CoRAL Lab at Virginia Tech
Compound flood hazard and Risk Assessment in Low-lying areas (CoRAL).
The CoRAL Lab integrates physics-informed and data-driven models to understand complex compound extreme dynamics in human and natural systems. We are interested in statistical analysis, hydrodynamic modeling, machine learning, remote sensing, and data collection using drones.
Vision:
The CoRAL Lab aims at enhancing the characterization, modeling, and prediction of compound extreme dynamics at different scales as well as their impacts on vulnerable communities to flooding.
We aim at providing actionable flood hazard maps, adaptation and planning strategies that integrate green infrastructure and risk-flood information through scenario-based simulations.
news
| Jan 29, 2026 | Our collaborative research on “Evaluating the three-cornered hat method for hourly satellite precipitation fusion in hydrological forecasting: A case study in a Tropical Andean Basin” has been accepted for publication in the Journal of Hydrology: Regional Studies. Congratulations to the team! |
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| Dec 23, 2025 | The CoRAL Lab members presented their work in the AGU Fall 2025 meeting: “Modeling Water Level Propagation with Sparse Gauges using Enhanced Deep Learning and Spatiotemporal Transfer Learning Approaches”, “Compound Effects of Synthetic, Low-frequency Tropical Cyclones and Sea Level Rise Scenarios on Nonlinear Tide–Surge Interactions”, and “A Comparative Analysis of Operational Multi-Method Flood Inundation Mapping: Quantifying Fidelity–Accuracy Tradeoffs”. |
| Dec 23, 2025 | Dr. Muñoz co-convened the AGU Fall 2025 session: “Multihazard Flood Modeling: From Inland to Coast”. Thanks to those who submitted their work to our session. |
| Dec 20, 2025 | The CoRAL Lab is extremely proud to introduce Dr. Samuel Daramola to the research community. Sam will be joining the Stevens Institute of Technology as a posdoctoral researcher. All the best wishes to Dr. Daramola in this new adventure! |
latest posts
selected publications
- From local to regional compound flood mapping with deep learning and data fusion techniquesScience of the Total Environment, 2021
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning modelsHydrology and Earth System Sciences, 2024
- Predicting the evolution of extreme water levels with long short-term memory station-based approximated models and transfer learning techniquesWater Resources Research, 2025
- Breaking down annual and tropical cyclone-induced nonlinear interactions in total water levelsAdvances in water resources, 2025
- A transferable deep learning framework to propagate extreme water levels from sparse tide-gauges across spatial domainsExpert Systems with Applications, 2025