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ABSTRACT:

HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling researchers to more easily share data, model and workflow products resulting from their research and used to create and support reproducibility of the results reported in scientific publications. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) web application tools that can act on content in HydroShare for computational and visual analysis. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis and that allows researchers to employ services beyond their desktops to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe ongoing enhancements to HydroShare, some of the challenges being faced in its design and ongoing development. Content storage is being consolidated into a single primary resource type that may hold multiple content aggregation types. This better supports storage of the diverse data involved with hydrologic data and model studies in a single shareable unit. Reproducible and easy to use computational functionality is being advanced using JupyterHub as a gateway to XSEDE and other high performance compute resources. This presentation will describe the progress made and challenges being addressed for managing the storage and use of HydroShare resources from JupyterHub, and using containers to enabling simple and scalable access to these resources.

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ABSTRACT:

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

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

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Aug 21, 2019 at 2:40 p.m.
Resource type: Composite Resource

ABSTRACT:

When analyzing time series of streamflow and associated water quality sensor data such as turbidity, researchers and managers often are interested in isolating storm events since that is when behavior is dynamic and physical processes can be inferred. In this presentation, we will present preliminary development of a web-based data analysis tool that can be used for hydrological event detection and analysis (HEDA). Currently, a lack of options exist for detecting, delineating and analyzing hydrological events that don’t require utilizing a programming environment such as R or MATLAB. We will present a first look at a web-based tool capable of interfacing with time series stored on the CUAHSI HydroServer and USGS NWIS databases and then performing subsequent analysis. We will demonstrate the event-based analysis that can be obtained from the HEDA tool as well as encourage feedback from potential users of the tool.

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Aug 12, 2019 at 8:31 p.m.
Resource type: Composite Resource

ABSTRACT:

In this session, we will explain and demonstrate an alpha build of the CUAHSI-supported HydroQuality web application. In the interest of growing the contributions of smaller research teams and growing a diversity of data products uploaded to CUAHSI data discovery platforms, we propose a web application which provides a pipeline workflow for setting up and performing Quality Control (QC) processes on flat data files. This software will flag data values with meaningful metadata informing users at a glance: what processes were run on the data, what metrics these processes used, what software performed these checks and when these processes were undertaken. Over the course of this hour session, we will show you the design and technologies we used to build this software platform, walk you through a typical use case in a live demo, and open the floor for discussion and feedback from you, the water experts who may one day use this software package.

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Aug 12, 2019 at 5:33 p.m.
Resource type: Composite Resource

ABSTRACT:

(Full talk - Part of session: Unveiling new innovations in advanced cyberinfrastructure to support a community hydrologic modeling ecosystem)

Are there analysis tools that can work with my data? Have other researchers developed code that I can reuse? How can I find these code examples and ramp up quickly so that I can apply them to my project? Can platform developers provide better ways to help would-be collaborators share and find code and examples?

With MATLAB Online hosted directly on cuahsi.org using Hydroshare resources, researchers, educations and students can access the relevant data and shared models more easily. This talk will demonstrate the use of MATLAB Online to work with hydrological data. We’ll also cover how to share MATLAB work as notebooks, complete with embedded graphics, equations, and publication-quality formatting, using the new MATLAB Live Editor, enabling more transparent research and improved teaching and learning of water data science and more.

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Aug 12, 2019 at 4:55 p.m.
Resource type: Composite Resource

ABSTRACT:

Are there analysis tools that can work with my data? Have other researchers developed code that I can reuse? How can I find these code examples and ramp up quickly so that I can apply them to my project?

With MATLAB Online hosted directly on cuahsi.org using Hydroshare resources, researchers, educations and students can access the relevant data and shared models more easily. This talk will demonstrate the use of MATLAB Online to work with hydrological data using new geospatial data access, data analytics and visualization techniques. We’ll also cover how to share work as notebooks, complete with embedded graphics, equations, and publication-quality formatting, using the new MATLAB Live Editor, enabling more transparent research and improved teaching and learning of water data science and more.

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Aug 06, 2019 at 9:20 p.m.
Resource type: Composite Resource

ABSTRACT:

Groundwater supplies 70% of global irrigation water needs; 25% of total freshwater consumption the United States; and a source of safe drinking water to 90% of the United States rural population. Climate models are increasingly being used to simulate the groundwater recharge. However, these climate models often have uncertainty in their recharge predictions. These uncertainties in climate models’ predictions stem from the difference in the models’ structure, the models’ parameters, and the models’ physics. In this study, ten regional climate models (RCMs) are used to model groundwater recharge. The RCMs used in this study were obtained from the North American Regional Climate Change Assessment Program (NARCCAP). In order to combat the uncertainty in the RCMs’ recharge predictions, the predictions are averaged in machine learning frameworks. The machine learning models used in this study include the artificial neural network (ANN), the deep neural networks (DNNs), and the support vector regression (SVR) models. Results suggest that the radial basis function-based SVR model was the superior model in modelling recharge.

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ABSTRACT:

Poster presented at the 2019 meeting of the CUAHSI hydroinformatics conference.

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ABSTRACT:

Hydrologic research is tackling more and more complex questions, requiring researchers to collaborate in teams to build complex, integrated model simulations. Accordingly, the use of cyberinfrastructure is increasing due to the need for collaborative modeling, high throughput computing, and reproducibility and usability. However, the design and implementation in cyberinfrastructure to support community hydrologic modeling are still challenging because much functionality, such as the user interface for modeling, online data sharing, and different model execution environments are necessary to support modeling cyberinfrastructure. In this research, we present a collaborative, cloud-based modeling system built on the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model as an example paradigm for the design and implementation of cyberinfrastructure. The general paradigm consists of three main components: (i) a Python-based model Application Programming Interface (API) for interacting with hydrologic models, (ii) an online repository for storing model input and output files for different simulation runs, and (iii) a public JupyterHub environment for creating and running model simulations that leverages both the Python API and the online data repository. In this instance, we first created pySUMMA as an example API for interacting with the SUMMA modeling framework. Second, we used HydroShare as an online repository for sharing data and models. Finally, we used a JupyterHub instance tailored for running SUMMA model simulations and hosted by the Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI). Together, these three components serve as a general example of a cloud-based modeling environment that can be used along with other models and modeling frameworks, in addition to SUMMA, to foster a community supported cyberinfrastructure for collaborative hydrologic modeling.

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Aug 05, 2019 at 6:58 p.m.
Resource type: Composite Resource

ABSTRACT:

A presentation at the 2019 CUAHSI Conference.

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Aug 05, 2019 at 6:17 p.m.
Resource type: Composite Resource

ABSTRACT:

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

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Aug 05, 2019 at 6:16 p.m.
Resource type: Collection Resource

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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Type Title First Author Date Created Last Modified Subject Authors Permission Level Labels Favorite Last modified Sharing Status Date Created
Composite Resource Composite Resource
Public Resource Sharable Resource
HydroShare: An overview of new functionality developed in support of collaborative reproducible research David Tarboton 28 Jul, 2019 10:43 a.m. 29 Jul, 2019 1:27 p.m. 1564406848 1564310599 2
Composite Resource Composite Resource
Public Resource Sharable Resource
HydroLearn: Facilitating the development, adaptation and sharing of active-learning resources in hydrology education Habib, Emad 01 Sep, 2019 2 p.m. 01 Sep, 2019 2:14 p.m. 1567347240 1567346403 2
Composite Resource Composite Resource
Public Resource Non Sharable Resource
Hydrological Event Detection & Analysis for streamflow and water quality time series Hamshaw, Scott 21 Aug, 2019 11:26 a.m. 21 Aug, 2019 2:40 p.m. 1566398428 1566386790 2468
Composite Resource Composite Resource
Public Resource Sharable Resource
HydroQuality: Upload and Download Quality Data Chen, Chao 12 Aug, 2019 8:15 p.m. 12 Aug, 2019 8:28 p.m. 1565641705 1565640943 3357
Composite Resource Composite Resource
Public Resource Sharable Resource
Model and Code Sharing via CUAHSI-hosted MATLAB Online Kempler, Lisa 12 Aug, 2019 5:27 p.m. 12 Aug, 2019 5:33 p.m. 1565631206 1565630835 2897
Composite Resource Composite Resource
Published Resource
HIC Lightning talk: Model and Code Sharing via CUAHSI-hosted MATLAB Online Kempler, Lisa 09 Aug, 2019 6:34 p.m. 12 Aug, 2019 4:56 p.m. 1565629019 1565375660 2897
Composite Resource Composite Resource
Public Resource Sharable Resource
Modelling Groundwater Recharge with Multiple Climate Models in Machine Learning Frameworks A., Kevin 06 Aug, 2019 9:07 p.m. 06 Aug, 2019 9:19 p.m. 1565126358 1565125662 4951
Composite Resource Composite Resource
Public Resource Non Sharable Resource
National Water Model Dockerized Job Scheduler: A Reproducible Framework to Generate Parameter Based NWM Ensemble Raney, Austin 06 Aug, 2019 1:40 p.m. 06 Aug, 2019 1:44 p.m. 1565099099 1565098833 4606
Composite Resource Composite Resource
Public Resource Sharable Resource
Design and Implementation of Cyberinfrastructure to Support a Cloud-Based, Community Hydrologic Modeling Ecosystem CHOI, YOUNG-DON 29 Jul, 2019 1:17 p.m. 29 Jul, 2019 1:28 p.m. 1564406939 1564406232 545
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Regional Flood Forecasting Applications for the Dominican Republic - CUAHSI 2019 Biesinger, Jason 05 Aug, 2019 6:55 p.m. 05 Aug, 2019 7:02 p.m. 1565031728 1565031315 1904
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State of CUAHSI Community Cyberinfrastructure - 2019 Castronova, Anthony Michael 31 Jul, 2019 1:58 p.m. 31 Jul, 2019 2:01 p.m. 1564581716 1564581507 11
Collection Resource Collection Resource
Public Resource Sharable Resource
CUAHSI compute services for working with data in the cloud Castronova, Anthony Michael 26 Jul, 2019 1:43 p.m. 30 Jul, 2019 7:43 p.m. 1564515815 1564148629 11
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