Quantifying climate and pumping contributions to aquifer depletion using a highly parameterised groundwater model: Uley South Basin (South Australia)

The relative contributions of climate and human stresses to aquifer depletion in real-world settings are rarely quantified, particularly where complex patterns of depletion arise from the spatial and temporal variability in aquifer stresses. These impacts can be assessed using calibration-constrained model predictions of disturbed (i.e., subject to human activity) and undisturbed (i.e., natural) conditions. Prior investigations that adopt this approach employ lumped-parameter or one-dimensional models. Here, we extend previous studies by using a highly parameterised, spatially distributed groundwater model to investigate the relative impacts of climate variability and pumping on aquifer depletion. The Uley South Basin (USB), South Australia, where there is conjecture surrounding the cause of declining groundwater levels, serves as a case study. The relative contributions of climate variability and pumping to USB depletion are shown to be highly variable in time and space. Temporal trends reflect variability in rainfall and pumping, as expected. Spatial trends are primarily dependent on the proximity to both the coastal boundary and pumping wells, and to the distribution of recharge and hydraulic properties. Results show that pumping impacts exceed those of climate between 1978 and 2012, and over the majority of the spatial extent of USB. The contribution of pumping to aquifer depletion is shown to be 2.9 and 1.4 times that of climate in terms of the time-averaged and maximum-in-time basin-scale water budget, respectively. Confidence in model predictions is enhanced by the outcomes of a linear predictive uncertainty analysis, which indicates that predictive uncertainty is lower than climatic and pumping impacts. This study demonstrates the application of a relatively simple analysis that can be used in combination with highly parameterised, spatially distributed groundwater models to differentiate causal factors of aquifer depletion.Matthew J.Knowling, Adrian D.Werner, Daan Herckenrat

Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data

Abstract. Increasingly, ground-based and airborne geophysical datasets are used to inform groundwater models. Recent research focuses on establishing coupling relationships between geophysical and groundwater parameters. To fully exploit such information, this paper presents and compares a joint hydrogeophysical inversion (JHI) approach and sequential hydrogeophysical inversion (SHI) approach to inform a field-scale groundwater model with Time Domain Electromagnetic (TDEM) and Electrical Resistivity Tomography (ERT) data. The implemented SHI coupled inverted geophysical models with groundwater parameters, where the strength of the coupling was based on geophysical parameter resolution. To test whether the implemented SHI over- or underestimated the coupling strength between groundwater and geophysical model, we compared its results with a JHI in which a geophysical model is simultaneously inverted with a groundwater model using additional coupling constraints that explicitly account for an established petrophysical relationship and its accuracy. The first set of simulations for a synthetic groundwater model and TDEM data, employing a high-quality petrophysical and geometric relationship, showed improved estimates for groundwater model parameters that were coupled to relative well-resolved geophysical parameters. Compared to a SHI these improvements were insignificant and geophysical parameter estimates became slightly worse. In a second set of simulations, employing a low-quality petrophysical relationship, groundwater parameter improved less for both the SHI and JHI, where the SHI performed slightly better. For a real-world groundwater model and ERT data, different parameter estimates were obtained with a JHI and SHI. Parameter uncertainty was reduced but was similar for the SHI and JHI. The geometric constraint showed little impact while the petrophysical constraint showed significant changes in geophysical and groundwater parameters. For both cases investigated in this paper, the SHI seems favorable, taking in account parameter error, data fit and the complexity of implementing a JHI in combination with its larger computational burden. </jats:p