https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47106 Wed 14 Dec 2022 10:05:24 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:10817 Sat 24 Mar 2018 08:13:37 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:11039 Sat 24 Mar 2018 08:11:46 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:6097 Sat 24 Mar 2018 07:44:25 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:28513 Sat 24 Mar 2018 07:29:20 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23559 Sat 24 Mar 2018 07:14:11 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46240 Mon 14 Nov 2022 13:08:03 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53046 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.]]> Fri 17 Nov 2023 11:48:10 AEDT ]]>