Overview | Why the Name? | Key Features | Getting Started | Contact Us | Disclaimer

Overview

DOMINO-SEE (Detection Of Multi-layer INterconnected Occurrences for Spatial Extreme Events) is a data-driven statistical framework for detecting spatially co-occurrences of hydroclimatic extreme events across locations, inspired by complex network science, powered by xarray architecture. It's developed by Hui-Min Wang and Xiaogang He from the PREP-NexT Lab at the National University of Singapore.

This project is licensed under the GNU General Public License 2.0.

Why the Name?

The name DOMINO-SEE represents our approach to detecting and analyzing interconnected occurrences of hydroclimatic extreme events across spatial locations, inspired by the cascade effect of DOMINOes falling in a chain reaction. The SEE highlights the framework's ability to capture the spatial synchronization and propagation of extreme events, emphasizing the interconnectedness inherent in complex environmental systems.

Key Features

• Complex Network Generation: Fast and memory-efficient functions to build spatial networks from event series among spatial locations and multiple types (layers) of climate extreme events
• Multi-dimensional Support: Native support for xarray DataArrays to handle multi-dimensional gridded climate data
• Parallel Processing: dask integration for efficient processing of large-scale climate datasets
• Blockwise Computation: Utilities for splitting large spatial datasets into manageable blocks of netCDF datasets (see dominosee/utils/blocking.py).

Getting Started

This section includes a brief tutorial on running your first DOMINO-SEE model.

1. Clone the repo

   git clone https://github.com/Hem-W/dominosee-dev.git

2. Install the dependencies through conda

   cd dominosee-dev
   conda env create -f environment.yml
   conda activate dominosee

3. Install the package from source

   pip install -e .

Contact Us

If you have any questions, comments, or suggestions that aren't suitable for public discussion in the Issues section, please feel free to contact Hui-Min Wang.

Please use the GitHub Issues for public discussions related to bugs, enhancements, or other project-related discussions.

Disclaimer

The DOMINO-SEE model is an academic project and is not intended to be used as a precise prediction tool for risk assessment and management. The developers will not be held liable for any decisions made based on the use of this model. We recommend applying it in conjunction with expert judgment and other modeling tools in a decision-making context.