About

Avantika Gori is an Assistant Professor of Civil & Environmental Engineering at Rice University. Her research focuses on understanding and quantifying coastal flood risk under evolving climate and landscape conditions. Combining physics-based models and multivariate statistical methods, her work addresses climate change impacts on tropical cyclone climatology and hydrometeorological extremes, probabilistic methods for high-resolution delineation of compound flood hazard, and the interaction of coastal hazards, climate change, and the built and natural environment. Dr. Gori obtained a B.S. in 2016 and an M.S. in 2018 in Civil & Environmental Engineering from Rice University, and a Ph.D. in Civil & Environmental Engineering from Princeton University in 2023.

Research topics

• Environmental science
• Geography
• Climatology
• Geology
• Meteorology
• Oceanography
• Computer Security
• Computer Science
• Environmental planning
• Cartography
• Environmental resource management
• Engineering
• Transport engineering
• Atmospheric sciences

Selected publications

• Emerging Importance of Compound Flooding in Future Tropical Cyclone Hazard Profiles

  Open MIND · 2026-01-01

  article1st authorCorresponding

  Accurately assessing tropical cyclone (TC) flood risk requires capturing the dynamic interactions between rainfall‐driven and coastal flood processes. We simulate flooding from over 2,800 synthetic TCs impacting five HUC6 watersheds in eastern North and South Carolina under historical and future (SSP5‐8.5) conditions using climatologically consistent data derived from physics‐based numerical models. Projected future TCs in our data set exhibit slower translation speeds and produce higher rainfall rates, driving substantial increases in flood extent and depth despite a slight decrease in average wind speeds and storm surge. The 1% annual flood hazard grows by 36%, with the contribution of compound flooding nearly doubling from 12% to 25%. When sea level rise is included, coastal and compound flooding expand further inland and upriver. Importantly, we show that inundation is underestimated when runoff and coastal flood hazards are modeled in isolation. Additionally, we find that the joint‐probability of the peak or average boundary conditions for a storm does not equal the probability of the overland flood extent at the HUC6 scale. By leveraging a probabilistic framework, our results highlight that using process‐based models to capture the spatiotemporal dynamics of flooding is essential for accurate watershed‐scale TC flood hazard assessment. These findings underscore the growing importance of compound flooding under climate change and sea level rise and provide a foundation for improved risk‐informed planning, infrastructure design, and adaptation to future hazards in coastal areas. Hurricanes generating heavy rainfall, high winds, and large storm surges can cause damaging flooding inland and along the coast. In some cases, multiple flood processes can combine to exacerbate flooding. This is known as compound flooding. Hurricane characteristics, such as size, strength and forward speed, are projected to change as the climate warms and this will influence flood severity, but how much and where is poorly understood. This study examines how hurricane‐related flood hazards could change in eastern North and South Carolina in the future. Using advanced and technical computer models, we simulate flooding from thousands of synthetic hurricanes under past (1980–2000) and future (high‐emissions; 2070–2100) climate scenarios across a large region. In the future, hurricanes are projected to generate more rain, leading to more flooding inland and exacerbating compound flooding near the coast. The worst future floods come from slower‐moving storms, and we show that sea level rise would make the coastal and compound flooding from these storms even larger. We find that compound flooding not only contributes to hurricane inundation at the coast in the past, but that it could double in the projected future, suggesting that it is critical to simulate these processes in coastal hazard assessments. Slower, wetter TCs generate more inland and compound flooding, increasing the 1% annual flood extent by 36% in a high‐emissions future Compound flooding accounts for a larger share of the future 1% annual flood extent, increasing from 12% to 25% Sea level rise expands and shifts compound flooding inland, increasing hazard depth and extent in low‐lying areas of the Carolinas Slower, wetter TCs generate more inland and compound flooding, increasing the 1% annual flood extent by 36% in a high‐emissions future Compound flooding accounts for a larger share of the future 1% annual flood extent, increasing from 12% to 25% Sea level rise expands and shifts compound flooding inland, increasing hazard depth and extent in low‐lying areas of the Carolinas

• Climatological Benchmarking of AI‐Generated Tropical Cyclones

  Journal of Geophysical Research Atmospheres · 2026-01-21

  articleSenior author

  Abstract This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI‐based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025, we assess the ability of two AI models (Pangu‐Weather and Aurora) to reproduce observed TC track density, climatology of storm characteristics, and physical consistency with TC theory. By comparing AI‐simulated TCs with ERA5 reanalysis, we benchmark the distributions of intensity, size, forward speed, and evaluate the model's ability to credibly simulate extratropical transition. Results show that both Pangu and Aurora perform well in reproducing storm track density, forward speed distribution, and outer size distribution. Aurora shows an improved performance in simulating storm intensity compared to Pangu, with less bias in the distribution of minimum central pressure and maximum wind speed. However, both models overestimate the distribution of storm inner size (radius of maximum winds), especially for extreme events. AI models capture the relative frequency and temporal evolution of extratropical transition patterns with reasonable accuracy. The AI‐simulated TCs are also less likely to conform to gradient wind balance compared to ERA5, indicating that the AI TCs may not be physically realistic in many cases. This benchmarking identifies systematic biases that can guide future corrections and support extended applications of AI models for TC hazard and risk assessment. Our work establishes a foundation for future studies using AI weather models in the context of TC climatological and hazard research.

  PublisherDOI

• Towards a typology for hybrid compound flood modeling

  Hydrology and earth system sciences · 2026-03-17

  articleOpen access

  Abstract. Modeling compound flood events requires sophisticated approaches that can capture complex nonlinear interactions between multiple flood drivers. While combining different data-driven and physics-based modeling approaches has shown promise, the criteria for classifying such combinations and the underlying terminology to describe them remain inconsistent in the literature. To establish classification criteria, we introduce a systematic framework for defining and categorizing hybrid physical-statistical modeling approaches in compound flood modeling. Hybrid compound flood models offer significant advantages in terms of prediction accuracy and computational efficiency over traditional single-model approaches, particularly in coastal regions where multiple flooding mechanisms frequently interact. We identify three categories of hybrid models: sequential, feedback, and ensemble. Through illustrative examples, we demonstrate how each category leverages the strengths of its component models while also maintaining their independence. The proposed framework enables a systematic evaluation of different hybrid modeling strategies, enhancing model comparability and supporting the development of more effective compound flood prediction tools.

  PublisherOA PDFDOI

• Emerging Importance of Compound Flooding in Future Tropical Cyclone Hazard Profiles

  Earth s Future · 2026-02-01

  articleOpen accessSenior author

  Abstract Accurately assessing tropical cyclone (TC) flood risk requires capturing the dynamic interactions between rainfall‐driven and coastal flood processes. We simulate flooding from over 2,800 synthetic TCs impacting five HUC6 watersheds in eastern North and South Carolina under historical and future (SSP5‐8.5) conditions using climatologically consistent data derived from physics‐based numerical models. Projected future TCs in our data set exhibit slower translation speeds and produce higher rainfall rates, driving substantial increases in flood extent and depth despite a slight decrease in average wind speeds and storm surge. The 1% annual flood hazard grows by 36%, with the contribution of compound flooding nearly doubling from 12% to 25%. When sea level rise is included, coastal and compound flooding expand further inland and upriver. Importantly, we show that inundation is underestimated when runoff and coastal flood hazards are modeled in isolation. Additionally, we find that the joint‐probability of the peak or average boundary conditions for a storm does not equal the probability of the overland flood extent at the HUC6 scale. By leveraging a probabilistic framework, our results highlight that using process‐based models to capture the spatiotemporal dynamics of flooding is essential for accurate watershed‐scale TC flood hazard assessment. These findings underscore the growing importance of compound flooding under climate change and sea level rise and provide a foundation for improved risk‐informed planning, infrastructure design, and adaptation to future hazards in coastal areas.

  PublisherDOI

• Emerging Importance of Compound Flooding in Future Tropical Cyclone Hazard Profiles

  UNC Libraries · 2026-03-05

  articleOpen access1st authorCorresponding

  Accurately assessing tropical cyclone (TC) flood risk requires capturing the dynamic interactions between rainfall‐driven and coastal flood processes. We simulate flooding from over 2,800 synthetic TCs impacting five HUC6 watersheds in eastern North and South Carolina under historical and future (SSP5‐8.5) conditions using climatologically consistent data derived from physics‐based numerical models. Projected future TCs in our data set exhibit slower translation speeds and produce higher rainfall rates, driving substantial increases in flood extent and depth despite a slight decrease in average wind speeds and storm surge. The 1% annual flood hazard grows by 36%, with the contribution of compound flooding nearly doubling from 12% to 25%. When sea level rise is included, coastal and compound flooding expand further inland and upriver. Importantly, we show that inundation is underestimated when runoff and coastal flood hazards are modeled in isolation. Additionally, we find that the joint‐probability of the peak or average boundary conditions for a storm does not equal the probability of the overland flood extent at the HUC6 scale. By leveraging a probabilistic framework, our results highlight that using process‐based models to capture the spatiotemporal dynamics of flooding is essential for accurate watershed‐scale TC flood hazard assessment. These findings underscore the growing importance of compound flooding under climate change and sea level rise and provide a foundation for improved risk‐informed planning, infrastructure design, and adaptation to future hazards in coastal areas. Hurricanes generating heavy rainfall, high winds, and large storm surges can cause damaging flooding inland and along the coast. In some cases, multiple flood processes can combine to exacerbate flooding. This is known as compound flooding. Hurricane characteristics, such as size, strength and forward speed, are projected to change as the climate warms and this will influence flood severity, but how much and where is poorly understood. This study examines how hurricane‐related flood hazards could change in eastern North and South Carolina in the future. Using advanced and technical computer models, we simulate flooding from thousands of synthetic hurricanes under past (1980–2000) and future (high‐emissions; 2070–2100) climate scenarios across a large region. In the future, hurricanes are projected to generate more rain, leading to more flooding inland and exacerbating compound flooding near the coast. The worst future floods come from slower‐moving storms, and we show that sea level rise would make the coastal and compound flooding from these storms even larger. We find that compound flooding not only contributes to hurricane inundation at the coast in the past, but that it could double in the projected future, suggesting that it is critical to simulate these processes in coastal hazard assessments. Slower, wetter TCs generate more inland and compound flooding, increasing the 1% annual flood extent by 36% in a high‐emissions future Compound flooding accounts for a larger share of the future 1% annual flood extent, increasing from 12% to 25% Sea level rise expands and shifts compound flooding inland, increasing hazard depth and extent in low‐lying areas of the Carolinas Slower, wetter TCs generate more inland and compound flooding, increasing the 1% annual flood extent by 36% in a high‐emissions future Compound flooding accounts for a larger share of the future 1% annual flood extent, increasing from 12% to 25% Sea level rise expands and shifts compound flooding inland, increasing hazard depth and extent in low‐lying areas of the Carolinas

  PublisherDOI

• Hurricane Ida’s blackout-heatwave compound risk in a changing climate

  Nature Communications · 2025-05-15 · 14 citations

  articleOpen access

  The emerging tropical cyclone (TC)-blackout-heatwave compound risk under climate change is not well understood. In this study, we employ projections of TCs, sea level rise, and heatwaves, in conjunction with power system resilience modeling, to evaluate historical and future TC-blackout-heatwave compound risk in Louisiana, US. We find that the return period for a compound event comparable to Hurricane Ida (2021), with approximately 35 million customer hours of simultaneous power outage and heatwave exposure in Louisiana, is around 278 years in the historical climate of 1980–2005. Under the SSP5-8.5 emissions scenario, this return period is projected to decrease to 16.2 years by 2070–2100, a ~17 times reduction. Under the SSP2-4.5 scenario, it decreases to 23.1 years, representing a ~12 times reduction. Heatwave intensification is the primary driver of this increased risk, reducing the return period by approximately 5 times under SSP5-8.5 and 3 times under SSP2-4.5. Increased TC activity is the second driver, reducing the return period by 40% and 34% under the respective scenarios. These findings enhance our understanding of compound climate hazards and inform climate adaptation strategies. Employing climate projections and power system modeling, the study finds that the return period for a hurricane-blackout-heatwave compound event comparable to Hurricane Ida (2021) will decrease by ~12–17 times by the end of the century due to heatwave and hurricane intensification.

  PublisherOA PDFDOI

• Compound hazards during tropical cyclones

  Elsevier eBooks · 2025-01-01 · 2 citations

  book-chapter
  PublisherDOI

• Towards a typology for hybrid compound flood modeling

  2025-10-05

  articleOpen access

  Abstract. Modeling compound flood events requires sophisticated approaches that can capture complex nonlinear interactions between multiple flood drivers. While combining different data-driven and physics-based modeling approaches has shown promise, the criteria for classifying such combinations and the underlying terminology to describe them remain inconsistent in the literature. To establish classification criteria, we introduce a systematic framework for defining and categorizing hybrid physical-statistical modeling approaches in compound flood modeling. Hybrid compound flood models offer significant advantages in terms of prediction accuracy and computational efficiency over traditional single-model approaches, particularly in coastal regions where multiple flooding mechanisms frequently interact. Here, we introduce a systematic framework for defining hybrid models and establish clear classification criteria based on their structural and functional characteristics. We identify three categories of hybrid models: sequential, feedback, and ensemble. Through illustrative examples, we demonstrate how each category leverages the strengths of its component models while also maintaining their independence. The proposed framework enables a systematic evaluation of different hybrid modeling strategies, enhancing model comparability and supporting the development of more effective compound flood prediction tools.

  PublisherOA PDFDOI

• Emerging Importance of Compound Flooding in Future Tropical Cyclone Hazard Profiles

  2025-10-12 · 2 citations

  preprintOpen accessSenior author

  Accurately assessing tropical cyclone (TC) flood risk requires capturing the dynamic interactions between rainfall-driven and coastal flood processes. We simulate flooding from over 2,800 synthetic TCs impacting five HUC6 watersheds in eastern North and South Carolina under historical and future (SSP5-8.5) conditions using climatologically consistent data derived from physics-based numerical models. Projected future TCs in our dataset exhibit slower translation speeds and produce higher rainfall rates, driving substantial increases in flood extent and depth despite a slight decrease in average wind speeds and storm surge. The 1% annual flood hazard grows by 36%, with the contribution of compound flooding nearly doubling from 12% to 25%. When sea level rise is included, coastal and compound flooding expand further inland and upriver. Importantly, we show that inundation is underestimated when runoff and coastal flood hazards are modeled in isolation. Additionally, we find that the joint-probability of the peak or average boundary conditions for a storm does not equal the probability of the overland flood extent at the HUC6 scale. By leveraging a probabilistic framework, our results highlight that using process-based models to capture the spatiotemporal dynamics of flooding is essential for accurate watershed-scale TC flood hazard assessment. These findings underscore the growing importance of compound flooding under climate change and sea level rise and provide a foundation for improved risk-informed planning, infrastructure design, and adaptation to future hazards in coastal areas.

  PublisherDOI

• Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas

  UNC Libraries · 2025-07-04

  articleOpen access
  PublisherDOI

Frequent coauthors

• Ning Lin

  23 shared

• Philip B. Bedient

  14 shared

• Antonia Sebastian

  University of North Carolina at Chapel Hill

  13 shared

• Andrew Juan

  Texas A&M University

  12 shared

• Dazhi Xi

  11 shared

• Michael Oppenheimer

  6 shared

• Jamie E. Padgett

  Rice University

  6 shared

• Benjamin Bass

  University of California, Los Angeles

  5 shared

Education

• Master of Science, Civil & Environmental Engineering

  Rice University

  2018

• Bachelor of Science, Civil & Environmental Engineering

  Rice University

  2016

Awards & honors

• Wallace Memorial Honorific Fellowship (2022-2023)
• AGU Natural Hazards Award for Graduate Research (2022)
• Future Cities Fellowship (2021)
• National Defense Science and Engineering Graduate Fellowship…
• Gordon Y. S. Wu Fellowship in Engineering (2018)