Spatial Modeling and Risk Zoning of Global Extreme Precipitation via Graph Neural Networks and r-Pareto Processes
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912584941174784 |
|---|---|
| author | Wang, Zimu Wu, Yifan Bi, Daning |
| author_facet | Wang, Zimu Wu, Yifan Bi, Daning |
| contents | Extreme precipitation events occurring over large spatial domains pose substantial threats to societies because they can trigger compound flooding, landslides, and infrastructure failures across wide areas. A hybrid framework for spatial extreme precipitation modeling and risk zoning is proposed that integrates graph neural networks with r-Pareto processes (GNN-rP). Unlike traditional statistical spatial extremes models, this approach learns nonlinear, nonstationary dependence structures from precipitation-derived spatial graphs and applies a data-driven tail functional to model joint exceedances in a low-dimensional embedding space. Using NASA's IMERG observations (2000-2021) and CMIP6 SSP5-8.5 projections, the framework delineates coherent high-risk zones, quantifies their temporal persistence, and detects emerging hotspots under climate change. Compared with two baseline approaches, the GNN-rP pipeline substantially improves pointwise detection of high-risk grid cells while yielding comparable clustering stability. Results highlight persistent high-risk regions in the tropical belt, especially monsoon and convective zones, and reveal decadal-scale persistence that is punctuated by episodic reconfigurations under high-emission scenarios. By coupling machine learning with extreme value theory, GNN-rP offers a scalable, interpretable tool for adaptive climate risk zoning, with direct applications in infrastructure planning, disaster preparedness, and climate-resilient policy design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatial Modeling and Risk Zoning of Global Extreme Precipitation via Graph Neural Networks and r-Pareto Processes Wang, Zimu Wu, Yifan Bi, Daning Applications Extreme precipitation events occurring over large spatial domains pose substantial threats to societies because they can trigger compound flooding, landslides, and infrastructure failures across wide areas. A hybrid framework for spatial extreme precipitation modeling and risk zoning is proposed that integrates graph neural networks with r-Pareto processes (GNN-rP). Unlike traditional statistical spatial extremes models, this approach learns nonlinear, nonstationary dependence structures from precipitation-derived spatial graphs and applies a data-driven tail functional to model joint exceedances in a low-dimensional embedding space. Using NASA's IMERG observations (2000-2021) and CMIP6 SSP5-8.5 projections, the framework delineates coherent high-risk zones, quantifies their temporal persistence, and detects emerging hotspots under climate change. Compared with two baseline approaches, the GNN-rP pipeline substantially improves pointwise detection of high-risk grid cells while yielding comparable clustering stability. Results highlight persistent high-risk regions in the tropical belt, especially monsoon and convective zones, and reveal decadal-scale persistence that is punctuated by episodic reconfigurations under high-emission scenarios. By coupling machine learning with extreme value theory, GNN-rP offers a scalable, interpretable tool for adaptive climate risk zoning, with direct applications in infrastructure planning, disaster preparedness, and climate-resilient policy design. |
| title | Spatial Modeling and Risk Zoning of Global Extreme Precipitation via Graph Neural Networks and r-Pareto Processes |
| topic | Applications |
| url | https://arxiv.org/abs/2509.10362 |