Laura Condon

Assistant Professor

University of Arizona

United States

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Laura Condon is an Assistant Professor at The University of Arizona, USA. Her research focuses on hydrology, particularly in the areas of machine learning applications for hydrologic simulations, groundwater modeling, and the interactions between surface water and groundwater. Recent publications include studies on diverse topics such as regionalized catchment dynamics, reservoir reliability, and spatio-temporal modeling in terrestrial hydrology.

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Collaborators (6)

Peter Melchior

Assistant Professor

Princeton University

UNITED STATES

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Anne Verhoef

Professor of Soil Physics and Micrometeorology

University of Reading

UNITED KINGDOM

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Graham Fogg

Professor of Hydrogeology

University of California, Davis

UNITED STATES

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Reed M. Maxwell

UNITED STATES

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Andrew Bennett

Assistant Professor

University of Arizona

UNITED STATES

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Jennie Steyaert

Utrecht University

NETHERLANDS

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Laura Condon

Research Interests

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About

Articles (24)

A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics

CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China

A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models

A scalable and modular reservoir implementation for large-scale integrated hydrologic simulations

Unravelling groundwater–stream connections over the continental United States

Spatio‐Temporal Machine Learning for Regional to Continental Scale Terrestrial Hydrology

Declining Reservoir Reliability and Increasing Reservoir Vulnerability: Long‐Term Observations Reveal Longer and More Severe Periods of Low Reservoir Storage for Major United States Reservoirs

hf_hydrodata: A Python package for accessing hydrologic simulations and observations across the United States

Investigating Economic and Climate Signals in Groundwater Data

Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models

Toward interpretable LSTM-based modeling of hydrological systems

Simulation-based inference for parameter estimation of complex watershed simulators

Exploring the Signal Filtering Properties of Idealized Watersheds Using Spectral Analysis

Climate-warming-driven changes in the cryosphere and their impact on groundwater–surface-water interactions in the Heihe River basin

ParFlow Sand Tank: A tool for groundwater exploration

Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology

Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model

A high-resolution, 3D groundwater-surface water simulation of the contiguous US: Advances in the integrated ParFlow CONUS 2.0 modeling platform

Global Groundwater Modeling and Monitoring: Opportunities and Challenges

A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes

Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML

Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States

Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Continental Hydrologic Intercomparison Project, Phase 1: A Large‐Scale Hydrologic Model Comparison Over the Continental United States

Collaborators (6)

Peter Melchior

Anne Verhoef

Graham Fogg

Reed M. Maxwell

Andrew Bennett

Jennie Steyaert

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