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.

In the past several decades, field studies have shown that woody plants can access substantial volumes of water from the pores and fractures of bedrock. If, like soil moisture, rock moisture serves as an important source of plant-available water, then conceptual paradigms regarding water and carbon cycling may need to be revised to incorporate bedrock properties and processes. Here we present a lower-bound estimate of the contribution of bedrock water storage to transpiration across the continental United States using distributed, publicly available datasets. Temporal and spatial patterns of bedrock water use across the continental United States indicate that woody plants extensively and routinely access rock moisture for transpiration across diverse climates and biomes. Bedrock water access is not confined to extreme drought conditions. On an annual basis in California, the volumes of bedrock water transpiration exceed the volumes of water stored in human-made reservoirs, and woody vegetation that accesses bedrock water accounts for over 50 per cent of the aboveground carbon stocks in the state. Our findings indicate that, like soil moisture, rock moisture is a critical component of terrestrial water and carbon cycling.

CODE AVAILABLE ON GITHUB: https://github.com/erica-mccormick/widespread-bedrock-water-use FOR MORE INFORMATION, SEE WEBPAGE: https://erica-mccormick.github.io/widespread-bedrock-water-use/

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)

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

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 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.

Presentation given to CUAHSI Informatics Conference, July 26, 2017.

HydroShare is an online, collaboration system for sharing of 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. 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; Use the web services application programming interface (API) to program automated and client access; 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 web apps to visualize, analyze and run models on data in HydroShare. HydroShare supports web apps to act on resources for cloud (server) based visualization and analysis, including large scale geographic and digital elevation model analysis at the CyberGIS center at the National Center for Supercomputing Applications (NCSA) and capability to execute hydrology models (e.g. SWAT and RHESSys models) and connect to geoscience modeling communities (e.g. Landlab). A pending proposal for the next phase of HydroShare development would extend the capabilities of HydroShare to enhance support for model hypothesis testing using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) approach, advance collaboration capability, integrate with 3rd party consumer cloud storage systems and establish an "App Nursery" to enable community coders to develop web apps linked to HydroShare. This presentation will describe the functionality and architecture of HydroShare comprising: (1) resource storage, (2) resource exploration, and (3) actions on resources by web apps. System 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 app that interacts with resources stored in HydroShare. We welcome discussion of the opportunities this enables for interoperability with other cyberinfrastructure tools, to the benefit of the hydrology and hydroinformatics research communities.

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.

The SnowClim Model and Dataset address the need for climate and snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains.

The SnowClim Model is a physics-based snow model that incorporates key energy balance processes necessary for capturing snowpack spatiotemporal variability, including under future climate scenarios, while optimizing computational efficiency throughout several empirical simplifications. The model code can be downloaded or run in the cloud using MATLAB Online through HydroShare.

The SnowClim Dataset consists of climate forcing data for and snow outputs from the SnowClim Model. Climate forcing data was downscaled from 4 km climate data from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) to ~210 m across the contiguous western United States. Climate forcings were downscaled from WRF directly for a present day (2000-2013) period and a thirteen year pseudo global warming scenario reflecting conditions between 2071-2100 under RCP 8.5. Climate forcings were prepared for a third time period by perturbing present-day downscaled climate data by the multi-model mean from CMIP5 to reflect conditions under pre-industrial conditions (1850-1879).

Additional details regarding the SnowClim model physics, model calibration, climate data downscaling, model application to the western US, and model performance are available in: Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

The goal of HydroShare is to advance hydrologic science by enabling the water-resources community to more easily and freely share products resulting from their research and/or data collection.

A collection of photos of the HydroShare team at various meetings, as well as photo's used on web pages.

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.

Collaboration is central to CIROH (https://ciroh.ua.edu/). Advancing the knowledge needed to support research to operations in hydrology depends on collaboration around model and data sharing. It requires open data supporting the integration of information from multiple sources; easy to use, generally accessible, shareable computing; and working together as a team and community. The CUAHSI HydroShare platform was developed to advance water research by enabling communities of researchers to more easily and freely share digital products resulting from their research, not just the scientific publications summarizing a study, but the data, models and workflows used to produce the results, consistent with Findable, Accessible, Interoperable, and Reusable (FAIR) principles of present-day research. This project advances HydroShare as a platform for CIROH collaborative research and education. The objectives are to (1) enhance community data access; (2) establish interoperability with scalable computing; (3) demonstrate computational reproducibility; and (4) establish and grow a CIROH Community on HydroShare. Work to date has, through a survey, identified high value datasets and developed Jupyter Notebooks to enhance accessing and working with this data. The HydroShare interoperability software stack has been added to CIROH 2i2c JupyterHub to enable execution of these Notebooks. A community of 5 HydroShare groups has been established to facilitate data sharing among CIROH themes. Ongoing efforts focus on providing access to computing resources enabling broader use of the NextGen modeling framework being used to develop future versions of the National Water Model.

This resource holds the powerpoint file for the HydroShare poster presented at the CIROH Science Meeting in Tuscaloosa Alabama October 14-17, 2024

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.

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 document is adapted from the iUTAH Research Data Policy (Horsburgh and Jones, 2017), which was adopted by a large group of collaborative researchers in Utah working on an NSF-funded research project. It is offered here for potential general use in defining research products and data sharing workflows. This document is intended to be used as an example from which specific data policies, timing, and best practices for data sharing can be defined and adopted for research projects like the Critical Zone Collaborative Network Thematic Cluster projects. Revised by Clara Cogswell, Shannon Syrstad, Jeff Horsburgh 2/17/2022

Water is the most precious and essential resource among all-natural resources. Some organism survives without oxygen and food such as Tardigrades. But no one can survive without water. The increase in the development of industries and human activities over the previous century is having an overwhelming impact on our environment. Most cities in the world have started to implement the aqua management system. The development of cloud computing, artificial intelligence, remote sensing, big data and the Internet of Things provide new opening and move toward the improvement and application of aqua resource monitoring system. For predicting water quality of rivers, dams and lakes in India, water quality parameter dataset is created. The name of the data set is Aquaattributes. Completely 1360 samples are presented in the Aquaattributes. The data set size is 190 KB. Attributes of the dataset location name along with its longitude and latitude values and water quality parameters.

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.

Context: This HydroShare resource is a prototype Help page. Since HydroShare is being designed to facilitate social, model and data organization, this resource is a snapshot in time of a Use Case for how HydroShare was used to advance science, education and outreach. Please use the comments below and let me know if this resource was useful to you as an example of how to use HydroShare to run a model.

Steps used to run a Landlab model in HydroShare at the CUAHSI 2016 Landlab Workshop: 1. Go to Collaborate on the HydroShare dashboard 2.'Ask to Join' the Landlab group. 3. Interactive step-- Owners of the Landlab group accepted requests to join the group.
4.Click on the Landlab Group, second tab 'Resources' 5.Scan list of search for Landlab CUAHSI Colloqium 2016 Workshop 6.You are now in what is called a 'collection resource' - scroll down to the Collection Contents and click on Thunder Creek Landlab Landslide Example 7.You are now in what is called a 'generic resource' which contain multiple files - all of Ronda's inputs a Jupyter notebook example. Click on Open With - a blue button on the upper right of the screen. 8.Select Open With Jupyter-HUB NCSA. This takes you to a landing page that is Python code for loading your landlab python notebook.
9.Skip down to the first code cell and run. The output will request your HydroShare user password. This is needed for security because you are running code from a server (currently a development server at USU, in a few days on the NCSA super computer at U of Illinois). 10.Run the next code block which results in generating a link to open your notebook from the HydroShare server. 11. Click on the hyperlink to your notebook to launch. landslide_driver.ipynb 12. Run the model.