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January e-Newsletter

Posted Jan 12, 2023

The CUAHSI January e-Newsletter came out today! Be sure to read on for more details about announcements, upcoming events and opportunities!

Hydroinformatics Blog - Monitor My Watershed Helps Lower Barriers for Real-time Data Sharing

Posted Jan 10, 2023

The Monitor My Watershed Data Sharing Portal evolved from work by the CUAHSI community for managing environmental data with the Observations Data Model. Monitor My Watershed accepts real-time sensor data and other observation types and provides data visualization and connection with CUAHSI’s HIS. Adoption and use of Monitor My Watershed is growing rapidly partly due to the extensive training resources for DIY hardware

Hydroinformatics Blog - Arc Hydro’s WIM: A Machine Learning Framework for Wetland Identification

Posted Dec 7, 2022

Arc Hydro’s Wetland Identification Model (WIM) is an automated framework for identifying wetlands using machine learning. Its baseline functionality infers wetland likelihood from geomorphologic indicators, but WIM can be configured and extended for your use case. WIM continues to be developed and improved as we gather feedback from users and collaborators in the wetland science community. Our goal is to build a tool that helps that community achieve its goal of better, faster, cheaper wetland mapping.

Hydroinformatics Blog - Advancing Technology for Understanding Residential Water Use

Posted Nov 9, 2022

Collecting, managing and analyzing high temporal (sub-minute) resolution residential water use data is challenging due to lack of existing tools, yet doing so enables better understanding of water use behavior that can help improve urban water management and planning by allowing direct identification of water appliances characteristics. This month's entry to the Hydroinformatics Blog presents low-cost, open-source tools designed to facilitate collection and analysis of this type of data.

Hydroinformatics Blog - Running research models for hands-on hydrology education

Posted Sep 14, 2022

Models are an integral part of hydrologic research and investigation. Over the past few years, we have made efforts to develop and implement solutions that allow graduate students to use a research hydrological model as part of their coursework. This setup has allowed us to introduce graduate students to research hydrological models and to perform meaningful model experiments.

Hydroinformatics Blog - Big Data Dreaming! A 42-Year CONUS Hydrologic Retrospective

Posted Jul 13, 2022

The latest NOAA National Water Model retrospective is a 42-year, >100 TB compendium of meteorological, land surface, and hydrologic states and fluxes across the Contiguous U.S. I've rounded up a handful of great example workflows for how to probe this rich data resource to help answer your looming scientific questions.

Hydroinformatics Blog - HydroLang: An Open-Source Web-Based Framework for Environmental and Hydrological Analyses

Posted Jun 8, 2022

HydroLang, an open-source and integrated community-driven computational web framework for hydrology and water resources research and education. HydroLang employs client-side web technologies and standards to carry out various routines aimed at acquiring, managing, transforming, analyzing, and visualizing hydrological datasets.

Water-Use Data in the United States: Challenges and Future Directions

Posted Jun 3, 2022

A recent publication in the Journal of the American Water Resources Association that outlines opportunities to improve access, use, and sharing of water-use data.

Hydroinformatics Blog - Whats new in RHESSys (Regional Hydro-Ecological Simulation System)

Posted May 11, 2022

RHESSys is a hydro-ecologic model that has been used in a wide range of modeling applications.

Hydroinformatics Blog - Flood Analytics Information System (FAIS): A National Scale Big Data Engineering and Gathering Pipeline To Improve Flood Situational Awareness

Posted Apr 13, 2022

The Internet of Things (IoT) and big data infrastructure are the emerging network and information technologies that can comprehend automatic monitoring and facilitate data engineering and problem-solving particularly for flood informatics research. By using these techniques, a national-scale Flood Analytics Information System (FAIS) is developed to advance and drive the next generation of flood informatics research and innovation.