Risk and Impact of Tropical Cyclones in a Warming Climate

Compound Extremes and Multi-dimensional Risk

Advanced Machine Learning and Data Mining

Our research themes aim to address the critical challenges posed by climate extremes in a warming world. We focus on understanding and mitigating climate risks, with particular emphasis on the impacts of tropical cyclones. By integrating advanced physics-based models and cutting-edge data-mining techniques, we seek to enhance resilience and develop more effective adaptation strategies.

Tropical cyclones (hurricanes) pose a significant threat to both human lives and the economy, with annual damages in the U.S. exceeding $28 billion on average. Our research advances physics-based models to downscale synthetic hurricanes and perform high-resolution numerical simulations of multiple hurricane-induced hazards. These hazards include storm surge-driven flooding caused by strong winds, inland flooding from intense rainfall, and the complex interaction of surge and rainfall-driven flooding in coastal urban areas. By simulating these individual and compound hazards at high temporal and spatial resolutions, we assess the impacts of climate change on their behavior and drivers under current and future warming scenarios.

Our modeling framework incorporates the effects of shifts in storm climatology and sea-level rise—key factors exacerbated by climate change—along with the expansion of urban areas. This allows for a deeper understanding of how these elements influence the magnitude and spatial extent of hurricane-induced compound hazards. Additionally, we integrate machine learning models to quantify the economic risks associated with these hazards, especially in the context of a warming climate. These empirical models help project economic losses stemming from intensifying hurricanes and their associated hazards.

Through these simulations, we also evaluate the cost-effectiveness of various adaptation strategies, including engineered defenses and nature-based solutions. This enables us to provide critical insights into how cities can better prepare for future hurricanes and their compound hazards, offering actionable solutions to enhance resilience and mitigate the increasing risks posed by climate change.

Risk and Impact of Tropical Cyclones in a warming climate

Designing Resilient Coastal Cities to Compound Flooding from Hurricanes

During hurricane landfall, storm surge-induced flooding and inland heavy rainfall-driven flooding can occur simultaneously, interacting synergistically to produce compound flooding, which is significantly more destructive than either hazard alone. In this study, we developed a computational and physics-based hydrodynamic model to simulate compound flooding events driven by synthetic hurricanes under current and future climate scenarios in New York City. These high-resolution numerical simulations enable a detailed assessment of key drivers of compound flooding, including shifts in storm climatology and rising sea levels, both of which amplify the magnitude and frequency of such events. Our analysis reveals that, under the current climate, a destructive compound flooding event similar to Hurricane Sandy may occur once every 150 years; however, by the end of the century, due to rising sea levels and changing storm patterns, the recurrence interval for such events could decrease to once every 30 years.

The increasing frequency and magnitude of compound flooding in a warming climate highlights the urgent need to integrate these projections into urban resilience planning to mitigate the growing risk from future hurricane-induced compound hazards.

Economic Damages from Intensifying Hurricane Compound Hazards

Our study presents a sophisticated computational framework that synergizes physics-based risk assessments with advanced machine learning techniques, including Conditional Random Fields and Deep Learning, to model the complex relationships between hurricane hazard magnitudes—such as extreme winds, storm surges, and compound flooding—and their resulting economic impacts on coastal cities. This framework provides a comprehensive approach for assessing both the current and future risks of multiple hurricane-induced hazards. Using high-resolution synthetic hurricanes in a warming climate, we are able to project future economic damages on critical infrastructure and residential areas.

By combining physical simulations with data-driven models, this approach enhances our understanding of how shifting storm climatology, rising sea levels, and expanding urban areas interact to magnify the economic risks posed by hurricanes. Through detailed risk quantification, our findings offer essential insights that support the development of robust adaptation strategies and resilient infrastructure. These strategies are crucial for mitigating both direct and indirect economic losses, especially as climate change accelerates the intensity and frequency of compound hazards associated with tropical cyclones. This study is currently in preparation, with the potential to guide future policy and urban planning aimed at minimizing the escalating impacts of hurricanes in a changing climate.

Compound Extremes and Multi-dimensional Risk

Anthropogenic global warming has profoundly reshaped the intensity, frequency, spatial distribution, and destructive potential of extreme climate events. Compound events—where multiple climate or weather phenomena co-occur—can lead to significantly amplified impacts on both human and natural systems.

To quantify and better understand these risks in a warming climate, we develop advanced Bayesian-based, time-evolving, multi-dimensional risk frameworks. These frameworks are designed to analyze the dynamic escalation of risks associated with compound extremes by quantifying their underlying causal mechanisms, their responses to a nonstationary climate system, and the socio-economic consequences they produce. This approach enables us to assess the influence of climate change on a range of compound extremes, including simultaneous hot and dry years across critical global agricultural regions and the increasing risks of concurrent meteorological and hydrological droughts in the U.S.

Our ongoing research addresses the complex challenges posed by high-dimensional, temporally and spatially varying compound and cascading extremes. By focusing on their causality and attribution, we aim to enhance climate resilience through a deeper, more comprehensive understanding of the dynamic risks involved.

Multi-dimensional Climate Risk in a Nonstationary World

This study introduces an advanced Bayesian framework to quantify the spatial and temporal co-occurrence of climate stressors, particularly the joint probability of warm and dry conditions, in the context of a nonstationary, human-driven climate. We found that, due to anthropogenic climate change, the likelihood of simultaneous warm and dry years has doubled globally compared to the 1961–1990 baseline. This elevated risk is especially significant in key global agricultural regions, where extreme heat and drought are increasingly linked to human activities.

Our research also highlights that ambitious climate actions, such as adherence to the Paris Agreement, can significantly mitigate these growing risks by reducing the probability of extreme events occurring simultaneously across multiple regions. This multidimensional risk assessment offers critical insights for sectors such as water resource management and energy, which have traditionally relied on historical climate patterns for resource planning.

By developing this methodology, we provide a valuable tool for assessing and managing the complex multi-dimensional risks associated with a warming and nonstationary climate.

Risk of Compound meteorological and hydrological droughts in a changing climate

This study investigates the increasing risk of compound droughts, where both meteorological and hydrological droughts occur simultaneously, across the contiguous U.S. The research highlights a 10-20% increase in the risk of moderate and severe compound droughts, with an 8-12% rise for extreme events over recent decades. Using a bivariate GARCH model, the study also reveals that these compound droughts exhibit strong short-term memory, particularly in the western U.S., where meteorological droughts significantly influence the likelihood of prolonged and severe drought conditions. These findings provide essential insights for adaptive water resource management and long-term mitigation strategies in the face of a changing climate.

Advanced Machine Learning and Data Mining

Anthropogenic global warming has triggered potentially dangerous shifts in climate and ocean systems, leading to more severe and frequent climate extremes.

To minimize the potential damage from these extremes in a warming climate, we should enhance our predictive capabilities through the development of innovative data-mining techniques.

In our research, we pioneer new data-mining methods, including supervised dimensionality reduction coupled with machine learning techniques, to improve the predictability of hydro-climate extremes in the context of statistical downscaling and predictions.

Risk and Impact of Tropical Cyclones in a warming climate

Designing Resilient Coastal Cities to Compound Flooding from Hurricanes

Economic Damages from Intensifying Hurricane Compound Hazards

Compound Extremes and Multi-dimensional Risk

Multi-dimensional Climate Risk in a Nonstationary World

Risk of Compound meteorological and hydrological droughts in a changing climate

Advanced Machine Learning and Data Mining

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