Hydrologic
CREST
Coupled Routing and Excess STorage — a distributed model for continental-scale rainfall–runoff, powering global flood-monitoring and real-time flash-flood systems. The Flood Lab is a core contributor.
Li's Flood Lab · University of Colorado Boulder
Flood science, from the raindrop to society.
We advance the scientific understanding and practical mitigation of floods through remote sensing, physics-based modeling, and AI — delivering accurate, timely information for the good of the community.
The mission
We study surface water across scales — local, continental, and global — to predict and monitor floods before they arrive.
Floods are the costliest natural hazard on Earth, and they are growing flashier and less predictable. Our lab couples remote-sensing platforms with AI-integrated hydrologic–hydraulic models to close the gap between raw observations and actionable warning.
Research featured in — click a logo to read
The research framework
SURGE — five letters, four thrusts, one pipeline.
S · U · R · G · E
The SURGE Flood Lab framework — click a component to explore
Open models
From rain to flood.
Our models transcend catchment-scale hydrology to reach hyper-local hydraulics — turning water into flood. From the CREST lineage to the GPU-native Inunda, the Flood Lab is one of the core contributors to each. “All models are wrong, but some are useful.”
Vectorized
CREST-VEC
A vectorized CREST that improves the efficiency and accuracy of streamflow simulation across scales.
Hydraulic · 2D
CREST-iMAP
CREST-inundation MApping and Prediction — a coupled hydrologic–hydraulic model for 2D flood prediction, uniting streamflow and inundation in one pipeline.
Ensemble · Operational
EF5
The Ensemble Framework For Flash Flood Forecasting — multiple water-balance and routing schemes for operational flash-flood prediction. The Flood Lab is a core contributor.
Nowcasting
FLASHCast
High-resolution flash-flood nowcasting with an open benchmark dataset — turning real-time inputs into fast, actionable inundation forecasts.
GPU-native · Differentiable
Inunda
A GPU-native, agent-enabled, differentiable hydrologic–hydraulic flood inundation model. Run a flood inundation model, fast — anywhere on the globe.
Open data
Two open flood datasets.
From a century of observed U.S. floods to simulation-grade benchmarks for AI — data the community can build on.
120 yr
U.S. Flood Database (USFD)
Remote sensing, stream gauges, flood reports, and crowdsourcing harmonized into one of the most comprehensive flood records in the United States.
FloodSimBench
A high-resolution physical flood inundation benchmark for AI
Physics-based, simulation-grade flood inundation truth for training and evaluating the next generation of AI flood models.
Agent benchmarks
Can frontier AI run a flood model?
We build the evaluations that answer it. Two benchmarks test LLM agents on real hydrologic modeling — wrangling data, configuring simulators, calibrating, and verifying results end to end.
Benchmark 01
Agent's Last Exam
An expert-level exam for scientific agents: end-to-end hydrologic modeling tasks that demand reasoning, tool use, and domain judgment.
Benchmark 02
terminal-bench-science
Real scientific computing in real terminals — agents drive models from raw data to verified simulation, graded automatically.
mean score · 0–1 · higher is better · n = evaluation runs
Yan, S., Chen, M., Li, Z., Wen, Y., Zhu, S., Zhang, M., Liu, D., Cao, J., Chen, X., Deng, C., Yang, T., & Hong, Y. (2026). AI Agent for Hydrologic Modeling: Definition, Development, and Application. Geophysical Research Letters, 53(13), e2025GL119814. doi:10.1029/2025GL119814
Yan, S., Li, Z., Zhu, S., Wen, Y., Zhang, M., Chen, M., et al. (2025). AQUAH: Automatic Quantification and Unified Agent in Hydrology. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2926–2935.
The team
People
Principal Investigator
Dr. Zhi Li (“Dr. Z”)
Dr. Z is an Assistant Professor at the University of Colorado Boulder. Previously he was the Stanford Doerr School of Sustainability Dean's Postdoctoral Fellow (2023–2025). He earned his PhD (2022) from the University of Oklahoma's Hydrometeorology and Remote Sensing Laboratory, directed by Prof. Yang Hong, with Master's and Bachelor's degrees from the National University of Singapore (2019) and Hohai University (2017).
He studies surface water across scales — spatially (local, continental, global) and temporally (hydrology, hydrometeorology, hydroclimatology) — focusing on flood prediction and monitoring with remote-sensing platforms and AI-integrated hydrologic–hydraulic models. He has published 50+ peer-reviewed articles and one invited book chapter, and serves as a reviewer for 10+ international journals.
Flood predictionRemote sensing
Hydrologic–hydraulic modelingScientific ML
Team members
Ke Zhu
PhD Student
Ke Zhu is a doctoral student at the University of Colorado Boulder. He earned his Master's degree (2025) from Southeast University (Civil & Hydraulic Engineering), directed by Prof. Qian Zhu, and his Bachelor's (2022) from Hohai University (Water Science & Engineering). His prior research focused on improving hydrologic models; he now studies the application of foundation models to flood identification.
Foundation modelsFlood identification
Hydrologic modeling
Collaborators
Our work is built with wonderful collaborators, including Prof. Yang Hong (Univ. of Oklahoma), Jonathan J. Gourley (NOAA/NSSL), Steven Gorelick (Stanford), Pierre Kirstetter, Mengye Chen, Shang Gao, Yixin Wen, Siyu Zhu, Songkun Yan, Guoqiang Tang, and Tiantian Yang.
The lab is growing. Dr. Z is recruiting PhD students, postdocs, and MS/undergraduate
researchers at CU Boulder. See Opportunities.
In the classroom
Teaching
Bridging environmental engineering, data science, and AI for the Earth system.
← Teaching
Linear Algebra
Calculus
Programming (Python)
CVEN 5833 · Spring 2026 · CU Boulder
AI for Earth System Science & Engineering
Course Overview
Course Information
- Course Number: CVEN-5833
- Course Title: AI in Earth System Science and Engineering
- Credits: 3
- Term: Spring 2026
Meeting Times
- Days: TTH
- Time: 8:30-9:45
- Location: SEEC N124
Course Description
This course explores the application of artificial intelligence and machine learning techniques to problems in Earth System Science. Students will learn how modern AI methods can be applied to analyze, model, and predict complex Earth system processes including climate dynamics, hydrology, atmospheric science, and environmental monitoring.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Understand fundamental AI/ML concepts relevant to Earth system science
- Apply machine learning techniques to analyze Earth observation data
- Develop predictive models for Earth system processes
- Evaluate model performance and interpret results in scientific context
- Critically assess the application of AI methods to environmental challenges
Instructor Information
Instructor
- Name: Zhi Li
- Email: Zhi.Li-2@colorado.edu
- Office: SEEC N104
- Office Hours: TBD
Teaching Assistant
- Name: TBD
- Email: TBD
- Office Hours: TBD
Prerequisites
Proposed Course Topics
| No. | Main Topic | Items |
|---|---|---|
| 1 | Introduction to Programming Language and AI & Earth System Data |
|
| 2 | Supervised Learning and Regression |
|
| 3 | Classification and Non-Linearity |
|
| 4 | Model-Based and Non-Parametric Methods |
|
| 5 | Deep Learning Fundamentals |
|
| 6 | Convolutional Neural Networks (CNNs) for Spatial Data |
|
| 7 | Recurrent and Sequence Models |
|
| 8 | Graph and Generative Models |
|
| 9 | Advanced Sequence Modeling |
|
| 10 | Foundation Models in Practice |
|
| 11 | Logistic Regression & Kernels | |
| 12 | Societal Impact & Ethics |
|
Assignments
Assignment 1: Data Analysis
Due Date: TBD
Assignment 2: TBD
Due Date: TBD
TBD
Assignment 3: TBD
Due Date: TBD
TBD
Final Project
Due Date:
Comprehensive project applying AI methods to an Earth system science problem...
Grading Policy
| Component | Weight |
|---|---|
| Assignments | 40% |
| Midterm Exam | 25% |
| Final Project | 30% |
| Participation | 5% |
Grading Scale
- A: 90-100%
- B: 80-89%
- C: 70-79%
- D: 60-69%
- F: <60%
Course Policies
Late Submissions
Assignments submitted after the due date will be penalized 10% per day late, up to a maximum of 3 days. After 3 days, assignments will not be accepted without prior approval from the instructor.
Academic Integrity
All work submitted must be your own. Collaboration on assignments is allowed up to the point of sharing code or solutions. Any violation of academic integrity will be reported to the Honor Code Council.
Accommodations
Students with disabilities who need accommodations should contact Disability Services and inform the instructor as early as possible in the semester.
Resources
Online books
Youtube videos
Software/Tools
- 100 Page Python Intro
- Python Data Science Handbook
- Deep Learning with PyTorch
- Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
Statistics
Peer-reviewed · 50+ articles
Publications
Selected recent work. The lab author is highlighted. Full list on Google Scholar — wire this to your BibTeX export at build time.
Join us
Opportunities
Please read the lab manual before you consider joining us. There are no grant-supported openings right now, but I strongly encourage you to pursue these fellowships — I'm glad to support strong applications.
Graduate
Graduate fellowships
- NASA FINESST — Future Investigators in NASA Earth & Space Science and Technology · due February
- NSF GRFP — Graduate Research Fellowship Program · due November
Student
Scholarships
- AWRA Colorado — Rich Herbert Memorial Scholarship · up to $5,000
- Open to CU Boulder students across levels
Postdoc
Postdoctoral fellowships
- CU Boulder Chancellor's Postdoctoral Fellowship · due early November
- Email a CV and short research statement anytime
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