David Fernando Muñoz Pauta

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Assistant Professor

Virginia Tech

United States

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David Fernando Muñoz Pauta is an Assistant Professor at Virginia Tech. His research focuses on the dynamics of water levels influenced by climate phenomena, including tropical cyclones and sea level rise. His recent articles explore topics such as satellite-based precipitation estimates, compound flood dynamics, and sediment trapping efficiency. He is based in the United States.

Recent Grants

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Collaborative Research: CAS-Climate: Nonstationarity of Compound Coastal Floods in the Anthropocene

Open Date: 2022-10-01

Close Date: 2025-09-30

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Collaborators (12)

Hamed Moftakhari

Associate Professor

The University of Alabama

UNITED STATES

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D. A. Jay

UNITED STATES

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Zuo Xue

UNITED STATES

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Nishani Moragoda

Teaching Assistant

The University of Alabama

UNITED STATES

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Hamid Moradkhani

Alton N. Scott Endowed Professor of Civil and Environmental Engineering

The University of Alabama

UNITED STATES

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Sagy Cohen

UNITED STATES

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Jennifer Irish

Professor

Virginia Polytechnic Institute and State University

UNITED STATES

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Paul Muñoz

Vrije Universiteit Brussel

BELGIUM

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Kyle Mandli

Assistant Professor

Columbia University in the City of New York

UNITED STATES

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Peyman Abbaszadeh

Assistant Professor

Portland State University

UNITED STATES

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Bradley Peter

Assistant Professor

University of Arkansas at Fayetteville

UNITED STATES

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Amir AghaKouchak

University of California, Irvine

UNITED STATES

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David Fernando Muñoz Pauta

Research Interests

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Recent Grants

Articles (19)

Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques

Breaking down annual and tropical cyclone-induced nonlinear interactions in total water levels

Reducing Latency in Satellite-Based Precipitation Estimates Using GOES-16 and Machine Learning

Escalating High Tide Flooding Along the Atlantic and Gulf Coast of the United States Due To Sea Level Rise

A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

Establishing flood thresholds for sea level rise impact communication

Nonlinear Interactions of Sea‐Level Rise and Storm Tide Alter Extreme Coastal Water Levels: How and Why?

Modeling and Analysis of Sediment Trapping Efficiency of Large Dams Using Remote Sensing

Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine

Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine

Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin

Compound Effects of Flood Drivers, Sea Level Rise, and Dredging Protocols on Vessel Navigability and Wetland Inundation Dynamics

Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers

From local to regional compound flood mapping with deep learning and data fusion techniques

Inter‐Model Comparison of Delft3D‐FM and 2D HEC‐RAS for Total Water Level Prediction in Coastal to Inland Transition Zones

Extreme Water Level Simulation and Component Analysis in Delaware Estuary during Hurricane Isabel

Collaborators (12)

Hamed Moftakhari

D. A. Jay

Zuo Xue

Nishani Moragoda

Hamid Moradkhani

Sagy Cohen

Jennifer Irish

Paul Muñoz

Kyle Mandli

Peyman Abbaszadeh

Bradley Peter

Amir AghaKouchak

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