Hierarchical prediction of soil water content time series

Quantifying the spatial and temporal dynamics of soil moisture is an important subject in vadose zone hydrology. A 1282-day field study was conducted to provide a hierarchy of data to assess neural network simulations of field soil water content time series theta(t). Volumetric water content was determined, typically once a week, by neutron thermalization at 20-, 40-, 60-, 80- and 100-cm depths. Soil samples were taken at 60 locations between 14- and 114-cm depths to determine soil properties, water retention, and saturated hydraulic conductivity. Prediction of hydraulic parameters from basic and extended soil properties yielded low correlation coefficients. Water content could be predicted reasonably with neural networks from soil properties or hydraulic parameters (0.880 < R < 0.942). Prediction of theta(t) based solely on rainfall data was not accurate. Independent networks could accurately simulate water content from one observed theta(t) based on the Nash-Sutcliffe Model Efficiency and Percent Bias. Once a network is trained for a particular depth, accurate predictions can be made beyond the training period using observations at just one location.

Automated summary

The study examined the spatial and temporal dynamics of soil moisture through field experiments and neural network simulations, showing that water content can be reasonably predicted from soil properties or hydraulic parameters. However, predicting soil moisture solely based on rainfall data was found to be inaccurate.