Here we present a geospatial dataset representing local- and regional-scale aquifer system boundaries, defined on the basis of an extensive literature review and published in GebreEgziabher et al. (2022). Nature Communications, 13, 2129, https://www.nature.com/articles/s41467-022-29678-7

The database contains 440 polygons, each representing one study area analyzed in GebreEgziabher et al. (2022). The attribute table associated with the shapefile has two fields (column headings): (1) aquifer system title (Ocala Uplift sub-area of the broader Floridan Aquifer System), and (2) broader aquifer system title (e.g., the Floridan Aquifer System).

• to explore the available data via an interactive app please visit the following URL: https://ucsb.maps.arcgis.com/apps/instant/nearby/index.html?appid=2a238e4ed2434d21b3b53c861a064d8f
• to explore the 3D nature of a dozen aquifer systems please see the following storymap: https://storymaps.arcgis.com/stories/a91f061759e64e44af38daf6cefa4259

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

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

Scientific and management challenges in the water domain require synthesis of diverse data. Many data analysis tasks are difficult because datasets are large and complex; standard data formats are not always agreed upon or mapped to efficient structures for analysis; scientists may lack training for tackling large and complex datasets; and it can be difficult to share, collaborate around, and reproduce scientific work. Overcoming barriers to accessing, organizing, and preparing datasets for analyses can transform the way water scientists work. Building on the HydroShare repository’s cyberinfrastructure, we have advanced two Python packages that make data loading, organization, and curation for analysis easier, reducing time spent in choosing appropriate data structures and writing code to ingest data. These packages enable automated retrieval of data from HydroShare and the USGS’s National Water Information System (NWIS) (i.e., a Python equivalent of USGS’ R dataRetrieval package), loading data into performant structures that integrate with existing visualization, analysis, and data science capabilities available in Python, and writing analysis results back to HydroShare for sharing and publication. While these Python packages can be installed for use within any Python environment, we will demonstrate how the technical burden for scientists associated with creating a computational environment for executing analyses can be reduced and how sharing and reproducibility of analyses can be enhanced through the use of these packages within CUAHSI’s HydroShare-linked JupyterHub server.

This HydroShare resource includes all of the materials presented in a workshop at the 2023 CUAHSI Biennial Colloquium.

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

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

Scientific and related management challenges in the water domain require synthesis of data from multiple domains. Many data analysis tasks are difficult because datasets are large and complex; standard formats for data types are not always agreed upon nor mapped to an efficient structure for analysis; water scientists may lack training in methods needed to efficiently tackle large and complex datasets; and available tools can make it difficult to share, collaborate around, and reproduce scientific work. Overcoming these barriers to accessing, organizing, and preparing datasets for analyses will be an enabler for transforming scientific inquiries. Building on the HydroShare repository’s established cyberinfrastructure, we have advanced two packages for the Python language that make data loading, organization, and curation for analysis easier, reducing time spent in choosing appropriate data structures and writing code to ingest data. These packages enable automated retrieval of data from HydroShare and the USGS’s National Water Information System (NWIS), loading of data into performant structures keyed to specific scientific data types and that integrate with existing visualization, analysis, and data science capabilities available in Python, and then writing analysis results back to HydroShare for sharing and eventual publication. These capabilities reduce the technical burden for scientists associated with creating a computational environment for executing analyses by installing and maintaining the packages within CUAHSI’s HydroShare-linked JupyterHub server. HydroShare users can leverage these tools to build, share, and publish more reproducible scientific workflows. The HydroShare Python Client and USGS NWIS Data Retrieval packages can be installed within a Python environment on any computer running Microsoft Windows, Apple MacOS, or Linux from the Python Package Index using the PIP utility. They can also be used online via the CUAHSI JupyterHub server (https://jupyterhub.cuahsi.org/) or other Python notebook environments like Google Collaboratory (https://colab.research.google.com/). Source code, documentation, and examples for the software are freely available in GitHub at https://github.com/hydroshare/hsclient/ and https://github.com/USGS-python/dataretrieval.

This presentation was delivered as part of the Hawai'i Data Science Institute's regular seminar series: https://datascience.hawaii.edu/event/data-science-and-analytics-for-water/

This FAIRsharing record describes: HydroShare is a system operated by The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that enables users to share and publish water-related data and models in a variety of flexible formats, and to make this information available in a citable, shareable and discoverable manner. HydroShare includes a repository for data and models, and tools (web apps) that can act on content in HydroShare providing users with a gateway to high performance computing and computing in the cloud. With HydroShare you can: share data and models with colleagues; manage access to shared content; share, access, visualize, and manipulate a broad set of hydrologic data types and models; publish data and models and obtain a citable digital object identifier (DOI); aggregate resources into collections; discover and access data and models published by others; use the web services application programming interface (API) to programmatically access resources; and use integrated web applications to visualize, analyze and run models with data in HydroShare.

This collection aggregates educational resources for instructors teaching hydrology, water resources, and related fields. Entries in this collection are owned and managed by the creators of the items within this collection - this entry serves to provide a single location to organize these files.

Attribution of content should be to the authors who have contributed in the 'collection contents', not to this HydroShare entry.

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

This HydroShare resource is intended to serve as the evolving publication list for HydroShare. All HydroShare team members should have edit access to this resource so everyone on the team can update this resource with new publications over time.

This resource represents new contributions to the national water service boundaries dataset, which also functions as the geoconnex.us reference feature set. This resource is managed by a workflow that incorporates community contributions to supplement, and intended to replace polygons available from https://www.hydroshare.org/resource/20b908d73a784fc1a097a3b3f2b58bfb . This workflow is available here: https://github.com/cgs-earth/ref_pws

This app is designed to run on Tethys Platform and helps support CUAHSI's HydroShare project. Its purpose is to allow HydroShare users to quickly preview hydrologic geospatial content stored in HydroShare resources.

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.

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

HydroShare (www.hydroshare.org) is a hydrology-domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare’s goal is to advance hydrologic science by enabling researchers to more easily share data, model and workflow products resulting from their research, creating and supporting reproducibility of the results reported in scientific publications. It supports the growing call for open data that is findable, accessible, interoperable and reusable (FAIR). HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) web application tools that can act on content in HydroShare for computational and visual analysis. Together these serve as a platform for collaboration and gateway for computation that integrates data storage, organization, discovery, and analysis and that allows researchers to employ services beyond their desktop computers to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe ongoing enhancements to HydroShare and some of the challenges being faced in its design and ongoing development. We report on efforts to refine the way data and model content are formatted and stored within the system to better support storage, management, and sharing of the diverse data involved with hydrologic data and model studies. We have developed techniques that enable scientists to organize and package data and models within a single shareable unit, while still providing value-added tools for known data types. Additionally, access to reproducible and easy to use computational functionality is being advanced using JupyterHub as a gateway to computing resources. This collaborative and computational functionality provides an important incentive by providing users with immediate value, while meeting open data mandates and sharing data using open standards.

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

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

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

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

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

Reservoirs are the key hydraulic infrastructure that regulates natural streamflow variability to fulfill various operation targets, including flood control, water supply, hydroelectricity generation and sustaining environmental flow. As an important anthropogenic interference in the hydrologic cycle, reservoir operation behavior remains challenging to be properly represented in hydrologic models, thus limiting the capability of predicting streamflow under the interactions between hydrologic variability and operational preferences. Data-driven models provide a promising approach to capture relationships embedded in historical records. This dataset contains historical daily operations of over 300 major reservoirs across the Contiguous United States with a wide range of streamflow conditions, including inflow, release, storage, elevation, etc. The eastern reservoir data is collected by Duke University (https://nicholasinstitute.duke.edu/reservoir-data/, Patterson et al., 2018. The western reservoir data is accessed via the United States Bureau of Reclamation (https://water.usbr.gov/api/web/app.php/api/).

Collection of presentations I have given about the HydroShare project

This resource comprises various files pertaining to time series data, particularly focusing on NWM (National Water Model) short-range forecast and USGS observations of streamflow data for three stations, measured in cubic feet per second (cfs). I added some spatial datasets in the form of vector and raster datasets just for one specific research area. The contents of each file serve distinct purposes: - "USGS Observation and NWM Outputs" is consisted of merged NWM forecast and USGS observation data; -"Data types" highlights some information including coordinates and reach ID and gage ID for specific locations in Arizona, Nevada, and Wisconsin in the USA; - "Results" showcases images associated with the statistical metrics for aforementioned locations, offering visual insights into data analysis outcomes; -"Data Collection and Analysis" summarizes merged data from the NWM and USGS, accompanied by statistical metrics for analysis; - "LSTM Paper" presents an incomplete paper on LSTM models application to the dataset, necessitating revision and completion in the near future; -"Big Sandy River " includes Vector data (shapefiles) for the delineated watershed shapefiles. -"Big Sandy River " includes the raster data for the delineated watershed which contains big sandy river. -"Arizona Raster" determines the raster data for Arizona state.