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\begin{document}

\title{Quantifying uncertainties in soil hydraulic parameters for dual-porosity
models using a null-space Monte Carlo method - implications for
groundwater recharge estimation.}



\author[1]{Sakambari Padhi}%
\author[2]{R Rangarajan}%
\author[3]{Tomochika Tokunaga}%
\author[4]{Philip Brunner}%
\affil[1]{The University of Tokyo}%
\affil[2]{National Geophysical Research Institute CSIR}%
\affil[3]{The University of Tokyo Graduate School of Frontier Sciences}%
\affil[4]{University of Neuchâtel}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Groundwater recharge can be significantly influenced by the macropores,
especially in fine structured soils. However, models considering
macropores require a number of additional parameters which are difficult
to determine by conventional methods. Thus, inverse modeling is often
applied to estimate soil hydraulic and solute transport properties of
the unsaturated zone. In this study, an efficient method for recharge
prediction and parameter uncertainty quantification by coupling a
dual-porosity model (DPM) to the null-space Monte Carlo (NSMC) algorithm
was developed, and the impact of uncertainty in the key model parameters
on groundwater recharge were analyzed. Recharge estimates were further
compared to the one by tritium peak method. Results showed that the
estimated recharge was much smaller than the one estimated from the
tritium peak method, indicating the possible overestimation of recharge
by conventional tritium peak method with piston flow model. Our study
further demonstrated that the conventional practice of deriving single
set of parameters through inverse modeling could result in biased
recharge prediction, and that for the complex subsurface flow and
transport models such as the DPM, NSMC method can provide a practical
solution for predictive uncertainty analysis.%
\end{abstract}%



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