Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912589883113472 |
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| author | Gong, Mengyi Nemeth, Christopher Killick, Rebecca Strauss, Peter Quinton, John |
| author_facet | Gong, Mengyi Nemeth, Christopher Killick, Rebecca Strauss, Peter Quinton, John |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis Gong, Mengyi Nemeth, Christopher Killick, Rebecca Strauss, Peter Quinton, John Applications Computation 62P12 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. |
| title | Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis |
| topic | Applications Computation 62P12 |
| url | https://arxiv.org/abs/2509.13293 |