Environmental extreme risk modeling via sub-sampling block maxima

2025-06-17

AI Summary

Addressing the challenges of block size selection and estimation bias in extreme value statistics under temporal dependence, this paper proposes the Subsampled Block Maxima (SBM) method to establish a robust, multi-scale framework for environmental extreme risk assessment. Methodologically, SBM introduces a novel subsampling-based block maxima modeling paradigm; designs a weighted least squares estimator for the extreme value index (EVI); jointly identifies the EVI and the extremal index (EI) via plateau detection grounded in the second-order moment of block maxima; and quantifies the mechanistic influence of EI on the Kullback–Leibler divergence. Validated on meteorite mass, earthquake energy, solar activity, and Greenland snow/sea-ice datasets, SBM significantly improves sample efficiency and robustness to temporal dependence. It enables cross-scale, operationally feasible risk quantification without manual hyperparameter tuning.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

This paper introduces a novel sub-sampling block maxima technique to model and characterize environmental extreme risks. We examine the relationships between block size and block maxima statistics derived from the Gaussian and generalized Pareto distributions. We introduce a weighted least square estimator for extreme value index (EVI) and evaluate its performance using simulated auto-correlated data. We employ the second moment of block maxima for plateau finding in EVI and extremal index (EI) estimation, and present the effect of EI on Kullback-Leibler divergence. The applicability of this approach is demonstrated across diverse environmental datasets, including meteorite landing mass, earthquake energy release, solar activity, and variations in Greenland's land snow cover and sea ice extent. Our method provides a sample-efficient framework, robust to temporal dependencies, that delivers actionable environmental extreme risk measures across different timescales. With its flexibility, sample efficiency, and limited reliance on subjective tuning, this approach emerges as a useful tool for environmental extreme risk assessment and management.

Problem

• Model environmental extreme risks using sub-sampling block maxima
• Estimate extreme value index with weighted least square method
• Assess risks in diverse environmental datasets efficiently

Innovation

• Sub-sampling block maxima technique
• Weighted least square EVI estimator
• Second moment for plateau finding