Use of Global Sensitivity and Data-Worth Analysis for an Efficient Estimation of Soil Hydraulic Properties

Abstract

Accurate simulation of flow and contaminant transport processes through unsaturated soils requires adequate knowledge of the soil parameters. This study deals with the hydraulic characterization of soils using laboratory experiments. A new strategy is developed by combining global sensitivity analysis (GSA) and Bayesian data-worth analysis (DWA) to obtain efficient data that ensure a good estimation of the soil properties. The strategy is applied for the estimation of soil properties from a laboratory infiltration experiment. Results of this study show that GSA allows identification of regions and periods of high sensitivity of each parameter and thereby, the observations prone to contain information for a successful calibration. Further, the sensitivity depicts a nonlinear behavior with regions of high influence and regions of weak influence inside the parameter space. Bayesian DWA, performed a priori, allows to quantify the improvement of the posterior uncertainty of the estimated parameters when adding a type of measurement. The results reveal that an accurate estimation of the soil properties can be obtained if the target parameter values are located in the regions of high influence in the parameter space.Water 2020, 12, 736 2 of 15 and evaporation experiments [13], unsaturated transport experiments [14][15][16][17], and hydrogeophysical experiments [18,19].Laboratory experiments are often time consuming, expensive, and tricky because of the risk of non-identifiability of the accepted parameters which depends on the measured data. Global sensitivity analysis (GSA) has been employed by Younes et al., [7] to help assess unsaturated soil hydraulic parameters. The GSA evaluates how output uncertainties are related to input parameter uncertainties with all the variation of inputs. In the last decades, GSA has been largely used for unsaturated flow in porous media. For instance, in Pan et al. [20], GSA has been conducted to assess the relative contribution of parameter uncertainties to the flow and transport uncertainties in a layered heterogeneous system. Brunetti et al. [21] used GSA to investigate the influence of soil hydraulic parameters on the behavior of a permeable pavement. Younes et al. [19] used GSA to investigate the influence of hydraulic and geophysical parameters on the streaming potential signals. In van Griensven et al. [22], GSA has been used to reduce the number of parameters and thus reduce over-parametrization of the numerical model.GSA allows to quantify the impact changes of parameters have on the model output. By this way, GSA enables the detection of the important parameters, their regions, and periods of influence [23]. This can facilitate the experimental procedure by identifying strategic measurements that could be relevant for soil characterization. However, collecting new output observations even when the output is highly sensitive to the parameters may not reduce parameter uncertainties if the information contained in the new observations are already contained in the previous observat...