Oral Presentation NCGRT/IAH Australasian Groundwater Conference 2019

Objectives

Hydrogeological studies typically focus on groundwater as a resource for both human and ecological activities. Shallow groundwater is often neglected as these resources may be considered too vulnerable to surface contamination, or the yield may be too variable. Despite shallow groundwater often not being a viable extractive resource, it can present a significant risk to infrastructure. These risks are likely to be exacerbated under changing climatic conditions (flooding, sea-level rise) and are applicable to a variety of risk mechanisms.

Design and methodology

Although shallow groundwater is subject to the same governing laws of flow as deeper groundwater resources, there is often far less information available about the spatial distribution of shallow groundwater surfaces. This lack of information makes traditional groundwater flow modelling difficult as data scarcity leads to greater model parameter uncertainty. As many shallow groundwater risks are dependent on hydraulic head, rather than flow or aquifer yield, simplified maps of shallow groundwater head can be used to understand these risks. We have developed a workflow for mapping shallow groundwater levels that incorporates a range of spatial and temporal information. The information is obtained from regional authority databases and environmental data sets. We used geostatistical relationships between variables to improve estimates of shallow hydraulic head in data-sparse areas.

Results

The results honour groundwater observation data and provide a continuous shallow groundwater surface across the area of interest. The workflow is flexible and allows for different scenarios (i.e. seasonal variation) to be efficiently mapped.

Conclusions

The shallow groundwater level maps produced in this study can be used to quantify risks to infrastructure, including liquefaction risks and groundwater inundation. Regional shallow groundwater maps are also applicable as design criteria for water-sensitive urban design and construction. The automation of mapping workflows allows for maps to be produced more efficiently, and offers the opportunity for further development using more advanced approaches, such as machine learning.