State-space prediction of field-scale soil water content time series in a sandy loam

The description of field soil water content time series can be affected by uncertainty due to measurement errors of the respective state variables, er rors due to assumptions underlying the model, and errors in the determinati on of boundary conditions. In this study, a simple state-equation was appli ed for predicting field soil water contents at three different soil depths. The simple state-model yielded large deviations of predictions from the me asured soil water content, especially for the upper soil depth. Apparently, the magnitude of the estimated evaporation rate was too high, The predicti on result could significantly be improved when the calculated evaporation w as reduced by a factor of 0.7. In order to account for uncertainty sources associated with this simple approach, the state-equation was combined with a stochastic technique, the so-called Kalman-Filter. Applying the Kalman-Fi lter, the prediction quality significantly increased, even when the erroneo usly high evaporation was assumed to be true. However, prediction uncertain ty increased for the same time periods, for which it was shown earlier that spatial correlation of soil water status was either random or very short. When the Kalman-Filter was applied in a scenario to the surface layer only, simulated soil water content in layers 2 and 3 agreed to measurements and were highly improved compared to simulations when layer 1 was not filtered. Hence, application of lab determined soil hydraulic property functions in combination with state observations of upper soil horizon water content and with the Kalman-Filter provides a promising opportunity to describe and pr edict soil water contents for entire soil profiles even under the presence of uncertainty sources. (C) 1999 Published by Elsevier Science B.V. All rig hts reserved.