Machine-Learned Emulators for Teleconnection Discovery and Uncertainty Quantification in Coupled Human-Natural Systems
Dec 27, 2025·,
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1 min read
Asim Zia
Patrick J. Clemins
Muhammad Adil
Andrew Schroth
Donna Rizzo
Panagiotis D. Oikonomou
Safwan Wshah
Abstract
This paper studies machine-learned emulators for discovering teleconnections and quantifying uncertainty in large coupled human-natural systems. Using scenario outputs from an integrated Lake Champlain CHANS model, the work evaluates several emulator families for predicting water-quality indicators and identifying the relative influence of internal and external drivers of harmful algal bloom dynamics.
Type
Publication
Water, 18(1), 79
Published in Water in 2026. This article examines machine-learned emulators for teleconnection discovery and uncertainty quantification in a coupled human-natural systems setting centered on Lake Champlain.
