Water 2026, 18(1), 79; https://doi.org/10.3390/w18010079 (registering DOI) - 27 Dec 2025
Abstract
Introduction: Traditional approaches to discover teleconnections and quantify uncertainty, such as global sensitivity analysis, Monte Carlo experiments, decomposition analysis, etc., are computationally intractable for large-scale process-based Coupled Human and Natural Systems (CHANS) models. This study hypothesizes that machine-learned emulator models provide “computationally efficient”
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Introduction: Traditional approaches to discover teleconnections and quantify uncertainty, such as global sensitivity analysis, Monte Carlo experiments, decomposition analysis, etc., are computationally intractable for large-scale process-based Coupled Human and Natural Systems (CHANS) models. This study hypothesizes that machine-learned emulator models provide “computationally efficient” algorithms for discovering teleconnections and quantifying uncertainty within and across dynamically evolving human and natural systems. Objectives: This study aims to harness machine-learned emulator models to discover the relative contributions of internal- versus external-to-the-lake teleconnected processes driving the emergence of Harmful Algal Blooms (HABs) and trophic regime shifts. Three objectives are pursued: (1) build emulators; (2); quantify uncertainty and (3) identify teleconnections. Methods: Six machine-learned emulator models are trained on ~3.8 million observations for ~52 features derived from 332 scenarios simulated in an integrated process-based CHANS model that predicts water quality in Missisquoi Bay of Lake Champlain under alternate hydro-climatic and nutrient management scenarios for the 2001–2047 timeframe. The regression random forest (RRF), regression LightGBM (RLGBM) and regression XGBoost (RXGB) models predict the average surface mean of ChlA. Further, the classifier random forest (CRF), classifier LightGBM (CLGBM) and classifier XGBoost (CXGB) predict four trophic states of Missisquoi Bay. Relative importance and partial dependence plots are derived from all six emulator models to quantify relative uncertainty and importance of external-to-the-lake (climatic, hydrological, nutrient management) and internal-to-the-lake (P and N sediment release) drivers of HABs. Results: RXGB (R2 = 96%, 48 features) outperforms RLGBM (R2 = 95%, 37 features) and RRF (R2 = 93%, 20 features) in predicting the average surface mean of ChlA. CLGBM (F1 = 96.15, 4 features) outperforms CXGB (F1 = 95.66, 48 features) and CRF (F1 = 93.06, 23 features) in predicting four trophic states. We discovered that predictor variables representing snow, evaporation and transpiration dynamics teleconnect hydro-climatic processes occurring in terrestrial watersheds with the biogeochemical processes occurring in the freshwater lakes. Conclusions: The proposed approach to discover teleconnections and quantify uncertainty through machine-learned emulator models can be scaled up in different watersheds and lakes for informing integrated water governance processes.
Full article
(This article belongs to the Special Issue Artificial Intelligence in Water Science: Opportunities, Prospects, and Concerns)
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