Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

Abstract

A b s t r a c t. Nonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the 'optimal' soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the 'optimal' solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.

In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results

“…In this case the existence of only one optimum combination of parameter values is dismissed on account of random distribution, which properties are to be determined in the model identification process. Probabilistic description of parameter space in the GLUE method provides also for the description of model uncertainty itself, being attributable to insufficient data number, but also imperfect representation of the real process (e.g., inaccurate data, inadequate mathematical description, simplifying assumptions) [38]. The problem of GLUE parameter estimation was formulated as Bayesian analysis:…”

“…In the case of models of surface runoff either soil moisture content or the structure of the parameters' distribution is not known, but only their range or allowable limits resulting from their physical interpretation. A constant character of the parameter distribution was adopted for soil moisture content modeling herein, which is a frequent case in such or similar applications [38]. Moreover, GLUE parameter identification embraced the transformation of a priori distribution to a posteriori one through reliability function as conditioning the probability of parameter combination due to the fitting quality of the modeled and measured values.…”

“…In Equation (13), in the same manner as (11), the first component describes part of the model variance, being a direct effect of the parameters X -i on the model output Y. The second component, however, represents the influence of Xi parameter being in interaction with other parameters (X -i ) [38]. On this basis, the total sensitivity index is defined (S Ti ) for the model against X i parameter as follows:…”

Available water capacity from a multidisciplinary and multiscale viewpoint. A review

“…In wind evaporation experiments, Mohrath et al (1997) demonstrated that inversion can have low error, except when soil samples are layered. Using more recent methods, such as generalized likelihood uncertainty estimation, Yan et al (2017) highlighted that the quality of inversion depends strongly on the parameters of the inversion method itself (i.e., hyper-parameters). Uncertainties in properties estimated from model inversion may depend on many factors.…”