Cloud computing for integrated stochastic groundwater uncertainty analysis

Abstract

One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties. A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision. This paper presents a scientific modeling service called ‘ModflowOnAzure’ which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud. Several technical issues were addressed, including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud, integration of Dropbox with Azure for improved usability and ‘Drop-and-Compute,’ and automated file exchanges between desktop and the cloud. Two scientific use cases are presented in this paper using this service with significant computational speedup. One case is from Arizona, where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios. Another case is from Texas, where a global sensitivity analysis is performed on a regional groundwater availability model. Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.

Keywords:

• MODFLOW
• groundwater sustainability
• uncertainty analysis
• global sensitivity analysis
• cloud computing
• Windows Azure
• Dropbox
• alternative conceptual model
• cyberinfrastructure
• eScience

Acknowledgements

The authors thank Wenming Ye from Microsoft Corporation who provided initial technical guidance on running models in the Azure platform at the early stage of our implementation. Ron Searl at the University of Illinois at Urbana-Champaign helped the implementation of this project in Azure. The first author also thanks Dr Yan Xu, Senior Research Program Manager of the Microsoft Research Connections' Environmental Informatics Program, who funded the ‘Digital Urban Informatics’ project at NCSA. Krishna Kumar at Microsoft provided no-cost access of an MSDN Premium Account on Azure for the computational experiment conducted in this paper. Benjamin Ruddell at the Arizona State University provided the initial suggestion and made connections on using ADWR's model as a case study for the cloud computing experiment. Lastly, we thank Barbara Jewett at NCSA who provided professional proofreading for this paper.