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

Abstract

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.

Groundwater Sensitivity to Climate Variations Across Australia

“…Estimating specific yield in practice, however, involves costly aquifer pumping tests (Healy & Cook, 2002), highly precise groundwater level observations, and subsequent earth‐tide analysis (Chowdhury et al., 2022). Consequently, estimates are rare and often have an order of magnitude uncertainty (Crosbie et al., 2019; Healy & Cook, 2002).…”

“…Encouragingly the S y is between the physical limits of 0% and 100% at all sites. Further evaluation of the validity of the results is, however, difficult given that very few independent estimates exist nationally, most likely because of the high costs of pumping tests and those that exits have a high uncertainty (Chowdhury et al, 2022;Crosbie et al, 2015). That said, for northern Australia it is estimated at 1.7%-3.6%, while in SA it is estimated at 7.5%-30%, and globally at 0%-43% (Crosbie et al, 2015(Crosbie et al, , 2019Lv et al, 2021).…”

“…Here we also introduce an approach to estimate the specific yield, which is a key aquifer hydraulic property and highly desirable for groundwater management. Given how sparse such estimates often are and their high utility in estimating the aquifer response time to pumping (Chowdhury et al, 2022;Crosbie et al, 2015), our results offer a considerable opportunity to the management of groundwater usage. The introduced approach enables to estimate specific yield at any site using long-term head and climate records, therefore avoiding the logistical and economic challenges of obtaining specific yield estimates through in situ measurement.…”

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

“…the surface-groundwater interactions [3], to estimate aquifer properties such as hydraulic conductivity and specific storage [4,5], to analyse the effects of stresses on groundwater recharge, storage and discharge [6], to establish models for groundwater flow pattern and gradients [7,8] and to investigate the effect of climate variability [9]. Thus, effective data-driven groundwater management is essential to meet the requirements of groundwater-dependent analysis.…”