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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A changepoint approach to modelling non-stationary soil moisture dynamics
AU - Gong, Mengyi
AU - Killick, Rebecca
AU - Nemeth, Christopher
AU - Quinton, John
PY - 2025/1/29
Y1 - 2025/1/29
N2 - Soil moisture dynamics provide an indicator of soil health that scientists model via drydown curves. The typical modelling process requires the soil moisture time series to be manually separated into drydown segments and then exponential decay models are fitted to them independently. Sensor development in recent years means that experiments that were previously conducted over a few field campaigns can now be scaled to months or years at a higher sampling rate. To better meet the challenge of increasing data size, this paper proposes a novel changepoint-based approach to automatically identify structural changes in the soil drying process and simultaneously estimate the drydown parameters that are of interest to soil scientists. A simulation study is carried out to demonstrate the performance of the method in detecting changes and retrieving model parameters. Practical aspects of the method such as adding covariates and penalty learning are discussed. The method is applied to hourly soil moisture time series from the National Ecological Observatory Network data portal to investigate the temporal dynamics of soil moisture drydown. We recover known relationships previously identified manually, alongside delivering new insights into the temporal variability across soil types and locations.
AB - Soil moisture dynamics provide an indicator of soil health that scientists model via drydown curves. The typical modelling process requires the soil moisture time series to be manually separated into drydown segments and then exponential decay models are fitted to them independently. Sensor development in recent years means that experiments that were previously conducted over a few field campaigns can now be scaled to months or years at a higher sampling rate. To better meet the challenge of increasing data size, this paper proposes a novel changepoint-based approach to automatically identify structural changes in the soil drying process and simultaneously estimate the drydown parameters that are of interest to soil scientists. A simulation study is carried out to demonstrate the performance of the method in detecting changes and retrieving model parameters. Practical aspects of the method such as adding covariates and penalty learning are discussed. The method is applied to hourly soil moisture time series from the National Ecological Observatory Network data portal to investigate the temporal dynamics of soil moisture drydown. We recover known relationships previously identified manually, alongside delivering new insights into the temporal variability across soil types and locations.
U2 - 10.1093/jrsssc/qlaf004
DO - 10.1093/jrsssc/qlaf004
M3 - Journal article
VL - 74
SP - 866
EP - 883
JO - Journal of the Royal Statistical Society. Series C: Applied Statistics
JF - Journal of the Royal Statistical Society. Series C: Applied Statistics
SN - 0035-9254
IS - 3
M1 - qlaf004
ER -