysis greatly reduces the computation burden of estimating the spatial-temporal structure of the GRACE TWSA.

A Bayesian approach is used to estimate the stochastic distributions for the model parameters ut , u0 , σ2ζ , β , and ξj. Bayesian priors are chosen for each parameter and Monte Carlo Makov Chain methods are used to estimate the distribution parameters following the algorithm:
I. Initialize the parameter values
II. Gibs sampler draws from the posterior conditional for parameters ut , σ2ζ , u0, and β
III. Slice sampler draws from the posterior conditional for the parameter ξj
IV. Repeat II and III until the Markov chain converges to a stationary distribution

The calculations to implement the model are provided as part of the data archive.

The SI dataset contains the following fields:
• ID: mascon ID assigned by NASA JPL
• Year: year of the TWSA
• Month: month of the TWSA
• Day: day of the TWSA
• TWSA_Obs: observed TWSA (NA if missing) in cm
• TWSA_Mod: observed TWSA in cm
• CI05: lower limit of the 90% credible interval for the modeled value in cm
• CI95: upper limit of the 90% credible interval for the modeled value in cm

This dataset was created on April 28, 2017.

Cressie, N., & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Hoboken, New Jersey: John Wiley & Sons, Inc.
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., & Landerer, F. W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons: Improved Gravity Observations from GRACE. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. https://doi.org/10.1002/2014JB011547
D. N. Wiese, D.-N. Yuan, C. Boening, F. W. Landerer, M. M. Watkins. 2016. JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2. Ver. 2. PO.DAAC, USA. Dataset accessed [2017-02-07] at http://dx.doi.org/10.5067/TEMSC-2LCR5.

ABSTRACT:

This dataset contains total water storage anomalies (TWSAs) over East Africa predicted from observations from the Gravity Recovery and Climate Experiment (GRACE) mission using a Bayesian spatiotemporal mixed effects model. The model was also used to estimate missing observations from the GRACE mission. We obtained the RL05M.1 CRI Filtered Version 2 (Wiese et al., 2016, Watkins et al., 2015) monthly mass grids of the NASA JPL global mascon solution from the JPL’s GRACE TELLUS site. TWSAs for 40 mascons covering East Africa were extracted from the dataset for May, 2002 through August, 2016. This dataset did not contain data for several months due to missing observations in the global mascon solution dataset. The missing values were predicted by the model.

The following Bayesian spatiotemporal mixed effects model was used to generate the modeled dataset. The GRACE TWSA data can be represented by the spatiotemporal mixed effects model: Zt = Xtβ + Yt + εt; where Zt is a vector of TWSAs observations at time t, Xt is a matrix of fixed seasonal affects, β is a vector of fixed covariate values for the seasonal affects, Yt is a vector of the true, underlying process, and εt is a vector of errors error terms.

The true TWSAs at time t can be modeled by the autoregressive process: Yt = ΦYt-1 +ηt; where Φ defines the spatial-temporal structure of the GRACE TWSA and ηt is a vector of errors error terms. However, estimating Φ is computationally difficult because of its high dimensionality.

Empirical orthogonal function (EOF) analysis (Cressie & Wikle, 2011) can be used to identify the principal spatial structures in the GRACE TWSA data. The dimensionality of the model is reduced by modeling the spatial structure using EOFs: Yt = Mut +ηt; ut = Ξut-1 + ζt; where M is a matrix of fixed, time-invariant basis functions defined as the first p empirical orthogonal functions (EOFs) of the data, ut is a vector representing a rank reduced process at time t, Ξ is a diagonal matrix defined as diag(ξ1.. ξp), representing the eigenvalues corresponding to the EOFs, and ζt is a vector of random errors error terms. EOF analysis greatly reduces the computation burden of estimating the spatial-temporal structure of the GRACE TWSA.

A Bayesian approach is used to estimate the stochastic distributions for the model parameters ut , u0 , σ2ζ , β , and ξj. Bayesian priors are chosen for each parameter and Monte Carlo Makov Chain methods are used to estimate the distribution parameters following the algorithm:
I. Initialize the parameter values
II. Gibs sampler draws from the posterior conditional for parameters ut , σ2ζ , u0, and β
III. Slice sampler draws from the posterior conditional for the parameter ξj
IV. Repeat II and III until the Markov chain converges to a stationary distribution

The calculations to implement the model are provided as part of the data archive.

The SI dataset contains the following fields:
• ID: mascon ID assigned by NASA JPL
• Year: year of the TWSA
• Month: month of the TWSA
• Day: day of the TWSA
• TWSA_Obs: observed TWSA (NA if missing) in cm
• TWSA_Mod: observed TWSA in cm
• CI05: lower limit of the 90% credible interval for the modeled value in cm
• CI95: upper limit of the 90% credible interval for the modeled value in cm

This dataset was created on April 28, 2017.

Cressie, N., & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Hoboken, New Jersey: John Wiley & Sons, Inc.
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., & Landerer, F. W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons: Improved Gravity Observations from GRACE. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. https://doi.org/10.1002/2014JB011547
D. N. Wiese, D.-N. Yuan, C. Boening, F. W. Landerer, M. M. Watkins. 2016. JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2. Ver. 2. PO.DAAC, USA. Dataset accessed [2017-02-07] at http://dx.doi.org/10.5067/TEMSC-2LCR5.

ABSTRACT:

Materials for WRSS package

ABSTRACT:

The files provided here are input files for the river temperature modeling components created for the calibration exercise presented in Buahin et al. 2019, "Parallel multi-objective calibration of a component-based river temperature model" in Environmental Modeling and Software (https://doi.org/10.1016/j.envsoft.2019.02.012). Input files are organized into different folders for the different components as follows:

1. Composition: Contains the coupled model composition files (i.e., *.hcp) used in the HydroCoupleComposer graphical user interface. It describes the components that have been coupled as well as the data exchange connections nodes between them.

2. CalibrationComponent: Contains the input files for the calibration component specifying the calibration objectives, decision variables, and optimization algorithm to use for calibration.

2. CSHComponent: Contains input files for the channel solute and heat transport component.

3. HTSComponent: Contains input files for the hyporheic transient storage component.

4. ObjectiveFunctionComponent: Contains input files used to specify the objectives to be minimized by the CalibrationComponent.

5. RHEComponent: Contains input files used in the radiative heat exchange component.

6. SWMMComponent: Contains input files used in the Stormwater Management Model (SWMM) component.

7. TimeSeriesProviderComponent: Contains input files used to prescribe time varying input data to other components.

The windows version of the HydroCoupleComposer executable needed to run this composition can be downloaded on Github (https://github.com/HydroCouple/HydroCoupleComposer/releases/download/v1.2.2/HydroCoupleComposer.msi)

ABSTRACT:

Concentration-discharge relationships are a key tool for understanding the sourcing and transport of material from watersheds to fluvial networks. Storm events in particular provide insight into variability in the sources of solutes and sediment within watersheds, and the hydrologic pathways that connect hillslope to stream channel. Here we examine high-frequency sensor-based specific conductance and turbidity data from multiple storm events across two watersheds (Quebrada Sonadora and Rio Icacos) with different lithology in the Luquillo Mountains of Puerto Rico, a forested tropical ecosystem. Our analyses include Hurricane Maria, a category 5 hurricane. To analyze hysteresis, we used a recently developed set of metrics to describe and quantify storm events including the hysteresis index (HI), which describes the directionality of hysteresis loops, and the flushing index (FI), which describes whether the mobilization of material is source or transport limited. We also examine the role of antecedent discharge to predict hysteretic behavior during storms. Overall, specific conductance and turbidity showed contrasting responses to storms. The hysteretic behavior of specific conductance was very similar across sites, displaying clockwise hysteresis and a negative flushing index indicating proximal sources of solutes and consistent source limitation. In contrast, the directionality of turbidity hysteresis was significantly different between watersheds, although both had strong flushing behavior indicative of transport limitation. Overall, models that included antecedent discharge did not perform any better than models with peak discharge alone, suggesting that the magnitude and trajectory of an individual event was the strongest driver of material flux and hysteretic behavior. Hurricane Maria produced unique hysteresis metrics within both watersheds, indicating a distinctive response to this major hydrological event. The similarity in response of specific conductance to storms suggests that solute sources and pathways are similar in the two watersheds. The divergence in behavior for turbidity suggests that sources and pathways of particulate matter vary between the two watersheds. The use of high-frequency sensor data allows the quantification of storm events while index-based metrics of hysteresis allow for the direct comparison of complex storm events across a heterogeneous landscape and variable flow conditions.

Additional scripts for hysteresis analysis are available here in the 'python scripts for analysis' folder and at https://github.com/miguelcleon/HysteresisAnalysis/

ABSTRACT:

Particle image velocimetry, PIV, is a non-invasive technique for measuring velocity fields. It is especially powerful when coupled with refractive index-matching (RIM) to map velocity fields around solid objects. The solid objects are typically removed from the flow field with a masking approach before performing the PIV analysis and mapping the velocity field. However, imasking required a-priory information on solid location and their geometric shape which is difficult in to select them when PIV is done with RIM. Here we report and store the data used in the contribution "Particle Seeded Grains to Identify Highly Irregular Solid Boundaries and Simplify PIV measurements" by Basham, Budwig and Tonina, 2019, in Frontiers in Earth Science, doi: 10.3389/feart.2019.00195.

ABSTRACT:

Data about water are found in many types of formats distributed by many different sources and depicting different spatial representations such as points, polygons and grids. How do we find and explore the data we need for our specific research or application? This seminar will present common challenges and strategies for finding and accessing relevant datasets, focusing on time series data from sites commonly represented as fixed geographical points. This type of data may come from automated monitoring stations such as river gauges and weather stations, from repeated in-person field observations and samples, or from model output and processed data products. We will present and explore useful data catalogs, including the CUAHSI HIS catalog accessible via HydroClient, CUAHSI HydroShare, the EarthCube Data Discovery Studio, Google Dataset search, and agency-specific catalogs. We will also discuss programmatic data access approaches and tools in Python, particularly the ulmo data access package, touching on the role of community standards for data formats and data access protocols. Once we have accessed datasets we are interested in, the next steps are typically exploratory, focusing on visualization and statistical summaries. This seminar will illustrate useful approaches and Python libraries used for processing and exploring time series data, with an emphasis on the distinctive needs posed by temporal data. Core Python packages used include Pandas, GeoPandas, Matplotlib and the geospatial visualization tools introduced at the last seminar. Approaches presented can be applied to other data types that can be summarized as single time series, such as averages over a watershed or data extracts from a single cell in a gridded dataset – the topic for the next seminar.

Cyberseminar recording is available on Youtube at https://youtu.be/uQXuS1AB2M0

ABSTRACT:

Model developed and documented in: O’Reilly, A.M., Holt, R.M., Davidson, G.R., Patton, A., Rigby, J.R., 2020. A dynamic water-balance/nonlinear-reservoir model of a perched phreatic aquifer–river system with hydrogeologic threshold effects: Water Resources Research 56(6): e2019WR025382. https://doi.org/10.1029/2019WR025382

Heterogeneity in the hyporheic zone or near-field geology can impart a threshold effect on groundwater-surface water (GW-SW) exchange. Variations in the texture of riverbed sediments and lithologic variations in adjacent and underlying geology are examples of common heterogeneities. Hydrologic interaction with these heterogeneities leads to distinct types of “behavior” that “switch” when surface-water or groundwater levels rise above or fall below the interface of the layers of differing lithology. A dynamic water-balance/nonlinear-reservoir model incorporating threshold effects was developed for a perched phreatic aquifer–river system. Four conceptualizations of the system were modeled, each of which simulates a perched aquifer as a dynamical system that receives recharge from the riverbank and loses water to an underlying regional aquifer, using combinations of zero, one, or two thresholds representing layered heterogeneity in riverbank and/or aquifer lithology. Application of the model code was demonstrated at a location in the Lower Mississippi River Valley, USA. Models were run using hourly river-gage measurements, calibrated to a 382-day period of corresponding measurements in a nearby well, and further assessed for a 3.5-year period representing varied hydrologic conditions. The best performance was demonstrated by the model incorporating threshold effects, which elucidated four modes of GW-SW system behavior controlled by both riverbank (riverbed hydraulic conductivity) and aquifer (hydraulic conductivity and storage coefficient) properties. The dynamical system modeling approach incorporates the salient hydrologic processes of a GW-SW system with layered heterogeneity. Based upon fundamental mass-conservation concepts, the simple dynamic water-balance/linear-reservoir model has broad applicability to many hydrogeologic settings.

ABSTRACT:

The data belong to a manuscript submitted to the Journal of Water Resources Management, Springer.

ABSTRACT:

This is the repository for the paper "Error Assessment for Height Above the Nearest Drainage Inundation Mapping" by Lukas Godbout, Jeff Y. Zheng, Sayan Dey, Damilola Eyelade, David Maidment, and Paola Passalacqua. Please direct all communication to LukasGodbout@utexas.edu.

Real time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage (HAND) method can be used in conjunction with Synthetic Rating Curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic modeling calibrated against observed stage heights. We find that SRCs are accurate enough for large scale approximate inundation mapping while not accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe that reach length and slope predict divergence between the two types of rating curves, and that SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.

ABSTRACT:

In coastal rivers, tides facilitate surface water-groundwater exchange and strongly coupled nitrification-denitrification near the fluctuating water table. We used numerical fluid flow and reactive transport models to explore hydrogeologic and biogeochemical controls on nitrogen transport along an idealized tidal freshwater zone based on field observations from White Clay Creek, Delaware, USA. The capacity of the riparian aquifer to remove nitrate depends largely on nitrate transport rates, which initially increase with increasing tidal range but then decline as sediments become muddier and permeability decreases. Over the entire model reach, local nitrification provides a similar amount of nitrate as surface and groundwater contributions combined. More than half (~66%) of nitrate removed via denitrification is produced in-situ, while the vast majority of remaining nitrate removed comes from groundwater sources. In contrast, average nitrate removal from surface water due to tidal pumping amounts to only ~1% of the average daily in-channel riverine nitrate load or 1.77 kg of nitrate along the reach each day. As a result, tidal bank storage zones may not be major sinks for nitrate in coastal rivers but can act as effective sinks for groundwater nitrate. By extension, tidal bank storage zones provide a critical ecosystem service, reducing contributions of groundwater nitrate, which is often derived from septic tanks and fertilizers, to coastal rivers.

ABSTRACT:

Do water borehole failures, in Uganda, correlate with geographic details, population, or political reasons? Is there a trend based on which orgnization oversaw the installation or raised the capital? Can we create an ML model to determine if correlations exist?

ABSTRACT:

Recirculating flume experiments tracking the evolution of sand bed forms through repeat 2d sonar scans. Experiments track change in bed form geometries across abrupt and gradual water discharge changes.

ABSTRACT:

Sample data for HESS-2019-205 submission

Description: This file contains the event magnitudes and spacing for Cases 1 & 3 presented in the submitted manuscript to HESS titled "Recession analysis 42 years later - work yet to be done".

CVS File: This file is an ordered set of the normalized event magnitude [-] and the start date fo the event (Time/Timescale [-])

Matlab File: The file is presented is in a .mat file extension created in Matlab. The data is divided into 3 columns: mag, value, and start_locs. The column of "Mag" defines the event magnitudes, which are log-normally distributed with a mean 1 of a standard deviation of 1. The column of "value" defines the event duration which has a mean of 2.5 and a standard deviation of 1. The "start_locs" column as the cumulative event durations that identify the start time of each event. Below is the associated Matlab code used to create the file:
%% Matlab Code %%
mag= lognrnd(1,1[number_of_events,1]); %create log-normally distributed dataset of event magnitudes for a defined number of events
mag(mag<0)=1; %remove any negative magnitudes
value=round(lognrnd(2.5,1,[number_of_events,1])); %create log-normally distributed dataset of event durations for a defined number of events
value(value<=0)=1; %remove any negative durations
start_locs=[2;cumsum(value)]; %create cumulative event start time-series

ABSTRACT:

This resource contains the use case results of web-based simulation for snowmelt modeling research. The model input files were created by executing the Python script (ueb_setup.py) in CUAHSI JupyterHub web app, which made web requests to HydroDS modeling web services (https://github.com/CI-WATER/Hydro-DS) for inputs preparation. The model output files were created by using the model input files and the UEB web app (https://appsdev.hydroshare.org/apps/ueb-app/). A JupyterHub Notebook file (Data_analysis_code.ipynb) includes the data analysis code to compare the model output created by this use case and another use case (https://doi.org/10.4211/hs.1be4d7902c87481d85b93daad99cf471) with different model grid resolutions (600 m vs 1200 m).

ABSTRACT:

This resource is linked to the following manuscript:
Lutz et al.: How Important is Denitrification in Riparian Zones? Combining Endmember Mixing and Isotope Modeling to Quantify Nitrogen Removal Processes, in review for Water Resources Research

This resource provides concentration and isotope data in a groundwater well field along a 2 km stream section in central Germany. We developed a mathematical model combining endmember mixing and isotope modeling to account for mixing of river water and groundwater, and quantify nitrate transformation at the study site (i.e., the SISS model). This enabled us to explicitly determine the extent of denitrification (as permanent nitrate removal process) and nitrate removal by additional processes associated with negligible isotope fractionation (e.g., plant uptake, microbial assimilation and dissimilatory nitrate reduction to ammonium) in the riparian system.

Content:
*the SISS model code
*chloride and nitrate concentration data from the field site
*nitrate and stable water isotope data from the field site

ABSTRACT:

The physical hydrodynamic model GOTM was used to reconstruct the past water temperature of Lake Erken (Sweden) in order to investigate possible changes in its thermal structure during the period 1961-2017. To run the model, seven climatic parameters were used as forcing data: Wind Speed (m/s), Air Temperature (°C), Air Pressure (hPa), Relative Humidity (%), Cloud Cover (dimensionless value between 0-1), Precipitation (mm/day) and Shortwave Solar Radiation (W/m-2). This resource contains the model configuration file, input data of the model, the observed water temperature used to calibrate the model, and the output data.

The file 'erken.xml' is the model configuration file.

The input data of the model are:
- 'Erken_DailyPrec_1960-2017.dat': Lake Erken Daily Precipitation
- 'Erken_MetFile_1960-2017.dat': hourly dataset that contains Lake Erken Wind Speed, Air Temperature, Air Pressure, Relative Humidity and Cloud Cover
- 'Erken_HourlySWR_1960-2017.dat': hourly dataset of Lake Erken Shortwave Solar radiation
- 'ErkenObsTemp_1961-2017.dat': real daily water temperature data of Lake Erken

The file 'ErkenObsTemp_1961-2017.obs' contains the real daily water temperature data of Lake Erken and it was used to calibrate the model.

The output data of the model are:
- 'Mod_temp_hr_z.txt': contains the depth profile of lake Erken
- 'Mod_temp_hr_temp.txt': contains the calibrated modeled water temperature profile on daily time-step between 1960-2017.

The input data used to run the model were available between 1961-2017. To avoid that the initial state of the model could increase the errors during calibration, a 1-year simulation spin-up was used to minimize calibration errors. To make full use of the available input data, a copy of the 1961 meteorological data was appended at the beginning of the input files 'Erken_DailyPrec_1960-2017.dat', 'Erken_MetFile_1960-2017.dat' and 'Erken_HourlySWR_1960-2017.dat'. The data referred to the year 1960 are therefore only a copy of the 1961 data and they were used as spin-up year. For this reason, the data referred to the year 1960 in the output files should be discarded.

ABSTRACT:

This project quantified the ability of salt marshes in the Chesapeake Bay to attenuate coastal hazards; including the attenuation of storm surge and the reduction of wave energy by these natural ecosystems. The project documented the interaction of storm surges and waves with marshes by measuring hydrodynamic conditions in the field during extreme events (waves, currents and water levels), vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density) and topo-bathymetric surveys in several natural preserves in the Chesapeake Bay during the extent of the project, including several coastal storms and hurricanes. All the field procedures, data processing, equipment and project methodology are described in the QAPP document. This dataset provides the information for the Eastern Shore of Virginia National Wildlife Refuge.

ABSTRACT:

Logan River Watershed data used for testing parallel implementations of Utah Energy Balance Snowmelt Model reported in:

Gichamo, T. Z. and D. G. Tarboton, (2020), "UEB parallel: Distributed snow accumulation and melt modeling using parallel computing," Environmental Modelling & Software, 125: 104614, https://doi.org/10.1016/j.envsoft.2019.104614.

ABSTRACT:

These are the companion cases with the book "Computational Fluid Dynamics: Applications in Water, Wastewater, and Stormwater Treatment" published by American Society of Civil Engineers (ASCE).

ISBN 9780784415313 (print)
ISBN 9780784482216 (pdf)

ABSTRACT:

Inputs to a spatially distributed hydrologic model incorporating the UEB snowmelt that evaluates the effect of snow and streamflow assimilation in streamflow forecasting.

ABSTRACT:

The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using CUDA GPU.

ABSTRACT:

The Upper Basin of the Colorado River, under current agreements, must prioritize releases between 7.48 and 9.0 million acre feet (maf) of water to the Lower Basin of the Colorado River per year. This delivery is controlled by outflows from Lake Powell which until recently could not be less than 8.23 maf. This release represents the downstream allocations for Mexico, regional Native American tribes, and the Lower Basin states. This report presents a management alternative that allows for proportional releases from Lake Powell based on inflows. This report recognizes that any alteration to the existing schedule of deliveries from Lake Powell downstream is politically fraught. However, there is a pressing need to ensure that water and electrical demands are met, even if in lesser quantities, during the projected driest years of the coming decades. The results of the new rule show that Lake Powell is kept above power pool elevation longer than without the rule in place.

ABSTRACT:

This study seeks to promote sustainable management of the two largest reservoirs in the Colorado River basin, Lakes Powell and Mead, by accounting for evaporation losses in Lower Basin deliveries. Although evaporation losses are currently ~7% of the total basin demands, they are currently unaccounted for in basin deliveries. This project assigns evaporation loss responsibility to water users when Mead’s pool elevation is ≤ 1090 ft by proportionally decreasing their allotment based on yearly evaporation totals and the user’s current delivery schedule. To achieve this, a new delivery rule is created in the Colorado River Simulation System (CRSS), as modeled in RiverWare. To determine the efficacy of evaporation loss inclusion as a solution to declining reservoir levels, a performance metric is defined, and reservoir elevation levels are compared between CRSS simulations with and without evaporation loss inclusion.

ABSTRACT:

The Upper Basin of the Colorado River, under current agreements, must prioritize releases between 7.48 and 9.0 million-acre feet of water releases to the Lower Basin each year. These releases control Lower Basin deliveries which, until recently, were at least 8.23 million-acre feet per year. This delivery represents downstream allocations for Mexico, Native American tribes, and Lower Basin states. This report presents a management alternative that allows for proportional releases from Lake Powell based on inflows. Our results in RiverWare (CRSS) modeling show that Lake Powell is kept above power pool elevation longer than without the rule in place.

ABSTRACT:

The construction of Flaming Gorge Dam in 1964 caused significant changes to the channel form and juvenile fish habitat. To mitigate for degraded habitat, spring high flow dam operations were changed to improve juvenile off-channel habitat for razorback sucker. In this study, we evaluated whether these environmental spring flow releases can be met with future hydrology that incorporates climate change. We found that the model performed poorly in years with an average spring runoff and well in dry, moderately dry, and wet years regardless of whether climate change was considered. Next, we sought to increase the reliability of meeting environmental flow recommendations by adjusting the threshold values that define the hydrologic classification for each year and days at power plant capacity. We found that changing the threshold for each hydrologic classification was more sensitive than changing days at power plant capacity and that only by making extreme alterations of those values could we get significant improvement in the reliability of meeting environmental flow objectives.

ABSTRACT:

This article provides supporting information for JAWER article "Dissolved oxygen concentration profiles in the hyporheic zone through the use of a high-density fiber optic measurement system" (https://doi.org/10.1080/23249676.2019.1611495). It gives additional details about sourcing, constructing and controlling the DO measurement system. Sample AutoHotKey ® scripts are presented. Additionally, sample calculations are presented for the evaluation of flowline residence time. Finally, additional details are provided to describe conditioning of the flume water, initiating the bacterial communities and preparation and consumption of the carbon amendments to support the metabolic activities of the microbial communities.

ABSTRACT:

Large river hydrodynamics studies inform global and regional issues pertaining to biogeochemical cycling, ecology, water availability, and flood risk. Such studies rely increasingly on satellite measurements, but these are limited by resolution, coverage and uncertainty, and their inability to directly measure bathymetry or discharge. We obtain new in-situ data covering 650 km of the Congo’s main stem, including elusive bathymetry and discharge measurements that complement space-borne datasets. Our key findings relate to our water surface elevation measurements which show that spatial coverage of existing satellite altimetry for deriving river water surface profiles may be adequate through the globally important Cuvette Centrale, but is not at its outlet where our field data reveals significant spatial variability in water surface slope. The findings have implications for altimetry-based hydrodynamics studies of other large rivers, such as those that involve estimating discharge or modelling multichannel river hydraulics.

ABSTRACT:

This project quantified the ability of salt marshes in the Chesapeake Bay to attenuate coastal hazards; including the attenuation of storm surge and the reduction of wave energy by these natural ecosystems. The project documented the interaction of storm surges and waves with marshes by measuring hydrodynamic conditions in the field during extreme events (waves, currents and water levels), vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density) and topo-bathymetric surveys in several natural preserves in the Chesapeake Bay during the extent of the project, including several coastal storms and hurricanes. All the field procedures, data processing, equipment and project methodology are described in the QAPP document. This dataset provides the information for the Magothy Bay Natural Area Preserve.

ABSTRACT:

This resource contains eddy covariance evapotranspiration, sap flow transpiration, and soil moisture measurements from a creosotebush‐dominated shrubland ecosystem at the Santa Rita Experimental Range in southern Arizona. These measurements were taken over an 18‐month period in 2013-2015 in conjunction with biweekly precipitation, shallow soil, deep soil, and stem stable water isotope samples.

US-SRC_Daily_2013320-2015091.csv:
--Precipitation (mm)
--Vapor pressure deficit (VPD) (kPa)
--Evapotranspiration (mm/day)
--Transpiration (mm/day)
--Volumetric water content at multiple depths (2.5 cm, 12.5 cm, 22.5 cm, 37.5 cm, 52.5 cm) (m3/m3)

US-SRC_WaterIsotope_2014169-2015091.xlsx:
--delta-oxygen-18 and delta-deuterium values (‰):
-Precipitation (canopy, intercanopy)
-Soil at multiple depths (10 cm, 20 cm, 25 cm, 30 cm, 35 cm, 40 cm)
-Stem (creosotebush)

This resource serves as the data for:
Szutu, D. J., & Papuga, S. A. (2019). Year‐round transpiration dynamics linked with deep soil moisture in a warm desert shrubland. Water Resources Research. https://doi.org/10.1029/2018WR023990

ABSTRACT:

Measurements were conducted in a headwater catchment of the Rappbode stream located in the Harz Mountains, Central Germany.

ABSTRACT:

Clinic materials to learn about calibration with Sandia National Lab's Dakota tool.

Current version tracks v0.1 of the following repository.
https://github.com/kbarnhart/calibration_with_dakota_clinic/releases/tag/v0.1