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Article

Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network

1
CSIRO Land and Water, Dutton Park, QLD 4102, Australia
2
CSIRO Data61, Dutton Park, QLD 4102, Australia
3
CSIRO Land and Water, Waite Road, Urrbrae, SA 5064, Australia
*
Author to whom correspondence should be addressed.
Water 2020, 12(1), 123; https://doi.org/10.3390/w12010123
Received: 29 November 2019 / Revised: 21 December 2019 / Accepted: 26 December 2019 / Published: 30 December 2019
(This article belongs to the Special Issue Advances in Groundwater and Surface Water Monitoring and Management)
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. View Full-Text
Keywords: groundwater monitoring; uncertainty; optimization groundwater monitoring; uncertainty; optimization
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MDPI and ACS Style

Janardhanan, S.; Gladish, D.; Gonzalez, D.; Pagendam, D.; Pickett, T.; Cui, T. Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network. Water 2020, 12, 123. https://doi.org/10.3390/w12010123

AMA Style

Janardhanan S, Gladish D, Gonzalez D, Pagendam D, Pickett T, Cui T. Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network. Water. 2020; 12(1):123. https://doi.org/10.3390/w12010123

Chicago/Turabian Style

Janardhanan, Sreekanth, Dan Gladish, Dennis Gonzalez, Dan Pagendam, Trevor Pickett, and Tao Cui. 2020. "Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network" Water 12, no. 1: 123. https://doi.org/10.3390/w12010123

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