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

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.

Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, the USGWD comprises over 14.2 million well records with attributes such as well purpose, location, depth, and capacity for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States.

How will you manage the data for your next big collaborative project? HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “hydrologic resources” which are data, or models in formats commonly used in hydrology. HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools. It can help you manage your data among collaborators and meet funding agency data management plan requirements. It can publish your data using citable digital object identifiers (DOIs). In this seminar you will learn how to load files into HydroShare so that you can share them with colleagues and publish them. I will show how to manage access to the content that you share, and how to easily add metadata, and in some cases how metadata is automatically completed for you. The capability to assign DOIs to HydroShare resources means that they are permanently citable helping researchers who share their data get credit for the data published. Models, and Model Instances, which in HydroShare are a model application to a specific site with its input and output data can also receive DOI's. Collections allow multiple resources from a study to be aggregated together providing a comprehensive archival record of the research outcomes, supporting transparency and reproducibility, thereby enhancing trust in the research findings. Reuse to support additional research is also enabled. Files in HydroShare may be analyzed through web apps configured to access HydroShare resources. Apps support visualization and analysis of HydroShare resources in a platform independent web environment. This presentation will demo some apps and describe ongoing development of functionality to support collaboration, modeling and data analysis in HydroShare.

HydroShare is the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI)'s web based hydrologic information system for users to share and publish data and models in a variety of flexible formats, and to make this information available in a citable, shareable and discoverable manner. It enables users to collaborate and work as teams in a web based collaborative environment, thereby enhancing research, education and application of hydrologic knowledge. Hydroshare includes tools (web apps) that can act on content in HydroShare providing users with a gateway to computing and analysis. HydroShare is being developed by a CUAHSI team supported by National Science Foundation awards ACI-1148453, ACI-1148090, EAR-1338606, OAC-1664018, OAC-1664061, OAC-1664119.

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.

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 dataset consists of water table elevation estimates for the High Plains (Ogallala) Aquifer from 1935 to 2016, updated in 2017 using methods described in the following paper:

Haacker, E.M., Kendall, A.D. and Hyndman, D.W., 2016. Water level declines in the High Plains Aquifer: Predevelopment to resource senescence. Groundwater, 54(2), pp.231-242.

The dataset also includes derived aquifer boundaries and aquifer base elevation, which can be used to derive an estimate of saturated thickness. In addition, county-level information for average water tables, water in storage (saturated thickness x area x specific yield), and variance is included in an Excel file. Please see the ReadMe tab in the Excel file for more information.

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

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.

How do you manage, track, and share hydrologic data and models within your research group? Do you find it difficult to keep track of who has access to which data and who has the most recent version of a dataset or research product? Do you sometimes find it difficult to share data and models and collaborate with colleagues outside your home institution? Would it be easier if you had a simple way to share and collaborate around hydrologic datasets and models? HydroShare is a new, web-based system for sharing hydrologic data and models with specific functionality aimed at making collaboration easier. Within HydroShare, we have developed new functionality for creating datasets, describing them with metadata, and sharing them with collaborators. In HydroShare we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. In this presentation, we will discuss and demonstrate the collaborative and social features of HydroShare and how it can enable new, collaborative workflows for you, your research group, and your collaborators across institutions. HydroShare’s access control and sharing functionality enable both public and private sharing with individual users and collaborative user groups, giving you flexibility over who can access data and at what point in the research process. HydroShare can make 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). Functionality for creating and sharing resources within collaborative groups can also make it easier to overcome barriers such as institutional firewalls that can make collaboration around large datasets difficult. Functionality for commenting on and rating resources supports community collaboration and quality evaluation of resources in HydroShare.

This presentation was delivered as part of a Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Cyberseminar in June 2016. Cyberseminars are recorded, and archived recordings are available via the CUAHSI website at http://www.cuahsi.org.

This data release provides the reanalysis streamflow data from versions 1.2, 2.0, and 2.1 of the National Water Model structured for timeseries extraction. The impact of this is that user can query time series for a given NHDPlusV2 COMID without downloading the hourly CONUS files and extracting the sample of relevant values.

The data is hosted on the RENCI THREDDS Data Server and is accessible via OPeNDAP at the follwoing URLs:

Version 1.2 (https://thredds.hydroshare.org/thredds/catalog/nwm/retrospective/catalog.html?dataset=NWM_Retrospective/nwm_retro_full.ncml) - Spans 1993-01-01 00:00:00 to 2017-12-31 23:00:00 - Contains 219,144 hourly time steps for - 2,729,077 NHD reaches

Version 2.0 (https://thredds.hydroshare.org/thredds/catalog/nwm/retrospective/catalog.html?dataset=NWM_Retrospective/nwm_v2_retro_full.ncml) - Spans 1993-01-01 00:00:00 to 2018-12-31 00:00:00 - Contains 227,903 hourly time steps for - 2,729,076 NHD reaches

Version 2.1 (https://cida.usgs.gov/thredds/catalog/demo/morethredds/nwm/nwm_v21_retro_full.ncml) - Spans 1979-02-02 18:00:00 to 2020-12-31 00:00:00 - Contains 227,903 hourly time steps for - 2,729,076 NHD reaches

Raw Data (https://registry.opendata.aws/nwm-archive/) - 227,000+ hourly netCDF files (depending on version)

DDS

The data description structure (DDS) can be viewed at the NcML page for each respective resource (linked above). More broadly each resource includes:

• A 1D time array - hours since 1970-01-01 00:00
• A 1D latitude array - coordinate (Y) information
• A 1D longitude array - coordinate (X) information WGS84
• A 1D feature_id array - NHDPlus V2 COMID (NWM forecast ID)
• A 2D streamflow array - Q (cms) [feature_id, time]

R package

The nwmTools R package provides easier interaction with the OPeNDAP resources. Package documentation can be found here and the GitHub repository here.

Collaborators:

Mike Johnson, David Blodgett

Support:

This effort is supported by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. under the HydroInformatics Fellowship. See program here

Publications

J.M. Johnson, David L. Blodgett, Keith C. Clarke, Jon Pollack. (2020). "Restructuring and serving web-accessible streamflow data from the NOAA National Water Model historic simulations". Nature Scienfic Data. (In Review)

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 document outlines the deployment of the HydroShare (https://github.com/hydroshare/hydroshare) application and it's components in a production manner. CentOS 7 (https://wiki.centos.org) will be used for purposes of documentation on a standard platform.

This web app allows the user to view Raster and Geographic Feature Resources from HydroShare in a customizable way. It is powered by the Tethys Platform (see http://www.tethysplatform.org/).

This resource is used to help me understand how to make a resource public

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

This resource was created to play around with the HS RDF

Ice-dammed lakes impounded behind glacier dams can undergo multiple fill-and-drain cycles, and rapid drainage can produce damaging floods with significant societal and ecological impacts. Using multitemporal satellite imagery (Landsat and Sentinel-2), we documented 1150 drainage events from 106 ice-dammed lakes over 1985–2020, with an average of 66 events per year over 2015–2020. This dataset provides each ice-dammed lake's location (latitude, longitude), and the image dates before and after a lake drainage event was observed.

This data is associated with the following publication: Rick, B., McGrath, D., McCoy, S.W. et al. Unchanged frequency and decreasing magnitude of outbursts from ice-dammed lakes in Alaska. Nat Commun 14, 6138 (2023). https://doi.org/10.1038/s41467-023-41794-6