CEVE 543: Statistical-Physical Methods for Hydroclimate Extremes and Catastrophes

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

Meetings

  • MWF
  • 1:00-1:50pm
  • TBD

Learning Objectives

After completing this course, you will be able to:

  1. Fit and interpret extreme value distributions for environmental data, including parameter estimation and uncertainty quantification
  2. Combine observations with process-based models to predict and quantify uncertainty in physical systems through calibration, validation, and uncertainty propagation
  3. Apply and understand quantitative model comparison techniques and communicate qualitative model strengths and limitations
  4. Integrate multiple data sources (observations, climate models, physical understanding) to address complex environmental problems

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

This is a graduate-level course and students are expected to bring some domain knowledge from their research. Different expertise in the class is a strength that enhances learning for everyone. Students should also bring some interest in and/or knowledge of specific methods in statistics, optimization, machine learning, or similar quantitative approaches that we can apply to the problems covered in the course.

An ungraded Problem Set Zero has been posted.

Required Materials

No textbook is required for this course. Reading will come from a free, online textbook developed specifically for this class, or will be provided through Canvas.

You do need a computer that you are able to install software on to complete the assignments and participate in class activities. If you do not have access to one, please contact me as soon as possible so that we can make arrangements.

Classroom Technology Policy

During regular class sessions, computers will not be allowed. You should take notes with pen and paper. Use of a tablet for a writing app is acceptable, but you should remain focused on the lesson. Computers are only permitted during labs or when explicitly mentioned for follow-along tutorials. Otherwise, you need to be present and offline.

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.

Collaboration Policy

You are encouraged to work together on problem sets and labs. Collaboration helps you learn from your peers and develop problem-solving skills through discussion. However, you must turn in your own work and acknowledge those you worked with.

When collaborating:

  • You must write your own code and explanations in your own words
  • Include the names of students you worked with in your submission
  • Understand every line of code and every step of your analysis
  • Do not copy and paste code or text from others

Since problem sets and labs are graded for completion rather than correctness, there is little incentive to cheat. These assignments are designed to help you practice concepts that will appear on module tests and the final exam. Collaborative learning on these assignments will better prepare you for individual assessments.

AI/LLM Resource Policy

AI tools, especially Large Language Models (LLMs), can be powerful aids in learning but also pose important risks. These include undermining your development of critical thinking skills, reducing your ability to work independently under time pressure, and preventing you from building the deep conceptual understanding needed for research and professional work. The use of these models must be informed by our values and goals, and we must be critical in thinking about when and how to use them.

Rather than focusing on preventing cheating, this course will help you use these tools in ways that support our goals (particularly your developing key skills and concepts) while designing grading such that using them in unproductive ways will not offer even a short-term benefit. Consequently, assessment will focus on approaches – like tests – that are not readily amenable to LLM use.

This AI policy is reciprocal. Your instructors may use LLMs to draft rubrics revise website content, but your assignments will not be graded by LLMs, and we will not use LLMs to write code or text from scratch or without complete understanding.

You are encouraged to explore the resources linked on the LLM resources page.

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 notice. Any changes to the syllabus will be automatically tracked through git version control, which you can see on the course’s GitHub repository.

Grading

Your final grade will be determined from a portfolio of work that includes regular low-stakes practice, in-class assessments of core concepts, and a final project. The goal is to reward consistent engagement, practice, and deep, applied learning.

  • Problem Sets (10%) provide comprehensive practice with all module concepts and are graded for completion to reward effort and exploration
  • Labs (10%) provide hands-on experience applying concepts and building technical skills. Labs are graded for completion to encourage exploration and learning from mistakes. Most labs have some work required before the lab date, posted on the lab page. Labs may change up to 1 week before the official date.
  • Module Tests (40%) closely mirror problem sets to assess deep understanding of practiced concepts. This creates multiple opportunities to demonstrate understanding and reduces high-stakes assessment anxiety.
  • Final Project (20%) allows students to explore methods beyond the course scope and contribute to the course knowledge base. Students select a hydroclimate risk problem and either apply a familiar method to a new context or learn and apply a new method. The final deliverables include: (1) a lecture notes contribution explaining your chosen method, (2) a 15-20 minute teaching segment during our Course Symposium, and (3) a 10-page research proposal showing how your method could address a significant problem.
  • Final Exam (20%) is an in-class, interpretation-focused assessment that demonstrates mastery of the complete course framework through peer review of a compound hazard analysis.

Canvas Usage

Canvas will be used only for tracking grades and for sharing solutions and other non-public material. Sometimes, this website will be updated faster than the Canvas page. If there are conflicts between this website and Canvas, assume this website is correct and Canvas is wrong.

Assignments will typically be posted to Canvas. However, if assignments are posted to the course website, they are considered officially assigned even if they are not visible on Canvas.

Late Work Policy

In general and by default, late work is not accepted. This is important because it allows me to post solutions immediately after assignments are due, helping everyone stay on track.

If you need to miss a class because of major illness, family emergency, or other unavoidable conflict, or for critical research travel, please let me know as soon as possible. In these cases, you will be assigned a score of 0/0, meaning it will not be reflected in the numerator or denominator.

Remote Learning Policy

This course is designed for in-person attendance on Mondays and Wednesdays, and remote attendance on Fridays. While a standing Zoom link has been created for the class, I will not livestream or record lectures unless specifically requested. If you need to attend remotely, please ask in advance.

Course schedule

The course schedule is given in the online schedule.