Dr. Zac McEachran

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Dr. Zac McEachran

Biography

Dr. Zac McEachran is a hydrologist and environmental data scientist. His research focuses on using advanced physics based and machine learning modeling tools to make better forecasts for complex environmental systems, and help understand the fundamental processes of streamflow generation. With a background in flood model development and operational forecasting at the US National Weather Service, I have seen first-hand the impacts of disasters and the need for better modeling tools. Creating better environmental modeling systems - from supporting the development of new crops that are more suited to current and future conditions, to developing more robust real-time and long-term flood models - will support a more resilient Minnesota today and tomorrow as the climate changes. As a lifelong Minnesotan with a passion for the outdoors, I am thrilled to be a part of this work and mission. 

Research Interests

Catchment Science; Knowledge-Guided Machine Learning; Physical Hydrology; Catastrophe and Peril Modeling; Envirotyping and Crop Modeling; Soil Physics; Stochastic Hydrology

Selected Publications and Presentations

McEachran, Z., Ghosh, R., Renganathan, A., Sharma, S., Lindsay, K., Steinbach, M., Nieber, J.L., Duffy, C. and Kumar, V. Knowledge-Guided Machine Learning for Operational Flood Forecasting. In Review at Water Resources Research. Preprint available on ESS Open Archive, May 21, 2025.
https://doi.org/10.22541/essoar.172900696.63551165/v2

McEachran, Z. P., Kietzmann, J., & Johnston, M. (2024). Parsimonious streamflow forecasting system based on a dynamical systems approach. Journal of Hydrology, 641, 131776. https://doi.org/10.1016/j.jhydrol.2024.131776
 
Twine, T.E., McEachran, Z.P., Kenney, M.A., Connelley, B., Peters, A., Williamson, D. and Perry, B. (2024). Building Knowledge to Support Equitable Climate Resilience in the Upper Mississippi River Basin. AGU Fall Meeting 2024, Oral Presentation. 
 
Invited Speaker: NOAA Administrator Executive Visit - Machine Learning for Forecasting Operations - Presented to NOAA leadership on integrating machine learning into flood forecasting operations.

Invited Speaker: Harnessing the Data Revolution: Knowledge Guided Machine Learning Conference 2024 - Knowledge-Guided Machine Learning for data assimilation and modeling multiscale processes: An application in Flood Forecasting

Invited Speaker: American Geophysical Union Water Science Conference 2024 - Hydrology at Our Doorstep - Presented a pop-up session to brief water scientists on the ongoing Summer 2024 Minnesota/Mississippi River major flooding event having significant shipping industry disruptions