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Transfer Function and Time Series Outlier Analysis: Modelling Soil Salinity in Loamy Sand Soil by Including the Influences of Irrigation Management and Soil Temperature
Author(s) -
Aljoumani Basem,
SànchezEspigares Josep A.,
Cañameras Nuria,
Wessolek Gerd,
Josa Ramon
Publication year - 2018
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.2187
Subject(s) - soil salinity , soil science , salinity , environmental science , pedotransfer function , loam , leaching model , irrigation , hydrology (agriculture) , soil water , hydraulic conductivity , geology , geotechnical engineering , agronomy , oceanography , biology
In variable interval irrigation, simply including soil salinity data in the soil salinity model is not valid for making predictions, because changes in irrigation frequency must also be taken into account. This study on variable interval irrigation used capacitance soil sensors simultaneously to obtain hourly measurements of bulk electrical conductivity ( σ b ), soil temperature ( t ) and soil water content ( θ ). Observations of σ b were converted so that the electrical conductivity of the pore water ( σ p ) could be estimated as an indicator of soil salinity. Values of θ , t and σ p were used to test a mathematical model for studying how σ p cross‐correlates with t and θ to predict soil salinity at a given depth. These predictions were based on measurements of σ p , t , and θ at a shallow depth. As a result, prediction at shallow depth was successful after integrating intervention analysis and outlier detection into the seasonal autoregressive integrated moving average (ARIMA) model. We then used the (multiple‐input/one‐output) transfer function models to logically predict soil salinity at the depths of interest. The model could also correctly determine the effect of the irrigation event on soil salinity. Copyright © 2017 John Wiley & Sons, Ltd.