The Present State and Future Application of Cloud Computing for Numerical Groundwater Modeling

Hayley, Kevin

doi:10.1111/gwat.12555

Cited by 25 publications

(17 citation statements)

References 36 publications

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“…The combination of PFAs with the large amount of computational power available on the cloud (Hayley ) makes the broad application of the Popper‐Bayes theory more feasible. It is now possible to launch thousand of independent simulations simultaneously.…”

Section: Research Perspectives In Pfasmentioning

confidence: 99%

Prediction‐Focused Approaches: An Opportunity for Hydrology

Hermans

2017

Groundwater

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Section: Research Perspectives In Pfasmentioning

confidence: 99%

Prediction‐Focused Approaches: An Opportunity for Hydrology

Hermans

2017

Groundwater

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“…The initial effort in setting up the cloud computing was far outweighed by the benefit of parallelization for the IES analysis and predictive runs. Extensions to the implementation of the IES method to take advantage of dynamic cloud computing environments with low costs [17] is a potential direction for future development.…”

Section: Discussionmentioning

confidence: 99%

“…Even with the efficiencies of the IES method, the Monte Carlo analysis of a computationally expensive numerical model is demanding and required a high degree of parallelization. High throughput computing employing cloud computing resources was used for this study and has been noted by previous studies to provide practitioners with access to high performance computing capability on an as needed basis [15][16][17] and make computationally demanding methods feasible.…”

Section: Introductionmentioning

confidence: 99%

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

Hayley

Valenza²,

White

et al. 2019

Water

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Numerical groundwater modelling to support mining decisions is often challenging and time consuming. Simulation of open pit mining for model calibration or prediction requires models that include unsaturated flow, large magnitude hydraulic gradients and often require transient simulations with time varying material properties and boundary conditions. This combination of factors typically results in models with long simulation times and/or some level of numerical instability. In modelling practice, long run times and instability can result in reduced effort for predictive uncertainty analysis, and ultimately decrease the value of the decision-support modelling. This study presents an early application of the Iterative Ensemble Smoother (IES) method of calibration-constrained uncertainty analysis to a mining groundwater flow model. The challenges of mining models and uncertainty quantification were addressed using the IES method and facilitated by highly parallelized cloud computing. The project was an open pit mine in South Australia that required predictions of pit water levels and inflow rates to guide the design of a proposed pumped hydro energy storage system. The IES calibration successfully produced 150 model parameter realizations that acceptably reproduced groundwater observations. The flexibility of the IES method allowed for the inclusion of 1493 adjustable parameters and geostatistical realizations of hydraulic conductivity fields to be included in the analysis. Through the geostatistical realizations and IES analysis, alternative conceptual models of fractured rock aquifer orientation and connections could be conditioned to observation data and used for predictive uncertainty analysis. Importantly, the IES method out-performed finite difference methods when model simulations contained small magnitude numerical instabilities.

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“…Recent advances in open-source software development facilitate the widespread use of sophisticated analysis techniques (e.g., Bakker and Schaars 2019). Further, the capabilities of cloud-based big data analysis (Hayley 2017) such as deep or machine learning are rapidly progressing (Shen 2018;Bergen et al 2019) and will inevitably play a major role in the discipline of hydrogeology. Such developments will, without doubt, lead to improved knowledge of groundwater system functioning, deliver much increased spatiotemporal understanding of subsurface resources and therefore also progress sustainable groundwater management efforts.…”

Section: Observations From Current Practicementioning

confidence: 99%

Future-proofing hydrogeology by revising groundwater monitoring practice

et al. 2020

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Groundwater is an important global resource and its sustainable use faces major challenges. New methods and advances in computational science could lead to much improved understanding of groundwater processes and subsurface properties. A closer look at current groundwater monitoring practice reveals the need for updates with a special focus on the benefits of high-frequency and high-resolution datasets. To future-proof hydrogeology, this technical note raises awareness about the necessity for improvement, provides initial recommendations and advocates for the development of universal guidelines.

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The Present State and Future Application of Cloud Computing for Numerical Groundwater Modeling

Abstract: Article impact statement: Cloud computing will make computationally demanding analysis such as stochastic uncertainty quantification measurement network design and groundwater management simulation possible and accessible to groundwater researchers and consultants

Cited by 25 publications

References 36 publications

Prediction‐Focused Approaches: An Opportunity for Hydrology

Prediction‐Focused Approaches: An Opportunity for Hydrology

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

Future-proofing hydrogeology by revising groundwater monitoring practice

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