On the identifiability of soil hydraulic parameters in lysimeter experiments: a Bayesian perspective 

Abstract

<p>Groundwater recharge through the vadose zone is an important yet hard to quantify variable. It is estimated from lysimeter experiments or mathematical modelling. For the simulation of groundwater recharge rates with a physically based model soil hydraulic properties (SHPs) have to be inversely estimated because SHPs from laboratory experiments can only be poorly transferred to field conditions. Still, also the inverse estimation of SHPs, is associated with experimental and modeling uncertainties that propagate into the recharge prediction. New methods are thus required improving the inverse estimation of SHPs and reducing the uncertainty in groundwater recharge prediction. Therefore, this study aims to investigate how the assimilation of different types of soil water measurements for the inverse estimation of SHPs with the HYDRUS-1D software affects the estimated uncertainty. For this purpose, observations from a monolithic lysimeter experiment (i.e. lysimeter outflow, soil pressure head and volumetric soil water content at two different depths) have been combined in the different modeling scenarios and coupled with a Bayesian analysis to inversely estimate SHPs and assess their uncertainty. Posterior predictive checks showed that the simultaneous assimilation of outflow and soil pressure head led to the smallest uncertainty in groundwater recharge prediction. This represented a reduction in uncertainty compared to assimilating lysimeter outflow alone. Additional information provided by measurements of soil water content resulted in a reduced parameter uncertainty for residual and saturated water content, however, it did not further reduce the uncertainty in recharge prediction. Overall, this study shows the applicability of a Bayesian analysis for determining uncertainties in the inverse estimation of SHPs with lysimeter data and for the quantification of the associated uncertainty in groundwater recharge prediction. Based on our results for the investigated site, we recommend simultaneous assimilation of lysimeter outflow and soil pressure head measurements to minimize uncertainty in groundwater recharge prediction. However, a more comprehensive analysis is required to make a generally valid recommendation for other soils or climates.</p><p> </p>