Publications

Upper-tail correction of multivariate synthetic environmental series using annual maxima

Víctor Collado, Fernando J. Méndez and Roberto Mínguez, (2026)

Keywords: Extreme value theory (EVT), Generalized extreme values (GEV), Annual maxima, Upper-tail correction, Quantile anchoring, Rank-preserving interpolation, Synthetic environmental risk simulations.

Article GitHub

Projects

TwinPeacs

Research project (2026-ongoing)

Description: Project within the AI research funding line, focused on applied research and methodological development.

MyEst

Research project (2024)

Description: Project focused on estimation and optimization methods. I contributed to the optimization side of the project.

Open-source tools

BlueMath

Python repository developed by GeoOcean

Description: Contribution to the BlueMath repository for distributions notebooks focused on Extreme Value Theory.

GitHub

BlueMath toolkit

Python package developed by GeoOcean

Description: Contribution to the BlueMath toolkit repository, extending the toolkit for statistical tools.

GitHub

Abstract

Reliable risk assessment of environmental systems often relies on long, multivariate synthetic time series produced by sampling, stochastic simulators and/or fast emulators. In practice, such simulations may reproduce bulk behavior and dependence reasonably well, yet misrepresent marginal upper tails, biasing estimates of rare, high-impact events. This short communication presents an annual maxima (AM)-centric, marginal post-processing method that (i) tests whether a correction is needed by comparing simulated AM to a target AM distribution based on historical records; and (ii) if needed, anchors simulated AM to target quantiles and propagates that adjustment to the full time series via a monotone interpolation that preserves ranks. Because the procedure preserves the empirical copula, rank-based dependence and copula-based tail dependence are retained, while marginal tails are aligned with the target. The method is intended as a lightweight safeguard against marginal tail misrepresentation in synthetic risk simulations, avoiding unnecessary complexity or retraining of the underlying generator. An application to climate-based wave simulations for Santoña (Spain) illustrates the approach and provides diagnostics for tail fit, bulk preservation, and dependence stability, showing that the method corrects return levels while minimally altering the time series.

Peer-Reviewed Work

Publications

Upper-tail correction of multivariate synthetic environmental series using annual maxima

Projects

TwinPeacs

MyEst

Open-source tools

BlueMath

BlueMath toolkit