Oral Presentation NCGRT/IAH Australasian Groundwater Conference 2019

Stochastic knowledge integration for groundwater exploration in data scarce areas (396)

Luk Peeters 1 , Dirk Mallants 1 , Tao Cui 2 , Andrew Taylor 1 , Trevor Pickett 2 , Mat Gilfedder 2 , Timothy Munday 3
  1. CSIRO Land and Water, Urrbrae, SA, Australia
  2. CSIRO Land and Water, Brisbane, QLD, Australia
  3. CSIRO Mineral Resources, Perth, WA, Australia

We have developed a systematic probabilistic framework to spatially assess the potential for sustainable groundwater development. The workflow starts by explicitly defining sustainable groundwater extraction, in our case study, a groundwater abstraction that can provide 1ML/d for 10 years with a salinity of less than 2500 mg/L without causing a drawdown of more than 5% of the saturated thickness at 1m from the borehole.

The methodology is applied to groundwater exploration in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands in central Australia. In this arid region, a crystalline basement is covered with regolith and a vast system of palaeovalleys that are filled with sediments. Both the regolith and the palaeovalley systems are known to host aquifer systems. An ensemble of interfaces that define the boundaries between the basement and the overlying weathered rocks and palaeovalley sediments is generated with a Bayesian Data Fusion methodology to ensure they are consistent with the available borehole, airborne electromagnetic and digital terrain information. The surfaces defined by these interfaces are combined with probability distributions of hydraulic conductivity and storage to create ensembles of equivalent transmissivity and storage. A similar procedure is used to generate ensembles of salinity that are consistent with the available knowledge of salinity distribution across the region.

Gridded water balance equations, in combination with the Theis equation, allow the rapid generation of ensembles of sustainable pumping volumes from these stochastic grids of hydraulic properties and salinity. The ensembles provide the probability of locating areas where the requirements of sustainable groundwater extraction are met.

The integration framework not only allows to rapidly identify prospective zones for sustainable groundwater extraction, it is transparent, can be iteratively and locally updated when new information becomes available.