Information Content of Data for Identifying Soil Hydraulic Parameters from Outflow Experiments

Process-oriented models of open systems often contain parameters that cannot be measured directly but can only be obtained by inverse modeling. A conventional inverse method is typically based on the minimization of an objective function that lumps the discrepancies in time series of observed values and predicted model response. However, problems are often encountered with the non-uniqueness of the parameter estimates. Non-unique parameter estimates result in case of low parameter sensitivity, mutual parameter dependency, and high measurement noise. These problems can be solved partly if we do not use the entire data set but focus on subsets where the model is most sensitive to changes in the unknown parameters. Therefore we propose PIMLI (Parameter Identification Method based on the Localization of Information), that uses the variability in time of the model sensitivity for the various parameters to split the total set of measurements into disjunctive subsets that each contain the most information on one of the model parameters. Thereupon, each distinguished subset is used to constrain its corresponding parameter. To illustrate PIMLI we chose a simulated multi-step outflow (MSO) experiment in which only cumulative outflow is measured because of its well-known problems with the uniqueness of the identified soil hydraulic properties. The results show that PIMLI not only leads to unique parameter sets of soil hydraulic properties for a range of soils but also significantly improves the understanding of uniqueness problems related to parameter identification.