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arXiv · 2509.13293

Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis

Mengyi Gong·Christopher Nemeth·Rebecca Killick·Peter Strauss·John Quinton
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Abstract

Continuous soil-moisture measurements provide a direct lens on subsurface hydrological processes, notably the post-rainfall "drydown" phase. Because these records consist of distinct, segment-specific behaviours whose forms and scales vary over time, realistic inference demands a model that captures piecewise dynamics while accommodating parameters that are unknown a priori. Building on Bayesian Online Changepoint Detection (BOCPD), we introduce two complementary extensions: a particle-filter variant that substitutes exact marginalisation with sequential Monte Carlo to enable real-time inference when critical parameters cannot be integrated out analytically, and an online-gradient variant that embeds stochastic gradient updates within BOCPD to learn application-relevant parameters on the fly without prohibitive computational cost. After validating both algorithms on synthetic data that replicate the temporal structure of field observations-detailing hyperparameter choices, priors, and cost-saving strategies-we apply them to soil-moisture series from experimental sites in Austria and the United States, quantifying site-specific drydown rates and demonstrating the advantages of our adaptive framework over static models.

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Mengyi Gong, Christopher Nemeth, Rebecca Killick, Peter Strauss, John Quinton. 2025-09-16. Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis. https://arxiv.org/abs/2509.13293

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