Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network

Abstract

In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling framework for obtaining the spatial distribution of predicted drawdown for a wide range of plausible combination of uncertain parameters pertaining to the deep sedimentary basin and groundwater flow processes. Reduced rank spatial prediction was used to characterize dominant trends in these spatial drawdown patterns using empirical orthogonal functions (EOF). A differential evolution algorithm was used to identify optimal locations for multi-level piezometers for collecting groundwater pressure data to minimize predictive uncertainty in groundwater drawdown. Data-worth analysis helps to explore the veracity of the design by using only the sensitivities of the observations to predictions independent of the absolute values of predictions. A 10-bore monitoring network that collects drawdown data from multiple depths at each location was designed. The data-worth analysis revealed that the design honours sensitivities of the predictions of interest to parameters. The designed network provided relatively high data-worth for minimizing uncertainty in the drawdown prediction at locations of interest.

Probabilistic Groundwater Flow, Particle Tracking and Uncertainty Analysis for Environmental Receptor Vulnerability Assessment of a Coal Seam Gas Project

“…This was considered insufficient to change regional hydraulic gradients and affect contaminant migration paths in the Pilliga aquifer. In Narrabri CSG development simulations, modelled CSG water extraction ranged from 4 to 107 million cubic metres over the life of the gas project as there was considerable uncertainty in estimates of CSG water production [21]. Particle advection in the Pilliga Sandstone was unlikely to be influenced by pumping during CSG well development compared to a no pumping scenario as drawdown in the Pilliga as a result of pumping was <0.3 m at the median level and <1.5 m at the 95th percentile level (Figure 4).…”

“…Adaptive management can then be used to adjust operational monitoring plans and responses according to the knowledge gained from new data. A related study applied a spatial optimization technique to identify locations for additional monitoring wells to reduce uncertainty [21].…”

“…A numerical groundwater flow model built using MODFLOW-USG [14] and developed as part of the Bioregional Assessment Program was modified and used in this study as described in detail by Janardhanan et al [21]. In the present study the model was applied for particle tracking analysis in the context of environmental receptor vulnerability analysis.…”

“…The previously reported version of this model [21] used a parsimonious parameterization of hydraulic properties considering observed depth dependence of hydraulic conductivity. Core data analysis identified spatial structure for the interburden formations [23].…”

“…Examples of realizations of the hydraulic conductivity for model layer 6 representing the Pilliga Sandstone aquifer obtained in this manner are shown in Figure 3. Independent parameters were also used to represent spatially variable flood and irrigation recharge and river conductance [21]. It is noteworthy that this approach enabled simulation of particle tracks for a wide range of hydraulic characteristics.…”