Multifactor analysis of specific storage estimates and implications for transient groundwater modelling

Specific storage (S$_S$) has considerable predictive importance in the modelling of groundwater systems, yet little is known about its statistical distribution and dependency on other hydrogeological characteristics. This study provides a comprehensive overview and compiles 430 values of S$_S$ from 183 individual studies, along with complementary hydrogeological information such as estimation methods, lithology, porosity, and formation compressibility. Further evaluation of different approaches to determine and utilize S$_S$ values for numerical groundwater modelling, along with the scale and source of uncertainty of different measurement methods, was carried out. Overall, S$_S$ values range across six orders of magnitude (from 3.2 × 10$^{–9}$ to 6 × 10$^{–3}$ m$^{–1}$) with a geometric mean of 1.1 × 10$^{–5}$ m$^{–1}$ and the majority (> 67%) of values are in the order of 10$^{–5}$ and 10$^{–6}$ m$^{–1}$. High S$_S$ values of ~10$^{–4}$ m$^{–1}$ were reported for glacial till and sandy lithologies, particularly for shallow and thin strata where leakage may obscure the estimation of S$_S$. A parallel assessment of 45 transient regional-scale groundwater models reveals a disconnect between findings of this study and the way S$_S$ is treated in practice, and that there is a lack of foundational S$_S$ data to conduct quantitative uncertainty analysis. ... mehrThis study provides the first probability density functions of S$_S$ for a variety of lithology types based on the field and laboratory tests collated from the literature. Log transformed S$_S$ values follow a Gaussian/normal distribution which can be applied to evaluate uncertainties of modelling results and therefore enhance confidence in the groundwater models that support decision making.