ABSTRACT:

This generic resource illustrates to students in the CEE6400 GIS in Water Resources Class at Utah State University how to prepare HydroShare resources to post term projects.

ABSTRACT:

This netCDF data is the simulation output from Utah Energy Balance (UEB) model.
It includes the simulation result of snow water equivalent during the period Oct. 2009 to June 2010 for TWDEF site in Utah.

ABSTRACT:

This resource contains the two talks, one by David Tarboton and one by Alva Couch presented at the iRODS users group meeting in Chapel Hill, June 11, 2015

ABSTRACT:

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

ABSTRACT:

Area volume data for the Great Salt Lake. This was provided by Biowest Inc. as part of work to model the potential impact of proposed evaporation ponds on the level of the Great Salt Lake.

ABSTRACT:

Presentation to National Weather Service National Hydrology Program Managers Meeting, May 13, 2015, Tuscaloosa Alabama

ABSTRACT:

Time series of level, area and volume in the Great Salt Lake. Volume and area of the Great Salt Lake are derived from recorded levels and bathymetry. The bathymetry used is included. Bathymetry is adjusted for the presence or absence of Magnesium corps pond. The area of the evaporation pond is not regarded as part of the lake except prior to its construction and during the time it was overtopped.

GSL_north_arm.txt is measured data from the USGS station 10010100 GREAT SALT LAKE NEAR SALINE, UT
GSL_north_arm_2017-04-23.txt Duplicate of above as run on 4/23/17

GSL_south_arm.txt is measured data from the USGS station 10010000 GREAT SALT LAKE AT SALTAIR BOAT HARBOR, UT
GSL_south_arm_2017-04-23.txt Duplicate of above as run on 4/23/17
GSL_south_arm_2016-03-01.txt Record downloaded 3/1/2016 that includes data prior to USGS changing format

GSLLAV.txt is time series of level, computed area and volume from level using bathymetry

Bathymetry folder. Lake bathymetry used in these calculations. This data is also stored separately in https://www.hydroshare.org/resource/b26090299ec947c692d4ee4651815579/

GSLLevelVol.csv is beginning of month time series of level and volume from 1/1/1915 used for modeling

LevelVolWork.R is the R script used to process this data

GSLLevelRecord.pptx Powerpoint file with some figures of this data

GSLFunctions.R R functions used by the script

Headings should be obvious. Note that separate levels in the north arm only started being recorded in 1966 so for dates prior to that Nlevel_ft is reported as NA (no data in R). Nlevel_m is converted from the measurement in ft, and filled in using the south arm when there is no north arm data (a few days after 1966 are also missing). The bathymetry was then used to compute area and volume in each arm separately and add them up.

ABSTRACT:

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

ABSTRACT:

Can your desktop computer crunch the large datasets that are becoming increasingly common in hydrology and across the sciences? Do you have access to, or the know how to, take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare is expanding the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial data used in hydrology. HydroShare will also include new capability to share models and model components, and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. The HydroShare web interface and social media functions are being developed using the Django web application framework. The integrated Rule-Oriented Data System (iRODS) is being used to manage federated data content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

ABSTRACT:

Digital Elevation Model for the Great Salt Lake Basin at 100 m cell size, used primarily for watershed delineation using TauDEM. This was derived and reprojected from National Elevation dataset sources. It is not the highest resolution currently available.

ABSTRACT:

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to, or the know how to, take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the storage and sharing of a broad class of hydrologic data including time series, geographic features and rasters, multidimensional space-time data and structured collections of data representing river geometry. Web service tools and a python client library provide researchers with access to high performance computing resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This talk will illustrate web and client based use of data services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Presentation at Kansas University GIS Days November 18, 2015

ABSTRACT:

This data provides an illustration of the height above the nearest stream approach to flood inundation mapping based on the TauDEM vertical distance to stream function. This example uses a 10 m resolution National Elevation dataset for Onion Creek in Texas. Height above the nearest stream may be thought of as a “relative elevation function” which measures for every DEM cell in the landscape the difference in elevation between that cell and the cell to which it flows on the stream channel. This is like a “water depth” or “stage height” function defined using terrain analysis continuously across the landscape. This relative elevation function, combined with a depth in each stream reach provide a simplified terrain based approach to flood inundation mapping premised on the following:

1. Each reach has a water depth hw, from a hydraulic model such as SPRNT or RAPID.
2. Each reach has an ID
3. Each grid cell has the ID of the reach it connects to (catchment grid) and the height above the stream hs
4. Flood extent is “rapidly” mapped as
If(hw(id) > hs(id))
Inundation depth = hw(id) - hs(id)
Else
Inundation depth = 0

The data here can also be used to compute reach averaged hydraulic properties as follows
1. For each reach the stream network file gives reach length L.
2. For a series of water depths using the height above nearest stream intersected with catchment raster the innundation water volume V, surface area As and bed area Ab are obtained.
3. Reach average properties are then computed as
Cross section Area A = V/L
Wetted perimeter P = Ab/L
Top width = As/L
Hydraulic Radius = A/P

This approach is a simplification over finer scale hydraulics, and the inaccuracy due to introduction of this simplification still needs evaluation. This approach is also dependent on how well the DEM represents the channel and flooded area. This is expected to improve as we get better LIDAR DEMs and develop better ways to hydrologically condition DEMs that do not involve pit filling.

ABSTRACT:

Presentations to the Elevation Hydrology Meeting of the USGS in Reston Virginia, December 9, 2015 outlining ideas for using height above the nearest stream to map flood inundation.

The file EleHydroTarboton.pptx is the main presentation.

The file SomeAdditionalThoughtsTarboton.pptx are some quickly assembled slides to support thoughts I was asked to present towards the end of the meeting.

ABSTRACT:

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “resources” which are defined primarily by standardized metadata, content data models for each resource type, and an overarching resource data model based on the Open Archives Initiative’s Object Reuse and Exchange (OAI-ORE) standard and a hierarchical file packaging system called “BagIt”. HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. It also supports web services and server/cloud based computation operating on resources for the execution of hydrologic models and analysis and visualization of hydrologic data. HydroShare uses iRODS as a network file system for underlying storage of datasets and models. Collaboration is enabled by casting datasets and models as "social objects". Social functions include both private and public sharing, formation of collaborative groups of users, and value-added annotation of shared datasets and models. The HydroShare web interface and social media functions were developed using the Django web application framework coupled to iRODS. Data visualization and analysis is supported through the Tethys Platform web GIS software stack. Links to external systems are supported by RESTful web service interfaces to HydroShare’s content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

Slides for AGU 2015 presentation H42A-04, December 17, 2015

ABSTRACT:

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Slides for AGU 2015 presentation IN51C-03, December 18, 2015

ABSTRACT:

Presentation on 2/22/16

ABSTRACT:

Digital Elevation Model in Geographic Coordinates

ABSTRACT:

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined. This dataset produced using methods described in Haacker, E. M., Kendall, A. D., & Hyndman, D. W. (2015), shows these declines.

ABSTRACT:

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined. This raster dataset gives the 1935 groundwater levels in the HPA. This dataset, produced using methods described in Haacker, E. M., Kendall, A. D., & Hyndman, D. W. (2015), is part of a collection of raster datasets that give HPA groundwater levels over time.

ABSTRACT:

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined. This raster dataset gives the 1936 groundwater levels in the HPA. This dataset, produced using methods described in Haacker, E. M., Kendall, A. D., & Hyndman, D. W. (2015), is part of a collection of raster datasets that give HPA groundwater levels over time.

ABSTRACT:

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined. This raster dataset gives the 1937 groundwater levels in the HPA. This dataset, produced using methods described in Haacker, E. M., Kendall, A. D., & Hyndman, D. W. (2015), is part of a collection of raster datasets that give HPA groundwater levels over time.

ABSTRACT:

This resource includes different command line used to convert the test rasters as one netCDF file. The tools used are GDAL(http://www.gdal.org/), netCDF4 python(http://unidata.github.io/netcdf4-python/), and NCO (http://nco.sourceforge.net/)

ABSTRACT:

Logan River Stream Network delineated from a digital elevation model using TauDEM

ABSTRACT:

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined. This collection gives the 1935 to 1937 groundwater levels in the HPA. This dataset, produced using methods described in Haacker, E. M., Kendall, A. D., & Hyndman, D. W. (2015).

ABSTRACT:

This includes the basic design idea of the composite resource type for discussion

ABSTRACT:

Presentation to NFIE Summer Institute June 9, 2016

ABSTRACT:

This is a collection of results derived using hydrologic terrain analysis from the Logan River Digital elevation model.

ABSTRACT:

Material for HydroShare workshop at CUAHSI biennial symposium, July 26, 2016.

HydroShare is a platform for data publication and collaboration for users to share multiple hydrologic data types, analytical tools, and models. During this portion of the workshop, participants will learn how to use HydroShare to:
(1) Upload, share and publish science products in HydroShare and receive a citable digital object identifier (DOI). This helps fulfill NSF’s data management requirements.
(2) Use HydroShare for collaboration, sharing data and models with individual users or a group
(3) Organize resources into collections in HydroShare
(4) Use the HydroShare GIS app to visualize and create web maps using content in HydroShare
(5) Use the CyberGIS TauDEM app to perform web based digital elevation model hydrologic terrain analysis

ABSTRACT:

This collection holds resources used to distribute material for HydroShare related workshop at CUAHSI biennial symposium, July 2016

ABSTRACT:

HydroShare is cyberinfrastructure developed as part of the CUAHSI Cyberinfrastructure suite to support collaborative hydrologic data sharing and modeling. It is being developed with support from NSF Software Infrastructure for Sustained Innovation (SI2) program to support the cyberinfrastructure needs of the CUAHSI community. This presentation describes how the HydroShare collaboration platform can support the sharing and publication of outcomes from the CUAHSI National Water Center Innovators Program Summer Institute held in Tuscaloosa Alabama, June to July 2016, and provides an opportunity for sharing summer institute work with the broader hydrologic community.

ABSTRACT:

Outlet of the Logan River stream network

ABSTRACT:

This HydroShare resource illustrates to students in the CEE6440 GIS in Water Resources Class at Utah State University how to prepare HydroShare Resources to post term projects!

ABSTRACT:

A 1/3 arc second digital elevation model from the National Elevation dataset. This DEM has had a flow direction conditioning procedure applied to it to remove barriers along high resolution NHD flowlines. The outlet.shp shapefile is the location where this Onion Creek enters the Colorado River of Texas and is used to specify the point upstream of which watersheds should be delineated.

ABSTRACT:

How do you share and publish hydrologic data and models for a large collaborative project? HydroShare is a new, web-based system for sharing hydrologic data and models with specific functionality aimed at making collaboration easier. HydroShare has been developed with U.S. National Science Foundation support under the auspices of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) to support the collaboration and community cyberinfrastructure needs of the hydrology research community. Within HydroShare, we have developed new functionality for creating datasets, describing them with metadata, and sharing them with collaborators. We cast hydrologic datasets and models as “social objects” that can be shared, collaborated around, annotated, published and discovered. In addition to data and model sharing, HydroShare supports web application programs (apps) that can act on data stored in HydroShare, just as software programs on your PC act on your data locally. This can free you from some of the limitations of local computing capacity and challenges in installing and maintaining software on your own PC. HydroShare’s web-based cyberinfrastructure can take work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This presentation will describe HydroShare’s collaboration functionality that enables both public and private sharing with individual users and collaborative user groups, and makes 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) when the work is complete. This presentation will also describe the web app architecture that supports interoperability with third party servers functioning as application engines for analysis and processing of big hydrologic datasets. While developed to support the cyberinfrastructure needs of the hydrology community, the informatics infrastructure for programmatic interoperability of web resources has a generality beyond the solution of hydrology problems that will be discussed.

Presentation IN33C-03: at AGU Fall Meeting December 14, 2016

ABSTRACT:

Presentation on managing and sharing research data using HydroShare for USU Climate Adaptation Class 2/2/17. Topics covered:
1. Bare essentials of data management
2. HydroShare overview
3. HydroShare Demo

ABSTRACT:

A collection of photos of the HydroShare team at various meetings, as well as photo's used on web pages.

ABSTRACT:

Results from Hydrologic terrain analysis performed on Logan River Basin Digital Elevation model using TauDEM

The input digital elevation model (DEM) is Logan.tif.

The sequence in the script script.py performs a TauDEM analysis that does the following
- Remove pits (by filling them)
- D8 Flow direction
- D8 Contributing area
- Peuker Douglas Valley skeleton
- Weighted D8 contributing area on Peuker Douglas valley skeleton
- Drop analysis to determine objective channel threshold
- Threshold to map stream indicator raster
- Streamnet to produce shapefile of the stream network

Dinfinity analysis for wetness index and height above the nearest drainage (HAND)
- Dinfinity flow direction
- Dinfinity contributing area
- Topographic wetness index
- Distance down to stream in the vertical direction

The file Logan10m.mxd is an ArcGIS map document file for visualizing the results in ArcGIS.

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:

Presentation to CUAHSI staff on 4/20/17 as a high level overview of HydroShare to orient new staff on how their work fits into the big picture of HydroShare.

ABSTRACT:

Collection of presentations I have given about the HydroShare project

ABSTRACT:

Go to meeting presentation to IWRSS model registry team on Nov 8, 2016

ABSTRACT:

Data from the Great Salt Lake and its basin. The Great Salt Lake is a highly saline terminal lake with considerable fluctuations in water surface elevation and salinity. The lake is divided into two arms by a railroad causeway. River inflows enter the larger south arm while the north arm only receives minimal surface runoff. Evaporation from both arms and limited exchange of water and salt through causeway openings result in complex water and salinity processes in the lake. This collection of HydroShare resources makes data from multiple studies on the lake and its hydrology and salinity available for public use

ABSTRACT:

Hand drawn Utah state border in geojson featurecollection format Projection: WGS84 (EPSG: 4326). This resource was created to test NWM viewer app.

ABSTRACT:

Presentation at UCGIS Summer School, May 15, 2017. Digital Elevation Model based Hydrologic And Water Resources Analysis and remarks on CyberGIS, interoperability, collaboration, remote computing and the HydroShare platform for analysis and modeling.

ABSTRACT:

This resource is just the Notebook images used in Logan Digital Elevation Model and Jupyter Notebook used as the starting point for UCGIS 2017 workshop Hydrologic Terrain Analysis Hands On Exercise in https://www.hydroshare.org/resource/56bb52dd88524a07ab76eccd173e397b/.

ABSTRACT:

HydroShare is an online, collaboration system for sharing hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “resources” which are defined by standardized content types for data formats and models commonly used in hydrology. Currently, with HydroShare you can: share your data and models with colleagues; manage who has access to the content that you share; share, access, visualize, and manipulate a broad set of hydrologic data types and models; publish data and models and obtain a citable digital object identifier (DOI); aggregate your resources into collections; discover and access data and models published by others; use the web services application programming interface (API) to programmatically access resources; and use integrated web applications to visualize, analyze and run models on data in HydroShare. Composite resources allow multiple file types from a study to be combined together, providing, as a single resource, an aggregation of all the data elements associated with a model or study. Hydroshare’s composite resource construct can be used to support software that enables transparency and reproducibility, and thereby enhance trust in the research findings. Toward this, as part of the EarthCube GeoTrust project we are investigating how the composite resource construct can be extended to support transparency and reproducibility. The EarthCube GeoTrust project is creating “geounits” which are self-contained packages of computational experiments that can be guaranteed to repeat or reproduce regardless of deployment issues. Since geounits provide a complete description of all the data elements with an instance (run) of a computational experiment, including input files, parameter files, the model executable, associated libraries, and output files produced, they can be mapped to a specialization of HydroShare’s composite resource type. This has a direct effect of transforming HydroShare into a repository of geounits, and making published and cited experiments not only accessible but also reproducible, thereby enhancing trust in them. Tools that create geounits use HydroShare’s REST API to load them into HydroShare, where they can then be shared with other users and downloaded for reproduction of the computational experiment, or further research with additional or alternate data. This presentation will describe the functionality and architecture of HydroShare that enables the creation of geounits comprising: (1) resource storage, (2) resource exploration, and (3) actions on resources by web applications. HydroShare’s components are loosely coupled and interact through APIs, which enhances robustness, as components can be upgraded and advanced relatively independently. The full power of this paradigm is the extensibility it supports, in that anybody can develop a web application that interacts with resources stored in HydroShare. We welcome discussion of the opportunities this enables for interoperability with other EarthCube tools, to the benefit of the geoscience research community.

ABSTRACT:

Logan Digital Elevation Model and Jupyter Notebook used as the starting point for Hydrologic Terrain Analysis Hands On exercise.

To use the Jupyter Notebook click on the "Open With" blue bottom at the top right of this page and choose "JupyterHUB NCSA". Then run the first few cells on the Welcome page. These cells establish a secure connection to the HydroShare and get the main notebook and the inputs to run the example. When the main code and the inputs are retrieved, you can click on "TauDEM.ipynb" to see the code and run it. The Jupyter Notebook also has steps to save all the inputs, outputs, and the main code into a new resource.

ABSTRACT:

Presentations at advancing Hydrologic and Environmental Science through Cyberinfrastructure: Lessons Learned and Paths Forward. Workshop at CUAHSI June 20-22.

ABSTRACT:

Presentation given to IAHS Scientific Assembly in Port Elizabeth, July 14, 2017

Researchers around the world expend tremendous resources to gather and analyze vast stores of hydrologic data and use them in a myriad of hydrologic models. The goal of HydroShare is to advance hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare is a web-based hydrologic information system developed with the goal of sharing, accessing and discovering hydrologic data and models with specific functionality aimed at making collaboration easier and supporting reproducibility, and thus trust in research results. HydroShare has been developed with U.S. National Science Foundation support under the auspices of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) to support the collaboration and community cyberinfrastructure needs of the hydrology research community. Within HydroShare, we have developed new functionality for creating datasets, describing them with metadata, and sharing them with collaborators. We cast hydrologic datasets and models as “social objects” that can be shared, collaborated around, annotated, published and discovered. In addition to data and model sharing, HydroShare supports web application programs (apps) that can act on data stored in HydroShare, just as software programs on your PC act on your data locally. This can free you from some of the limitations of local computing capacity and challenges in installing and maintaining software on your own PC. HydroShare’s web-based cyberinfrastructure can take work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This presentation will describe HydroShare’s collaboration functionality that enables both public and private sharing with individual users and collaborative user groups, and makes 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) when the work is complete. This presentation will also describe the web app architecture that supports interoperability with third party servers functioning as application engines for analysis and processing of big hydrologic datasets.

ABSTRACT:

Presentation given to CUAHSI Informatics Conference, July 26, 2017.

HydroShare is an online, collaboration system for sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “resources” which are defined by standardized content types for data formats and models commonly used in hydrology. With HydroShare you can: Share your data and models with colleagues; Manage who has access to the content that you share; Share, access, visualize and manipulate a broad set of hydrologic data types and models; Use the web services application programming interface (API) to program automated and client access; Publish data and models and obtain a citable digital object identifier (DOI); Aggregate your resources into collections; Discover and access data and models published by others; Use web apps to visualize, analyze and run models on data in HydroShare. HydroShare supports web apps to act on resources for cloud (server) based visualization and analysis, including large scale geographic and digital elevation model analysis at the CyberGIS center at the National Center for Supercomputing Applications (NCSA) and capability to execute hydrology models (e.g. SWAT and RHESSys models) and connect to geoscience modeling communities (e.g. Landlab). A pending proposal for the next phase of HydroShare development would extend the capabilities of HydroShare to enhance support for model hypothesis testing using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) approach, advance collaboration capability, integrate with 3rd party consumer cloud storage systems and establish an "App Nursery" to enable community coders to develop web apps linked to HydroShare. This presentation will describe the functionality and architecture of HydroShare comprising: (1) resource storage, (2) resource exploration, and (3) actions on resources by web apps. System components are loosely coupled and interact through APIs, which enhances robustness, as components can be upgraded and advanced relatively independently. The full power of this paradigm is the extensibility it supports, in that anybody can develop a web app that interacts with resources stored in HydroShare. We welcome discussion of the opportunities this enables for interoperability with other cyberinfrastructure tools, to the benefit of the hydrology and hydroinformatics research communities.

ABSTRACT:

HydroShare is a system operated by CUAHSI for sharing hydrologic data and models aimed at giving hydrologists the cyberinfrastructure needed to manage data, innovate, and collaborate in research to solve water problems. HydroShare addresses the challenges of sharing data and hydrologic models to support collaboration and reproducible hydrologic science through the publication of hydrologic data and models. With HydroShare users can: (1) share data and models with colleagues; (2) manage who has access to shared content; (3) share, access, visualize and manipulate a broad set of hydrologic data types and models; (4) use the web services API to program automated and client access; (5) publish data and models to meet the requirements of research project data management plans; (6) discover and access data and models published by others; and (7) use web apps to visualize, analyze, and run models on data in HydroShare. This workshop will introduce participants to HydroShare and show new features recently deployed. Participants will learn how to use HydroShare to:
• Upload, share and publish science products in HydroShare and receive a citable digital object identifier (DOI). This helps fulfill NSF’s data management requirements.
• Use HydroShare for collaboration, sharing data and models with individual users or a group
• Organize resources into collections in HydroShare
• Use the HydroShare GIS app to visualize and create web maps using content in HydroShare
• Use the HydroShare Jupyter Notebook app to write scripts and short programs to analyze and model with data in HydroShare.
• Use Apps to access and visualize data from the National Water Model.

ABSTRACT:

This is the poster presented at the Earthcube all hands meeting June 7, 2017.

HydroShare is an online, collaboration system for sharing hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “resources” which are defined by standardized content types for data formats and models commonly used in hydrology. Currently, with HydroShare you can: share your data and models with colleagues; manage who has access to the content that you share; share, access, visualize, and manipulate a broad set of hydrologic data types and models; publish data and models and obtain a citable digital object identifier (DOI); aggregate your resources into collections; discover and access data and models published by others; use the web services application programming interface (API) to programmatically access resources; and use integrated web applications to visualize, analyze and run models on data in HydroShare. Composite resources allow multiple file types from a study to be combined together, providing, as a single resource, an aggregation of all the data elements associated with a model or study. Hydroshare’s composite resource construct can be used to support software that enables transparency and reproducibility, and thereby enhance trust in the research findings. Toward this, as part of the EarthCube GeoTrust project we are investigating how the composite resource construct can be extended to support transparency and reproducibility. The EarthCube GeoTrust project is creating “geounits” which are self-contained packages of computational experiments that can be guaranteed to repeat or reproduce regardless of deployment issues. Since geounits provide a complete description of all the data elements with an instance (run) of a computational experiment, including input files, parameter files, the model executable, associated libraries, and output files produced, they can be mapped to a specialization of HydroShare’s composite resource type. This has a direct effect of transforming HydroShare into a repository of geounits, and making published and cited experiments not only accessible but also reproducible, thereby enhancing trust in them. Tools that create geounits use HydroShare’s REST API to load them into HydroShare, where they can then be shared with other users and downloaded for reproduction of the computational experiment, or further research with additional or alternate data. This presentation will describe the functionality and architecture of HydroShare that enables the creation of geounits comprising: (1) resource storage, (2) resource exploration, and (3) actions on resources by web applications. HydroShare’s components are loosely coupled and interact through APIs, which enhances robustness, as components can be upgraded and advanced relatively independently. The full power of this paradigm is the extensibility it supports, in that anybody can develop a web application that interacts with resources stored in HydroShare. We welcome discussion of the opportunities this enables for interoperability with other EarthCube tools, to the benefit of the geoscience research community.

ABSTRACT:

This HydroShare resource illustrates to students in the CEE6440 GIS in Water Resources Class at Utah State University how to prepare HydroShare Resources to post term projects!

ABSTRACT:

Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of data and populate models to better understand the process they are studying. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance knowledge beyond their original purpose? It is these questions that are motivating the movement towards open data, better data management and collaboration and sharing in the use of data and models. In short, researchers are relying more on teamwork to tackle the big problems of the day. This seminar will describe research being done at Utah State University and other collaborating organizations developing a system, called HydroShare, to address these questions in the context of water data and models. HydroShare is advancing hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. This capability is necessary for community model development, execution, and evaluation and to improve reproducibility and community trust in scientific findings through transparency. As a platform for collaboration and running models on advanced computational infrastructure, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. This seminar will provide information for you on the data management resources available to you at Utah State University and how you could take advantage of HydroShare in your own work.

ABSTRACT:

Logan Digital Elevation Model and Jupyter Notebook used as the starting point for Hydrologic Terrain Analysis Hands On exercise.

To use the Jupyter Notebook click on the "Open With" blue bottom at the top right of this page and choose "JupyterHUB NCSA". Then run the first few cells on the Welcome page. These cells establish a secure connection to the HydroShare and get the main notebook and the inputs to run the example. When the main code and the inputs are retrieved, you can click on "TauDEM.ipynb" to see the code and run it. The Jupyter Notebook also has steps to save all the inputs, outputs, and the main code into a new resource.

This version is trimmed down from the original version (linked as older version) to just have basic TauDEM processing, The older version also has information on CyberGIS TauDEM app.

ABSTRACT:

SMFlowlineGeo.zip contains a shapefile in geographic (NAD 1983) coordinates

SMFlowlineWebM.zip contains a shapefile in Web Mercator coordinates.

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Taudem is wonderful. This example is for the Sauk watershed.

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HydroShare logo to use in HydroShare presentations or promotions

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Taudem is awesome!

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Digital Elevation Model for the Great Salt Lake, lake bed bathymetry. This is an integration of data from the National Elevation Dataset and multiple bathymetry datasets as described in the README.txt file.

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This resource holds files to test the various file types under development for composite resources.

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October 30, 2017 presentation to Utah Governors Executive Water Finance Board.

ABSTRACT:

Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of data and populate models to better understand the process they are studying. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance knowledge beyond their original purpose? It is these questions that are motivating the movement towards open data, better data management and collaboration and sharing in the use of data and models. In short, researchers are relying more on teamwork to tackle the big problems of the day. This seminar will describe research being done developing a system, called HydroShare, to address these questions in the context of water data and models. HydroShare is advancing hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. This capability is necessary for community model development, execution, and evaluation and to improve reproducibility and community trust in scientific findings through transparency. As a platform for collaboration and running models on advanced computational infrastructure, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. This seminar will provide information for you on how you could take advantage of HydroShare in your own work.

ABSTRACT:

HydroShare is an online, collaboration system for sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “resources” which are defined by standardized content types for data formats and models commonly used in hydrology. With HydroShare you can: Share your data and models with colleagues; Manage who has access to the content that you share; Share, access, visualize and manipulate a broad set of hydrologic data types and models; Use the web services application programming interface (API) to program automated and client access; Publish data and models and obtain a citable digital object identifier (DOI); Aggregate your resources into collections; Discover and access data and models published by others; Use web apps to visualize, analyze and run models on data in HydroShare. This presentation will describe the functionality and architecture of HydroShare highlighting its use as a virtual environment supporting education and research. HydroShare has components that support: (1) resource storage, (2) resource exploration, and (3) web apps for actions on resources. The HydroShare data discovery, sharing and publishing functions as well as HydroShare web apps provide the capability to analyze data and execute models completely in the cloud (servers remote from the user) overcoming desktop platform limitations. The HydroShare GIS app provides a basic capability to visualize spatial data. The HydroShare JupyterHub Notebook app provides flexible and documentable execution of Python code snippets for analysis and modeling in a way that results can be shared among HydroShare users and groups to support research collaboration and education. We will discuss how these developments can be used to support different types of educational efforts in Hydrology where being completely web based is of value in an educational setting as students can all have access to the same functionality regardless of their computer.

Plain Language Summary

HydroShare is a web based hydrologic information system designed to enhance collaboration within the hydrology community through data sharing. Advancing hydrologic understanding requires combining information from multiple sources which requires collaboration and working as a team or community. HydroShare is a computer system that supports this by enabling users to share units of content referred to as “resources” that hold either data or hydrologic computer models in standardized formats. This presentation will describe the HydroShare data discovery, sharing and publishing capability as well how web apps (computer programs accessed through a web browser) can be used with HydroShare to analyze data and run models completely in servers remote from the user overcoming local desktop computer limitations.

Tarboton, D. G., R. Idaszak, J. S. Horsburgh, D. P. Ames, J. L. Goodall, A. Couch, R. P. Hooper, P. K. Dash, M. Stealey, H. Yi, T. Gan, C. Bandaragoda, A. M. Castronova and The HydroShare Development Team, (2017), "HydroShare: A Platform for Collaborative Data and Model Sharing in Hydrology," Abstract ED23D-0330 presented at 2017 Fall Meeting, AGU, New Orleans, Mississippi., 11-15 Dec, https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/298917.

ABSTRACT:

HydroShare is an online, collaboration system for sharing of hydrologic data, analytical tools, and models. It supports the sharing of, and collaboration around, “resources” which are defined by standardized content types for data formats and models commonly used in hydrology. With HydroShare you can: Share your data and models with colleagues; Manage who has access to the content that you share; Share, access, visualize and manipulate a broad set of hydrologic data types and models; Use the web services application programming interface (API) to program automated and client access; Publish data and models and obtain a citable digital object identifier (DOI); Aggregate your resources into collections; Discover and access data and models published by others; Use web apps to visualize, analyze and run models on data in HydroShare. This presentation will describe the functionality and architecture of HydroShare highlighting our approach to making this system easy to use and serving the needs of the hydrology community represented by the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI). Metadata for uploaded files is harvested automatically or captured using easy to use web user interfaces. Users are encouraged to add or create resources in HydroShare early in the data life cycle. To encourage this we allow users to share and collaborate on HydroShare resources privately among individual users or groups, entering metadata while doing the work. HydroShare also provides enhanced functionality for users through web apps that provide tools and computational capability for actions on resources. HydroShare’s architecture broadly is comprised of: (1) resource storage, (2) resource exploration website, and (3) web apps for actions on resources. System components are loosely coupled and interact through APIs, which enhances robustness, as components can be upgraded and advanced relatively independently. The full power of this paradigm is the extensibility it supports. Web apps are hosted on separate servers, which may be 3rd party servers. They are registered in HydroShare using a web app resource that configures the connectivity for them to be discovered and launched directly from resource types they are associated with.

Plain Language Summary
HydroShare is a web based hydrologic information system designed to enhance collaboration within the hydrology community through data sharing. Advancing hydrologic understanding requires combining information from multiple sources, which requires collaboration and working as a team or community. HydroShare is a computer system that supports this by enabling users to share units of content referred to as “resources” that hold either data or hydrologic computer models in standardized formats. HydroShare can thus serve as a repository for hydrologic information. This presentation will describe the functionality and architecture of HydroShare highlighting our approach to making this system easy to use and serving the needs of the hydrology community. HydroShare strives to make it easy for users to have their resources well described with metadata (data about data) by collecting this information automatically or having easy to use interfaces for metadata entry. HydroShare also attracts users by providing useful functionality in the form of web apps (computer programs accessed through a web browser). The system is flexible in that web apps can be set up on any web servers to access HydroShare resources, making the system extensible.

Tarboton, D. G., R. Idaszak, J. S. Horsburgh, D. P. Ames, J. L. Goodall, A. Couch, R. P. Hooper, P. K. Dash, M. Stealey, H. Yi, C. Bandaragoda, A. M. Castronova and The HydroShare Development Team, (2017), "The HydroShare Collaborative Repository for the Hydrology Community," Abstract IN12B-02 presented at 2017 Fall Meeting, AGU, New Orleans, Mississippi., 11-15 Dec, https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/298088.

ABSTRACT:

This resource holds the narrative text of the proposal funded by NSF to support development of HydroShare from 2017-2021. A second document lists the scope of work adjustments due to NSF not being able to provide the full funding requested.

This work is being pursued through three collaborative NSF Awards
https://nsf.gov/awardsearch/showAward?AWD_ID=1664061
https://nsf.gov/awardsearch/showAward?AWD_ID=1664018
https://nsf.gov/awardsearch/showAward?AWD_ID=1664119

Summary
Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of hydrologic data and populate a myriad of models to better understand hydrologic phenomena and find solutions to vexing water problems. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance hydrologic understanding beyond their original purpose? HydroShare is a system to advance hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare supports the sharing and publication of hydrologic data and models. This capability is necessary for community model development, execution, and evaluation and to improve reproducibility and community trust in scientific findings through transparency. As a platform for collaboration and running models on advanced computational infrastructure, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. HydroShare is designed to help researchers easily meet the sharing requirements of data management plans while at the same time providing value added functionality that makes metadata capture more effective and helps researchers improve their work productivity. This project will extend the capabilities of the HydroShare cyberinfrastructure to: (1) enhance support for scientific methods enabling systematic data and model analysis and hypothesis testing; (2) advance the social capabilities of HydroShare to enable improved collaborative research; (3) integrate with 3rd party consumer data storage systems to provide more flexible and sustainable data storage; and (4) establish an application testing environment to empower researchers to develop their own computer programs to act on and work with data in HydroShare.

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/]

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Model My Watershed® (MMW) is a watershed-modeling web app that enables citizens, conservation practitioners, municipal decision-makers, educators, and students to
- Analyze land use and soil data in their neighborhoods and watersheds
- Model stormwater runoff and water-quality impacts
- Compare how different conservation or development scenarios could modify runoff and water quality
With this App, you can create a new MMW project for an area of interest, or load an existing MMW project, that has been exported as a HydroShare resource. You can modify the land cover for an area of interest or add best management practices (BMPs) to evaluate the impact on stormwater runoff and water quality.

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This is an overview of the status of the HydroShare project for the CUAHSI Informatics Standing Committee.

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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:

Presentation to Research Data Alliance Interest Group session on Preservation Tools, Techniques and Policies at 11th RDA Plenary https://www.rd-alliance.org/plenaries/rda-eleventh-plenary-meeting-berlin-germany/rda-11th-plenary-programme.

HydroShare is operated CUAHSI to support data management and sharing needs of the water research community. It is an easy to use web based hydrologic information system that enables users to share and publish data and models in a variety of flexible formats, and to make this information available in a citable, shareable and discoverable manner. HydroShare includes a repository for data and models, and tools (web apps) that can act on content in HydroShare providing users with a gateway to high performance computing and computing in the cloud.

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Planimetric Coverage containing the delineation of impervious surfaces for studying and calculating drainage runoff. This coverage shows surface features that are visible on the aerial photography, and is sometimes referred to as the landbase.

Dataset hosted at PASDA Pennsylvania Spatial Data Access, The Pennsylvania Geospatial Data Clearinghouse

Web map services links.
http://maps.psiee.psu.edu/preview/map.ashx?layer=1136
REST: http://maps.pasda.psu.edu/ArcGIS/rest/services/pasda/CityPhilly/MapServer
WMS: http://maps.pasda.psu.edu/arcgis/services/pasda/CityPhilly/MapServer/WMSServer?SERVICE=WMS&request=getcapabilities
Data: ftp://ftp.pasda.psu.edu/pub/pasda/philacity/data/PhiladelphiaImperviousSurfaces2015.zip
GeoJSON: http://www.pasda.psu.edu/json/PhiladelphiaImperviousSurfaces2015.geojson
Metadata: http://www.pasda.psu.edu/uci/FullMetadataDisplay.aspx?file=PhiladelphiaImperviousSurfaces2015.xml

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This presentation for the Big Data Hubs (BDHubs) Data Sharing and CI Working Group describes HydroShare, a web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare consists of 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 server based data visualization and analysis capability, and a gateway to high performance computing and computing in the cloud.

Presentation via webinar April 6, 2018

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:

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for, and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

ABSTRACT:

Advances in hydrology, as in many domains of science, increasingly requires integration of information from multiple sources, reuse and repurposing of data, and collaboration. The complex, multi-faceted problems faced in hydrology such as predicting floods and droughts in the face of climate and watershed changes cannot be addressed by scientists, either experimentalists or modelers, working individually. Instead, team science and collaboration is required, with data and models open, accessible and transparent to support reproducibility and enhance trust in findings and results. Cyberinfrastructure is needed to help scientists move into this new paradigm of collaborative research. HydroShare is a web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that is available for use worldwide as a service to the hydrology community. 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. This presentation will describe the functionality and architecture of HydroShare, highlighting its use as a virtual research environment for managing individual research contributions within collaborative groups to advance science on complex questions. We will illustrate the use of HydroShare for collecting and making accessible to the community data from the US National Water Model and 2017 Atlantic Hurricanes Harvey, Irma and Maria that had significant impacts on parts of the US and islands in the Caribbean. HydroShare is being used to assemble, document and archive hydrologic data from these events to support research to improve our understanding of, and capability to prepare for and respond to, such extreme events in the future. HydroShare has components that support: (1) resource storage, (2) resource exploration, and (3) web apps for actions on resources. The HydroShare data discovery, sharing and publishing functions as well as HydroShare web apps provide the capability to analyze data and execute models completely in the cloud, overcoming desktop platform limitations. The HydroShare Jupyter Notebook app provides flexible and documentable execution of Python code snippets for analysis and modeling in a way that results can be shared among HydroShare users and groups to support research collaboration. The Jupyter platform is embedded in high performance and data intensive cyberinfrastructure so that code blocks may include preparation and execution of advanced and data intensive models on the host infrastructure. We will discuss how these developments can be used to support collaborative research in hydrology, where being web based is of value as collaborators can all have access to the same functionality regardless of their computer or location. The architecture of HydroShare is built for extensibility with system components loosely coupled and configured to interact through application programming interfaces (APIs). This enhances robustness, as components can be upgraded and advanced relatively independently. Web apps are hosted on separate servers, which may be 3rd party servers set up by different teams. They are registered in HydroShare using a web app resource that configures the connectivity for them to be discovered and launched directly from resource types they are associated with.

Tarboton, D. G., R. Idaszak, J. S. Horsburgh, D. P. Ames, J. L. Goodall, A. Couch, R. Hooper, S. Wang, M. Clark, P. Dash, H. Yi, C. Bandaragoda, A. Castronova, T. Gan, Z. Li, M. Morsy, M. Ramirez, J. Sadler, D. Yin and Y. Liu, (2018), "HydroShare: A Platform for Collaborative Data and Model Sharing in Hydrology," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-9834, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-9834.pdf.

ABSTRACT:

This resource contains material for the SUMMA-Hydroshare hands on modeling session at the workshop on improving the theoretical underpinnings of hydrologic models, Sopron, Hungary, April 15-18, 2018.

ABSTRACT:

Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of data and populate models to better understand the process they are studying. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance knowledge beyond their original purpose? It is these questions that are motivating the movement towards open data, better data management and collaboration and sharing in the use of data and models. In short, researchers are relying more on teamwork to tackle the big problems of the day. This presentation will describe the HydroShare web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that is available for use as a service to the hydrology community. 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. HydroShare has components that support: (1) resource storage, (2) resource exploration, and (3) web apps for actions on resources. The HydroShare data discovery, sharing and publishing functions as well as HydroShare web apps provide the capability to analyze data and execute models completely in the cloud, overcoming desktop platform limitations. We will discuss how these developments can be used to support collaborative research and modeling in Hydrology, where being web based is of value as collaborators can all have access to the same functionality regardless of their computer. We will illustrate the use of HydroShare for collecting and making accessible to the community data from the US National Water Model and 2017 Atlantic Hurricanes Harvey, Irma and Maria that had significant impacts on parts of the US and islands in the Caribbean. HydroShare is being used to assemble, document and archive hydrologic data from these events to support research to improve our understanding of and capability to prepare for and respond to such extreme events in the future.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/.

ABSTRACT:

Demo of Wikiwatershed.

ABSTRACT:

This Multi-Year Model GWLF-E/Mapshed model for the Eagleville Watershed was generated as a demonstration of WikiWatershed toolkit functionality applied to watersheds delineated using the Rapid Watershed delineation approach described in a presentation at the 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/.

Tarboton, D. G., N. Sazib and A. Aufdenkampe, (2018), "The Model My Watershed Rapid Watershed Delineation Tool " 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/. https://www.hydroshare.org/resource/d752efeae812478898fb78327f25c87c/

ABSTRACT:

River hydraulic geometry is an important input to hydraulic and hydrologic models that route flow along streams, determine the relationship between stage and discharge, and map the potential for flood inundation give the flow in a stream reach. Traditional approaches to quantify river geometry have involved river cross-sections, such as are required for input to the HEC-RAS model. Extending such cross-section based models to large scales has proven complex, and, in this presentation, an alternative approach, the Height Above Nearest Drainage, or HAND, is described. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM software (http://hydrology.usu.edu/taudem) to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter. Together with slope (also determined from the DEM) and roughness (Manning's n) these provide all the inputs needed for establishing a Manning's equation uniform flow assumption stage-discharge rating curve and for mapping potential inundation from discharge. This presentation will describe the application of this approach across the continental US in conjunction with NOAA’s National Water Model for prediction of stage and flood inundation potential in each of the 2.7 million reaches of the National Hydrography Plus (NHDPlus) dataset, the vast majority of which are ungauged. The continental US scale application has been enabled through the use of high performance parallel computing at the National Center for Supercomputing Applications (NCSA) and the CyberGIS Center at the University of Illinois.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/.

ABSTRACT:

Model My Watershed (MMW) is a free web application for modeling the influences of land use and best management practices on stormwater runoff and water quality. The public can access this tool at https://app.wikiwatershed.org/. One component of this tool is a function to define the model domain, or area of interest for analysis and modeling by interactively setting the outlet location and delineating the watershed draining to that location. This functionality has been developed using enhancements to the TauDEM hydrologic terrain analysis software ((http://hydrology.usu.edu/taudem) and includes a tool on the user interface and RESTFul Application Program Interface that accesses backend data generated from NHDPlus Version 2.1 gridded flow directions. The continental US was preprocessed into subwatersheds that include gridded flow directions and the polygon shapefile for the entire watershed draining to the subwatershed outlet. Thus when a point within the domain is input (clicked or entered to RESTFul API), the subwatershed that it falls in is first identified. It is then snapped to the stream by moving down to the first stream (NHDPlus medium resolution stream) encountered along the flow directions. Then the local watershed within the subwatershed is delineated based on subwatershed flow direction grid using an adaptation of the TauDEM gauge watershed function. This local subwatershed is then merged with shapefiles for any upstream watersheds to which it attaches. Small watersheds are delineated within a few seconds, with larger watersheds taking up to 40 s (entire Mississippi). The most time consuming step is the merging and generalization of shape information for display. The polygon that result from this process may be downloaded, and subject to size limitations also entered into the MMW analyze area function to summarize land use, hydrologic soils and other information of interest to hydrologic and water quality modeling within the delineated area. The resulting watershed polygon may also be entered into one of the stormwater or water quality models supported by MMW.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/.

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:

This resource contains a HydroShare Map Project file created using the HydroShare GIS web app. The Map Project file is in JSON format and contains data regarding the state of the project upon creating this resource.

ABSTRACT:

Green River Site Storm Model

ABSTRACT:

Resource to hold this icon files used in web app connectors.
- Model My Watershed Tool
- Jupyter Notebook Viewer

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:

Hydrologic Terrain Analysis Jupyter Notebook used to demonstrate the use of the Jupyter Notebook App for watershed delineation.

To use the Jupyter Notebook click on the "Open With" blue bottom at the top right of this page and choose "Jupyter". Then run the first few cells on the Welcome page. These cells establish a secure connection to the HydroShare and get the main notebook and the inputs to run the example. When the main code and the inputs are retrieved, you can click on "TauDEM.ipynb" to see the code and run it. The Jupyter Notebook also has steps to save all the inputs, outputs, and the main code into a new resource.

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

ABSTRACT:

Blacksmith

ABSTRACT:

This paper addresses the open collaborative data and model sharing opportunities offered by the HydroShare web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare users share and publish data and models in a variety of flexible formats, in order to make this information available in a citable, shareable and discoverable format for the advancement of hydrologic science. HydroShare includes a repository for data and models, and tools (web apps) that can act on content in HydroShare and save results back into the repository that represents a flexible web based architecture for collaborative environmental modeling research. This presentation will focus on the key functionalities of HydroShare that support web based collaborative research that is open and enhances reproducibility and trust in research finding through sharing of the data, models and scripts used to generate results. The HydroShare Jupyter Notebook app provides flexible and documentable execution of Python or R code snippets for analysis and modeling. An analysis or modelling procedure documented in a Jupyter Notebook may be saved as part of a HydroSHare resource along with the associated data, and shared with other users or groups. These users may then open the notebook to modify or add to the analysis or modelling procedure, and save results back to the same, or a new resource. Passing information back and forth this way serves to support collaboration on common data in a shared modelling platform. The Jupyter platform is embedded in high performance and data intensive cyberinfrastructure so that code blocks may include preparation and execution of advanced and data intensive models on the host infrastructure. We will discuss how these developments can be used to support collaborative research, where being web based is of value as collaborators can all have access to the same functionality regardless of their computer or location.

Presentation at 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" June 24-28 2018, Fort Collins, USA, http://iemss2018.engr.colostate.edu/

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 presentation describes the HydroShare web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare users share and publish data and models in a variety of flexible formats, in order to make this information available in a citable, shareable and discoverable format for the advancement of hydrologic science. HydroShare includes a repository for data and models, and tools (web apps) that can act on content in HydroShare and save results back into the repository. This presentation will focus on the key capabilities of, and concepts behind HydroShare that support web based collaborative research that is open and enhances reproducibility and trust in research findings, through sharing of the data, models and scripts used to generate results. I will also describe work in progress to advance HydroShare using JupyterHub to provide flexible and documentable analyses and to serve as a gateway to high performance computing. For this workshop I will address the question of what I would like and why from a digital data resource and repository, giving my ideas about the need for a platform for collaboration and computation that integrates data storage, organization, discovery, and programmable actions through web applications (web apps) and that allows researchers to easily employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving ability to collaborate and reproduce results.

Presentation at GeoDaRRS workshop August 7-9, 2018, https://www2.cisl.ucar.edu/events/workshops/geodarrs-workshop/2018/geoscience-digital-data-resource-and-repository-service-geodarrs-workshop

ABSTRACT:

This HydroShare Resource illustrates to student in the GIS in Water Resources Classes at Utah State University and University of Texas at Austin how to prepare HydroShare resources to post term projects.

ABSTRACT:

Scraper to create PASDA resources

ABSTRACT:

Script to generate web pages for GIS in Water Resources Class

ABSTRACT:

The purpose of this notebook is to illustrate the use of the Terrain Analysis Using Digital Elevation Models (TauDEM) software for Watershed Delineation and calculation of Height above the nearest Drainage (HAND), using data from HydroShare and saving results back to HydroShare. TauDEM is a free and open source set of Digital Elevation Model (DEM) tools for watershed delineation and extraction and analysis of hydrologic information from topography as represented by DEM.

This notebook is intended as a brief introduction to guide a reader through the steps of running some of the more important functions required to delineate a stream network using TauDEM. Once you recognize the pattern from this, you can refer to documentation on the use of each TauDEM function found at http://hydrology.usu.edu/taudem/taudem5/documentation.html, and construct other analyses to meet your needs.

To use the Jupyter Notebook click on the "Open With" blue bottom at the top right of this page and choose "Jupyter". Then run the first few cells on the Welcome page. These cells establish a secure connection to the HydroShare and get the main notebook and the inputs to run the example. When the main code and the inputs are retrieved, you can click on "TauDEM.ipynb" to see the code and run it. The Jupyter Notebook also has steps to save all the inputs, outputs, and the main code into a new resource.

ABSTRACT:

HydroShare (www.hydroshare.org) enables researchers to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. Join us for a presentation describing the tools, methods and practices for data and model publication.

Presentation to University of Idaho Water Resources Research Institute Seminar Series, November 27, 2018

ABSTRACT:

Documents for CUAHSI ad hoc committee on HydroShare

ABSTRACT:

This resource includes the script, called script_NWM_dl_thredds.py, written in Python 3.6 to download the National Water Model products (specifically the analysis and assimilation) from HydroShare THREDDS data server. The other script, called script_NWM_readncfile.py, is also written in Python 3.6 to read the streamflow values from downloaded NetCDF files for a specific period (which is set to be February 15, 2017, but can be set to any other time if needed).

ABSTRACT:

This resource includes the code (written in Python 3.6) and the documentation of a technique which is presented for adjusting the slopes of a Digital Elevation Model (DEM) derived drainage network where the slope is zero. The procedure uses the stream river network delineated from the grid-based DEM using Terrain analysis using Digital Elevation Models (TauDEM) software and re-compute the slopes considering the length and slope of all the upstream, downstream, and side entrance reaches. The results of this procedure is that all of the DEM-derived drainage network will have a positive (“downhill”) slope which are constrained to be greater than 0 m/m even when the elevation smoothing process produces equal upstream and downstream elevations on a flow line.

ABSTRACT:

Flood inundation remains stubbornly challenging to map, model, and forecast with high precision for decision making because it requires a detailed
representation of the hydrologic and hydraulic processes, which are computationally demanding, and data limited. Recently, an empirical approach,
Continental-Scale Flood Inundation Mapping (CFIM), having fewer data demands and perhaps offering a more practical alternative, has been
presented as a scientific workflow where a Height Above Nearest Drainage (HAND) terrain model along with the National Water Model (NWM)
forecast discharge is employed for near real-time flood inundation mapping. In February 2017, a record flood occurred on the Bear River in Box
Elder County due to rapid snowmelt and rain on snow. In this study, we evaluated the CFIM method over the reach of the Bear River where this
flooding occurred. We evaluated the performance of the CFIM in terms of its accuracy in representing flooded and non-flooded areas when
comparing the results with flood inundation observed in imagery from the high-resolution Planet CubeSat RapidEye Satellites. The results indicate
that there were differences between CFIM flood inundation predictions and flooded area recorded by CubeSat Imagery. We used evaluation of these
differences to address challenges of CFIM and present a set of improvements to overcome some of the limitations and advance the outcome of
CFIM. The improvements utilize (1) the high-resolution (1:24,000) National Hydrography Dataset (NHD) to provide an obstacle-removed and
hydrologically conditioned topography, and (2) a higher-resolution Digital Elevation Model (DEM) dataset available for this area. The results indicate
that differences between CFIM flood inundation predictions and flooded area recorded by CubeSat Imagery were attributed to differences in observed
and forecast discharges, but also notably due to shortcomings in the HAND method and the derivation of HAND from the national elevation dataset
as implemented in CFIM. Examination of the causes for these differences has led us to develop proposed improvements to the CFIM methods,
which in this study were evaluated only for this single location. Nonetheless, the proposed improvements have the potential, following further
evaluation, to improve the broad application of the CFIM methodology.

PLAIN LANGUAGE SUMMARY:
Flood inundation is difficult to map, model, and forecast because of the data needed and computational demand. Recently an approach based on
the Height Above Nearest Drain (HAND) derived from a digital elevation model along with using the National Water Model forecasts has been
suggested, for both flood mapping and obtaining reach hydraulic properties. This approach was tested for a recent snowmelt flood on the Bear River
and compared to inundated area mapped using CubeSat satellite imagery. Initial differences found were reduced by addressing shortcomings in the
terrain analysis evaluation of HAND both in terms of the digital elevation model resolution and method used to condition the digital elevation model
using streamline information.

Slides for AGU Fall Meeting 2018 presentation H34G-08 at Washington D.C., December 12, 2018
Session: H34G: Research, Development, and Evaluation of the National Water Model and Facilitation of Community Involvement II

ABSTRACT:

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.

ABSTRACT:

Model My Watershed® (MMW) is a watershed-modeling web app that enables citizens, conservation practitioners, municipal decision-makers, educators, and students to
- Analyze land use and soil data in their neighborhoods and watersheds
- Model stormwater runoff and water-quality impacts
- Compare how different conservation or development scenarios could modify runoff and water quality
With this App, you can create a new MMW project for an area of interest, or load an existing MMW project, that has been exported as a HydroShare resource. You can modify the land cover for an area of interest or add best management practices (BMPs) to evaluate the impact on stormwater runoff and water quality.

ABSTRACT:

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

ABSTRACT:

Database maintained at http://waterisotopes.org containing measurements of the stable H- and O-isotopes in water. This includes

- JPEG maps and GIS datasets of geographic water isotope ratio patterns.
- An interactive calculator allowing you to estimate water isotope values for any site.
- Data tables related to water isotope publications.
- References and papers describing geospatial projects involving the stable water isotopes.

ABSTRACT:

This resource gives links to the data that underpins the Continental US Medium resolution watershed delineation Model My Watershed online watershed delineation system deployed at https://modelmywatershed.org/

ABSTRACT:

This resource gives links to the data that underpins the Delaware River Basin high resolution watershed delineation Model My Watershed online watershed delineation system deployed at https://modelmywatershed.org/

ABSTRACT:

This is a web app connector that launches https://nbviewer.jupyter.org/ to view a jupyter notebook file (with extension .ipynb) from HydroShare.

To enable this for specific .ipynb files in HydroShare click on the "Add web app to open with list" grid icon above.

Note that the web app at nbviewer that is launched only supports viewing of a Jupyter Notebook. It does not support editing or execution.

ABSTRACT:

The Watershed where Sara is doing her work

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:

This is also part of https://github.com/hydroshare/hydroshare/issues/3351

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:

This script executes the HydroDS tasks required to prepare TOPNET inputs for the use case reported in

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: 104623, https://doi.org/10.1016/j.envsoft.2020.104623.

ABSTRACT:

This resource was created using the HydroShare UEB model input preparation web application (UEB web app) that utilizes the HydroDS modeling web services. The model input 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, siteinitial.dat, inputcontrol.dat, outputcontrol.dat. This model instance resource is complete for model simulation. The corresponding model output is also included in the resource. Use a HydroShare user account and the link to access the UEB web app (https://appsdev.hydroshare.org/apps/ueb-app/) to run and reproduce this model instance.

ABSTRACT:

This resource collects all the data and code to illustrate reproducibility of hydrologic modeling research, which coupled Utah Energy Balance (UEB) snowmelt model with the Sacramento Soil Moisture Accounting (SAC-SMA) run-off model as part of the National Weather Service Research Distributed Hydrologic Modeling Framework to simulate the basin snowmelt process and discharge for the Dolores River watershed. This research is aimed to evaluate the Utah Energy Balance (UEB) model performance using Daymet input for water supply forecast in the study watershed. It is also aimed to demonstrate how to use different web based apps and software to support reproducible hydrologic modeling research.

ABSTRACT:

This resource stores the Sciunit object to help repeat the model input preparation and model execution for basin discharge simulation in Dolores River watershed using the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model. The rain plus melt input for SAC-SMA model was created from the Utah Energy Balance snowmelt model output (https://www.hydroshare.org/resource/8f9320123baa4ef1b4a304f0ce20ab08/)

This Sciunit object is a container that enables reproduction of the modeling process without software installation. The JupyterHub web app in HydroShare can help execute the Sciunit object.

In this resource, there are 2 Sciunit objects:
earthcube_use_case.zip: a Sciunit object which supports SAC-SMA model simulation for a water year
use_case.zip: a demo Sciunit object which supports SAC-SMA model simulation for 1 month

ABSTRACT:

This resource includes the simulation and observation discharge data and corresponding data analysis code for Dolores River watershed. The observation data is from USGS gage station. The basin discharge simulation was created by coupling Utah Energy Balance snowmelt model and Sacramento Soil Moisture Accounting (SAC-SMA) runoff model.
This resource is aimed to demonstrate how to use the Jupyter Notebook to repeat the data analysis process. To test please use the link to access the app (https://jupyter.cuahsi.org/hub/login)

ABSTRACT:

The CUAHSI JupyterHub platform is linked to HydroShare and provides a vital interface for users to conduct reproducible science from an interactive interface. This Earthcube 2019 Annual Meeting breakout session will demonstrate conducting reproducible science using Sciunit, Jupyter, and Hydroshare by working with a Hydrology model. The goal of this session is to solicit feedback from users on capabilities and interfaces that are needed for conduct of reproducible science. The breakout follows from last year's presentation at AHM which demonstrated the CUAHSI JupyterHub platform. We are particularly interested in determining requirements from science communities beyond Hydrology and learning how our interfaces can better serve their reproducibility requirements.

ABSTRACT:

Achieving reproducible computational models and workflows is an important challenge that calls for open and reusable code and data, well-documented workflows, and controlled environments that allow others to verify published findings. Several scientists have highlighted the reproducibility crisis in science, but tools to help achieve reproducibility are limited. This presentation will describe cyberinfrastructure developed as part of the Geotrust and HydroShare projects that is enabling reproducible hydrologic science in the CUAHSI JupyterHub platform linked to HydroShare. Snow modeling plays an important role in the prediction of seasonal runoff and water supply forecasting for water resources management in snow-fed river basins. Physically based modeling is generally assumed better suited to reproduce snow processes under changing conditions. However, challenges exist in the application of snowmelt models that make them hard to reproduce, in terms of (1) tracking the preparation of model inputs and preserving the precise version of input data used, (2) repeating the execution of model code to duplicate model outputs, and (3) reproducing analyses used to report the results. This presentation will describe the use of Sciunit, which is software for creating self-contained and annotated containers that describe and package computational experiments, as deployed in the CUAHSI JupyterHub platform address these challenges. We will describe a research application of the Utah Energy Balance (UEB) snowmelt model to investigate water supply forecasts improvement for test watersheds in the Colorado River Basin that is made reproducible through the use of Sciunit. This work illustrates how reproducibility of the complete hydrologic science modeling cycle can be enhanced. The contents can be shared with other users in HydroShare to repeat or build on the work and can be permanently published to receive a digital object identifier for citation in papers to fulfill the open data mandate.

ABSTRACT:

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 researchers to more easily share data, model and workflow products resulting from their research and used to create and support reproducibility of the results reported in scientific publications. 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) web application tools that can act on content in HydroShare for computational and visual analysis. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis and that allows researchers to employ services beyond their desktops to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe ongoing enhancements to HydroShare, some of the challenges being faced in its design and ongoing development. Content storage is being consolidated into a single primary resource type that may hold multiple content aggregation types. This better supports storage of the diverse data involved with hydrologic data and model studies in a single shareable unit. Reproducible and easy to use computational functionality is being advanced using JupyterHub as a gateway to XSEDE and other high performance compute resources. This presentation will describe the progress made and challenges being addressed for managing the storage and use of HydroShare resources from JupyterHub, and using containers to enabling simple and scalable access to these resources.

ABSTRACT:

This resource contains the data and scripts used for: Garousi-Nejad, I., D. G. Tarboton, M. Aboutalebi and A. F. Torres-Rua, (2019), "Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method," Water Resources Research, http://doi.org/10.1029/2019WR024837.

Abstract from the paper:
Flood inundation remains challenging to map, model, and forecast because it requires detailed representations of hydrologic and hydraulic processes. Recently, Continental‐Scale Flood Inundation Mapping (CFIM), an empirical approach with fewer data demands, has been suggested. This approach uses National Water Model forecast discharge with Height Above Nearest Drainage (HAND) calculated from a digital elevation model to approximate reach‐averaged hydraulic properties, estimate a synthetic rating curve, and map near real‐time flood inundation from stage. In 2017, rapid snowmelt resulted in a record flood on the Bear River in Utah, USA. In this study, we evaluated the CFIM method over the river section where this flooding occurred. We compared modeled flood inundation with the flood inundation observed in high‐resolution Planet RapidEye satellite imagery. Differences were attributed to discrepancies between observed and forecast discharges but also notably due to shortcomings in the derivation of HAND from National Elevation Dataset as implemented in CFIM, and possibly due to sub optimal hydraulic roughness parameter. Examining these differences highlights limitations in the HAND terrain analysis methodology. We present a set of improvements developed to overcome some limitations and advance CFIM outcomes. These include conditioning the topography using high‐resolution hydrography, dispersing nodes used to subdivide the river into reaches and catchments, and using a high‐resolution digital elevation model. We also suggest an approach to obtain a reach specific Manning's n from observed inundation and validated improvements for the flood of March 2019 in the Ocheyedan River, Iowa. The methods developed have the potential to improve CFIM.

The file Readme.md describes the contents and steps for reproducing the analyses in the paper.

ABSTRACT:

Lightning presentation and workshop presented at CUAHSI HydroInformatics Conference, 2019. https://www.cuahsi.org/community/cuahsi-science-meetings/

This workshop is offered for hydrology faculty interested in implementing or adapting active-learning, data-driven resources to their educational settings. The workshop aspires to create faculty networking and development opportunities with the overall goal of promoting and reducing barriers against adoption of active-learning resources in hydrology. The workshop will use the recently developed NSF-sponsored HydroLearn platform, along with resources from CUAHSI, HydroShare and other community platforms, to enable participating faculty to develop and share educational resources. The workshop will showcase existing seed modules and will cover best practices in developing student-centered learning activities, including the design of pedagogically-sound learning objectives and assessment rubrics. Faculty who currently teach hydrology-related courses are encouraged to participate, especially those who teach undergraduate or early-level graduate courses. Interested faculty may also be invited to participate in a follow-up funded fellowship program to engage in a semester-long adoption and field testing of the HydroLearn platform and its content. The workshop will be jointly conducted by hydrology faculty along with an expert in education research.

ABSTRACT:

Presentation on HydroShare to GeoEDF Stakeholder workshop

ABSTRACT:

Presentation given at TopoBathy Workshop Sept 17-18, 2019, Tuscaloosa, Alabama.

Flood inundation is difficult to map, model, and forecast because of the data needed and the computational demand. Recently an approach based on the relative elevation, or Height Above Nearest Drainage (HAND), which is derived from a digital elevation model (DEM), has been suggested for both flood mapping and obtaining reach hydraulic properties and synthetic rating curves. These products are only as good as the underlying DEM from which they are derived and thus better inland bathymetry offers the potential for incorporating bathymetry into national scale models to improve flood inundation modeling and mapping. This presentation will review the approach for using relative elevation in flood modeling, describing how HAND is calculated, how it is used to map flood inundation for stream reach catchments and how it is used to determine stream reach properties, identifying shortcomings and giving ideas for improvements. As we obtain more detailed information on bathymetry, topography and hydrography it is important to establish a consistent data model for the river bed that is used in HAND related work that aligns and reconciles elevation and hydrography. This presentation will discuss approaches for using hydrography to remove DEM obstacles, the segmentation of streams used in deriving HAND reach average hydraulic properties and ideas for quantifying reach average roughness based on HAND and flood inundation mapped from remote sensing of previous floods with measured discharges.

ABSTRACT:

Presentation at AWRA National Conference in Salt Lake City. November 4, 2019.

HydroShare (www.hydroshare.org) is a hydrology-domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare’s goal is to advance hydrologic science by enabling researchers to more easily share data, model and workflow products resulting from their research, creating and supporting reproducibility of the results reported in scientific publications. It supports the growing call for open data that is findable, accessible, interoperable and reusable (FAIR). 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) web application tools that can act on content in HydroShare for computational and visual analysis. Together these serve as a platform for collaboration and gateway for computation that integrates data storage, organization, discovery, and analysis and that allows researchers to employ services beyond their desktop computers to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe ongoing enhancements to HydroShare and some of the challenges being faced in its design and ongoing development. We report on efforts to refine the way data and model content are formatted and stored within the system to better support storage, management, and sharing of the diverse data involved with hydrologic data and model studies. We have developed techniques that enable scientists to organize and package data and models within a single shareable unit, while still providing value-added tools for known data types. Additionally, access to reproducible and easy to use computational functionality is being advanced using JupyterHub as a gateway to computing resources. This collaborative and computational functionality provides an important incentive by providing users with immediate value, while meeting open data mandates and sharing data using open standards.

ABSTRACT:

Inputs to Research Distributed Hydrologic Model (RDHM) spatially distributed hydrologic model incorporating the UEB snowmelt model that evaluates the effect of snow and streamflow assimilation on streamflow forecasting.

This is data for the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472

ABSTRACT:

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

This is the model used in the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472

ABSTRACT:

Presentation IN32A-02 at 2019 AGU Fall Meeting, San Francisco, CA, December 11, https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/424998.

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 and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. It is comprised of two sets of functionalities: (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 move us towards 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 advances in the tools, methods and practices for data and model publication implemented in HydroShare.

ABSTRACT:

The Jupyter Notebook and data in this resource illustrate the use of Terrain Analysis Using Digital Elevation Model (TauDEM) software deployed on JupyterHub for watershed delineation.

ABSTRACT:

Jupyter Notebook TauDEM was used to define streamflow and subwatersheds in the Logan River Watershed in Utah. To start, "logan.tif" Digital Elevation Model (DEM) data and "LoganOultet.shp" Logan Outlet were used as the main inputs. The final results were "loagnw.tif" subwatershed and "logannet.shp" stream networks. This resource includes both the inputs to and the outputs from Jupyter Notebook TauDEM used for hydrologic terratin analysis in the Logan River Watershed in Utah.

ABSTRACT:

Slides on HydroShare interoperability with HydroFrame for 2019 AGU HydroFrame Workshop

ABSTRACT:

Presentation for Science Gateway Computational Institute Federal Agency Workshop, Washington DC, March 4, 2020

HydroShare is a web based hydrologic information system 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 and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. It is comprised of two sets of functionalities: (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 move us towards 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 describes HydroShare and how it fits into the broader cyberinfrastructure software ecosystem where interoperability is important as a gateway for computation, and some of the challenges faced, from both an organizational and technical perspective.

ABSTRACT:

This resource illustrates how data and code can be combined together to support hydrologic analyses. It was developed June 2020 as part of a HydroLearn Hackathon.

ABSTRACT:

HydroShare is a web based hydrologic information system 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 and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. It is comprised of two sets of functionalities: (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 move us towards 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 summarizes the functionality of HydroShare as a data and model sharing platform and poses some issues and questions for discussion by the CUAHSI board related to the governance and sustainability of HydroShare as well as coordination of CUAHSI informatics activities within the broader informatics and data and model publishing community.

ABSTRACT:

Logan River Watershed delineated using TauDEM

ABSTRACT:

Results from Hydrologic terrain analysis performed on Logan River Basin Digital Elevation model using TauDEM

The input digital elevation model (DEM) is Logan.tif.

The sequence in the script script.py performs a TauDEM analysis that does the following
- Remove pits (by filling them)
- D8 Flow direction
- D8 Contributing area
- Peuker Douglas Valley skeleton
- Weighted D8 contributing area on Peuker Douglas valley skeleton
- Drop analysis to determine objective channel threshold
- Threshold to map stream indicator raster
- Streamnet to produce shapefile of the stream network

Dinfinity analysis for wetness index and height above the nearest drainage (HAND)
- Dinfinity flow direction
- Dinfinity contributing area
- Topographic wetness index
- Distance down to stream in the vertical direction

ABSTRACT:

Presentation for AWRA Geospatial Technologies Conference held Virtually August 4-13, 2020. This presentation on August 6. https://www.eventscribe.com/2020/AWRAGIS/

HydroShare (www.hydroshare.org) is a hydrology-domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare’s goal is to advance hydrologic science by enabling researchers to more easily share data, model and workflow products resulting from their research, creating and supporting reproducibility of the results reported in scientific publications. It supports the growing call for open data that is findable, accessible, interoperable and reusable (FAIR). HydroShare is comprised of two sets of functionalities: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) web application tools that can act on content in HydroShare for computational and visual analysis. Together these serve as a platform for collaboration and gateway for computation that integrates data storage, organization, discovery, and analysis and that allows researchers to employ services beyond their desktop computers to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe ongoing enhancements to HydroShare and some of the challenges being faced in its design and ongoing development. We report on efforts to support geospatial data types as aggregations of content within the Open Archives Initiative Object Reuse and Exchange standard resource data model used by HydroShare and describe how geospatial data services are enabled for public resources holding geospatial aggregations. This enables geospatial data in HydroShare to be consumed by third party web applications adding to the functionality supported by HydroShare as a content storage element within a software ecosystem of interoperating systems.

ABSTRACT:

This dataset holds streamflow sequences for each of three drought scenarios developed to characterize plausible future drought conditions in the Colorado River Basin. These sequences were produced using the methods described in Center for Colorado River Studies Future of the Colorado River Project white paper 4 entitled “The Future Hydrology of the Colorado River Basin” by Salehabadi, Tarboton et al. (2020) and paper Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061. This study defined three drought scenarios: (1) Millennium Drought, (2) Mid 20th Century Drought and (3) Paleo Tree Ring Severe Drought. The first two were defined using the US Bureau of Reclamation Natural flows from 2000-2018 and 1953-1977 respectively. The last was defined using the years 1576-1600 from the Meko et al., 2017 tree ring reconstruction of streamflow at Lees Ferry. 100 streamflow traces, each 42 years long were produced for each scenario by resampling years with replacement. Resampling from identified past drought scenarios, provides test droughts based on past flows that are more severe, due to the variety in the sampling, than any past droughts that have actually occurred. They are nevertheless plausible, since they are derived from past records. We used a nonparametric resampling approach referred to as “Water Year Block Disaggregation” to split the simulated annual flow at Lees Ferry into monthly flow at each of the 29 Colorado River Simulation System (CRSS) natural inflow sites. For the first two scenarios where there are historic natural flows at the 29 CRSS sites, this selects the entire water year block of monthly flows across sites for the corresponding drought year. For the paleo scenario, where there are not flows at each of the sites, the historic natural flow year with annual flow at Lees Ferry closest to the paleo flow is selected, and then flows across the sites and months adjusted by the ratio of paleo flow to closest historic flow.

ABSTRACT:

This dataset holds scripts for Duration-Severity and Cumulative Deficit analyses developed to examine the severity of sustained droughts that have impact on storage and water supply in the Colorado River Basin. These analyses were performed using the methods described in Salehabadi, Tarboton et al. (2022; 2020). These studies analyzed the US Bureau of Reclamation Natural flow and Tree Ring Reconstructed flow from Meko et.al., 2017, both at Lees Ferry, using the Duration-Severity and Cumulative Deficit plots, which show the mean flow and cumulative magnitude of departure from average conditions, or “deficit”, for different durations. These plots presented by Salehabadi, Tarboton et al. (2020; 2022) characterize the severity of past droughts that have occurred in the Colorado River Basin. Based on examination of these plots, this study defined three drought scenarios: (1) Millennium Drought, (2) Mid-20th Century Drought, and (3) Paleo Tree Ring Drought. The first two were defined using the US Bureau of Reclamation Natural flows from 2000-2018 and 1953-1977 respectively. The last was defined using the years 1576-1600 from the Meko et al., 2017 tree ring reconstruction of streamflow at Lees Ferry.

- Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061.
- Salehabadi, H., D. G. Tarboton, E. Kuhn, B. Udall, K. G. Wheeler, D. E. Rosenberg, S. A. Goeking and J. C. Schmidt, (2020), "The Future Hydrology of the Colorado River Basin," White Paper 4, Future of the Colorado River Project, Center for Colorado River Studies, Utah State University, 71 p., https://qcnr.usu.edu/coloradoriver/files/WhitePaper4.pdf.

ABSTRACT:

This collection holds the data and analysis scripts for Salehabadi, Tarboton et al. (2022; 2020). These studies examined historical natural flow, tree-ring flow reconstruction, and projected streamflow from climate change models to generate plausible severe drought scenarios to consider during planning in the Colorado River Basin. This collection has been developed to provide access to and preserve the data used in these studies, in the interests of transparency and reproducibility of this work.

- Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061.
- Salehabadi, H., D. G. Tarboton, E. Kuhn, B. Udall, K. G. Wheeler, D. E. Rosenberg, S. A. Goeking and J. C. Schmidt, (2020), "The Future Hydrology of the Colorado River Basin," White Paper 4, Future of the Colorado River Project, Center for Colorado River Studies, Utah State University, 71 p., https://qcnr.usu.edu/coloradoriver/files/WhitePaper4.pdf.

ABSTRACT:

This notebook has been developed to download specific variables at specific sites from National Water Model (NWM) Retrospective run results in Google Cloud. It has been set up to retrieve data at SNOTEL sites. An input file SNOTEL_indices_at_NWM.csv maps from SNOTEL site identifiers to NWM X and Y indices (Xindex and Yindex). A shell script (gget.sh) uses Google utilities (gsutil) to retrieve NWM grid file results for a fixed (limited) block of time. A python function then reads a set of designated variables from a set of designated sites from NWM grid files into CSV files for further analysis.

The input file SNOTEL_indices_at_NWM.csv was generated using Garousi-Nejad and Tarboton (2022), https://www.hydroshare.org/resource/7839e3f3b4f54940bd3591b24803cacf/.

ABSTRACT:

Presentation for AWRA Geospatial Technologies Conference held Virtually August 4-13, 2020. This presentation on August 12. https://www.eventscribe.com/2020/AWRAGIS/

Flood inundation is difficult to map, model, and forecast because of the data needed and the computational demand. Recently an approach based on the relative elevation, or Height Above Nearest Drainage (HAND), which is derived from a digital elevation model (DEM), has been suggested for both flood mapping and obtaining reach hydraulic properties and synthetic rating curves. These products are only as good as the underlying DEM from which they are derived and better DEMs offers the potential for improving model representations of streams and parameters derived from DEMs used in hydrologic and flood inundation modeling and mapping. This presentation will review the approach for using relative elevation in flood modeling, describing how HAND is calculated, how it is used to map flood inundation for stream reach catchments and how it is used to determine stream reach properties, identifying shortcomings and giving ideas for improvements. As we obtain more detailed information on bathymetry, topography and hydrography it is important to establish a consistent data model for the river bed that is used in HAND related work that aligns and reconciles elevation and hydrography. This presentation will discuss approaches for using hydrography to remove DEM obstacles, the segmentation of streams used in deriving HAND reach average hydraulic properties and ideas for quantifying reach average roughness based on HAND and flood inundation mapped from remote sensing of previous floods with measured discharges.

ABSTRACT:

Presentation about HydroShare to the American Society of Civil Engineers (ASCE) Northern Utah branch lunch 'learn webinar on November 19, 2020.

ABSTRACT:

Resource to help installation of TauDEM on Linux.

TauDEM.sh does full install but takes about an hour
TauDEM.zip holds all the compiled files.
TauDEMPaths.sh sets paths to the compiled files

Paths may need adjusting based on your local configuration

ABSTRACT:

This is an example of Geoscience Use Case 4: Height Above the Nearest Drainage (HAND) of "Improving Reproducibility of Geoscience Models with Sciunit" in the Geological Society of America publication. In this resource, there are two notebooks: 1) HANDWorkFlow.ipynb and 2) HAND_Sciunit.ipynb.

Using these two notebooks, we demonstrate the capabilities of Sciunit to encapsulate the HAND TauDEM workflow and create a Sciunit Container, and evaluate differences in HAND due to changing the contributing area threshold used to map the drainage network. During computation of the drainage network, a minimum contributing area threshold is used to identify the channel beginning. With a lower threshold value, the density of the resulting drainage network increases. Scientists running this experiment might be interested in finding out how the threshold makes a difference in the execution and result of the HAND model.

The first notebook demonstrates the general procedure to calculate HAND (Height above the Nearest Drainage) using TauDEM (https://hydrology.usu.edu/taudem/taudem5/).
Then using the second notebook we demonstrate how to create a Sciunit container for HAND Workflow and compare two Sciunit containers (5000 vs 50000 thresholds) using `diff` command.

ABSTRACT:

This JavaScript code has been developed to retrieve NDSI_Snow_Cover from MODIS version 6 for SNOTEL sites using the Google Earth Engine platform. To successfully run the code, you should have a Google Earth Engine account. An input file, called NWM_grid_Western_US_polygons_SNOTEL_ID.zip, is required to run the code. This input file includes 1 km grid cells of the NWM containing SNOTEL sites. You need to upload this input file to the Assets tap in the Google Earth Engine code editor. You also need to import the MOD10A1.006 Terra Snow Cover Daily Global 500m collection to the Google Earth Engine code editor. You may do this by searching for the product name in the search bar of the code editor.

The JavaScript works for s specified time range. We found that the best period is a month, which is the maximum allowable time range to do the computation for all SNOTEL sites on Google Earth Engine. The script consists of two main loops. The first loop retrieves data for the first day of a month up to day 28 through five periods. The second loop retrieves data from day 28 to the beginning of the next month. The results will be shown as graphs on the right-hand side of the Google Earth Engine code editor under the Console tap. To save results as CSV files, open each time-series by clicking on the button located at each graph's top right corner. From the new web page, you can click on the Download CSV button on top.

Here is the link to the script path: https://code.earthengine.google.com/?scriptPath=users%2Figarousi%2Fppr2-modis%3AMODIS-monthly

Then, run the Jupyter Notebook (merge_downloaded_csv_files.ipynb) to merge the downloaded CSV files that are stored for example in a folder called output/from_GEE into one single CSV file which is merged.csv. The Jupyter Notebook then applies some preprocessing steps and the final output is NDSI_FSCA_MODIS_C6.csv.

ABSTRACT:

The notebooks in this resource have been developed to retrieve precipitation, air temperature, and snow water equivalent measured at Natural Resources Conservation Service (NRCS) SNOTEL sites by calling associated Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) web services.

ABSTRACT:

The HydroShare resources in this collection contain the data and scripts used for: Garousi-Nejad, I. and Tarboton, D. (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States", Hydrological Processes, https://doi.org/10.1002/hyp.14469.

Abstract from the paper:
This study compares the US National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow‐covered area fraction (SCAF) at snow telemetry (SNOTEL) sites across the Western United States SWE was obtained from SNOTEL sites, while SCAF was obtained from moderate resolution imaging spectroradiometer (MODIS) observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modelled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under‐predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under‐modelling of SWE. There was also under‐modelling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modelled and observed SCAF as well as the binary comparison of snow cover presence that hampered useful interpretation of SCAF comparisons. This is in part due to the shortcomings associated with both model SCAF parameterization and MODIS observations, particularly in vegetated regions. However, when SCAF was aggregated across all sites and years, modelled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements.

Order to follow the developed scripts:
1. Notebook to get the indices of National Water Model grid cells containing SNOTEL sites
2. Notebook for retrieval of National Water Model Retrospective run results at SNOTEL sites
3. Notebooks for post-processing the retrieved National Water Model Retrospective run results and inputs at SNOTEL sites
4. Notebook for retrieval of precipitation, air temperature, and snow water equivalent measurements at SNOTEL sites
5. JavaScript code for retrieval of MODIS Collection 6 NDSI snow cover at SNOTEL sites to be run using Google Earth Engine
6. Notebooks for combining the National Water Model results/inputs with observations from SNOTEL and MODIS at SNOTEL sites
7. Notebooks for visualizations reported at A Comparison of National Water Model Retrospective Analysis Snow Outputs at SNOTEL Sites Across the Western U.S.

ABSTRACT:

This resource contains Jupyter Notebooks that are used for post-processing the retrieved National Water Model retrospective simulations (NWM-R2), which are geospatial gridded results with a spatial resolution of 1 km and temporal resolution of 3 h. The NWM-R2 grid cells used were from https://doi.org/10.4211/hs.7839e3f3b4f54940bd3591b24803cacf and snow water equivalent and snow-covered area fraction at these grid cells from https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a that retrieved this information from the NOAA Google Cloud.

ABSTRACT:

This resource includes Jupyter Notebooks that combine (merge) model results with observations. There are four folders:

- NWM_SnowAssessment: This folder includes codes required for combining model results with observations. It also has an output folder that contains outputs of running five Jupyter Notebooks within the code folder. The order to run the Jupyter Notebooks is as follows.
First run Combine_obs_mod_[*].ipynb where [*] is P (precipitation), SWE (snow water equivalent), TAir (air temperature), and FSNO (snow covered area fraction). This combines the model outputs and observations for each variable. Then, run Combine_obs_mod_P_SWE_TAir_FSNO.ipynb.

- NWM_Reanalysis: This folder contains the National Water Model version 2 retrospective simulations that were retrieved and pre-processed at SNOTEL sites using https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a and https://doi.org/10.4211/hs.1b66a752b0cc467eb0f46bda5fdc4b34.

- SNOTEL: This folder contains preprocessed SNOTEL observations that were created using https://doi.org/10.4211/hs.d1fe0668734e4892b066f198c4015b06.

- GEE: This folder contains MODIS observations that we downloaded using https://doi.org/10.4211/hs.d287f010b2dd48edb0573415a56d47f8. Note that the existing CSV file is the merged file of the downloaded CSV files.

ABSTRACT:

This resource contains Jupyter Notebooks used to create Figures of Garousi-Nejad, I. and D. G. Tarboton, (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States," Hydrological Processes, 36(1): e14469, https://doi.org/10.1002/hyp.14469.

ABSTRACT:

The Jupyter Notebook shared here determines X and Y indices of the National Water Model grid cells that contain snow telemetry (SNOTEL) sites. It uses two inputs: one CSV file that includes SNOTEL site information and one NetCDF file that is a land surface model output of the NWM reanalysis results. You can open this resource with CUAHSI JupyterHub and run the notebook within the code folder. The output is a CSV file that gives X and Y indices of the National Water Model grid cells associated with each SNOTEL site.

ABSTRACT:

Geospatial Data For the Logan Little Bear River. Flow lines and stream gages. Used to illustrate WMS capability.

ABSTRACT:

This notebook is created to support SUMMA general application workflows using CAMELS forcing, watershed attributes, and streamflow observation.
CAMELS datasets cover 671 basins across the USA, so users can apply SUMMA models in 671 basins.

ABSTRACT:

This is the web app connector for the HydroShare THREDDS (Thematic Real-time Environmental Distributed Data Services) server for content aggregations within Composite resources in HydroShare. THREDDS data services are available only for the "Public" composite resources. This THREDDS data server supports access to netCDF data through OPeNDAP using the DAP2 protocol that supports subsetting directly from a number of clients using the DAP2 data access protocol as well as direct file download. This resource connects to a CUAHSI deployment of the UCAR Unidata THREDDS server https://www.unidata.ucar.edu/software/tds/current/TDS.html.

ABSTRACT:

This HydroShare resource is an example to demonstrate the vPICO presentations in EGU General Assembly 2021 (https://meetingorganizer.copernicus.org/EGU21/session/40092#vPICO_presentations).
- Session: EOS5.3 session - The evolving open-science landscape in geosciences: open data, software, publications, and community initiatives
- Title: An Approach for Open and Reproducible Hydrological Modeling using Sciunit and HydroShare

Using this notebook, you can test how to create an immutable and interoperable Sciunit Container for open and reproducible hydrological modeling.

You can start using "NB_01_An_Approach_for_Open_and_Reproducible_Hydrological_Modeling_using_Sciunit_and_HydroShare.ipynb" notebook in "CyberGIS-Jupyter for water" after clicking "Open with...". in Right-Above.

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks created for the study "An Approach for Creating Immutable and Interoperable End-to-End Hydrological Modeling Computational Workflows" led by researcher Young-Don Choi submitted to the 2021 EarthCube Annual meeting, Notebook Sessions.

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file provided in this resource.

For the sake of completeness, the abstract for the study submitted to the EarthCube session is mentioned below:

"Reproducibility is a fundamental requirement to advance science. Creating reproducible hydrological models that include all required data, software, and workflows, however, is often burdensome and requires significant work. Computational hydrology is a rapidly advancing field with fast-evolving technologies to support increasingly complex computational hydrologic modeling. The growing model complexity in terms of variety of software and cyberinfrastructure capabilities makes achieving computational reproducibility extremely challenging. Through recent reproducibility research, there have been efforts to integrate three components: 1) (meta)data, 2) computational environments, and 3) workflows. However, each component is still separate, and researchers must interoperate between these three components. These separations make verifying end-to-end reproducibility challenging. Sciunit was developed to assist scientists, who are not programming experts, with encapsulating these three components into a container to enable reproducibility in an immutable form. However, there were still limitations to support interoperable computational environments and apply end-to-end solutions, which are an ultimate goal of reproducible hydrological modeling. Therefore, the objective of this research is to advance the existing Sciunit capabilities to not only support immutable, but also interoperable computational environments and apply an end-to-end modeling workflow using the Regional Hydro-Ecologic Simulation System (RHESSys) hydrologic model as an example. First, we create an end-to-end workflow for RHESSys using pyRHESSys on the CyberGIS-Jupyter for Water platform. Second, we encapsulate the aforementioned three components and create configurations that include lists of encapsulated dependencies using Sciunit. Third, we create two HydroShare resources, one for immutable reproducibility evaluation using Sciunit and the other for interoperable reproducibility evaluation using library configurations created by Sciunit. Finally, we evaluate the reproducibility of Sciunit in MyBinder, which is a different computational environment, using these two resources. This research presents a detailed example of a user-centric case study demonstrating the application of an open and interoperable containerization approach from a hydrologic modeler’s perspective."

ABSTRACT:

This is an example use case for designing where on the landing page to put OpenDap web service URLs

ABSTRACT:

This presentation will introduce HydroShare, a repository developed for sharing data and models within the hydrology and water resources community served by CUAHSI. It will describe HydroShare functionality for capturing and holding metadata as well as tools for acting on data in HydroShare that make data sharing attractive beyond open data mandates and enable problem solving through data integration. I will discuss lessons learned and challenges we still face in the management and reuse of water related data for integrated problem solving.

ABSTRACT:

An experiment to measure snow energy balance and sublimation from snow in the winter of 1992 - 1993 was conducted at the USU drainage and evapotranspiration experimental farm located in Cache Valley near Logan, Utah, USA (41.6˚ N, 111.6˚ W, 1350 m elevation). The weather station and instrumentation were in a small fenced enclosure at the center of a large open field. There are no obstructions to wind in any direction for at least 500 m. Cache Valley is a flat bottomed valley surrounded by mountains that reach elevations of 3000 m. During the period of this experiment the ground was snow covered from November 20, 1992 to March 22, 1993. Air temperatures ranged from -23 ˚C to 16 ˚C and there was 190 mm of precipitation (mostly snow, but some rain). The snow accumulated to a maximum depth of 0.5 m with maximum water equivalence of 0.14 m. Data collected included measurements of snow water equivalent, snow surface temperature and the meteorological variables necessary to drive the UEB snowmelt model. Temperatures within the snow were measured using a ladder of thermocouples suspended on fishing line strung between two upright posts at 75 mm spacing. The instrumentation also included two weighing lysimeters comprising 1 x 1 x 1 m metal boxes embedded flush with the surface and filled with soil, vegetated with grass similar to the surrounding agricultural field. Load cells (underneath in the case of one lysimeter and at the corners for the other) record the weight of soil, grass, soil moisture and snow over the 1 m2 areas. Meltwater infiltrates into the lysimeter and so does not result in a weight change. Changes in weight are due only to addition or removal of mass from the surface, which in the case of snow can be due to precipitation, condensation, sublimation and wind drifting.

This work has been described in

You, J., Tarboton, D. G., and Luce, C. H.: Modeling the snow surface temperature with a one-layer energy balance snowmelt model, Hydrol. Earth Syst. Sci., 18, 5061–5076, https://doi.org/10.5194/hess-18-5061-2014, 2014.

Luce, C. H. and D. G. Tarboton, (2010), "Evaluation of alternative formulae for calculation of surface temperature in snowmelt models using frequency analysis of temperature observations," Hydrol. Earth Syst. Sci., 14(3): 535-543, http://www.hydrol-earth-syst-sci.net/14/535/2010/.

You, J., (2004), "Snow Hydrology: The Parameterization of Subgrid Processes within a Physically Based Snow Energy and Mass Balance Model," PhD Thesis, Civil and Environmental Engineering, Utah State University, https://hydrology.usu.edu/dtarb/yjs_dissertation.pdf.

Luce, C. H., (2000), "Scale Influences on the Representation of Snowpack Processes," PhD Thesis, Civil and Environmental Engineering, Utah State University, https://hydrology.usu.edu/dtarb/luce_dissertation.pdf, 188 pp.

Among others

ABSTRACT:

This resource contains data from the Central Sierra Snow Laboratory (CSSL) used in the development of the Utah Energy Balance (UEB) Snowmelt model. This data was provided by Bruce McGurk to David Tarboton around 1991. The CSSL is located 1 km east of Soda Springs, California, and measures and archives comprehensive data relevant to snow. It is at latitude 39°19'N and at elevation 2100 m. The meteorological data is reported each hour and consists of temperature, radiation, humidity, precipitation, and wind measurements at two levels in a 40 x 50 m clearing and in a mixed conifer fir forest with 95% forest cover. Only data from the clearing was used in UEB development. Snow depths and water equivalent are measured daily (except on weekends) and eight lysimeters record melt outflow each hour. We used the temperature, precipitation, radiation (incoming solar and net), humidity and wind measurements to drive UEB and compared model output to measurements of snow water
equivalence, melt outflow and snow surface temperature (infrared sensor).

This work is described in

Tarboton, D. G. and C. H. Luce, (1996), "Utah Energy Balance Snow Accumulation and Melt Model (UEB)," Computer model technical description and users guide, Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station, Logan, Utah, 64 p., https://hydrology.usu.edu/dtarb/snow/snowrep.pdf.

Recent information on CSSL is at https://cssl.berkeley.edu/

ABSTRACT:

This resource contains data from Niwot Ridge used in the development of the Utah Energy Balance (UEB) Snowmelt model. This data was provided by Mark Williams. The Niwot Ridge Subnivean snow laboratory is on the eastern slope of the Front Range of Colorado (3517 m above MSL, 40 deg 03’ N, 105 deg 35’ W). Data were collected during the 1995~1996 winter seasons. The instrument site is located in a relatively flat area above the treeline within a broad saddle of the ridge. The high elevation and exposure of Niwot Ridge, and typically dry atmospheric conditions result in large clear-sky atmospheric transmissivity, increased solar insolation, and low magnitudes of incident longwave radiation, low air temperatures, and high wind velocities. The dataset includes measurements of air temperature, wind speed, relative humidity, and incident shortwave radiation from April 28, 1996 to September 30, 1996 with a time step of 2 hours. Measured lysimeter data is also available although there are concerns as to how representative it is due to preferential flow paths (finger-flow) in the snow resulting in under-catch of meltwater (Cline DW, 1997, Effect of seasonality of snow accumulation and melt on snow surface energy exchanges at a continental alpine site. Journal of Applied Meteorology 36: 22-41).

This work is described in

You, J., Tarboton, D. G., and Luce, C. H.: Modeling the snow surface temperature with a one-layer energy balance snowmelt model, Hydrol. Earth Syst. Sci., 18, 5061–5076, https://doi.org/10.5194/hess-18-5061-2014, 2014.

You, J., (2004), "Snow Hydrology: The Parameterization of Subgrid Processes within a Physically Based Snow Energy and Mass Balance Model," PhD Thesis, Civil and Environmental Engineering, Utah State University, https://hydrology.usu.edu/dtarb/yjs_dissertation.pdf.

ABSTRACT:

This dataset holds CRSS-ready flow sequences adjusted for temperature increases in the 21st century (2018 to 2100) as well as temperature increases that occurred during the natural flow period from 1906 to 2017. These flow traces were produced using the methods described in the file memo by Udall (2020). This study developed six different temperature-adjusted natural flow datasets based on considering three different temperature sensitivities (-3%/°C, -6.5%/°C, and -10%/°C) times two different temperature projections (RCP4.5 and RCP8.5). The flows were generated by modifying the existing Reclamation natural flow dataset (USBR, 2019) at each of the 29 Colorado River Simulation System (CRSS) natural inflow sites. Two main steps of temperature adjustment process in this study were: 1- Create a set of natural flows that would have occurred were temperatures constant from 1906 to 2017 assuming 2017 temperatures, and 2- Further adjust downward the constant 2017 temperature flows to account for expected losses due to warming in the 21st century. The flow data are provided in an CRSS-ready Indexed Sequential Method (ISM) series with a starting year of 2018 and an ending year of 2100.

ABSTRACT:

Presentation University of Saskatchewan Global Institute for Water Security 2021 Distinguished Lecture Series, November 3, 2021, https://water.usask.ca/events/2021/11/dls8.php.

HydroShare is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) for users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research. This presentation will step away from the science of water security breakthroughs and instead describe the development of CUAHSI HydroShare with a focus on the information technology key to supporting the transparency and reproducibility needed to enhance trust in the findings of hydrologic research by making hydrologic information more findable, accessible, interoperable and reusable (FAIR), and through linked computational systems simplifying the workflows needed for hydrologic modeling and analysis.

ABSTRACT:

This resource contains the data and scripts used for:
Goeking, S. A. and D. G. Tarboton, (2022). Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach. Water Resources Research, 58, e2021WR031575. https://doi.org/10.1029/2021WR031575.

Abstract from the paper:
Forest cover and streamflow are generally expected to vary inversely because reduced forest cover typically leads to less transpiration and interception. However, recent studies in the western US have found no change or even decreased streamflow following forest disturbance due to drought and insect epidemics. We investigated streamflow response to forest cover change using hydrologic, climatic, and forest data for 159 watersheds in the western US from the CAMELS dataset for the period 2000-2019. Forest change and disturbance were quantified in terms of net tree growth (total growth volume minus mortality volume) and mean annual mortality rates, respectively, from the US Forest Service’s Forest Inventory and Analysis database. Annual streamflow was analyzed using multiple methods: Mann-Kendall trend analysis, time trend analysis to quantify change not attributable to annual precipitation and temperature, and multiple regression to quantify contributions of climate, mortality, and aridity. Many watersheds exhibited decreased annual streamflow even as forest cover decreased. Time trend analysis identified decreased streamflow not attributable to precipitation and temperature changes in many disturbed watersheds, yet streamflow change was not consistently related to disturbance, suggesting drivers other than disturbance, precipitation, and temperature. Multiple regression analysis indicated that although change in streamflow is significantly related to tree mortality, the direction of this effect depends on aridity. Specifically, forest disturbances in wet, energy-limited watersheds (i.e., where annual potential evapotranspiration is less than annual precipitation) tended to increase streamflow, while post-disturbance streamflow more frequently decreased in dry water-limited watersheds (where the potential evapotranspiration to precipitation ratio exceeds 2.35).

The following scripts (R language and environment for statistical computing) produce the results, figures, and tables in this paper (in the order in which they appear in the paper; requires either running data compilation/aggregation scripts first OR using provided data files watersheds.csv and wb_filtered.csv):
1. Map_watersheds.r
2. Analysis_M-K_trend_test.r
3. analysis_M-K_quadrant_figure.r
4. analysis_timetrend_linear.r
5. analysis_regressn_w-veg.r

The following scripts (R) compile the data, aggregated from other sources prior to the analyses in the scripts listed above:
1. compilation_CAMELS.r
2. compilation_Daymet.r
3. compilation_USGS.r
4. compilation_FIA.r
5. compilation_CAMELS_Daymet_USGS.r (must run scripts #1-3 first)
6. watershed_compilation.r (must run scripts #1-5 first)

ABSTRACT:

HW Abstract

ABSTRACT:

This resource illustrates how data and code can be combined together to support hydrologic analyses. It was developed June 2020 as part of a HydroLearn Hackathon.

ABSTRACT:

Dear CUAHSI community members,

We are pleased to announce a new quarterly release of the CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes several new capabilities and features summarized as follows.

Transition to CyberGIS-Compute V2:
CyberGIS-Compute V2 is a new development phase of the CyberGIS-Compute framework that was initially released (denoted as V1) one year ago through CJW 2020-Q4. Compared with V1, V2 includes several major enhancements: 1) a new workflow for model contribution to facilitate adding new hydrologic models by community developers; 2) a GUI in the notebook environment to simplify and guide users through the job submission process; 3) transparent and bi-directional data transfers between CJW and high-performance computing (HPC) resources using Globus by default, and 4) detailed tracking of usage and metrics. It is worth noting that due to the upgraded architecture in V2, existing models implemented in V1 would need to be migrated. For a smooth transition and backward compatibility, services in V1 will remain available in parallel to those in V2, and all the old notebooks that use V1 remain functional.

SUMMA Model Migrated to CyberGIS-Compute V2:
We have migrated the SUMMA model to CyberGIS-Compute V2, and end users can now benefit from the new features mentioned above in SUMMA modeling work. Please refer to the example notebook below for details. In addition, the implementation of SUMMA in CyberGIS-Compute V2 is accessible on a Github repo (https://github.com/cybergis/cybergis-compute-v2-summa), which can serve as a real-world example to model developers who may want to contribute their models for sharing with the community. A “HelloWorld” implementation is also available serving as a model-agnostic example (https://github.com/cybergis/cybergis-compute-mpi-helloworld).

New Modules and Kernel Customization:
Upon user requests, two new easybuild-based modules have been added to the CJW toolbox and are now ready to use: NCL (https://www.ncl.ucar.edu/) for scientific data analysis and visualization (e.g., NetCDF, GRID, HDF); and CDO (https://code.mpimet.mpg.de/projects/cdo) for manipulation of climate and Numerical Weather Prediction (NWP) data. Furthermore, for advanced users who may want to customize the provided software environment and kernels, an example notebook (see below) is available for users to walk through the basics on how to install new libraries on top of existing environments or set up a Conda environment from scratch.

New UI Elements on CJW:
CJW has further customized the Jupyter Notebook user interface to include a virtual Announcement Board (in the header area) for timely communicating with users on upcoming events including downtimes and new releases, and a Bug Report button (at the upper-right corner) that opens an issue tracker page in a publicly accessible Github repo for quick bug reporting.

Please refer to the following resources for details and examples:
Run ensemble SUMMA 3.0 model with CyberGIS-Compute V2
https://www.hydroshare.org/resource/deac1b0b5b46415aaedb886b9dc16f45/

Customize Software Environment on CJW
https://www.hydroshare.org/resource/461a8a853d8e42a8ae170c68c4cfa8f1/

Implementation of SUMMA model using CyberGIS-Compute V2
https://github.com/cybergis/cybergis-compute-v2-summa

Implementation of HelloWorld model using CyberGIS-Compute V2
https://github.com/cybergis/cybergis-compute-mpi-helloworld

See Release Notes on HydroShare
https://www.hydroshare.org/resource/2086b241b16b453d827db847e8640475/

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.

Best regards,
CyberGIS-Hydro team

ABSTRACT:

Sciunit simple demo

ABSTRACT:

Illustration of General idea of use case for sciunit container.
1. User creates sciunit (sciunit create Project1)
2. User initiates interactive capturing (sciunit exec -i)
3. User does their work. For now assume this is a series of shell commands
4. User saves or copies the sciunit
5. User opens the sciunit on a new computer and can re-execute the commands exactly as they would have on the old computer, from command line, from bash shell escapes or python in Jupyter
6. User sees a list of the commands that were in the sciunit and could use editing of them to reproduce

The setup.
On CUAHSI JupyterHub the user has a resource (the one above) with some code that is a simple example for modeling the relationship between streamflow and snow

There is a python "dependency" GetDatafunctions.py in a folder on CUAHSI JupyterHub. This is not part of the directory where the user is working. It is added to the python path for the programs to execute. This is a simple example of what could be a dependency the user may not exactly be aware of (e.g. if it is part of the CUAHSI JupyterHub platform, but not part of other platforms).

An export PYTHONPATH command is used to add the dependency to the python path.

Then the analysis is illustrated outside of sciunit.

Then sciunit is installed and the analysis repeated using sciunit exec.

Finally sciunit copy copies the sciunit to the sciunit repository

Then on a new computer

Sciunit open retrieves the sciunit

After repeating one of the executions, the sciunit directory has the dependency container unpacked

Setting the PYTHONPATH to the unpacked dependency allows the commands to be run on the new computer, just as they were on the old computer.

This is the vision - running on the new computer with dependencies from the old computer resolved.

Would like the dependencies to be “installed” on the new computer so that they work with Jupyter and Jupyter escape bash commands.

All is done from the command line - the Jupyter Notebook is just used as a convenient notepad.

ABSTRACT:

The Office of Water Prediction (OWP) National Water Center provides water information products from version 2.1 of the National Water Model (NWM). Information about NWM products available through the OWP website can be found in this Product Description Document. Advisory: NWM products do not yet incorporate anthropogenic influence and should be used with some caution. The NWM is currently undergoing extensive validation and verification to identify where scientific updates to the model can make the most improvement. The next version of the NWM will be released in the late spring 2020 time frame. For more information about the NWM, go here.

Please note, the mapping interface and NWM products and web services are experimental. In addition to products from the NWM (streamflow, soil saturation), two products from the National Snow Analysis (snow depth, snow water equivalent) are available, as well as several useful reference maps from various sources. The OWP is seeking to improve the availability and quality of its products and services based on user feedback. Comments regarding any of the experimental NWM products and web services should be submitted through the NWM online survey form.

The OWP also provides a range of NWS official water information through the following web sites.

Official river observations and forecast information: https://water.weather.gov/ahps
Snow Information: https://www.nohrsc.noaa.gov
Precipitation Frequency Estimates: https://www.weather.gov/owp/hdsc
Comments? Questions? Please Contact nws.nwc.ops@noaa.gov.

ABSTRACT:

This resource holds files to test the various file types under development for composite resources.

ABSTRACT:

This notebook demonstrates how to prepare a WRFHydro model on CyberGIS-Jupyter for Water (CJW) for execution on a supported High-Performance Computing (HPC) resource via the CyberGIS-Compute service. First-time users are highly encouraged to go through the [NCAR WRFHydro Hands-on Training on CJW](https://www.hydroshare.org/resource/d2c6618090f34ee898e005969b99cf90/) to get familiar WRFHydro model basics including compilation of source code, preparation of forcing data and typical model configurations. This notebook will not cover those topics and assume users already have hands-on experience with local model runs.

CyberGIS-Compute is a CyberGIS-enabled web service sits between CJW and HPC resources. It acts as a middleman that takes user requests (eg. submission of a model) originated from CJW, carries out the actual job submission of model on the target HPC resource, monitors job status, and retrieves outputs when the model execution has completed. The functionality of CyberGIS-Compute is exposed as a series of REST APIs. A Python client, [CyberGIS-Compute SDK](https://github.com/cybergis/cybergis-compute-python-sdk), has been developed for use in the CJW environment that provides a simple GUI to guide users through the job submission process. Prior to job submission, model configuration and input data should be prepared and arranged in a certain way that meets specific requirements, which vary by models and their implementation in CyberGIS-Compute. We will walk through the requirements for WRFHydro below.

The general workflow for WRFHydro in CyberGIS-Compute works as follows:

1. User picks a Model_Version of WRFHydro to use;
2. User prepares configuration files and data for the model on CJW;
3. User submits configuration files and data to CyberGIS-Compute;
4. CyberGIS-Compute transfers configuration files and data to target HPC;
5. CyberGIS-Compute downloads the chosen Model_Version of WRFhydro codebase on HPC;
6. CyberGIS-Compute applies compile-time configuration files to the codebase, and compiles the source code on the fly;
7. CyberGIS-Compute applies run-time configuration files and data to the model;
8. CyberGIS-Compute submits the model job to HPC scheduler for model execution;
9. CyberGIS-Compute monitors job status;
10. CyberGIS-Compute transfers model outputs from HPC to CJW upon user request;
11. User performs post-processing work on CJW;

Some steps in this notebook require user interaction. "Run cell by cell" is recommended. "Run All" may not work as expected.

How to run the notebook:
1) Click on the OpenWith button in the upper-right corner;
2) Select "CyberGIS-Jupyter for Water";
3) Open the notebook and follow instructions;

ABSTRACT:

CyberGIS-Jupyter for Water Quarterly Release Announcement (2022 Q1) [full-version]

Dear CUAHSI community members,

We are pleased to announce a new release of the CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes several new capabilities and features summarized as follows.

1) Integration of WRFHydro model with CyberGIS-Compute V2 to simplify access to High-Performance Computing (HPC) environments: A newly developed computation job template in CyberGIS-Compute enables users to configure a WRFHydro model and submit it to a HPC resource for execution. The client tool of the CyberGIS-Compute suite, CyberGIS-Compute SDK, walks users through job configuration, data transfer, job submission, and job status monitoring in a guided graphical interface. Since the overhead of HPC access is handled by CyberGIS-Compute, users can now focus on the modeling work. Currently, the implementation allows users to change almost every setting and configuration for a WRFHydro 5.x “offline run”. The whole process described above can be accomplished entirely within a notebook environment on CJW. Please refer to the example notebooks below for additional details.

2) Transition to JupyterLab: Starting with this release, CJW will launch the “next-generation notebook interface”, JupyterLab, as the default user environment. Although the new interface is different from the classic Notebook interface in many places, we anticipate this transition would be easy and smooth for most users. All existing notebooks should continue to run without modification, and the bug report and announcement UI elements have been migrated to the Lab interface. In addition, we have integrated the CUAHSI “HydroShare-on-Jupyter” extension - a handy tool that enables users to move data between CJW and HydroShare through a simple graphical user interface.

3) The “cjw” Command Line Interface (CLI): The “cjw” CLI is designed to help users manage different kernels on CJW for advanced use cases. For example, users can use this capability to set up personal kernels that will persist between sessions. For a quick start, open a terminal on CJW and try out the "cjw -h" command. Check out the documentation and examples below.

4) New Modules and Kernels: To support the latest RHESSys codebase, we have added Clang, a new C family compiler supplementing the existing GCC suite, to the CJW Easybuild-based toolbox. Accordingly, a new versioned RHESSys (2022-03) kernel has been created with Clang and other development tools pre-activated that are necessary for compilation of the RHESSys source code. Upon user requests, a new versioned WRFHydro (2022-03) kernel has been created to include the hvPlot toolset for advanced data visualization and updated versions of all the libraries from the previous WRFHydro (2021-09) kernel.

Please refer to the following resources for details and examples:
Run WRFHydro 5.x model on HPC with CyberGIS-Compute V2
https://www.hydroshare.org/resource/cc28d769943046fdac0f9b0c0fc2afc6/

Implementation of WRFHydro 5.x model using CyberGIS-Compute V2
https://www.hydroshare.org/resource/329ede31b88942c489aca3111b076446/

Customize Software Environment on CJW
https://www.hydroshare.org/resource/461a8a853d8e42a8ae170c68c4cfa8f1/

“cjw” Command Line Interface Documentation
https://cybergis.github.io/cybergisx-cli/cjw/

See Release Notes on HydroShare
https://www.hydroshare.org/resource/b0d094eef336427ab605066e166135d3/

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.

Best regards,
CyberGIS-Hydro team

ABSTRACT:

Testing the TauDEM Kernel on CJW

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:

Presentation for AWRA Geospatial Technologies Conference May 10, 2022 https://www.awra.org/Members/Events_and_Education/Events/2022_GIS_Conference/2022_GIS_Conference.aspx

HydroShare is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) for users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research. It serves as a repository for data and models to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. Beyond content storage, the HydroShare repository also links with connected computational systems providing immediate value to users through the ability to reduce the needs for software installation and configuration and to document workflows, enhancing reproducibility. These approaches have facilitated considerable sharing and publication of data associated with research in HydroShare, enabling its re-use and the integration of data from multiple users to support broader synthesis studies. Data types supported include multidimensional netCDF, time series, geographic rasters and features. For some of these, standard data services, such as OpenDAP services for netCDF or Open Geospatial Consortium web services for geographic data types are automatically established when data is made public, improving machine readability and system interoperability. This presentation will describe geospatial data in HydroShare focusing on the geospatial feature and raster aggregations used to hold geospatial data and the functionality developed to automatically harvest metadata from these data types, simplifying the process of metadata creation for users. We will also describe how geospatial data services established for public resources holding geospatial data in HydroShare enable the data to be accessed by third party web applications adding to the functionality supported by HydroShare as a content storage element within a software ecosystem of interoperating systems.

ABSTRACT:

Presentation for AGU Frontiers in Hydrology Conference, June 22, 2022, Hydroinformatics Roundtable I Online Discussion Session https://agu.confex.com/agu/hydrology22/meetingapp.cgi/Session/143096

Founded on the notion that solving big research problems in hydrology requires teamwork and integration of information from multiple sources, the CUAHSI HydroShare model and data repository was developed to support collaboration throughout the scientific data lifecycle of data preparation, analysis, management and publication. Content type aggregations within HydroShare resources enable users to organize multiple types of content within a single resource. Harvesting of metadata from uploaded known file types simplifies metadata creation. Data format standardization enables tools for data reuse. Incentives for users to put their data in HydroShare include publisher and funding agency requirements to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. However, these incentives do not always provide immediate value to users. HydroShare has also established cloud-computing functionality via web applications linked to the repository through web services which provides a gateway to computing using data in HydroShare. Anyone can set up an application software service to operate on HydroShare resources. JupyterHub platforms, hosted in the Google Cloud and on XSEDE, interoperate with data in HydroShare, provide immediate value to researchers through reduced needs for software installation and configuration, and enable access to higher performance computing. They also provide a built-in means for documenting workflows, thus enhancing reproducibility and moving towards a fully web-based hydrologic innovation environment. In this round table I will discuss the contributions of HydroShare to hydroinformatics and give thoughts on ongoing opportunities and challenges.

ABSTRACT:

Presentation for AGU Frontiers in Hydrology Conference, June 23, 2022, Emphasizing F, I, and R in FAIR Hydrology: Bottlenecks and Solutions to Making Hydrologic Science More Reproducible Session https://agu.confex.com/agu/hydrology22/meetingapp.cgi/Session/142683

HydroShare is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) for users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research. It serves as a repository for data and models to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. Beyond content storage, the HydroShare repository also links with connected computational systems providing immediate value to users through the ability to reduce the needs for software installation and configuration and to document workflows, enhancing reproducibility. Drawing upon experience with the development of HydroShare I will contribute to the discussion in this session through reflection on the contributions and challenges to each of Findable, Interoperable and Reusable FAIR elements. Findable is addressed through resource schemas (schema.org) that draw searchers to HydroShare and discover functionality within HydroShare that enables text, facet and geographic filtering. Interoperable is addressed through machine readability via an applications programming interface (API) and framework for linking computational systems to operate on HydroShare resources. Reusable is addressed through standards used to represent common data content types, and the overall Open Archives Initiative Object Exchange and Reuse based resource data model. HydroShare has capability to automatically recognize supported data types and harvest metadata from these data types, simplifying the process of metadata creation for users. Data types supported include multidimensional netcdf, time series, geographic rasters and features. For some of these, standard data services, such as OpenDAP services for netCDF or Open Geospatial Consortium web services for geographic data types are automatically established when data is made public, improving machine readability and system interoperability. Ongoing work is also advancing the use of containerization to encapsulate software and data dependencies needed to ensure persistent reproducibility of computational workflows, especially for data and computationally-intensive modeling. However, bottlenecks and challenges remain in the use of this functionality. In this session I will discuss some of these contributions from HydroShare and give thoughts on ongoing opportunities and challenges.

ABSTRACT:

This paper summarizes the current understanding of future hydrology from the perspective of how that understanding can be incorporated into the Colorado River Simulation System and other river planning models. We also provide scenarios that characterize and estimate plausible future drought conditions, based on the record of past droughts in historic and tree ring-estimated natural flow. Scenarios described in this report, although sometimes of low probability, are based on flows that have occurred in the past or can be reconstructed from the past record of streamflow. If such conditions have happened in the past, they might occur in the future, and these scenarios should be considered in future planning.

ABSTRACT:

This resource contains the data and scripts used for:
Goeking, S. A. and D. G. Tarboton, (2022). Spatially distributed overstory and understory leaf area index estimated from forest inventory data. Water. https://doi.org/10.3390/w1415241.

Abstract from the paper:
Abstract: Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and re-mote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of under-story LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how for-est change influences hydrologic processes.

This resource contains one CSV file, two shapefiles (each within a zip file), two R scripts, and multiple raster datasets. The two shapefiles represent the boundaries of the Middle Fork Flathead river and South Fork Flathead River watersheds. The raster datasets represent annual leaf area index (LAI) at 30 m resolution for the entire modeling domain used in this study. LAI was estimated using method LAI4, which produced separate overstory and understory LAI datasets. Filenames contain years, e.g., "LAI4_2019" is overstory LAI for 2019; "LAI4under_2019" is understory LAI for 2019.

The CSV files in this Resource contain annual time series of LAI and ET ratio (annual evapotranspiration divided by annual precipitation) for the South Fork Flathead River and Middle Fork Flathead River watersheds, 1984-2019. LAI methods represented in this time series are LAI1 and LAI4 from the paper. LAI1 consists of only overstory LAI, and LAI4 consists of overstory (LAI4), understory (LAI4_under), and total (LAI4_total) LAI. For each LAI estimation method, summary statistics of the entire watershed are included (min, first quartile, median, third quartile, and max).

The two R scripts (R language and environment for statistical computing) summarize Forest Inventory & Analysis (FIA) data from the FIA database (FIADB) to estimate LAI at FIA plots.
1) FIADB_queries_public.r: Script for compiling FIA plot measurements prior to estimating LAI
2) LAI_estimation_public: Script for estimating LAI at FIA plots using the four methods described in this paper

Before running the R scripts, users must obtain several FIADB tables (PLOT, COND, TREE, and P2VEG_SUBP_STRUCTURE; all four tables must be renamed with lower-case names, e.g., "plot"). These tables can be obtained using one of two methods:
1) By downloading CSV files for the appropriate U.S. state(s) from the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html). If this method is used, the CSV files must be imported (read) into R before proceeding.
2) By using r package 'rFIA' to download the tables from FIADB for the U.S. state(s) of interest.

Note that publicly available plot coordinates are accurate within 1 km and are not true plot locations, which are legally confidential to protect the integrity of the sample locations and the privacy of landowners. Access to true plot location data requires review by FIA's Spatial Data Services unit, who can be contacted at SM.FS.RMRSFIA_Help@usda.gov.

ABSTRACT:

This Jupyter Notebook and associated python file illustrates the use of the ModelMyWaterShed API for watershed delineation from an arbitrary point on or near NHD Plus streams in the U.S. This is documented at https://modelmywatershed.org/api/docs/.

ABSTRACT:

This notebook demonstrates the setup for a typical WRF-Hydro model on HydroShare leveraging different tools or services throughout the entire end-to-end modelling workflow. The notebook is designed in such a way that the user/modeler is able to retrieve datasets only relevant to a user-defined spatial domain (space domain), for example, a watershed domain of interest and time domain using a graphical user interface (GUI) linked to HPC. In order to help users submitting a job on HPC to run the model, they are provided with a user-friendly interface that abstracts away details and complexities involved in the HPC use such as authorization, authentication, monitoring and scheduling of the jobs, data and job management, and transferring data back and forth. Users can interact with this GUI to perform modeling work. This GUI is designed in such a way to allow users/modeler to 1) select the remote server where the HPC job will run, 2) upload the simulation directory, which contains the configuration files, 3) specify the parameters of the HPC job that the user is allowed to utilize, 4) set some parameters related to the model compilation, 5) follow-up on the submitted job status and 6) retrieve the model output files back to local workspace. Once the model execution is completed, users can easily have access to the model outputs on HPC and retrieve them to the local workspace for visualization and analysis.

ABSTRACT:

CyberGIS-Jupyter for Water Quarterly Release Announcement (2022-Q3)

Dear CJW users,

We are pleased to announce a new release of the CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes several new capabilities and features summarized as follows.

(1) Cern Virtual Machine File System (CVMFS): We have redesigned how we deliver software within CyberGIS-Jupyter. This new design drastically increases computational performance and reproducibility, and allows the platform to make the software environment available in a variety of settings. From an end-user perspective, there should be no change to your accessing and utilizing the CJW services.

(2) Improved user experience for CyberGIS-Compute: In previous versions, we introduced the capability for users to “Restore” their previously submitted jobs of interest. Based on user feedback, we’ve further refined the interface to support viewing and downloading outputs of all previously submitted jobs by simply navigating to the “Past Results” section. The result/output of any completed job can be accessed with a single click.

(3) Support for new High Performance Computing (HPC) backend in CyberGIS-Compute: Anvil is now available as a new HPC resource for CyberGIS-Compute. Supported by NSF, Anvil is a HPC system hosted at Purdue University that contains 1000 CPU nodes based on the third generation AMD EPYC "Milan" processor, delivering a peak performance of 5.3 petaflops. Allocations on Anvil are managed by NSF's ACCESS program (https://access-ci.org/). The large numbers of CPU nodes and cores (i.e., 128) enable superior computational performance for scalable codes, short queuing times, and fast execution for hydrologic models via CyberGIS-Compute. For more information on Anvil, refer to the documentation at: https://www.rcac.purdue.edu/anvil. The WRFHydro model is supported on Anvil via CyberGIS-Compute. Please refer to the example notebook below.

Please refer to the following resources for details and examples:

A Brief Overview Of Cern Virtual Machine File System (CVMFS)
http://www.hydroshare.org/resource/ab1555c0c8d34d3496997353ba8060d9

CyberGIS-Compute updates - 2022-Q3
http://www.hydroshare.org/resource/3b472641c3504161bb13a19d4c9fbc87

Submission of WRFHydro model to Anvil HPC
https://www.hydroshare.org/resource/cc28d769943046fdac0f9b0c0fc2afc6/

See Release Notes on HydroShare
http://www.hydroshare.org/resource/bf463f07e1244de4a17b3ea7b2d95916

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.
Best regards,
CyberGIS-Hydro team

ABSTRACT:

This Jupyter Notebook and data serve as an illustration for the evaluation of Height Above the Nearest Drainage (HAND) using TauDEM. This depends on TauDEM functions and a user either needs to install TauDEM (see http://hydrology.usu.edu/taudem) or use a platform where this is pre-installed such as CyberGIS Jupyter for Water (https://go.illinois.edu/cybergis-jupyter-water/) with a TauDEM Kernel.

ABSTRACT:

Great Salt Lake water withdrawals for use in mineral extraction evaporation ponds. This data is part of a study "The Salinity of the Great Salt Lake and its Deep Brine Layer." This study analyzes the lake’s historical salinity and level data record. Its purpose is to better understand the movement and changes of salt in time and space within the lake and evaluate the occurrence and extent of the deep brine layer. Mineral extraction water withdrawals contribute to changes in the salinity of the lake.

ABSTRACT:

Shapefile giving geographic boundaries of the Great Salt Lake Basins. There are 6 features in this shapefile
- The lake itself. This is at a nominal high extent as extracted from common data sources
- Bear River Basin
- Weber River Basin (Which includes Davis County)
- Jordan/Provo River River Basin
- West Desert
- Strawberry

Strawberry is not topographically within the Great Salt Lake drainage, but is the area draining to Strawberry reservoir that supplies water to the GSL basin through the central Utah Project.

The west desert was delineated from digital elevation model analysis separating out the area more adjacent to the lake from a larger area that extends much further south but deemed not relevant for Great Salt Lake hydrology studies.

The original source of this data is not known. For a more precise and current basin delineation I suggest referring to the USGS watershed boundary dataset. This data is the underling data for maps in the following papers
Mohammed, I. N. and D. G. Tarboton, (2012), "An examination of the sensitivity of the Great Salt Lake to changes in inputs," Water Resour. Res., 48(11): W11511, http://dx.doi.org/10.1029/2012WR011908.
Mohammed, I. N. and D. G. Tarboton, (2011), "On the Interaction between Bathymetry and Climate in the System Dynamics and Preferred Levels of the Great Salt Lake," Water Resour. Res., 47: W02525, http://dx.doi.org/10.1029/2010WR009561.

ABSTRACT:

This describes work on the calculation of Great Salt Lake (GSL) inflows for modeling and analyses in support of declining lake levels. This derives from the procedures of USGS 00-4221 (Loving et al., 2000) who estimated GSL streamflow inputs for 12 years from 1987-1998, and Mohammed ant Tarboton (2012), who modeled GSL levels for 1950-2010 water years. Some deviations from the methods given in USGS 00-4221 (Loving et al., 2000) were necessary due to limited availability of data for the extended period and where there were opportunities for improvement.

ABSTRACT:

Time series of level, area and volume in the Great Salt Lake from the earliest South Arm level record on 10/18/1847 to 6/24/2024. Volume and area of the Great Salt Lake are derived from recorded levels and bathymetry. The bathymetry used is included. Bathymetry is adjusted for the presence or absence of Magnesium corps pond. The area of the evaporation pond is not regarded as part of the lake except prior to its construction and during the time it was overtopped.

GSL_north_arm.txt is measured data from the USGS station 10010100 GREAT SALT LAKE NEAR SALINE, UT
GSL_north_arm_2024-06-25.txt Duplicate of above as run on 6/25/24
GSL_north_arm_2023-10-07.txt Previous file from 10/7/23

GSL_south_arm.txt is measured data from the USGS station 10010000 GREAT SALT LAKE AT SALTAIR BOAT HARBOR, UT
GSL_south_arm_2024-06-25.txt Duplicate of above as run on 6/25/24
GSL_south_arm_2023-10-07.txt Previous file from 10/7/23

GSLLAV.txt is time series of level, computed area and volume from level using bathymetry on the days of each observation

Bathymetry folder. Lake bathymetry used in these calculations. This data is also stored separately in https://www.hydroshare.org/resource/b26090299ec947c692d4ee4651815579/

GSLLevelVol.csv is beginning of month time series of level and volume. This is interpolated when there is not an observation on the first day of the month.

LevelVolWork.R is the R script used to process this data
LevelVolPlots.R is the R script used to make plots used to check this data

GSLLevelRecord.pptx Powerpoint file with some figures of this data

GSLFunctions.R R functions used by the script

Headings should be obvious. Note that separate levels in the north arm only started being recorded in 1966 so for dates prior to that the North Arm Level is taken from the South Arm. The bathymetry was then used to compute area and volume in each arm separately and add them up.

ABSTRACT:

CIROH research necessitates collaboration and data sharing. Advancing the knowledge needed to support research to operations in hydrology depends on collaboration around model and data sharing. It requires open data supporting the integration of information from multiple sources; easy to use, generally accessible, shareable computing; and working together as a team and community. The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare platform established over the last 10 years enables best (FAIR, Findable, Accessible, Interoperable and Reusable) practices for data sharing and collaboration, and for improving reproducibility and reusability of research outcomes, through sharing and publishing both the data and models and analyses that underpin research findings. This workshop will overview best practices for collaboration and data sharing and the use of HydroShare and linked computing. It will focus on capability presently available, for immediate use, and outline advances forthcoming through upcoming CIROH supported research. It will provide an overview of HydroShare, examples of data resources in HydroShare and best practices, and JupyterHub Platforms linked to HydroShare for access to NWM data and computing.

ABSTRACT:

This resource includes the streamflow record from the Logan River USGS stream gage site number 10109000. This is in file usgs10109000.txt. It also included shapefiles giving the watershed boundary and sites comprising the stream gage location and snotel locations. Maximum snow water equivalent observed at snotel locations is a predictor of maximum streamflow. The jupyter notebook file included develops linear regression relationships between maximum snow water equivalent and maximum daily streamflow.

ABSTRACT:

This resource is an updated version of the following resource:
Salehabadi, H., D. Tarboton (2022). Hydrology scenarios that characterize plausible future drought conditions in the Colorado River Basin, HydroShare, https://doi.org/10.4211/hs.ca2e152c9fca4b2aa7c3294a388c522d

The previous dataset was updated using the most recent version (last updated on 12/15/2022) of the US Bureau of Reclamation Natural Flow database, covering the time period from 1906 to 2020. In addition to this update, the dataset now provides CRSS-ready-to-use input files including:
• Flow inputs at 29 CRSS sites
• MWD_ICS.SacWYType (Sacramento Water Year Type)
• TMD_East_Slope_Supply.St_Vrain_Annual_Flow (St Vrain Annual Flows)
• HydrologyParameters.SupplyScenario
• HydrologyParameters.TraceNumber
• MeadFloodControlData.hydrologyIncrement

This dataset holds streamflow sequences for three drought scenarios developed to characterize plausible future drought conditions in the Colorado River Basin. These sequences were produced using the methods described in Center for Colorado River Studies Future of the Colorado River Project white paper 4 entitled “The Future Hydrology of the Colorado River Basin” by Salehabadi, Tarboton et al. (2020) and paper Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061. The three defined drought scenarios are as follows: (1) Millennium Drought, (2) Mid 20th Century Drought, and (3) Paleo Tree Ring Drought. The first two droughts were defined using the US Bureau of Reclamation Natural flows from 2000-2020 and 1953-1977, respectively. The last was defined using the years 1576-1600 from the Meko et al., 2017 tree ring reconstruction of streamflow at Lees Ferry. 100 streamflow traces, each 50 years long were produced for each scenario by resampling years with replacement. Resampling from identified past drought scenarios, provides test droughts based on past flows that are more severe, due to the variety in the sampling, than any past droughts that have actually occurred. They are nevertheless plausible, since they are derived from past records. We used a nonparametric resampling approach referred to as “Water Year Block Disaggregation” to split the simulated annual flow at Lees Ferry into monthly flows at each of the 29 Colorado River Simulation System (CRSS) natural inflow sites. For the first two scenarios where there are historical natural flows at the 29 CRSS sites, this selects the entire water year block of monthly flows across sites for the corresponding drought year. For the paleo scenario, where there are no flows at the sites, the historical natural flow year with the annual flow at Lees Ferry closest to the paleo flow is selected and then flows across the sites and months adjusted by the ratio of paleo flow to closest historical flow.

ABSTRACT:

Collaboration is central to CIROH (https://ciroh.ua.edu/). Advancing the knowledge needed to support research to operations in hydrology depends on collaboration around model and data sharing. It requires open data supporting the integration of information from multiple sources; easy to use, generally accessible, shareable computing; and working together as a team and community. The CUAHSI HydroShare platform was developed to advance water research by enabling communities of researchers to more easily and freely share digital products resulting from their research, not just the scientific publications summarizing a study, but the data, models and workflows used to produce the results, consistent with Findable, Accessible, Interoperable, and Reusable (FAIR) principles of present-day research. HydroShare supports and enables private (e.g., social science) and open data sharing, transparent workflows, and computational reproducibility, thereby improving reliability and trust in research findings. These are crucial as research is transferred into operations.

The goal of this project is to enhance the performance, reliability, usability, and scalability of HydroShare’s linkages with cloud storage and computational systems to fulfill CIROH’s community collaboration and linked computing needs and enable CIROH researchers to easily integrate and analyze national scale datasets required for their research using high-performance and cloud computing systems.

The objectives are to (1) enhance community data access; (2) establish interoperability with scalable computing; (3) demonstrate computational reproducibility; and (4) establish and grow a CIROH Community on HydroShare. Work under objective (1) will use community input to identify, prioritize, and establish easy to use access to multiple high-value community datasets. Work under objective (2) will establish or extend interfaces to high performance computing, leveraging tools for model input preparation such as the CUAHSI Domain Subsetter and I-GUIDE (the Institute for Geospatial Understanding through an enhanced Discovery Environment, https://iguide.illinois.edu). Work under objective (3) will establish and document CIROH community best practices for enhancing the reproducibility of high-performance computing and analysis workflows so that CIROH modeling workflows can be accessed, re-executed, and analyzed by multiple researchers. Work under objective (4) will establish a CIROH “Community” within the HydroShare repository to support collaboration around and sharing of CIROH research products.

Forecasting operations will benefit from the transparency of research products hosted in HydroShare and linked to computing platforms for reproducibility and evaluation. Linking publications, data, and code (often in GitHub), with methods and findings that are well documented and tested will support their evaluation by the National Water Center for operational adoption.

This project runs 6/1/2023 to 5/31/2025.

ABSTRACT:

Precipitation and Temperature data over the Great Salt Lake (GSL) and its basin for modeling and analyses in support of declining lake levels.

ABSTRACT:

The objective of this HydroShare resource is to query AORC v1.0 Forcing data stored on HydroShare's Thredds server and create a subset of this dataset for a designated watershed and timeframe. The user is prompted to define their temporal and spatial frames of interest, which specifies the start and end dates for the data subset. Additionally, the user is prompted to define a spatial frame of interest, which could be a bounding box or a shapefile, to subset the data spatially.

Before the subsetting is performed, data is queried, and geospatial metadata is added to ensure that the data is correctly aligned with its corresponding location on the Earth's surface. To achieve this, two separate notebooks were created - [this notebook](https://github.com/CUAHSI/notebook-examples/blob/main/thredds/query-aorc-thredds.ipynb) and [this notebook] (https://github.com/CUAHSI/notebook-examples/blob/main/thredds/aorc-adding-spatial-metadata.ipynb) - which explain how to query the dataset and add geospatial metadata to AORC v1.0 data in detail, respectively. In this notebook, we call functions from the AORC.py script to perform these preprocessing steps, resulting in a cleaner notebook that focuses solely on the subsetting process.

ABSTRACT:

This collection of resources is was assembled to illustrate ways to use and work with data in HydroShare through its applications programming interface (API), the web application connector resource type, and web services for some content types. The resources collected here are intended to serve as tutorial examples for others to model in creating scripts and tools that act on HydroShare stored data.

ABSTRACT:

Poster for AGU Fall Meeting, December 11, 2023
https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1336263

HydroShare (http://www.hydroshare.org) is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) that enables users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. The HydroShare repository also links with connected computational systems, enabling users to reproducibly run models and analyses and share documented workflows. This presentation will overview the capabilities and best practices developed for collaboration and sharing of data and other research products along with the use of HydroShare and linked computing. It will focus on successes and challenges in engaging scholars, researchers, and practitioners as individuals and as communities, including lessons learned in sharing data across large scientific communities such as the Critical Zone Collaborative Network. It will also include collaboration functions being developed for the Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) and Cooperative Institute for Research to Operations in Hydrology (CIROH), where challenges associated with large scale input/output data preparation, staging, and sub setting along with execution of large-scale models and data are faced.

ABSTRACT:

This resource contains the R Scripts developed to characterize and assess annual streamflow ensembles using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree-based classification approach to categorize both ensembles and metrics. The results, also included here, provide a way to visualize and interpret differences between streamflow ensembles. The presented metrics and their classification provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non-stationarity, and contributing to better planning in river basins.

This resource contains the data and scripts used in
Salehabadi, H., Tarboton, D. G., Wheeler, K. G., Smith, R., & Baker, S. (2024). Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence-Based Analysis: Colorado River Case Study. Water resources research, 60, e2024WR037225. https://doi.org/10.1029/2024WR037225

ABSTRACT:

This resource aggregates data on the reservoirs and time series of reservoir storage within the Great Salt Lake Basin. It has been assembled by combining information from the United States Department of Agriculture (USDA) and the United States Bureau of Reclamation (USBR).

ABSTRACT:

This HydroShare resource provides Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA Analysis of Record for Calibration (AORC) Dataset. There are two Jupyter Notebooks
1. AORC_Point_Data_Retrieval.ipynb
2. AORC_Zone_Data_Retrieval.ipynb
The first retrieves data for one point. The second retrieves data for areas specified interactively or via an uploaded shapefile.
These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/noaa-nws-aorc/), and in the case of Zone data retrieval average the data over the zones specified.
The notebooks provided are coded to retrieve data from AORC version 1.1 released in ZARR format in December 2023.
From https://registry.opendata.aws/noaa-nws-aorc/ the Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas. It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the native AORC process generates netCDF output, the data was post-processed to create a cloud optimized Zarr formatted equivalent for dissemination using cloud technology and infrastructure.

ABSTRACT:

This HydroShare resource provides Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA National Water Model CONUS Retrospective Dataset. There are two Jupyter Notebooks
1. NWM_output_variable_retrieval_with_FID_identifier.ipynb
2. NWM_output_variable_retrieval_with_XY_identifier.ipynb
The first retrieves data for one point (feature ID). The second retrieves data for areas specified interactively or via an uploaded shapefile.
These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/nwm-archive/), and in the case of Zone data retrieval average the data over the zones specified.
The notebooks provided are coded to retrieve data from NWM retrospective analysis version 3.0 released in ZARR format in December 2023.
From https://registry.opendata.aws/nwm-archive/ The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations

ABSTRACT:

Material for CIROH Developers Conference Workshop, May 29, 2024.

CIROH research necessitates collaboration, data and model sharing, easy to use, generally accessible, shareable computing, and working together as a team and community. The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare platform enables best (FAIR, Findable, Accessible, Interoperable, and Reusable) practices for data sharing and collaboration and for improving reproducibility and reusability of research outcomes through sharing and publishing both the data and models and analyses that underpin research findings. This workshop provided information on using HydroShare for collaboration and data and model sharing in CIROH, including links between HydroShare and CIROH computing.

ABSTRACT:

The HydroData data catalog, associated python functions hf_hydrodata, and API are products of the HydroFrame project and are designed to provide easy access to a variety of other gridded model input datasets and point observations as well as national hydrologic simulations generated using the National ParFlow model (ParFlow-CONUS1 and ParFlow-CONUS2).

ABSTRACT:

This HydroShare collection contains multiple Jupyter notebook that enable user to retrieve data from different data sources.

ABSTRACT:

This HydroShare resource is developed to subset and retrieve the HydroFabric dataset (Johnson, J. M. (2022), https://lynker-spatial.s3-us-west-2.amazonaws.com/copyright.html) needed to execute the NOAA Next Generation (NextGen) Water Resource Modeling framework. The NextGen hydrofabric describes the representation, discretization, and topology of the hydrologic landscape and drainage network as a three-part data product that includes: (1) catchment and flowpath features, (2) their connectivity, and (3) the attribute sets needed to execute models. For more details about the HydroFabric data, please visit this website: https://noaa-owp.github.io/hydrofabric/

ABSTRACT:

This resource holds the MERRA Spatial Downscaling for Hydrology (MSDH) downscaling tool developed to provide sub-daily high spatial resolution surfaces of weather variables for distributed hydrologic modeling from NASA Modern Era Retrospective-Analysis for Research and Applications reanalysis products. The tool uses spatial interpolation and physically based relationships between the weather variables and elevation to provide inputs at the scale of a gridded hydrologic model, typically smaller (∼100 m) than the scale of weather reanalysis data (∼20–200 km).

Detailed information on and an evaluation of MSDH is given in Sen Gupta, A. and D. G. Tarboton, (2016), "A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications," Environmental Modelling & Software, 84: 50-69, http://dx.doi.org/10.1016/j.envsoft.2016.06.014.

ABSTRACT:

test

ABSTRACT:

The shrinking GSL is a problem that has received a lot of attention in Utah, and around the country. It has been selected as one of the I-GUIDE convergence catalyst problems to bring multidisciplinary convergence focus to it. This presentation review the problem, showing data on the shrinking GSL and associated climate and hydrology. It provides a historical context and describes what we know about how GSL as a terminal lake responds to drivers, specifically streamflow and climate. It examines water use and what we know about how human consumptive water use depletes streamflow into the lake, and the consequences. It ends with discussion of some solutions that are being pursued, describing where additional research could help and where I-GUIDE could be more involved.

Presentation for I-GUIDE Virtual All Hands Meeting, March 21, 2024
Tarboton, D., U. Lall, C. Flint, B. Holdaway, R. Morovati, I. Haqiqi, T. Hertel, B. Ghimire, A. Nassar, M. Merck and M. Abualqumboz, (2024), "Seeking Solutions to Restore the Great Salt Lake," I-GUIDE All Hands Meeting, Virtual, March 21, 2024, https://i-guide.io/i-guide-ahm/i-guide-virtual-all-hands-meeting-2024/, https://www.hydroshare.org/resource/1365b150f90440f8af94e938eacb9926/