Soil volumetric water content; Autoregressive integrated moving average (ARIMA); Outlier detection; Transfer function model

Understanding the field soil water regime is fundamental in scheduling irrigation as well as for monitoring water flow and solute transport. This study was carried out on variable interval irrigation and used time series analysis techniques to predict the soil water content at the interested depth by measuring one single depth in order to precisely determine the next irrigation time and its effect on soil water content at the interested depth. Volumetric water content of silty loam soil in Barcelona was measured in situ with capacitance soil moisture sensors at five depths within the root zone for a horticultural crop during its life cycle in 2010. The time series consisted of hourly measurements of soil water content and was transformed to a stationary situation. Subsequently, the transformed data were used to conduct analyses in the time domain in order to obtain the parameters of a seasonal autoregressive integrated moving average (ARIMA) model. In the case of variable interval irrigation, predicting the soil water content time series cannot be properly explained by the ARIMA model and its underlying normality assumption. By completing the ARIMA model with intervention analysis and outlier detection, the prediction of soil water content in variable interval irrigation can be made. The transfer function models were then used to predict water contents at depths of interest (0.20, 0.35, 0.50 and 0.60 m depths) as well as the average water content WAVG in the top 0.60 m soil profile by measuring water content at 0.10 m depth. As a result, the predictions were logical. Also, the next irrigation time and its effect on soil water content at the depth of interest were correctly estimated. To confirm results of the models, the experiment was repeated in 2011, and the predicted and observed values agree reasonably well.

Peer Reviewed