Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Aug 5, 2026

Beavers alter river corridor hydrology by creating ponds and inundating floodplains, improving surface water storage, yet groundwater impacts in mountainous alluvial settings remain uncertain. This study develops a model–data integration workflow to quantify groundwater response to beaver-induced inundations in the Upper Colorado River Basin, addressing uncertainty from seasonal dynamics, hydraulic conductivities, floodplain structures, and meteorological forcing. An ensemble of groundwater models is calibrated with a neural density estimator, yielding posterior parameter distributions and estimates of vertical flux from soil to permeable gravel, down-valley underflow, and their ratios. Results show vertical flux rises from 2% during dry pond periods to 20% during wet periods, highlighting structure-driven effects on storage and water quality.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

AccessFree to download

Abstract

Key findings

Methodology and uncertainty sources

Implications for groundwater storage and water quality

Lawrence Berkeley National Laboratory LBL Publications Title Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration Permalink [https://escholarship.org/uc/item/43f1j9m2](https://escholarship.org/uc/item/43f1j9m2) Journal Water Resources Research, 61(9) ISSN 0043-1397 Authors Wang, Lijing Babey, Tristan Perzan, Zach et al. Publication Date 2025-09-01 DOI 10.1029/2024wr039192 Copyright Information This work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California RESEARCH ARTICLE 10.1029/2024WR039192 Key Points: • Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains • Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality • Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters Correspondence to: L. Wang, [lijing.wang@uconn.edu](lijing.wang@uconn.edu) Citation: Wang, L., Babey, T., Perzan, Z., Pierce, S., Briggs, M., Boye, K., & Maher, K. (2025) . Quantifying groundwater response and uncertainty in beaver‐influenced mountainous floodplains using machine learning‐based model calibration. Water Resources Research, 61, e2024WR039192. [https://doi.org/10.1029/](https://doi.org/10.1029/)[ ](https://doi.org/10.1029/)2024WR039192 Received 15 OCT 2024 Accepted 4 SEP 2025 Author Contributions: Conceptualization: Lijing Wang, Tristan Babey, Zach Perzan, Kate Maher Data curation: Lijing Wang, Zach Perzan, Sam Pierce, Martin Briggs Formal analysis: Lijing Wang Funding acquisition: Kristin Boye, Kate Maher Investigation: Lijing Wang Methodology: Lijing Wang, Tristan Babey, Zach Perzan, Kate Maher Project administration: Kristin Boye, Kate Maher Resources: Lijing Wang Software: Lijing Wang Supervision: Kate Maher Validation: Lijing Wang Visualization: Lijing Wang Writing – original draft: Lijing Wang © 2025 The Author(s) . This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration Lijing Wang1,2 , Tristan Babey3, Zach Perzan4, Sam Pierce5 , Martin Briggs6 , Kristin Boye7, and Kate Maher5 1Department of Earth Sciences, University of Connecticut, Storrs, CT, USA, 2Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, CA, USA, 3Department of Geosciences, University of Rennes, Rennes, France, 4Department of Geoscience, University of Nevada Las Vegas, Las Vegas, NV, USA, 5Department of Earth System Science, Stanford University, Stanford, CA, USA, 6Observing Systems Division, U.S. Geological Survey, Hydrologic Remote Sensing Branch, Storrs, CT, USA, 7SLAC National Accelerator Laboratory, Menlo Park, CA, USA Abstract Beavers (Castor canadensis) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This st

FAQs

What uncertainty factors does the study address in modeling groundwater response to beaver ponds?

The study considers uncertainty from seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings.

How does the machine learning approach contribute to estimating groundwater model parameters?

It uses a neural density estimator to calibrate an ensemble of groundwater models and obtain posterior distributions for model parameters.

What do the results indicate about the relationship between vertical flux and down-valley underflow?

Vertical flux increases substantially relative to down-valley underflow, rising from 2% in dry periods to 20% in wet periods, which informs analogous conditions with and without beaver ponds.

Aug 5, 2026

Upload by Evangeline