StormLab: Space-time Nonstationary Rainfall Model for Large Area Basins

Figure: (A) Graphical showing how StormLab converts large-scale climate model predictions into thosands of realistic high-resolution rainfall simulations. (B) Rainfall "frequency curve" for Mississippi Basin rainfall, showing close agreement between high-resolution StormLab simulations and validation measurements.

Introduction

This repository contains code to implement StormLab, a stochastic rainfall generator that simulate 6-hour, 0.03° resolution rainfall fields over large-area river basins conditioned on global climate model data.

Dependencies

Required packages are listed in requirements.txt

Usage

1. Download the example data and unzip at the code folder "/StormLab."
   • Google Drive: https://drive.google.com/file/d/1x090aVFbe0c3PPZnvwevptN4nP7GuCf7/view?usp=drive_link
   • The folder should look like: "/StormLab/data"
2. Rainstorm tracking
   • See /examples/Storm_tracking_on_CESM2_data.ipynb
3. TNGD distribution fitting
   • See /examples/TNGD_distribution_fitting.ipynb
4. Noise generation
   • See /examples/Noise_generation.ipynb
5. Rainfall simulation
   • See /examples/Rainfall_simulation.ipynb

Citation

If you use this model in your work, please cite:

Liu, Y., Wright, D. B., & Lorenz, D. J. (2024). A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins. Water Resources Research, 60(5), e2023WR036826. https://doi.org/10.1029/2023WR036826

Contributing

Feel free to open an issue for bugs and feature requests.

License

StormLab is released under the MIT License.

Authors

• Yuan Liu - research & developer
• Daniel B. Wright - research
• David J. Lorenz - research

Attribution

This project uses code from the following repositories:

• pysteps
• STREAM
• starch