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Related Experiment Video

Updated: Jun 14, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Using a cloud to replenish parched groundwater modeling efforts.

Randall J Hunt¹, Joseph Luchette, Willem A Schreuder

¹U.S. Geological Survey, Middleton, WI 53562, USA. rjhunt@usgs.gov

|March 25, 2010

Summary

This summary is machine-generated.

Cloud computing offers powerful, on-demand computational resources to enhance groundwater models. This enables complex model calibration and uncertainty analysis, previously limited by high computational demands.

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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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Last Updated: Jun 14, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Environmental science
Hydrogeology
Computational modeling

Background:

Groundwater models benefit from increased parameter flexibility and soft-knowledge integration.
These advanced methods require significant computational resources, posing a barrier to widespread adoption.
Traditional computing infrastructure limits the scope and frequency of complex groundwater modeling tasks.

Purpose of the Study:

To explore the application of cloud computing for enhancing groundwater model calibration and uncertainty analysis.
To demonstrate how cloud-based resources can overcome computational limitations in sophisticated groundwater modeling.

Main Methods:

Utilizing cloud computing platforms to access scalable and on-demand virtual computing power.
Implementing techniques for creating, launching, and terminating virtual machines as needed.
Leveraging cloud storage for saving and reusing machine images for future modeling sessions.

Main Results:

Cloud computing provides cost-effective and flexible access to high-performance computing for groundwater modelers.
The use of virtual computers allows for routine execution of complex model calibration and uncertainty analyses.
Enables advanced modeling approaches that were previously computationally prohibitive.

Conclusions:

Cloud computing democratizes access to advanced computational resources for hydrogeological studies.
Modelers can now perform sophisticated groundwater model calibration and uncertainty analysis more efficiently and routinely.
This technological shift empowers more robust and comprehensive groundwater resource management and research.