About

Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science at Princeton University, serving as a professor of Civil and Environmental Engineering and the High Meadows Environmental Institute. His research centers on understanding terrestrial freshwater resources on Earth, specifically focusing on how much freshwater is available and the rates at which it is replenished or depleted. His work addresses complex hydrological problems, including groundwater, evapotranspiration, and snow, with an emphasis on understanding the connections within the hydrologic cycle and their relation to water quantity and quality under anthropogenic stresses. Maxwell's research group employs a broad range of approaches, including integrated hydrologic modeling, field observations, and remote sensing products, to investigate these critical issues. He has contributed significantly to the field through his research on hydrology, water resources, and environmental systems. Maxwell has been recognized for his expertise with honors such as the Distinguished Henry Darcy Lecturer by the American Geophysical Union in 2020 and being named a Fellow of the same organization in 2019. He also served as the Boussinesq Lecturer and held the Belle van Zuylen Chair as a visiting professor at the University of Utrecht. In addition to his research, Maxwell teaches courses such as Physical Hydrology and actively contributes to the academic community through his leadership roles, including directing the Integrated Groundwater Modeling Center.

Research topics

• Computer Science
• Environmental science
• Geology
• Ecology
• Engineering
• Geography
• Meteorology
• Mechanics
• Environmental resource management
• Physics
• Parallel computing
• Cartography
• Geotechnical engineering
• Oceanography
• Computational science
• Environmental engineering
• Water resource management

Selected publications

• 20 years of trials and insights: bridging legacy and next generation in ParFlow and Land Surface Model Coupling

  Geoscientific model development · 2026-03-04

  articleOpen accessSenior author

  Abstract. Groundwater plays a vital role in terrestrial water and energy cycles. Yet, it remains oversimplified in most Earth system models (ESMs), limiting their ability to represent key land-atmosphere interactions, including evapotranspiration partitioning, drought propagation, and boundary layer development. The original coupling of ParFlow with the Common Land Model (CoLM) in 2005 not only demonstrated the feasibility of integrating physically based groundwater models into ESMs, but also revealed emergent behaviors – such as lateral moisture redistribution, along with the buffering effects that emerge from enhanced subsurface connectivity – that cannot be captured by traditional land surface models (LSMs). This study reviews key findings from two decades of ParFlow–land/atmosphere coupled modeling efforts, highlighting how groundwater–land–atmosphere interactions shape surface energy balance and hydrologic connectivity across three dimensions: upward feedbacks, downward influences, and the critical zone of coupling. Given the substantial advances in LSMs such as CoLM over the past two decades, a renewed recoupling effort is warranted to enhance our understanding of groundwater's role across a broader range of Earth system processes. Preliminary efforts to recouple ParFlow with the updated water and energy modules of CoLM demonstrate improved performance when evaluated against reanalysis and observational data. To ensure long-term sustainability, we propose a modular and maintainable coupling framework addressing functional extensibility, data/code interoperability, and parallel computing needs, in which area, TerrSysMP2 has taken early steps and may be considered an initial forerunner. Finally, we summarize existing ParFlow-based coupled systems and highlight the need for a community-led model intercomparison project (PLCMIP) to benchmark performance, evaluate process coupling under varied configurations, and foster cross-community collaboration.

  PublisherOA PDFDOI

• A high-resolution water table depth (WTD) map for the contiguous United States

  Open MIND · 2026-02-06

  datasetSenior author

  A 1-arcsec (~30 m) resolution water table depth (WTD) map for the contiguous United States using machine learning methods trained on over one million well observations compiled from multiple groundwater databases spanning 1914-2023. A random forest model with 300 decision trees was trained on 80% of these data using input variables including climatology (precipitation, temperature, PME), subsurface properties (hydraulic conductivity, soil texture), and topographic features (elevation, slope, distances to streams), achieving test performance of r = 0.79, RMSE = 14.94 m, and NSE = 0.62. Ma, Y., Condon, L.E., Koch, J. et al. High resolution US water table depth estimates reveal quantity of accessible groundwater. Commun Earth Environ 7, 45 (2026). https://doi.org/10.1038/s43247-025-03094-3 Data also accessible via the HydroData platform https://hydroframe.org/hydrodata

  DOI

• Learning, creating, and sharing: A science communication framework for water and climate education

  2026-03-14

  articleOpen accessSenior authorCorresponding

  Since 2015, the Integrated GroundWater Modeling Center has engaged diverse audiences in water and climate science through community education and outreach programs including STEM fairs, university courses, teacher workshops, and week-long camps for high school students. Across these varied contexts, science communication has served as a consistent throughline, informing both how participants learn scientific content and how they share it with others.Over this period of engagement, participant groups took part in parallel learning of hydrology-focused scientific content and science communication principles, applying both to the creation of communication products, and synthesizing new knowledge and tools to engage effectively with peers and public audiences. Participants across this collection of programs created a wide range of science communication products, including hands-on activities, videos, games, audio products, and digital tools. Together, these methods and outcomes supported participants in communicating complex water and climate topics in accessible and meaningful ways.This presentation will highlight educational approaches refined over a decade of programming, reaching over 10,000 in-person participants and a similarly sized audience through digital tools and lessons. Evaluation metrics collected across program iterations indicate consistent gains in self-reported knowledge and suggest positive participant experiences. It will also share core elements of the instructional framework and key lessons learned from a decade of communication and outreach, including observed impacts and practical insights for designing hands-on science communication experiences. By providing structured opportunities to both learn and practice science communication, these programs support participants in understanding how scientific knowledge is developed and communicated, with the broader goal of building trust in scientists and the scientific process.

  PublisherDOI

• Comparing Snow Water Equivalent Estimations From Long Short‐Term Memory Networks and Physics‐Based Models in the Western United States

  Water Resources Research · 2026-01-29 · 1 citations

  articleOpen accessSenior author

  Abstract In snowmelt‐dominated regions like the western U.S., mountain snowpacks supply 50%–70% of the total runoff. Accurate estimation of snow water equivalent (SWE) is critical for informed management of water resources, including reservoir operations, flood risk assessments, and drought mitigation. However, the spatial variability and complexity of snow accumulation and dispersion processes are challenging to accurately model. Recently, machine learning (ML) techniques, particularly Long Short‐Term Memory (LSTM) neural networks, have demonstrated success in modeling complex hydrological processes; however, their application to SWE estimation remains relatively underexplored. In this paper, we develop and train an LSTM model to estimate SWE at point locations in the western U.S. and assess its sensitivity, transferability, and robustness to biased forcing in comparison with two representative physics‐based models, ParFlow‐CLM and the University of Arizona SWE product. We identify strengths of the LSTM model, including superior performance on metrics of magnitude and temporal accuracy and higher adaptability across snowpack regimes and erroneous forcing conditions, as well as weaknesses, including a lack of physical constraints on estimations. We also show the LSTM model relies on physically relevant characteristics—both static and meteorological—to predict snowpack. Overall, this paper represents a novel investigation into the behavior and characteristics of an LSTM model of SWE compared to a range of physics‐based models, extending beyond traditional performance‐focused assessments. Developing a deeper understanding of LSTM models in comparison to physics‐based models can pinpoint areas for improvement, representing an important step toward the development of operational ML models of SWE.

  PublisherDOI

• A Hybrid Machine Learning and Physics-Based Approach Accurately Models High-Resolution Inundation

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A high-resolution water table depth (WTD) map for the contiguous United States

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-06

  datasetOpen accessSenior author

  A 1-arcsec (~30 m) resolution water table depth (WTD) map for the contiguous United States using machine learning methods trained on over one million well observations compiled from multiple groundwater databases spanning 1914-2023. A random forest model with 300 decision trees was trained on 80% of these data using input variables including climatology (precipitation, temperature, PME), subsurface properties (hydraulic conductivity, soil texture), and topographic features (elevation, slope, distances to streams), achieving test performance of r = 0.79, RMSE = 14.94 m, and NSE = 0.62. Ma, Y., Condon, L.E., Koch, J. et al. High resolution US water table depth estimates reveal quantity of accessible groundwater. Commun Earth Environ 7, 45 (2026). https://doi.org/10.1038/s43247-025-03094-3 Data also accessible via the HydroData platform https://hydroframe.org/hydrodata

  PublisherDOI

• Supplementary Material for "Produced water imprints in groundwaters predicted vulnerable to spills from shale gas development"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-01

  datasetOpen access

  SUPPLEMENTARY MATERIAL FOR Title: "Produced water imprints in groundwaters predicted vulnerable to spills from shale gas development"Authors: Mario A. Soriano Jr., Joshua L. Warren, Yingcan Cathy Wang, David van Velden, Shuqi Lin, Reed M. Maxwell, and James E. SaiersJournal: Published article doi: CONTENTS [1] pathline_histogram.nb.html - R Markdown Notebook documenting code for pathline histogram visualization (Figure 2 in journal manuscript) [2] regression_and_sensitivity_analysis.nb.html - R Markdown Notebook documenting code for statistical analysis (including analysis for Table 1 and Supplementary Table 2) [3] Sample_MODFLOW_MODPATH_Simulation.zip - Zip file containing Jupyter Notebook and sample input files for running MODFLOW groundwater flow and MODPATH particle tracking simulations. [4] V_pop_hexpoly.zip - Zip file containing shapefile of groundwater dependent populations served by domestic wells in locations highly vulnerable (V ≥ 0.5) to groundwater contamination from unconventional oil and gas development. Shown as Figure 3 in journal manuscript. Attributes: Population - Total vulnerable population within hexagon (number of individuals); Pct_TotPop - Total vulnerable population divided by Total population within hexagon (%); V_mean - Mean vulnerability to all UOGD, aggregated from original 250-m x 250-m grid computations; n_UOG - Number of UOGD wells within hexagon; n_spill - Number of UOGD spills within hexagon

  PublisherDOI

• A prototype hyper-resolution groundwater digital twin for the contiguous United States: integrating physics-based modeling, machine learning, and observations

  Journal of Hydrology · 2026-02-24

  articleOpen accessSenior authorCorresponding

  • We develop a physics-guided machine learning model for groundwater modeling. • Our model reliably estimates groundwater dynamics at large scales. • We provide daily 1 arcsec water table depth data for the contiguous United States. • This model is a prototype groundwater digital twin for the United States. To advance large-scale hyper-resolution groundwater modeling, we leverage existing physically-based simulation results and water table depth (WTD) observations to develop a prototype groundwater digital twin for the contiguous United States (CONUS). This framework represents a continuously updatable virtual representation that integrates observations with physics-based predictions to support operational decision making. An adjusted random forest model is trained to downscale 1 km simulation results from the integrated physically-based hydrologic model ParFlow-CLM to 1 arcsec (∼30 m) and bias-correct to observations, producing daily 1 arcsec WTD and associated uncertainties across the CONUS. Trained on water year 2003 (WY2003), the model reliably estimates temporal variations in WTD at most previously unseen grid cells, achieving a median Spearman’s ρ of 0.66. Over half of the grid cells that contain continuous daily records in WY2003 exhibit good performance, with ρ ≥ 0.5. At the subbasin scale, the digital twin captures more detailed groundwater variability than ParFlow-CLM, especially in areas with strong surface–groundwater interactions. During the future time period (WY2024), the model consistently outperforms ParFlow-CLM, increasing the median ρ by 0.13. Enabled by multi-GPU computing, the digital twin generates each daily 1-arcsec resolution WTD map in approximately 35 min of GPU time, providing insights into groundwater systems across multiple scales. The success of the physics-guided machine learning (ML) digital twin highlights the advantage of combining ML and physically-based modeling in groundwater applications. This groundwater digital twin demonstrates a path toward operational capability, enabling near-real-time monitoring, scenario exploration, and decision support at unprecedented spatial resolution.

  PublisherDOI

• Vegetation groundwater use drives streamflow declines in Colorado River headwaters

  Research Square · 2026-02-09

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Accurately and Efficiently Predicting High-Resolution Inundation using a Hybrid Machine Learning and Physics-Based Approach

  2026-03-14

  articleOpen accessSenior authorCorresponding

  Predicting high-resolution inundation at large spatial and temporal scales is important in understanding future water availability and flood risk. Classically, hydrologic models have been used to model inundation, but the computational expense associated with applying hydrologic models at large scales has motivated the use of other methodologies, such as machine learning. We propose a hybrid physics-based and machine learning modeling approach to produce high-resolution inundation maps at a much lower computational cost than high-resolution physics-based modeling while still maintaining high accuracy. This methodology is then tested in a representative watershed in Colorado, USA. The proposed hybrid physics-based and machine learning modeling approach consists of a coarse spatial resolution hydrologic model and a random forest downscaling postprocessing step. First, a 1km resolution integrated surface-subsurface hydrologic model, ParFlow-CLM, is ran for the spatial and temporal domain of interest. Then, the resultant modeled inundation as well as additional climate and geographical parameters are fed into a random forest model which predicts inundation at a higher spatial resolution. We tested this methodology in a 1800km2 watershed in Colorado, USA during the 2019 water year to predict modeled inundation produced by a 100m resolution hydrologic model. In our test case, this hybrid methodology predicted whether each fine resolution cell is inundated at each hourly timestep correctly >97% of the time and maintained high accuracy in unseen timesteps as well as in unseen locations within the same region. We will also discuss next steps to predict real-world inundation by training the random forest model on 30m resolution satellite data. This study shows the potential for this methodology to be applied at the continental scale to predict high-resolution inundation accurately and efficiently.

  PublisherDOI

Recent grants

• An Integrated Hydrologic Model Intercomparison Workshop to Develop Community Benchmark Problems

  NSF · $26k · 2011–2014

• WSC-CATEGORY 2 COLLABORATIVE: WATER QUALITY AND SUPPLY IMPACTS FROM CLIMATE-INDUCED INSECT TREE MORTALITY AND RESOURCE MANAGEMENT IN THE ROCKY MOUNTAIN WEST

  NSF · $2.3M · 2012–2018

• Collaborative Research: Framework: Software: NSCI : Computational and data innovation implementing a national community hydrologic modeling framework for scientific discovery

  NSF · $594k · 2020–2023

• Collaborative Research: Sustainability in the Food-Energy-Water nexus; integrated hydrologic modeling of tradeoffs between food and hydropower in large scale Chinese and US basins

  NSF · $243k · 2018–2021

• Collaborative Research: Sustainability in the Food-Energy-Water nexus; integrated hydrologic modeling of tradeoffs between food and hydropower in large scale Chinese and US basins

  NSF · $174k · 2020–2024

Frequent coauthors

• Laura E. Condon

  135 shared

• Stefan Kollet

  Forschungszentrum Jülich

  63 shared

• Kenneth H. Williams

  Lawrence Berkeley National Laboratory

  39 shared

• Jun Zhang

  Nanyang Technological University

  30 shared

• Hoang Tran

  Pacific Northwest National Laboratory

  28 shared

• L. A. Bearup

  United States Bureau of Reclamation

  28 shared

• Claire Welty

  28 shared

• James M. Gilbert

  University of California, Santa Cruz

  26 shared

Awards & honors

• 2020 Distinguished Henry Darcy Lecturer American Geophysical…
• Fellow (2019) American Geophysical Union
• 2018 Boussinesq Lecturer
• Belle van Zuylen Chair (visiting), University of Utrecht