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Anthony Michael Castronova

CUAHSI | Hydrologic Scientist

Subject Areas: Hydrology, Hydroinformatics, Hydrologic Modeling

 Recent Activity

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.

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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

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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.

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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.

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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.

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Resources
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HIS Referenced Time Series 0
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Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Generic Generic
JupyterHub Terrain Processing
Created: June 15, 2016, 3:37 p.m.
Authors: Tony Castronova

ABSTRACT:

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

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Geographic Raster Geographic Raster
Logan Digital Elevation Model
Created: July 22, 2016, 3:45 p.m.
Authors: Anthony Castronova

ABSTRACT:

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

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Generic Generic
Beaver Divide Air Temperature
Created: July 22, 2016, 8:46 p.m.
Authors: Anthony Castronova

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.

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RHESSys Sample Data
Created: Aug. 12, 2016, 4 p.m.
Authors: Anthony Castronova

ABSTRACT:

sample rhessys data

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Time Series Time Series

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.

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Site 1: Charters TWP., Plum run
Created: Feb. 23, 2017, 8:55 p.m.
Authors: Anthony Castronova

ABSTRACT:

This is my abstract

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Climate Data Download for NOCA Observatory
Created: March 22, 2017, 6:02 p.m.
Authors: Anthony Castronova

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.

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Services for Supporting Hydrologic Science
Created: March 27, 2017, 3:25 p.m.
Authors: Anthony Castronova

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.

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Daily Aggregate Temperature for Beaver Divide
Created: April 17, 2017, 3:47 p.m.
Authors: Anthony Castronova

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.

[Modified in JupyterHub on 2017-04-17 15:47:39.041903]
This daily average air temperature for the Beaver Divide gauging station that is maintained by iUtah researchers.

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Web App Resource Web App Resource
CUAHSI JupyterHub
Created: April 25, 2017, 2:32 p.m.
Authors: Anthony Castronova

ABSTRACT:

This web app launches HydroShare resources in a online Python environment using the JupyterHub software stack. The software is hosted at the Renaissance Computing Instituted (RENCI) located in Raleigh, NC.

JupyterHub Version 1.1.2
https://github.com/hydroshare/hydroshare-jupyterhub/releases/tag/v1.1.2

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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.

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Summer Institute - JupyterHub Demos
Created: June 22, 2017, 3:01 a.m.
Authors: Anthony Castronova

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.

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Model Program Resource Model Program Resource
Storm Water Management Model (SWMM)
Created: June 3, 2015, 7:17 p.m.
Authors: Lewis Rossman · Trent Schade · Daniel Sullivan · Robert Dickinson · Carl Chan · Edward Burgess

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.

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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.

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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.

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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

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CZO Data Managers Workshop Resources
Created: Aug. 1, 2017, 5:09 a.m.
Authors: Anthony Castronova

ABSTRACT:

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

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Time Series Time Series
Water temperature data from the Little Bear River, UT
Created: Nov. 2, 2017, 10:19 p.m.
Authors: Anthony Castronova

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.

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Time Series Time Series
Water temperature data from the Little Bear River, UT
Created: Nov. 3, 2017, 3:55 p.m.
Authors: Anthony Castronova

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.

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Composite Resource Composite Resource

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

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Terrain Processing - TauDem Example
Created: Feb. 22, 2018, 5:43 p.m.
Authors: Anthony Castronova

ABSTRACT:

An example workflow for processing DEM terrain processing. The Jupyter notebook included within this resource uses the TauDEM software package for parallel terrain processing operations.

More information on TauDEM can be found at: http://hydrology.usu.edu/taudem/taudem5/

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Collection Resource Collection Resource
JupyterHub Example Notebooks
Created: Feb. 22, 2018, 5:58 p.m.
Authors: Anthony Castronova

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.

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SUMMA TestCase Data - Celia 1990
Created: March 16, 2018, 6:55 p.m.
Authors: Anthony Castronova

ABSTRACT:

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

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Composite Resource Composite Resource
test data
Created: March 23, 2018, 2:49 p.m.
Authors: Liza Brazil(DEMO)

ABSTRACT:

abstract

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Collection Resource Collection Resource

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

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Composite Resource Composite Resource

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.

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Generic Generic

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.

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Hurricane Harvey Streamflow Preview Notebook
Created: May 11, 2018, 3:28 p.m.
Authors: Anthony Castronova

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.

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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

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Geographic Feature (ESRI Shapefiles) Geographic Feature (ESRI Shapefiles)
HUC 120200
Created: June 13, 2018, 1:43 p.m.
Authors: Anthony Castronova

ABSTRACT:

This is a HUC boundary that Hurricane Harvey hit.

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Hurricane Harvey NWM Subsetting Exercise
Created: June 13, 2018, 2:54 p.m.
Authors: Anthony Castronova

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

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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

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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.

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Hydroshare and community data sharing tools
Created: Jan. 15, 2019, 6:49 p.m.
Authors: Danielle Tijerina

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.

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ABSTRACT:

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

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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.

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ABSTRACT:

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

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HUC8 - CONUS Shapefile
Created: March 7, 2019, 6:45 p.m.
Authors: Danielle Tijerina

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.

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Waterhackweek Instructor Info
Created: March 15, 2019, 5:56 p.m.
Authors: Anthony Michael Castronova

ABSTRACT:

HydroShare data sharing instructions for Waterhackweek presenters.

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Waterhackweek Sample HydroShare Data
Created: March 25, 2019, 2:45 p.m.
Authors: Anthony Michael Castronova

ABSTRACT:

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

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ODM2 Example - CZIMEA Workshop
Created: April 5, 2019, 5:29 a.m.
Authors: Anthony Michael Castronova

ABSTRACT:

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

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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.

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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.

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CyberCarpenrty_01
Created: July 17, 2019, 2:13 p.m.
Authors: essawy, bakinam

ABSTRACT:

this resource is made for testing purposes.

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CUAHSI HIC 2019
Created: July 22, 2019, 3:07 p.m.
Authors: Castronova, Anthony Michael

ABSTRACT:

data for the 2019 HIC MATLAB workshop

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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.

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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.

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ABSTRACT:

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

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CUAHSI Time Series Data Explorer
Created: Aug. 1, 2019, 2:53 p.m.
Authors:

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.

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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

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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.

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Waterhackweek 2019 Cyberseminar: Workflows for gridded climate datasets
Created: Aug. 27, 2019, 2:18 p.m.
Authors: Nijssen, Bart · Diana Gergel

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.

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Waterhackweek 2019 Cyberseminar: Visualization of water datasets
Created: Aug. 27, 2019, 2:23 p.m.
Authors: Anthony Cannistra

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.

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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

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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.

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