Given error-free measurements, the estimated conductivities and specific storages are accurate within 10% of the true values.
Measurement errors are often hidden in the data and are only revealed when the measurement process is repeated or responses are compared to a gold standard (i.e., error-free measurements).
Finally, our estimates were based on targets that changed across measurements, and we could not assume error-free measurements.
Conclusions on the asymptotic growth rate of the bias continue to hold even with unbiased measurements or error-free translation measurements.
Assuming NLOS error with a Gaussian distribution N μ, σ NLOS 2, for a given BS, let us denote the error-free TOA measurement as m.
These "ideal" conditions include the resolution of the water flux on a weekly basis, error-free temperature measurements below the soil freezing zone, and model parameter values (thermal conductivity and heat capacity of the soil) known a priori with no uncertainty.
Typically, this involves replacing presumably error-free laboratory salinity measurements with indirect measurements that are however affected by various source of uncertainties, and these uncertainties need to be accounted for in order to avoid compromising the quality of the final results.
The full model lead to perfect estimates for both, δ and cb, when applying kLD and kDBL, which is of course expected for a simulated, measurement error-free data set.
In contrast to the Bayesian approach, where the model parameters are considered random variables, in the frequentist approach the model parameters are assumed to be constants, i.e. we assume the existence of a true parameter vector θ t which would predict the measurement error-free data.
