Robust Statistical Processing of Long-Time Data Series to Estimate Soil Water Content

The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An...

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Bibliographic Details
Published in:Mathematical geosciences 2024, Vol.56 (1), p.3-26
Main Authors: Anello, Mirko, Bittelli, Marco, Bordoni, Massimiliano, Laurini, Fabrizio, Meisina, Claudia, Riani, Marco, Valentino, Roberto
Format: Article
Language:English
Subjects:
Air temperature
Chemistry and Earth Sciences
Computer Science
Earth and Environmental Science
Earth Sciences
Experimental data
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical models
Meteorological data
Model testing
Moisture content
Physics
Robustness
Soil
Soil temperature
Soil water
Special Issue
Statistical analysis
Statistical models
Statistics for Engineering
Water content
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Summary:The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes.
ISSN:1874-8961
1874-8953
DOI:10.1007/s11004-023-10100-x