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Jun-Whan Lee

University of Texas at Austin

SCHOLARLY PAPERS

5

DOWNLOADS

547

TOTAL CITATIONS

0

Scholarly Papers (5)

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1.

PyFlood: Rapid High-Resolution Coastal Flood Mapping with Digital Elevation Model, Land Cover and Water Level Data

Number of pages: 45 Posted: 09 Aug 2024 Last Revised: 05 Jun 2026
Armando Ulises Santos Cruz, Jun-Whan Lee and Wonhyun Lee
University of Texas at Austin, University of Texas at Austin and University of Texas at Austin
Downloads 245 (311,323)

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Flood Modeling, Land Cover, Python, Bayesian Optimization Algorithm, Coastal Engineering

2.

Spatiotemporal Quantification of Nonlinearity and Shapley Value-Based Relative Contribution of Compound Flood Drivers During Hurricane Beryl (2024)

Number of pages: 60 Posted: 20 May 2025
Md Enayet Chowdhury, Jun-Whan Lee and Wonhyun Lee
The University of Texas at Austin, TX, USA, University of Texas at Austin and University of Texas at Austin
Downloads 120 (600,462)

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Compound Flooding, Nonlinearity, Relative Contribution, Shapley Value, Hurricane Beryl, SFINCS

3.

A Hybrid Framework Combining Machine Learning and Static Flood Models for Rapid, High-Resolution Peak Storm Surge Inundation Prediction

Number of pages: 28 Posted: 14 Apr 2025 Last Revised: 07 Aug 2025
Hyunje Yang, Jun-Whan Lee and Armando Ulises Santos Cruz
University of Texas at Austin, University of Texas at Austin and University of Texas at Austin
Downloads 109 (645,052)

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peak storm surge, tropical cyclone, machine learning, static flood model, hybrid model, flood inundation mapping

4.

What is Causing Flood Prediction Error? A Shapley-Based Attribution of Compound Hurricane Flood 

Number of pages: 56 Posted: 23 Mar 2026 Last Revised: 13 Apr 2026
Md Enayet Chowdhury, Jun-Whan Lee, Naveen Sudharsan, Shyama Mohanty, Alka Tiwari and Dev Niyogi
The University of Texas at Austin, TX, USA, University of Texas at Austin, The University of Texas at Austin, TX, USA, The University of Texas at Austin, TX, USA, The University of Texas at Austin, TX, USA and University of Texas at Austin
Downloads 58 (1,157,785)

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Compound flood, Water level error decomposition, Shapley value, GraphCast, Digital twin, SFINCS

5.

Reflection-induced Hydrodynamic Amplification from Tsunami Debris Damming in Macro-roughness Environments: A Material Point Method Study

Number of pages: 81 Posted: 26 Jun 2026
Youngchul Choi, Jun-Whan Lee, Justin Bonus and Che-Wei Chang
The University of Texas at Austin, TX, USA, University of Texas at Austin, University of California, Berkeley and University of Rhode Island
Downloads 15

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Tsunami, Debris Damming, Macro-roughness, Wave Reflection, Hydrodynamic Amplification, Material Point Method, ClaymoreUW