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| Created: | Dec 04, 2023 at 6:45 p.m. | |
| Last updated: | Dec 04, 2023 at 6:48 p.m. | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content |
| Sharing Status: | Discoverable |
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Abstract
River and groundwater geochemistry reflect the integrated results of natural and anthropogenic biogeochemical processes that generate and transport solutes from rocks and soil to rivers and aquifers. These solutes can be derived from multiple sources, such as the weathering of silicate, carbonate, and evaporite rocks, meteoric precipitation, and human pollution. Delineating the contribution of each source to measured solute chemistry is critical for understanding biogeochemical processes and for helping policymakers improve management strategies to safeguard water resources under a changing climate.
End Member Mixing Analysis (EMMA) refers to a group of methods that are increasingly used to identify solute sources and quantify their contributions to water chemistry. EMMA techniques include statistical methods, inverse models, and emerging tools based on machine learning such as non-negative matrix factorization (NMF). As the availability of large hydrogeochemistry datasets and computational resources improves, there is an increased need for the hydrogeochemistry community to become fluent in a variety of EMMA approaches. In this half-day workshop, we will teach how to apply EMMA techniques with genuine river and groundwater hydrogeochemistry datasets in R and MATLAB to solve Earth and environmental sciences problems.
Workshop attendees will use the CUAHSI JupyterHub and MATLAB online cloud computing environment to complete workshop activities. This computing environment will be pre-configured with all necessary software in order to maximize engagement in end member mixing analysis workflows. While attendees are required to bring a personal laptop, all software libraries and hardware requirements will be pre-configured for them to use, no software installation will be necessary.
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| Title | Owners | Sharing Status | My Permission |
|---|---|---|---|
| AGU 2023 EMMA Workshop | Tao Wen | Discoverable & Shareable | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | Collaborative Research: Frameworks: DeCODER (Democratized Cyberinfrastructure for Open Discovery to Enable Research) | OAC-2209864 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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