Root- zone soil moisture estimation using data- driven methods

The soil moisture state partitions both mass and energy fluxes and is important for many hydro-geochemical cycles, but is often only measured within the surface layer. Estimating the amount of soil moisture in the root-zone from this information is difficult due to the nonlinear and heterogeneous nature of the various processes which alter the soil moisture state. Data-driven methods, such as artificial neural networks (ANN), mine data for nonlinear interdependencies and have potential for estimating root-zone soil moisture from surface soil moisture observations. To create an ANN root-zone model that was nonsite-specific and physically constrained, a training set was generated by forcing HYDRUS-1D with meteorological observations for different soil profiles from the unsaturated soil hydraulic database. Ensemble ANNs were trained to provide soil moisture at depths of 10, 20, and 50 cm below the surface using surface soil moisture observations and local meteorological information. Insights into the processes represented by the ANNs were derived from a clamping sensitivity analysis and by changing the ANNs input data. Further model testing based on synthetic soil moisture profiles from three McMaster Mesonet and three USDA soil climate analysis network sites suggests that ANNs are a flexible tool capable of predicting root-zone soil moisture with good accuracy. It was found that ANNs could well represent soil moisture as estimated by HYDRUS-1D, but performance was reduced in comparison to in situ soil moisture observations outside the training conditions. The transferability of the model appears limited to the same geographic region.