2.1.3 Hydrogeology and groundwater quality

Summary

This section details various statistical techniques to assess the spatial and/or temporal variability of four hydrogeological datasets: groundwater levels, hydraulic parameters, groundwater quality and allocations (Section 2.1.3.2 and Section 2.1.3.3). The objective of this study is to collate, process and analyse the pre-existing hydrogeological and hydrochemical data to support the development of a conceptual model of causal pathways and a numerical groundwater model in the context of coal mining and coal seam gas (CSG) extraction.

For example, groundwater bores were assigned to major aquifers by comparing their screen intervals to relevant aquifer boundaries. Water levels were standardised to the Australian Height Datum (AHD) using a 1-second DEM to enable contour maps to be generated. The TGUESS approach was adopted to derive transmissivities from pumping tests available in the state databases. Subsequently, hydraulic conductivities were derived from the transmissivities and available bore screen information. The process of aquifer assignment allowed the grouping of available bore data into aquifers. Section 2.1.3.1.5 briefly describes the chloride mass balance method, which was used for estimating groundwater recharge, and presents the resulting recharge estimates for the bioregion.

A brief description of different observation data groups, their sources and their spatial distributions is given in Section 2.1.3.1 . Water level time series data analysis is reported in Section 2.1.3.3.1 for a 13-year period (2000 to 2012), with the period 2000 to 2007 representing drought conditions. The period from 2008 to 2012 reflects how groundwater levels have changed following the break of the drought and severe flooding. Mean groundwater levels and depths to groundwater and the associated uncertainties were determined for various aquifers of the Clarence-Moreton bioregion including the alluvial aquifers, the Walloon Coal Measures, the Gatton Sandstone and the Woogaroo Subgroup. Generally, mean water levels and flow directions in the alluvial aquifers follow the topographic gradient. They follow a predominantly north-easterly direction in the Queensland part of the Clarence-Moreton bioregion, and an easterly direction in the NSW part. However, in some areas of the Clarence-Moreton Basin, the groundwater flow direction differs from these patterns, highlighting the complex nature of groundwater dynamics in this sedimentary basin. The temporal variability in water levels was assessed by two statistical metrics: the Mann-Kendall tau statistic and the Sen’s slope estimate. The alluvial aquifers exhibited a rapid response to the break of the drought in 2008. In contrast, the response of water levels in the bedrock aquifers to rainfall events and the break of drought was more subdued.

The spatial variability of transmissivities and hydraulic conductivities in the alluvial aquifers are reported in Section 2.1.3.3.2 . Alluvial aquifers have higher transmissivities than the bedrock formations. Mean hydraulic conductivity estimates for the Lockyer and the Bremer alluvial aquifer systems were generally higher than for their counterparts in the Logan-Albert and Richmond river alluvia. The hydraulic properties exhibited considerable spatial variability within and across different alluvial systems.

The results of the analysis of water chemistry data are presented in Section 2.1.3.3.3 . Multivariate statistical analysis was used to identify patterns within the large datasets, based on groundwater chemistry records from several thousand bores. The multivariate statistical analysis of water chemistry patterns in the sedimentary and volcanic bedrock showed that some of the stratigraphic units have distinct patterns that allow differentiation from other units. However, the analysis also showed that there is considerable variability within the stratigraphic units, suggesting complex processes of groundwater hydrochemical evolution and recharge. The water chemistry analysis of the alluvial aquifer systems in the different regions confirmed that groundwater recharge processes (e.g. stream recharge) and the groundwater hydrochemistry in the underlying bedrock are the major controls on groundwater quality in the alluvial aquifer systems. In the headwaters where alluvial aquifers overlie volcanic bedrock, groundwater quality is generally good, whereas higher salinities are often observed in floodplain environments and particularly at the edge of alluvial aquifer systems

Groundwater recharge, one of the most important input parameters for the groundwater numerical model, was estimated using chloride mass balance for the volcanic and sedimentary bedrock units and the relationship of soil, vegetation and rainfall for the alluvial aquifers.

A gap analysis of the hydrogeological data is provided in Section 2.1.3.4 . The data gap analysis highlights that sufficient hydrogeological and hydrochemical data for analysis of temporal patterns only exist for some alluvial aquifer systems (i.e. Lockyer Valley, Bremer river basin and Logan-Albert river basin), whereas limited groundwater monitoring data are available for the Richmond and Clarence river basins. Additionally, the major data gap identified is that very limited information exists relating to groundwater heads, flow directions and hydraulic properties in deeper bedrock formations.

This section provides a comprehensive hydrogeological assessment for the Clarence-Moreton bioregion. It informs the conceptualisation (see companion product 2.3 of the Clarence-Moreton bioregion (Raiber et al., 2016)) that underpins the numerical groundwater model for the Richmond river basin (see companion product 2.6.2 for the Clarence-Moreton bioregion (Cui et al., 2016)). The relevant hydrogeological datasets sourced from NSW and Queensland state agencies underwent a rigorous process of standardisation to guarantee the integrity of this process and to ensure that they are fit for modelling purposes.

Mean groundwater levels and depths to groundwater and the associated uncertainties were determined for various aquifers of the Clarence-Moreton bioregion including the alluvial aquifers, the Walloon Coal Measures, the Gatton Sandstone and the Woogaroo Subgroup. This information is crucial to the identification of recharge/discharge areas and associated groundwater flow systems, as well as for the development of the groundwater balance for the bioregion. The inferred flow directions provide an insight into groundwater dynamics and aid preliminary identification of areas that can potentially be impacted by CSG and coal mining development. The temporal variability in water levels represented by the Mann-Kendall tau statistic and the Sen’s slope estimate analysis illuminates system dynamics and highlights how shallow groundwater and deep aquifers respond to contrasting climate conditions. Hydraulic parameters provide critical input into the numerical groundwater model – the identification of uncertainties in the hydraulic parameter fields is essential as they can contribute significantly to the overall uncertainty associated with the modelling of impacts.

The multivariate statistical analysis identifies patterns within thousands of groundwater chemistry records that inform critical hydrological processes such as recharge/discharge and the complex processes of groundwater hydrochemical evolution. It also helps to explain how geological structures influence aquifer connectivity and enables the identification of areas where surface water – groundwater interactions occur.

The gap analysis provides an overall assessment of data availability in the three-dimensional space, that is, spatially and across aquifers of various depths. This knowledge constitutes a crucial, first-step input that informs the modelling capability that can be conducted in the Clarence-Moreton bioregion. Informed decisions can then be made on crucial aspects such as model complexity, as represented by the extent of the domain and its temporal and spatial resolution, and the ability to refine a model in certain areas where CSG or coal mining developments are likely to occur.

Last updated:
11 July 2017
Thumbnail images of the Clarence-Moreton bioregion

Product Finalisation date

6 October 2016
PRODUCT CONTENTS