- Title
- Multifactor analysis of specific storage estimates and implications for transient groundwater modelling
- Creator
- Chowdhury, Faysal; Gong, Jinzhe; Rau, Gabriel C.; Timms, Wendy A.
- Relation
- Hydrogeology Journal Vol. 30, p. 2183-2204
- Publisher Link
- http://dx.doi.org/10.1007/s10040-022-02535-z
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2022
- Description
- Specific storage (SS) 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 SS 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 SS values for numerical groundwater modelling, along with the scale and source of uncertainty of different measurement methods, was carried out. Overall, SS 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 SS 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 SS. A parallel assessment of 45 transient regional-scale groundwater models reveals a disconnect between findings of this study and the way SS is treated in practice, and that there is a lack of foundational SS data to conduct quantitative uncertainty analysis. This study provides the first probability density functions of SS for a variety of lithology types based on the field and laboratory tests collated from the literature. Log transformed SS 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.
- Subject
- aquifer properties; groundwater management; specific storage; groundwater modelling; probability distribution; SDG 6; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1491716
- Identifier
- uon:53046
- Identifier
- ISSN:1431-2174
- Rights
- x
- Language
- eng
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