Nicholas Engdahl
Washington State University, WA·Earth & Environmental Science
Predicting where groundwater, and any pollutant it carries, will flow is hard because nobody can see the full structure of the rock and sediment underground. Groundwater models normally depend on uncertain guesses about that structure, so their predictions can be wrong without saying how wrong.
- Fields
Earth & Environmental Science
Engineering
Mathematics & Statistics
- Topics
groundwater modeling
contaminant transport
uncertainty quantification
- Grants
- 1 known grant, through Aug 2029
What they work on
- Develop a Madelung-style amplitude–phase groundwater transport model in which amplitudes describe probable transport modes across sediment types (facies) and phases act as velocity potentials, recovering the classical advection–dispersion equation in limiting cases.
- Build a facies-aware transport model for a complex amplitude field with a dephasing operator tied to facies length scales, to capture mixing below grid resolution and early-time plume behavior.
- Represent contaminant transport with a density-matrix evolution whose diagonal is concentration and whose off-diagonals encode short-range correlation, as a compact closure for unresolved heterogeneity.
- Validate all three frameworks on synthetic plumes, heterogeneous ensembles and MADE field data, comparing accuracy and CPU cost against MODFLOW/MT3DMS with PEST++.
More about the research
This project proposes a different way to build uncertainty into the models themselves. It borrows mathematics from quantum mechanics, which can represent several possible states at once, such as several possible paths a contaminant plume might take.
Why it matters
Decisions about cleaning up contaminated aquifers depend on groundwater predictions that often hide large uncertainties. Models that report uncertainty plainly could help engineers and environmental managers judge remediation risk more realistically and decide when extra model complexity is worth it. The open code and AI-assisted guide aim to make these tools usable in practitioners' own workflows. The frameworks may also be suited to future quantum computers.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
William Johnson
University of Utah, UT·Earth & Environmental Science
Quantify how particles such as microplastics and pathogens attach to sediment grains after repeated encounters, using laboratory transport experiments in porous media combined with surface characterization.
Collin Sutton
Colorado School of Mines, CO·Earth & Environmental Science
Develop a reduced-order, bipartite graph framework that captures the essential flow, solute transport and geochemical reactions in fractured rock without the cost of full-physics simulation.
Jonghyun Lee
University of Hawaii at Manoa, HI·Earth & Environmental Science
Build hybrid surrogate models from neural operators, networks that imitate expensive simulations, coupled through an open-source framework so groundwater–ocean exchange is simulated quickly while conserving mass at the land–sea boundary.
The school
Washington State University: 22 labs on PI Pages, $403M a year on research, 259 research doctorates a year.
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