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Chaopeng Shen

Assistant Professor

Pennsylvania State University

United States

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Research Interests

Hydrology

50%

Climatology

20%

Water Resource Management

30%

Circulation Modeling

20%

Climate Dynamics

20%

Groundwater Modeling

20%

Geophysical Fluid Dynamics

20%

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Articles20

Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models

Article Link

Year:

2025

Improving differentiable hydrologic modeling with interpretable forcing fusion

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Year:

2025

Deep Learning and Remote‐Sensed Observations Reveal Global Underestimation of River Obstructions

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Year:

2025

The Value of Terrain Pattern, High‐Resolution Data and Ensemble Modeling for Landslide Susceptibility Prediction

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Year:

2025

Probabilistic Diffusion Models Advance Extreme Flood Forecasting

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Year:

2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

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Year:

2025

Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?

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Year:

2025

Global Daily Discharge Estimation Based on Grid Long Short‐Term Memory (LSTM) Model and River Routing

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Year:

2025

Calibration of the High‐Resolution Common Land Model in Simulating the Soil Moisture Over the Northeastern China Using an Adaptive Parameter Learning Method

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Year:

2025

Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?

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Year:

2025

Applying Knowledge-Guided Machine Learning to Slope Stability Prediction

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Year:

2023

Differentiable modelling to unify machine learning and physical models for geosciences

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Year:

2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

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Year:

2023

Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration

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Year:

2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

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Year:

2023

Hybrid forecasting: blending climate predictions with AI models

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Year:

2023

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

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Year:

2023

Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation

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Year:

2022

Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers

Article Link

Year:

2022

Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy

Article Link

Year:

2022

Collaborators12

Daniel Kifer

Pennsylvania State University

UNITED STATES

Alexandre M. Tartakovsky

UNITED STATES

Marie-Amélie Boucher

Professor

Université de Sherbrooke

CANADA

Andrew Wood

Research Professor

Colorado School of Mines

UNITED STATES

Linda Speight

University of Oxford

UNITED KINGDOM

Yalan Song

Pennsylvania State University

UNITED STATES

Peishi Jiang

Assistant Professor

University of Alabama, Tuscaloosa

UNITED STATES

Robert Wilby

Loughborough University

UNITED KINGDOM

Li Li

Pennsylvania State University

UNITED STATES

Louise Slater

University of Oxford

UNITED KINGDOM

Te Pei

Assistant Professor

The City College of New York

UNITED STATES

Paul Ullrich

tahoe environmental research center university of california davis california usa

UNITED STATES

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