CEVE 543 (Statistical-Physical Methods for Hydroclimate Extremes and Catastrophes) Syllabus

Fall 2025

This course covers the use of tools from data science (statistics, machine learning, and programming) to model climate hazards such as floods and droughts. Through hands-on programming assignments based on state-of-the-art published research, students will learn to apply methods to real-world problems with a strong emphasis on probabilistic methods and uncertainty quantification.

Course Information

Instructor

TA

Meetings

  • MWF
  • 1:00-1:50pm
  • TBD

Learning Objectives

After completing this course, you will be able to:

  1. Apply statistical and physical modeling to characterize hydroclimate extremes using modern techniques
  2. Evaluate and critique methodologies in research and technical reports on hydroclimate extremes
  3. Quantify and communicate uncertainty in probabilistic assessments of extreme events
  4. Design appropriate modeling strategies by comparing different statistical-physical methods
  5. Implement and validate models for hydroclimate extremes using good programming practices
  6. Communicate technical concepts effectively through presentations, discussions, and writing

Prerequisites & Preparation

  • Linear algebra (you should be comfortable with matrix notation and basic operations)
  • A course in applied statistics (e.g., STAT 419/519)
  • Some exposure to Python, Julia, Matlab, R, or another programming language is strongly encouraged, though not strictly required

I will post a “Homework Zero” assignment before the start of the semester to help you assess your readiness for the course.

Required Materials

No textbook is required for this course.

Rice Honor Code

In this course, all students will be held to the standards of the Rice Honor Code, a code that you pledged to honor when you matriculated at this institution. If you are unfamiliar with the details of this code and how it is administered, you should consult the Honor System Handbook at honor.rice.edu/honor-system-handbook/. This handbook outlines the University’s expectations for the integrity of your academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process.

AI/LLM Resource Policy

AI tools, especially Large Language Models (LLMs), can be powerful aids in learning. They can outline, summarize, explain, and write code. However, these tools also pose risks, including the potential for plagiarism and for students to rely on them without developing their own understanding.

The rapid evolution of AI tools raises important questions about learning, assessment, and the skills you need for the future.

For this course, the goal of learning is articulated in the course learning objectives. Rather than focusing on preventing or punishing “cheating,” we recognize that students will use LLMs on take-home and written material, and should learn to do so responsibly. Assessment will focus on approaches—including tests and in-class activities—that are not readily amenable to LLM use (note: the use of any hypothetical wearable AI device is prohibited). You are encouraged to explore the resources linked on the LLM resources page and elsewhere.

For this course, the goal of learning is articulated in the course learning objectives. Rather than focusing on preventing or punishing “cheating,” we recognize that students will use LLMs on take-home and written material, and should learn to do so responsibly. Assessment will focus on approaches—including tests and in-class activities—that are not readily amenable to LLM use (note: the use of any hypothetical wearable AI device is prohibited).

This AI policy is reciprocal. For example, your instructors may use LLMs to draft rubrics or add comments to example code, but your assignments will not be graded by LLMs, and we will not use LLMs to write code or text we do not understand and fully stand behind.

Disability Resource Center

If you have a documented disability or other condition that may affect academic performance you should: (1) make sure this documentation is on file with the Disability Resource Center (Allen Center, Room 111 / adarice@rice.edu / x5841) to determine the accommodations you need; and (2) talk with me to discuss your accommodation needs.

Syllabus Change Policy

This syllabus is only a guide for the course and is subject to change with advanced notice.

Grading

TBD

Late Work Policy

Because assignments are designed to be “LLM-proof”, assessments will be primarily in-class. This makes it hard to make up late or missed work.

If you need to miss a class because of illness, family emergency, or other unavoidable conflict, or for critical research travel, please let me know as soon as possible. Your grade will not be penalized for excused absences (you will be assigned zero out of zero).

Module Learning Objectives

This course is organized into four modules, each building on the previous one.

Module 0: Introduction, Motivation, and Foundations

By the end of this module, you will be able to:

  1. Articulate the core challenge in hydroclimate extremes assessment: balancing limited observations with imperfect models
  2. Explain the importance of quantifying uncertainty for engineering applications
  3. Connect hydroclimate hazards to underlying weather patterns and climate drivers
  4. Discuss model validation challenges, especially for rare events
  5. Use key terminology from probability, statistics, and machine learning correctly
  6. Set up the programming environment needed for computational assignments
  7. Describe the course approach to integrating statistical and physical methods

Module 1: Extreme Value Theory (EVT) for Hazard Assessment

By the end of this module, you will be able to:

  1. Compare and apply block maxima (GEV) and peaks-over-threshold (GPD) approaches
  2. Implement parameter estimation using Maximum Likelihood and Bayesian methods
  3. Construct hazard curves with uncertainty bounds for events like floods
  4. Incorporate nonstationarity in EVT models using time-varying parameters
  5. Apply regionalization techniques to improve estimates in data-scarce environments
  6. Evaluate model fit using diagnostic plots and goodness-of-fit tests
  7. Critically analyze published studies that use EVT for hazard assessment

Module 2: Synthetic Events and Catastrophe Modeling

By the end of this module, you will be able to:

  1. Explain the need for synthetic event generation for low-probability, high-impact events
  2. Implement components of a basic synthetic event generation scheme
  3. Evaluate how physical constraints and statistical properties are incorporated into models
  4. Connect synthetic event sets to probabilistic hazard assessment
  5. Identify key sources of uncertainty in synthetic event generation
  6. Critically evaluate different synthetic methodologies and justify your selections

Module 3: Climate Model Projections and Stochastic Weather Generators

By the end of this module, you will be able to:

  1. Distinguish between climate model projections and weather forecasts
  2. Apply distribution adjustment techniques to improve climate model outputs
  3. Explain statistical downscaling approaches for connecting large-scale to local variables
  4. Implement and evaluate stochastic weather generators (SWGs)
  5. Analyze future changes in extreme events using climate-informed methods
  6. Assess limitations of statistical post-processing techniques
  7. Characterize uncertainty across the modeling chain from emissions to impacts

Module 4: Synthesis and Special Topics

By the end of this module, you will be able to:

  1. Research and synthesize information on an advanced topic of your choice
  2. Explain complex methodologies clearly to your peers
  3. Evaluate specialized approaches for their strengths, weaknesses, and appropriate contexts
  4. Connect your special topic to core course concepts and cross-cutting themes
  5. Create effective learning materials about your chosen topic for others