Contributions to Statistical Modeling and Estimation of Rainfall Intensity–Duration–Frequency Curves
Date of Award
12-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mathematical Sciences
Committee Chair/Advisor
Dr. Brook Russell
Committee Member
Dr. Whitney Huang(Co-advisor)
Committee Member
Dr. Angeline Close Scheinbaum
Committee Member
Dr. Patrick Gerard
Abstract
Extreme rainfall can cause flooding, damage infrastructure, and create serious risks for communities. Engineers use Intensity-Duration-Frequency (IDF) curves to estimate precipitation extremes over different lengths of time, such as one hour or one day, and the average time one would expect wait for one of these events to occur. Several approaches exist for estimating IDF curves, but no single approach is known to be best in all cases. In this dissertation, I study statistical methods that aim to improve IDF curve estimation. I use the Canadian Regional Climate Model (CanRCM4) Large Ensemble, which contains 35 independent climate simulations. These simulations act like multiple versions of the climate system and allow me to calculate the “ground truth” return levels from the simulations. I can then assess how well different statistical methods perform.
I first investigate modeling approaches that describe how rainfall extremes change over different durations. I find that some previous approaches do not represent this relationship well, especially for very rare and intense rainfall events. I then develop a more flexible statistical approach that better captures the ways that intensity varies with duration in a stable climate. We find that this approach provides improved estimators for modeling IDF curves.
I then explore the ways in which extreme rainfall changes over time in response to a changing climate. I begin by focusing on a 24-hour rainfall duration and show that return level estimators improve as a result of linking extreme rainfall intensity to annual atmospheric carbon dioxide concentration levels. Additionally, I find that by using spatial modeling approach that is able to share information from nearby locations.
Finally, I combine information across temporal duration and across spatial locations to develop a cohesive modeling framework. Importantly, this approach is applied under a stable climate assumption. Leveraging information from both sources reduces uncertainty and leads to more reliable estimators of extreme rainfall. This work in this dissertation is critical, because communities depend on accurate estimates of rainfall extremes when designing stormwater systems, managing flood risks, and planning climate-resilient infrastructure. The methods here aim to strengthen the statistical foundation for estimating IDF curves and may help to support safer and more informed decision-making under a changing climate.
Recommended Citation
Huang, Jiyun, "Contributions to Statistical Modeling and Estimation of Rainfall Intensity–Duration–Frequency Curves" (2025). All Dissertations. 4148.
https://open.clemson.edu/all_dissertations/4148