ABSTRACT:

An example workflow for processing DEM's. The JupyterHub notebook included within this resource uses the TauDEM library for parallel terrain processing operations.

ABSTRACT:

This is a digital elevation model for the Logan River watershed that is being used to test ipynbs.

[Modified in JupyterHub on 2016-07-19 15:57:50.298217]
This a group of files that were derived from the Logan River watershed.

ABSTRACT:

A digital elevation model encompassing the Logan River watershed in northern Utah.

ABSTRACT:

iUTAH researchers have developed and deployed an ecohydrologic observatory to study water in ‘Gradients Along Mountain to Urban Transitions’ (GAMUT). The GAMUT Network measures aspects of climate, hydrology, and water quality along a mountain-to-urban gradient in three watersheds that share common water sources (winter-derived precipitation) but differ in the human and biophysical nature of land-use transitions. Designing GAMUT was a 12-month process involving faculty and technicians from across Utah’s research-intensive institutions: Brigham Young University, the University of Utah, and Utah State University.

This dataset contains raw data for all of the air temperature in degrees Celsius measured for the iUTAH GAMUT Network climate site near Beaver Divide.

ABSTRACT:

sample rhessys data

ABSTRACT:

This dataset contains time series of observations of water temperature in the Little Bear River, UT. Data were recorded every 30 minutes. The values were recorded using a HydroLab MS5 multi-parameter water quality sonde connected to a Campbell Scientific datalogger.

ABSTRACT:

This is my abstract

ABSTRACT:

This a download of VIC fluxesw data and vizualization processing results from the Daily_VIC_1915_2011 (Livneh et al. 2013); Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.

ABSTRACT:

This is a presentation that Tony Castronova gave at the 2017 Mason Water Forum at George Mason University. It outlines the data, training, and education/outreach services that CUAHSI provides to the water science community.

ABSTRACT:

This is a step-by-step demonstration of how to view and download forecasts from any stream in the National Hydrography Dataset with the National Water Model App.

ABSTRACT:

Jupyterhub demos that were presented to CUAHSI Summer Institute participants at the National Water Center in Tuscaloosa Alabama on 05/22/2017. This resource consists of the following material:

1. Powerpoint presentation outlining JupyterHub
2. NWM-preview.ipynb -- Uses iRODs to inspect and plot NWM data
3. HAND.ipynb -- Uses TauDEM to calculate height above nearest drainage for the Onion Creek watershed in Texas
4. Oniondata.tar -- Supplementary data for the Onion Creek watershed, used in the HAND.ipynb.
5. HAND-HydroTerre.ipynb -- Uses HydroTerre and TauDEM to extend HAND.ipynb for a different region of the U.S.

ABSTRACT:

The EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas on which rain falls and runoff is generated. The routing portion of SWMM transports this runoff through a conveyance system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. SWMM was first developed back in 1971 and has undergone several major upgrades since then. The current edition, Version 5, is a complete re-write of the previous release. Running under Windows, EPA SWMM 5 provides an integrated environment for editing drainage area input data, running hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color-coded drainage area maps, time series graphs and tables, profile plots, and statistical frequency analyses.

ABSTRACT:

Jupyter notebooks are becoming popular among the geoscience community for there ability to clearly present, disseminate, and describe scientific findings in a transparent and reproducible manner. This also makes them a desirable mechanism for sharing and collaborating scientific data and workflows with colleagues during the research process, especially when addressing large-scale cross-disciplinary geoscience issues. This work extends Jupyter notebooks to operate in a pre-configured cloud environment that is integrated with HydroShare for its data sharing and collaboration functionality, and notebooks are executed on the Resourcing Open Geospatial Education and Research (ROGER) supercomputer hosted in the CyberGIS center. This design enables researchers to address problems that are often larger in scale than can be done on a typical desktop computer. Additionally, the integration of these technologies enables researchers to collaborate on notebook workflows that execute in the cloud and are shared through the HydroShare platform. The goals of this work are to establish an open source platform for domain scientists to (1) conduct data intensive and computationally intensive collaborative research, and (2) organize data driven educational material via classroom modules, workshops, or training courses. This presentation will discuss recent efforts towards achieving these goals, and describe the architectural design of the notebook server in an effort to support collaborative and reproducible science.

ABSTRACT:

Data archival and dissemination is a challenging task for scientists and engineers, and if not done properly, hinders discovery of data applicable to an ongoing project. The next generation of scientists must be well versed in numerous methods of discovering, collecting, and processing water-related data, which is further complicated by the variety in data repositories, le formats, and encoding standards. CUAHSI (the Consortium of Universities for the Advancement of Hydrologic Science Inc.; https://www.cuahsi.org/) offers free data services and tools to address these issues. This workshop presents recent software development efforts to support (1) advanced data searching via the HIS Catalog API and (2) cloud-based hydrologic data analysis. The structure of this workshop will be a mixture of short presentations and interactive tutorials aimed to provide an introduction to research-oriented data discovery and analysis. Participants are encouraged to work through examples to better understand how they can leverage CUAHSI resources to supplement their research activities. We welcome participants that wish to learn how our tools and services can be leveraged in a research capacity.

ABSTRACT:

This resource was developed as a HydroShare workshop demonstration for the CUAHSI Hydroinformatics conference, July 25-27, 2017, Tuscaloosa, AL.

When you open this resource with the CUAHSI JupyterHub server (upper right, click on Open With, Select JupyterHub NCSA), you will launch a Welcome Notebook that will connect you to the CyberGIS virtual machine on the ROGER super computer at the University of Illinois, Urbana-Champagne. When you execute (Run Step 1 and Step 2 only) in the Jupyter Notebook cells on the Welcome Notebook, you will download related data and two Notebooks designed to explore hydrologic research problem solving using data and model integration in HydroShare . Skip Step 3 "Welcome" tutorial steps unless you want to explore how to do work and Save back to HydroShare.

Click on the hyperlink to ThunderCreek_DataIntegration_Beginner.ipynb. The beginner notebook is an Introduction for new HydroShare users who may have limited experience with Python code and Jupyter Notebooks. The advanced notebook explores how to combine watershed data with hydrology models (e.g. DHSVM) and the Landlab modeling framework (e.g. landsliding).

The problem: Researchers need a modeling workflow that is flexible for developing their own code, with easy access to distributed datasets, shared on a common platform for coupling multiple models, usable by science colleagues, with easy publication of data, code, and scientific studies.

The emerging solution: Collaborate with the CUAHSI HydroShare community to use and contribute to water data software and hardware tools, so that you can focus on your science, be efficient with your time and resources, and build on existing research in multiple domains of water science.

Beginner Notebook (time savings ~ 9 months)
Download water data from CUAHSI HIS
Develop your own utilites (e.g. download hydrometeorology)
Save your results on HydroShare for your colleagues

Advanced Notebook (time savings ~2.5 years)
Run a preconfigured hydrology model installed on the CUAHSI JupyterHub server
Run a published Landlab landslide model
Publish your results and get a DOI

This is a Watershed Dynamics Model developed by the Watershed Dynamics Research Group in the Civil and Environmental Engineering Department at the University of Washington for the Thunder Creek basin in the Skagit Watershed, WA, USA in collaboration with CUAHSI.

The resource was originally derived from a reproducible demonstration of the landslide modeling results from: Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2017-39, in review, 2017.

How are you using HydroShare?
https://docs.google.com/forms/d/e/1FAIpQLSeD4K9faWoHjy_ZwZhz3zHWxYH2vIhBFsvz5uhVbMvsXNuoeA/viewform

ABSTRACT:

This resource contains the data for a workshop held on August 1st in Boulder Colorado

ABSTRACT:

This dataset contains time series of observations of water temperature in the Little Bear River, UT. Data were recorded every 30 minutes. The values were recorded using a HydroLab MS5 multi-parameter water quality sonde connected to a Campbell Scientific datalogger.

ABSTRACT:

This dataset contains time series of observations of water temperature in the Little Bear River, UT. Data were recorded every 30 minutes. The values were recorded using a HydroLab MS5 multi-parameter water quality sonde connected to a Campbell Scientific datalogger.

ABSTRACT:

The development and adoption of technologies by the water science community to improve our ability to openly collaborate and share workflows will have a transformative impact on how we address the challenges associated with collaborative and reproducible scientific research. Jupyter notebooks offer one solution by providing an open-source platform for creating metadata-rich toolchains for modeling and data analysis applications. Adoption of this technology within the water sciences, coupled with publicly available datasets from agencies such as USGS, NASA, and EPA enables researchers to easily prototype and execute data intensive toolchains. Moreover, implementing this software stack in a cloud-based environment extends its native functionality to provide researchers a mechanism to build and execute toolchains that are too large or computationally demanding for typical desktop computers. Additionally, this cloud-based solution enables scientists to disseminate data processing routines alongside journal publications in an effort to support reproducibility. For example, these data collection and analysis toolchains can be shared, archived, and published using the HydroShare platform or downloaded and executed locally to reproduce scientific analysis. This work presents the design and implementation of a cloud-based Jupyter environment and its application for collecting, aggregating, and munging various datasets in a transparent, sharable, and self-documented manner. The goals of this work are to establish a free and open source platform for domain scientists to (1) conduct data intensive and computationally intensive collaborative research, (2) utilize high performance libraries, models, and routines within a pre-configured cloud environment, and (3) enable dissemination of research products. This presentation will discuss recent efforts towards achieving these goals, and describe the architectural design of the notebook server in an effort to support collaborative and reproducible science

This was presented as an EPoster at the 2017 American Geophysical Union and can be found at:
https://agu2017fallmeeting-agu.ipostersessions.com/default.aspx?s=2B-C4-70-3C-B8-A0-0D-77-35-04-7C-F2-A4-1B-36-10

ABSTRACT:

This is a collection of resources that demonstrate the CUAHSI JupyterHub platform. Each of these can be launched using the JupyterHub WebApp and executed in the cloud.

ABSTRACT:

This is the input data for the Celia 1990 SUMMA testcase.

ABSTRACT:

Scientists are faced with many challenges throughout the research lifecycle ranging from data collection and management to collaboration and reproducible science. These challenges are exacerbated for large studies by increased scope and complexity that results from the interdisciplinary nature of water science. The next generation of water scientists must be comfortable using a variety of software, tools, and platforms on a daily basis to efficiently and effectively conduct their research. The Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI) aims to assist scientists in these efforts by investing in community-driven cyberinfrastructure research projects. This cyberseminar series presents efforts made by CUAHSI to alleviate the burden common of data-related tasks and is separated into three distinct seminars that collectively discuss the challenges associated with data management, collaboration, and reproducible science. Each seminar will focus on a specific scientific use cases and will demonstrate how free and open source software can be used to overcome data-related research challenges. Participants will learn about new technologies that can assist both academic and educational water science settings.

Dates, Speakers, and Topics:

April 13: Data Archiving and Dissemination Tools to Support Water Science Research | Liza Brazil, CUAHSI
April 20: Cloud-hosting Water Science Data for Collaborative Research | Mark Henderson, CUAHSI
April 27: Cyberinfrastructure to Support Water Science Education and Reproducible Science | Anthony Castronova, CUAHSI

ABSTRACT:

Fluency in software tools, libraries, and languages is becoming an essential skill of scientists that can directly influence the effectiveness and efficiency of their work. While graduate students often learn these skills while conducting research, these are difficult to teach undergraduate students. This seminar will discuss CUAHSI’s investment in cyberinfrastructure to support water science research, training, and education. Topics will range from designing educational modules and hosting workshops to hydrologic modeling and data analysis. Participants can expect a primer on JupyterHub and the cyberinfrastructure that has been designed to support these workflows, as well as detailed demonstrations of common educational and research use cases. Participants are expected to have a basic understanding of HydroShare.org and the Python programming language, and are encouraged to participate in the live demonstrations.

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 is a Jupyter notebook that demonstrates how to preview Hurricane Harvey Streamflow data that is stored in an Observations Data Model v.2 SQLite file. It's meant to be executed on the https://jupyter.cuahsi.org notebook server and relies on several pre-installed packages, e.g. utilities.timeseries and utilities.hydroshare. This notebook will need to be modified for it to be executed elsewhere.

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:

This is a HUC boundary that Hurricane Harvey hit.

ABSTRACT:

This notebook provides a very brief introduction to the process of subsetting NWM forecast results for small watershed areas using Thredds and OpenDAP technologies. It was originally designed to be executed on the HydroShare-JupyterHub environment, but can also be executed offline. This notebook was presented at the 2018 Summer Innovators program to stimulate a dialog about how forecast subsets can be used for hydrological analysis, methods for standardizing this basic approach so that it easily be applied to other watersheds, and executing similar code as a batch job for the entire CONUS. For more information about the Hurricane Harvey dataset see: https://www.hydroshare.org/group/41

ABSTRACT:

Continued investment and development of cyberinfrastructure (CI) for water science research is transforming the way future scientists approach large collaborative studies. Among the many challenges, that we as a community need to address, are integrating existing CI to support reproducible science, enabling open collaboration across traditional domain and institutional boundaries, and extending the lifecycle of data beyond the scope of a single project. One emerging solution for addressing these challenges is HydroShare JupyterHub which is an open-source, cloud-based, platform that combines the data archival and discovery features of HydroShare with the expressive, metadata-rich, and self-descriptive nature of Jupyter notebooks. This approach offers researchers a mechanism for designing, executing, and disseminating toolchains with supporting data and documentation. The goals of this work are to establish a free and open source platform for domain scientists to (1) conduct data intensive and computationally intensive collaborative research, (2) utilize high performance libraries, models, and routines within a pre-configured cloud environment, and (3) enable dissemination of research products. This presentation will discuss our approach for hydrologic model simulation, sensitivity analysis, and optimization applications in this platform by establishing a generic CI pattern that can be adopted to support research, classroom, and workshop activities

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:

There are many MATLAB users in the Hydrology space. These scientists work as researchers and educators in academia and in agencies and institutes. Many of these institutions partner with CUAHSI and use their resources to share data and research. For data analysis and visualization, HydroShare provides integrations with Jupyter notebooks and other tools, via an ‘open with’ affordance.

MATLAB Online provides access to MATLAB from any standard web browser wherever you have internet access. It is ideal for teaching, learning and convenient, lightweight access. With MATLAB Online, you can share your scripts, live scripts, and other MATLAB files with others directly. Additionally, you can publish your scripts and live scripts to the web as PDFs or HTML and share the URL with anyone.

This web application enables the interactive exploration of MATLAB artifacts (such as Live Scripts) through a similar ‘open with’ affordance. When working with Live Scripts, users are presented with the option to open these artifacts in the Live Editor environment.

ABSTRACT:

Data management, sharing, and publication are integral parts of a robust data management plan, a core requirement of all NSF funded research grants and many other funding agencies. This seminar will discuss some common challenges and present solutions for managing and sharing data using CUAHSI tools, specifically utilizing HydroShare. HydroShare is an online repository system for water data and models that aims to advance hydrologic science through enabling users to manage, share, and publish products resulting from their research and data collection. We will introduce attendees to approaches for managing current and archived data, collaboration within a research group, documentation of metadata, and publication. The webinar will center around tools and techniques within HydroShare to facilitate these activities, employing both discussions and demos.

ABSTRACT:

This daily average air temperature for the Little Bear River gauging station near Mendon, UT.

ABSTRACT:

The water science community continually develops and adopts technologies to improve our ability to openly collaborate and share workflows. Ultimately, this will have a transformative impact on how we address the challenges associated with collaborative and reproducible scientific research. One solution to these problems is to utilizing Jupyter notebooks, an open-source platform for creating metadata-rich toolchains for modeling and data analysis applications. Combining this technology with publicly available datasets from agencies such as USGS, NASA, and EPA enables researchers to easily prototype and execute data-intensive toolchains. CUAHSI has invested in this technology by establishing a free and open source web platform for scientists to (1) conduct data intensive and computationally intensive collaborative research, (2) utilize high performance libraries, models, and routines within a pre-configured cloud environment, and (3) enable dissemination of research products. This seminar will discuss CUAHSI’s investment in JupyterHub for supporting water science research, training, and education. Participants can expect a primer on JupyterHub and the cyberinfrastructure that has been designed to support these workflows, as well as detailed demonstrations of common educational and research use cases. A basic understanding of HydroShare.org and the Python programming language are helpful, but not required for participation in the live demonstrations.

ABSTRACT:

This daily average air temperature for the Little Bear River gauging station near Mendon, UT.

ABSTRACT:

This resource contains a shapefile of HUC-8 (eight digit Hydrologic Unit Codes) for the Continental United States (CONUS).

The Watershed Boundary Dataset (WBD) is a comprehensive aggregated collection of hydrologic unit data consistent with the national criteria for delineation and resolution. It defines the areal extent of surface water drainage to a point except in coastal or lake front areas where there could be multiple outlets as stated by the "Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD)" “Standard” (http://pubs.usgs.gov/tm/11/a3/). Watershed boundaries are determined solely upon science-based hydrologic principles, not favoring any administrative boundaries or special projects, nor particular program or agency. This dataset represents the hydrologic unit boundaries to the 12-digit (6th level) for the entire United States. Some areas may also include additional subdivisions representing the 14- and 16-digit hydrologic unit (HU). At a minimum, the HUs are delineated at 1:24,000-scale in the conterminous United States, 1:25,000-scale in Hawaii, Pacific basin and the Caribbean, and 1:63,360-scale in Alaska, meeting the National Map Accuracy Standards (NMAS). Higher resolution boundaries are being developed where partners and data exist and will be incorporated back into the WBD. WBD data are delivered as a dataset of polygons and corresponding lines that define the boundary of the polygon. WBD polygon attributes include hydrologic unit codes (HUC), size (in the form of acres and square kilometers), name, downstream hydrologic unit code, type of watershed, non-contributing areas, and flow modifications. The HUC describes where the unit is in the country and the level of the unit. WBD line attributes contain the highest level of hydrologic unit for each boundary, line source information and flow modifications.

ABSTRACT:

HydroShare data sharing instructions for Waterhackweek presenters.

ABSTRACT:

These are a set of Jupyter notebooks that have been prepared for the 2019 Waterhackweek event in Seattle WA. These notebooks have been designed to demonstrate the connection between HydroShare and the CUAHSI-JupyterHub web application. To being working with these data, either (1) download the notebooks and execute them locally or (2) use the "Open With" button and select "CUAHSI JupyterHub" to execute them in the cloud.

ABSTRACT:

This resource contains sample data for participants at the 2019 Waterhackweek event.

ABSTRACT:

This is a notebook designed to be executed in the CUAHSI JupyterHub web app to demonstrate how to work with odm2.sqlite data.

ABSTRACT:

The purpose of this resource is to demonstrate how the CUAHSI JupyterHub platform can be used to perform basic hydrologic data analysis. Temperature data is collected via the CUAHSI Hydrologic Information System (HIS) using web services. These data are organized using Python classes, and plotted in various ways to demonstrate common data analysis steps.

ABSTRACT:

The purpose of this resource is to provide a workflow of how to use the CUAHSI Domain Subsetter - NWM edition. The subsetter application (subset.cuahsi.org) introduces a collaborative effort for preparing, publishing, and sharing subsets of the National Water Model input data at watershed scales. Our hope is that these efforts will lower the barrier of entry for using and applying these models and engage a wide variety scientists from a diverse spectrum of expertise. With a combination of modern cyberinfrastructure techniques and state-of-the-science modeling tools, researchers will have access to subsets of National Water Model information that would otherwise require extensive computational resources. This work provides the foundation onto which similar efforts can be applied to other large-scale model simulations and input data.

ABSTRACT:

this resource is made for testing purposes.

ABSTRACT:

This Live Script demonstrates an approach for transforming point-based precipitation observations onto a rectilinear grid that can be used for hydrological modeling. The goal of this script is to introduce the general process of working with observation data, stimulate discussion around interpolation methods, and introduce the HydroShare data repository. It was presented at the 2019 CUAHSI HydroInformatics Conference in Provo, UT.

ABSTRACT:

Advancements in cyberinfrastructure (CI) to support cloud-based tools and services for the water science community have changed how researchers conduct, share, and publish scientific workflows. These have had a transformative impact on how our community addresses the challenges associated with interdisciplinary collaboration, reproducing scientific findings, and developing real-world educational modules. The Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) facilitates discussion around these topics, with the water science community, to better identify the shortcomings of current CI approaches and define the requirements for the next generation of cloud services. The purpose of this workshop is to introduce and solicit feedback on the current suite of CUAHSI community to computational tools to that have been designed to improve the way water science research and education is conducted in the cloud. This workshop will consist of several technologies that are actively being developed for working with data Earth surface data. Our goal is to demonstrate how these compute environments can be used in educational applications, workshops, reproducing published work, and conducting research. Participants will be presented with several approaches for working with their data within the CUAHSI ecosystem of tools. The workshop will focus heavily on interactive examples and will feature several programming languages including Python, R, and MATLAB. Participants are not required to be proficient in these languages but should bring a laptop computer, be ready to work through live examples, and willing to provide constructive feedback.

ABSTRACT:

Presentation given at the 2019 CUAHSI Hydroinformatics Conference in Provo, UT.

ABSTRACT:

The CUAHSI Hydrologic Information System (HIS) is an internet-based system for sharing hydrologic data. It is comprised of databases and servers, connected through web services, to client applications, allowing for the publication, discovery and access of data.

ABSTRACT:

Managing and sharing scientific code is an invaluable skill researchers today should possess. However, existing version control systems which facilitate are sometimes hard to master. In this training, you will learn how to use Git & Github to:

- put your code under version control and publish it online
- track changes in your code, and retrieve old versions
- collaborate with others on the same project

ABSTRACT:

Studies of water and environmental systems are becoming increasingly complex and require the integration of knowledge across multiple domains. At the same time, technological advances have enabled the collection of massive quantities of data for studying earth system changes. Fully leveraging these datasets and software tools requires fundamentally new approaches in the way researchers store, access and process data. Waterhackweek, supported by the National Science Foundation Cybertraining program, serves the national interest by motivating a culture shift within the hydrologic and more broadly earth science communities toward open and reproducible software practices that will enhance interdisciplinary collaboration and increase capacity for addressing complex science challenges around the availability, risks and use of water. This cyberseminar series consists of presentations from the Cybertraining investigators and research software developers, each focusing on a specific water-related use cases, tool, or library. Topics will consist of both introductory and advanced concepts that are relevant to a wide range of water and informatics use-cases, e.g. publishing large datasets, running numerical models, organizing collaborative research projects, and meeting journal requirements by following open data standards. The goal of the 2019 series is to prepare the incoming Waterhackweek (March 25-29, 2019) participants for the in-person capstone event in which their skills and creativity will be used to address natural hazards, however, these topics and technologies are also relevant to the broader water science community. We welcome all undergraduate, graduate, and early career scientists to join us in this public cyberseminar series.

ABSTRACT:

Climate change, forecasting, satellite datasets, large model ensembles ... Large gridded datasets are everywhere in hydrology and earth science. While accessing and analyzing these datasets required some serious programming skills not so long ago, a number of toolkits are now available that let you easily access, ingest, analyze and display gridded climate datasets. In this webinar we’ll discuss one of the most common file formats used in our field for large data sets, the Network Common Data Format (NetCDF), and step through a Jupyter notebook to showcase python packages, such as xarray and cartopy, that can be used to examine them. No prior experience required, although we will build on some of the skills you have acquired in earlier webinars in the series.

ABSTRACT:

Geospatial data, especially those in hydrology, are uniquely suited to compelling and practical visualization. Maps, in particular, are not only tools for developing an initial understanding of a new set of data but are also used widely to disseminate analytical results in a native manner. This seminar will develop both a high-level understanding of the practice of visualizing geospatial data and practical skills in Python for easily creating geospatial visualizations. In particular, we will discuss the importance of (and historical precedent for) creating a visual narrative for the dissemination of information, concerns regarding cartographic projections, a brief overview of common geospatial data types, and provide live demonstrations of common open-source geospatial data visualization packages in Python using hydrologic datasets.

ABSTRACT:

Tethys is a python-based web platform that can be used to develop and host environmental applications. Its free and open source and includes commonly used web tools including PostGIS, GeoServer, mapping gizmos with open layers and google maps and HTCondor for distributed computing. Tethys Platform is powered by the Django Python web framework giving it a solid web foundation with excellent security and performance. Tethys aims at lowering the technical barrier required for scientists and engineers to develop environmental web applications and share their work with the world. In this cyberseminar, we will go through a brief introduction to Tethys and walk through the Beginner Concepts section of the Tethys Tutorial. We will discuss Tethys in detail during the WaterHackWeek when we will employ some of the more advanced concepts to develop our projects. Prior experience in Python and Django will be of help.

Docs: http://docs.tethysplatform.org/en/stable/tutorials/getting_started.html

ABSTRACT:

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

ABSTRACT:

Output of a WRF-Hydro (configured as the NWM) simulation for the Clear Creek IA CZO. The domain was obtained using the CUAHSI Subsetting service (subset.cuahsi.org).

ABSTRACT:

A binderhub configuration for running WRF-Hydro configured as the National Water Model v1.2.2

ABSTRACT:

This is an example resource that launches an R Jupyter environment in the CUAHSI compute cloud using Binder. For more information on how this resource was configured, see https://mybinder.readthedocs.io/en/latest/config_files.html

ABSTRACT:

Launch a Hydroshare Resource into MyBinder.org

ABSTRACT:

This collection contains a variety of HydroShare resources that have been specifically designed to run in Binder environments. Each resource contains a README that explains the software that is built using Binder.

ABSTRACT:

This resource describes how to get started using Binder in HydroShare.

ABSTRACT:

This is basic example of a binder-compliant HydroShare resource.

ABSTRACT:

Studies of water and environmental systems are becoming increasingly complex and require integration of knowledge across multiple domains. At the same time, technological advances have enabled the collection of massive quantities of data for studying earth system changes. Fully leveraging these datasets and software tools requires fundamentally new approaches in the way researchers store, access and process data. The project serves the national interest by motivating a culture shift within the hydrologic and more broadly earth science communities toward open and reproducible software practices that will enhance interdisciplinary collaboration and increase capacity for addressing complex science challenges around the availability, risks and use of water. Project's CyberTraining approach provides virtual learning experiences throughout an academic year, with online learning modules oriented around a one-week in-person workshop (WaterHackWeek) that will focus on hands-on real-world research projects. These research projects are designed to serve the national interest by preparing for natural hazards such as floods, hurricanes and climate change, and to advance the nation's health by making tools and data accessible to health researchers, local governments, and citizens.

New cyberinfrastructure that emphasizes data sharing and open, reproducible software practices is currently in development, but requires a mode of knowledge transfer, or CyberTraining, that extends beyond currently available university curriculum. Project's aim is to ensure successful use of community cyberinfrastructure to 1) publish large datasets, 2) run numerical models, 3) organize collaborative research projects, and 4) meet journal requirements to follow open data standards. The activities take advantage of HydroShare, a National Science Foundation funded cyberinfrastructure platform, operated by the Consortium of Universities Allied for Hydrologic Sciences (CUAHSI), for sharing hydrologic data and models. The short-term goals are to develop new CyberTraining modules; the long-term goals are to have an annually recurring WaterHackWeek, to distribute curriculum CUAHSI to more than 130 member universities, and advance cyberinfrastructure education for the broader geoscience community. The use of the hackweek educational model extends the use of cyberinfrastructure to promote the progress of science by including a specific emphasis on graduate student training as instructors, training coordinators, and building research networks with data providers who are stakeholders outside of academia. For example, case studies include data and resource management by Native American tribal governments, Hurricane Maria data archive for research in Puerto Rico, improving flood forecasting, and tool-building using complex numerical models such as the National Water Model. This project allows to test the educational model in the water research community, in addition to connecting team's research and curriculum to annually recurring hackweeks in neuro, astro, ocean, and geo sciences. The team of researchers is actively engaged in experimenting with this new model, and in testing its efficacy through robust evaluation metrics. The proposed activities encourage collaboration and support for use of cyberinfrastructure at all stages of the educational pipeline and provides participants with opportunities for networking, career development, community building and design of open-source software tools.

ABSTRACT:

The binder recipe for running Parflow in JupyterHub. This is designed to work with the subset.cuahsi.org web application.

To launch with a domain from subset.cuahsi.org, invoke:

https://mybinder.org/v2/hydroshare/8f7c2f0341ef4180b0dbe97f59130756/?urlpath=urlparams?unpack=http://subset.cuahsi.org/data/[SUBSET_GUID].tar.gz

Docker Info:
- CentOS 7
- TCL
- Python 3
- Parflow
- hs_restclient
- hstools
- hydrofetch

ABSTRACT:

this resource is testing dockerfiles for binder.

ABSTRACT:

adf

ABSTRACT:

aaa

ABSTRACT:

This is an example for making a HydroShare resource "Binder Capable" by extending the HydroShare Ubuntu image. There are several advantages to using this base image:

1. Binder configurations can use Dockerfiles in addition to all other configuration files, e.g. apt.txt, requirements.txt, postbuild, etc.
2. The image is preinstalled with JupyterHub, Python3, and tools for accessing HydroShare data (e.g. iRODs, hs_restclient, nbfetch, and hstools) to facilitate interaction with the CUAHSI HydroShare.

ABSTRACT:

this is a resource containing a binder build for the Olin SCOPE Sync project

ABSTRACT:

A Flask application for viewing COVID-19 data.

ABSTRACT:

Testing Flask in Binder

ABSTRACT:

The CUAHSI JupyterHub is a web application that allows HydroShare users to execute scientific code in the cloud. It’s hosted on the Google Cloud Platform and is maintained by the CUAHSI Compute staff. While this application supports the execution of a variety of codes, it’s primarily designed to write, build, and run Jupyter Notebooks. A Jupyter notebook is thus an enhanced computational environment that combines rich text and code execution into a single script-like container. The CUAHSI JupyterHub combines this functionality with the HydroShare data repository to provide a rich computational environment for water scientists.

The CUAHSI Jupyterhub is a scalable computational environment hosting on the Google Cloud Platform that has been carefully designed to provide maximum interoperability with the HydroShare data repository. The CUAHSI JupyterHub is a web application that allows HydroShare users to run code and models in the CUAHSI Compute Cloud. This application offers free, limited, computational resources primarily aimed at education and reproducible science.

ABSTRACT:

The purpose of this resource is to demonstrate how the CUAHSI JupyterHub platform can be used to perform basic hydrologic data analysis. Temperature data was collected from the NOAA Global Historical Climatology network for two sites in the greater Seattle area. These data are organized using Python classes, and plotted in various ways to demonstrate common data analysis steps.

For more information about the GHCN data included in this resource, see; https://docs.opendata.aws/noaa-ghcn-pds/readme.html

ABSTRACT:

These are the static domain datasets for the Saluda River watershed at Columbia, SC for the Parflow, derived from the PF-CONUS v1.0 dataset. The static domain data were extracted using the CUAHSI Subsetter (http://subset.cuahsi.org). Additional metadata regarding these data are listed below:

Model: Parflow CONUS
Version: 1.0
Date processed: 2020-11-17 19:24:05.517056+00:00
Included HUCs: 030501091403, 030501091401, 030501091402, 030501090603, 030501090701, 030501090702, 030501090703, 030501090704, 030501090803, 030501090804, 030501090806, 030501090807, 030501090808, 030501090903, 030501090904, 030501090906, 030501090907, 030501090908, 030501091001, 030501091002, 030501091003, 030501091101, 030501091102, 030501091103, 030501091104, 030501091105, 030501091201, 030501091202, 030501091203, 030501091205, 030501091206, 030501091207, 030501091301, 030501091302, 030501091303, 030501091304, 030501091305, 030501091306, 030501091307, 030501091308, 030501091309, 030501091310, 030501091311, 030501090101, 030501090102, 030501090202, 030501090203, 030501090201, 030501090204, 030501090301, 030501090302, 030501090303, 030501090304, 030501090305, 030501090306, 030501090307, 030501090401, 030501090402, 030501090403, 030501090404, 030501090501, 030501090502, 030501090601, 030501090801, 030501090901, 030501090503, 030501090602, 030501090604, 030501090802, 030501090805, 030501090902, 030501090905, 030501091204

ABSTRACT:

This is a shapefile of watershed boundaries for CONUS using 12-digit Hydrologic Unit Codes. Data are projected into the WRF-Hydro Lambert Conformal Conic (variant) spatial reference system. This is defined as "+proj=lcc +lat_1=30 +lat_2=60 +lat_0=40.0000076293945 +lon_0=-97 +x_0=0 +y_0=0 +a=6370000 +b=6370000 +units=m +no_defs"

ABSTRACT:

NWM 2.0 domain for:

- Upper-left corner (44.764626, -97.19455)
- Lower-right corner (40.008854, -89.30267)

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:

National Water Model v2.0 domain data files for the HUC 0101 Subregion -- St. John. Domain data was extracted using the CUAHSI subsetter: subset.cuashi.org.

Accounting Unit 010100 -- St. John. Maine., Area = 7330 sq.mi.
Cataloging Units 01010001 -- Upper St. John. Maine., Area = 2120 sq.mi.
01010002 -- Allagash. Maine., Area = 1250 sq.mi.
01010003 -- Fish. Maine., Area = 908 sq.mi.
01010004 -- Aroostook. Maine., Area = 2420 sq.mi.
01010005 -- Meduxnekeag. Maine., Area = 634 sq.mi.

For more information, see: https://water.usgs.gov/GIS/huc_name.html

ABSTRACT:

A Jupyter notebook for exploring LZO data for the RAPID Maria Workshop.

ABSTRACT:

This resource contains data for a NWM simulation that was used in a 2021 AWRA MAC workshop.
This is a dataset that we are using to demonstrate HydroShare functionality. We can collaborate on the metadata, add authors, and specify keywords.
This is the line Sunny created.

ABSTRACT:

launch resource or resource file in custom BinderHub instance running on GCP.

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the original (HydroShare) approach at Coweeta Subbasin18, NC. This resource is derived from "4b7d16df30794a829466d1f032262cef" and modified to leverage repo2docker and BinderHub to support re-usablilty and reproducibility.

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

ABSTRACT:

this resource is for testing the CUAHSI BinderHub deployment

ABSTRACT:

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This is an auto-generated abstract
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This resource contains static domain data extracted using the CUAHSI Subsetter (http://subset.cuahsi.org). Additional metadata regarding these data are listed below:

Model: Parflow CONUS
Version: 1.0
Date processed: 2021-12-28 15:17:43.761795+00:00
Included HUCs: 051202011203, 051202011202, 051202011205

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 contains example data used in a 2022 guest lecture at Virginia Tech. The purpose of these data are to demonstrate the value added capabilities of HydroShare and associated web applications, specifically automatic metadata extraction and code execution.

ABSTRACT:

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

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

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

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

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

ABSTRACT:

A Jupyter Notebook to demonstrate the steps of extracting inundated area

ABSTRACT:

This resource contains a Jupyter notebook that demonstrates how someone can query the I-GUIDE data catalog, retrieve data, and execute a code workflow.

ABSTRACT:

This is a sample of AORC Forcing Data (v1.1) for evaluating subsetting via THREDDS

ABSTRACT:

This resource contains data used in the 2022 CyberWater workshop that featured the WRF-Hydro model

ABSTRACT:

This resource contains a small subset of AORC v1.1 data that is being used to test programatic access (and subsetting) via Python and Thredds. The AORC data was uploaded as a zip archive and extracted inside HydroShare. When this operation initiated, HydroShare automatically created "multidimensional" content aggregations for each NetCDF file within the archive. Note that this only works if the files contain a *.nc extension, which was added prior to uploading to HydroShare. The aorc-hs-thredds.ipynb notebook demonstrates how these data can be queried directly from the HydroShare Thredds server.

ABSTRACT:

AORC Forcing Data from AWS subsetted to the Great Salt Lake basin. This is data for one day: 01/01/2016

ABSTRACT:

An example for accessing NWM retrospective streamflow predictions using Python and Zarr.

- 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.
One application of this dataset is to provide historical context to current near real-time streamflow, soil moisture and snowpack conditions. The retrospective data can be used to infer flow frequencies and perform temporal analyses with hourly streamflow output and 3-hourly land surface output. This dataset can also be used in the development of end user applications which require a long baseline of data for system training or verification purposes.

ABSTRACT:

These are the HUC 12 watershed boundaries for the Great Basin.

ABSTRACT:

A NetCDF file containing geospatial metadata that can be added to the NWM v2.0

ABSTRACT:

This is the shapefile of the Logan River head watershed in Utah. The associated HUC12 ID is 160102030302.

ABSTRACT:

test geopackage

ABSTRACT:

Vector boundaries for the NGEN HydroFabric v 1.*

ABSTRACT:

This resource was first created for a live demo during an online I-GUIDE VCO meeting on May 9, 2023. It was then modified for another live demo during the 1st annual CIROH users and developers conference in Salt Lake City, May 16-18. Recently, it was used for the National Water Center Summer Institute 2023.

It contains codes and inputs for a precipitation analysis across the Logan River Watershed. In this analysis, we will obtain modeled precipitation from two products: AORC and PRISM, compare the basin's average daily precipitation, and save results back to HydroShare.

ABSTRACT:

NGEN HydroFabric VPU Boundaries - Pre-release

ABSTRACT:

A collection of NGEN HydroFabric VPU Boundaries (Pre-release version).

ABSTRACT:

HUC 01 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 02 NGEN HydroFabric VPU Boundaries - Pre-release

ABSTRACT:

This dataset contains the VPU vector boundaries for a pre-release version of the NOAA OWP NextGen Hydrofabric for the HUC 03N region. These VPU regions have been extracted from the full dataset (accessible at https://lynker-spatial.com/), for use in Jupyter Notebooks that demonstrate the Hydrofabric subsetting process. This work work was demonstrated at the 2023 CIROH User's Conference. VPU geometries are accessed programmatically using the GeoServer API endpoints provided by HydroShare.

For more information about the NextGen Hydrofabric see the following:
- https://mikejohnson51.github.io/hyAggregate/
- https://github.com/NOAA-OWP/hydrofabric
- https://lynker-spatial.com/

ABSTRACT:

HUC 03S NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 03W NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 04 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 05 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 06 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 07 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 08 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 09 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 10L NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 10U NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 11 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 12 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 13 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 15 NGEN HydroFabric VPU Boundaries - Pre-release

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HUC 16 NGEN HydroFabric VPU Boundaries - Pre-release

ABSTRACT:

HUC 17 NGEN HydroFabric VPU Boundaries - Pre-release

ABSTRACT:

HUC 18 NGEN HydroFabric VPU Boundaries - Pre-release

ABSTRACT:

HUC 14 NGEN HydroFabric VPU Boundaries - Pre-release

ABSTRACT:

CIROH 2i2c JupyterHub - Production Server

CIROH 2i2c Production environment - https://ciroh.awi.2i2c.cloud/

To gain access to this environment please reach out to ciroh-it-admin@ua.edu

ABSTRACT:

Computational hydrology and real world decision-making increasingly rely on simulation-based, multi-scenario analyses. Enabling scientists to align their research with national-scale efforts is necessary to facilitate knowledge transfer and sharing between operational applications and those focused on local or regional water issues. Leveraging existing large-domain datasets with new and innovative modeling practices is vital for improving operational prediction systems. The scale of these large-domain datasets presents significant challenges when applying them at smaller spatial scales, specifically data collection, pre-processing, post-processing, and reproducibly disseminating findings. Given these challenges, we propose a cloud-based data processing and modeling pipeline, leveraging existing open source tools and cloud technologies, to support common hydrologic data analysis and modeling procedures. Through this work we establish a scalable and flexible pattern for enabling efficient data processing and modeling in the cloud using workflows containing both publicly accessible and privately maintained cloud stores. By leveraging modern cloud computing technologies such as Kubernetes, Dask, Argo, and Analysis Ready Cloud Optimized data, we establish a computationally scalable solution that can be deployed for specific scientific studies, research projects, or communities. We present an approach for using large-domain meteorological and hydrologic modeling datasets for local and regional applications using the NOAA National Water Model, the NOAA NextGen Hydrological Modeling Framework, and Parflow. We discuss how this approach can be used to advance our collective understanding of hydrologic processes, creating reusable workflows, and operating on large-scale data in the cloud.

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:

This resource contains files for the workshop.

ABSTRACT:

This resource contains data and codes demonstrating how to build and execute scientific workflows in the cloud using cyberinfrastructure developed as part of the “HydroShare Modernization” CIROH research project. The goal is to illustrate how the general-purpose cloud analysis workflows developed to support common data archival operations can also be leveraged for scientific computing. This notebook describes the process for using the outcomes of the aforementioned CIROH project; however, these capabilities are still under active development and are not ready for widespread public use.

ABSTRACT:

This resource provides codes and data to demonstrate a use case for evaluating the National Water Model (NWM) locally and enhancing its accessibility. The objective is to explore how NOAA’s NWM can be utilized by a new audience of potential users. The NWM, managed by NOAA’s National Water Center, is a comprehensive hydrologic model focusing on river and streamflow data. It offers insights into historical water conditions (with a 40-year retrospective capability), current water status, and future projections (ranging from 18 hours to 10-day and 30-day forecasts). Since its initial release in 2016, the NWM has been updated to version 3.0, with several planned enhancements and new services, including the Next Generation Framework and Flood Inundation Mapping, which are expected to be introduced within the next 24 months.

Working Group's Project: “Evaluating the NWM as a Data Source for Resilient Transportation Planning” and “Building Trust Around Predictive Hydrologic Resources

Funding for this project was provided by the National Oceanic and Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research on Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama, NA22NWS4320003

ABSTRACT:

This resource provides codes and data to demonstrate a use case for evaluating the National Water Model (NWM) locally and enhancing its accessibility. The objective is to explore how NOAA’s NWM can be utilized by a new audience of potential users. The NWM, managed by NOAA’s National Water Center, is a comprehensive hydrologic model focusing on river and streamflow data. It offers insights into historical water conditions (with a 40-year retrospective capability), current water status, and future projections (ranging from 18 hours to 10-day and 30-day forecasts). Since its initial release in 2016, the NWM has been updated to version 3.0, with several planned enhancements and new services, including the Next Generation Framework and Flood Inundation Mapping, which are expected to be introduced within the next 24 months.

Working Group's Project: "Evaluating the NWM in a Local Context” and “Using the NWM as a Data Source for Emergency Planning"

Funding for this project was provided by the National Oceanic and Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research on Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama, NA22NWS4320003

ABSTRACT:

This resource includes materials for two workshops: (1) FAIR Data Management and (2) Advanced Application of Python for Hydrology and Scientific Storytelling, both prepared for presentation at the NWC Summer Institute BootCamp 2024.

ABSTRACT:

This resource contains detailed information about the basin characteristics and streamflow statistics for the USGS stream gage Chester C at Arctic Boulevard in Anchorage, Alaska (Station ID: 15275100). These data have been obtained from the USGS StreamStats web application on June 19, 2024. This resource was created during the demo session of the National Water Center Summer Institute Program and the goal was to access data, share it on HydroShare and describe it.

ABSTRACT:

A resource for testing the file-based metadata representation of HydroShare content. This resource contains all recognized HS filetypes.