Allied for Water Science

DEADLINE TO SUBMIT ABSTRACT FOR ICRW8 2023

JANUARY 16, 2023 / 11:59 PM - 11:59 PM PT

SUBMIT ABSTRACT

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

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.

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.

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.

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.