Calibration of watershed models using cloud computing

2012 IEEE 8th International Conference on E-Science, e-Science 2012

Volumn , Issue , 2012, Pages

  Humphrey, Marty ^a   Beekwilder, Norm ^a   Goodall, Jonathan L ^b   Ercan, Mehmet B ^b  

^a University of Virginia   (United States)

^b University of South Carolina   (United States)

Author keywords

Cloud computing; Model calibration; SWAT; Watershed modeling; Windows Azure

Indexed keywords

BEST MATCH; CLOUD-BASED; COMPUTATIONAL MODEL; CONTINUOUS CALIBRATIONS; EXTREME EVENTS; HYDROLOGIC SYSTEMS; INTERACTIVE MODELS; LARGE WATERSHEDS; MICROSOFT WINDOWS; MODEL CALIBRATION; RIVER BASINS; SEARCH ALGORITHMS; SWAT; WATERSHED MODELING; WATERSHED MODELS; WINDOWS AZURES;

CALIBRATION; CLOUD COMPUTING; LANDFORMS; LAPTOP COMPUTERS; WATER QUALITY; WINDOWS OPERATING SYSTEM;

WATERSHEDS;

EID: 84873627952     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/eScience.2012.6404420     Document Type: Conference Paper

References (35)

1. M. Sloboda, V. Sharma, S. Hood, D. Swayne, W. Booty, D. Lam and I. Wong. Innovative Autocalibration Techniques Using High Performance Computing. 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009.
2. E. Rouholahnejad, K.C. Abbaspour, M. Vejdani, R. Srinivasan, R. Schulin, A. Lehmann, A parallelization framework for calibration of hydrological models, Environmental Modelling & Software, Available online 5 January 2012, ISSN 1364-8152, 10.1016/j.envsoft.2011.12.001. (http://www.sciencedirect.com/science/ article/pii/S1364815211002829)
3. P. Gassman, M.R. Reyes, C. Green, J. Arnold.The soil and water assessment tool: development, applications, and future research directions. Transactions on the ASABE. 50(4), pp. 1211-1250.
4. Soil and Water Assessment Tool (SWAT). http://swatmodel.tamu.edu/
5. Microsoft Windows Azure. http://www.windowsazure.com
6. J. Li, D. Agarwal, M. Humphrey, C. van Ingen, K. Jackson, and Y. Ryu. eScience in the Cloud: A MODIS Satellite Data Reprojection and Reduction Pipeline in the Windows Azure Platform. In Proceedings of the 24th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2010), Apr 19-23, 2010. Atlanta, Georgia.
7. M. Humphrey, Z. Hill, C. van Ingen, J. Jackson, and Y. Ryu. Assessing the Value of Cloudbursting: A Case Study of Satellite Image Processing on Windows Azure. In IEEE e-Science 2011 Conference. Stockholm, Sweden. Dec 5-8, 2011.
8. B.A. Tolson and C. A. Shoemaker, Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43, W01413, doi:10.1029/2005WR004723.
9. B.A. Tolson, Sharma, V., Swayne, D., and Fan, L. (2007) "A parallel implementation of the Dynamically Dimensioned Search (DDS) algorithm." International Symposium on Environmental Software Systems (ISESS) 2007. Prague, Czech Republic. May 22 - 25, 2007.
10. A. Matsunaga, M. Tsugawa, J. Fortes. CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications Proceedings of 4th IEEE International Conference on e-Science (eScience 2008), Indianapolis, IN, Dec 2008.
11. C. Evangelinos and C. Hill. Cloud Computing for parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon's EC2. Cloud Computing and its Applications (CCA-08). Chicago, IL. Oct 22-23, 2008.
12. R. Grossman, Y. Gu, Data mining using high performance data clouds: experimental studies using sector and sphere. Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'08). Las Vegas, NV, 2008.
13. G. Mackey, S. Sehrish, J. Bent, J. Lopez, S. Habib, J. Wang. Introducing Map-Reduce to High End Computing. 3rd petascale data storage workshop (held in conjunction with SC'08). Austin, TX. Mon Nov 17, 2008.
14. E. Deelman, G. Singh, M. Livny, B. Berriman, J. Good. The Cost of Doing Science on the Cloud: The Montage Example. Proceedings of Supercomputing 2008, Austin, TX, Nov 15-21, 2008.
15. K. Jackson, L. Ramakrishnan, K. Runge, and R. Thomas. "Seeking Supernovae in the Clouds: A Performance Study". 1st Workshop on Scientific Cloud Computing (ScienceCloud 2010), Chicago, IL, June 21 2010.
16. M. Palankar, A. Lamnitchi, M. Ripeanu, S. Garfinkel. Amazon S3 for Science Grids: A Viable Solution? International Workshop on Data-Aware Distributed Computing, Boston, Massachusetts, June 2008.
17. W. Lu, J. Jackson, and R. Barga. "AzureBlast: A Case Study of Developing Science Applications on the Cloud." 1st Workshop on Scientific Cloud Computing (ScienceCloud 2010), Chicago, IL, June 21 2010.
18. W. Lu, J. Jackson, J. Ekanayake, R. S. Barga, N. Araujo. Performing Large Science Experiments on Azure: Pitfalls and Solutions. IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2010), Nov. 30-Dec.1, 2010, Bloomington, Ind.
19. Community Surface Dynamics Modeling System (CSDMS). http://csdms. colorado.edu
20. Earth System Modeling Framework (ESMF). http://www.earthsystemmodeling. org/
21. Object Modeling System (OMS). http://www.javaforge.com/project/oms
22. Open Modeling Interface (OpenMI). http://www.openmi.org
23. B.A. Allan, R. Armstrong, D.E. Bernholdt, F. Bertrand, K. Chiu, T.L. Dahlgren, K. Damevski, W.R. Elwasif, T.G.W. Epperly, M. Govindaraju, D.S. Katz, J.A. Kohl, M. Krishnan, G. Kumfert, J.W. Larson, S. Lefantzi, M.J. Lewis, A.D. Malony, L.C. McInnes, J. Nieplocha, B. Norris, S.G. Parker, J. Ray, S. Shende, T.L. Windus, and S.J. Zhou, "A component architecture for high-performance scientific computing," International Journal of High Performance Computing Applications, vol. 20, 2006, pp. 163-202.
24. I. Foster, "Service-Oriented Science," Science, vol. 308, 2005, pp. 814-817.
25. N.S. Christensen, A.W. Wood, N. Voisin, D.P. Lettenmaier, and R.N. Palmer, "The effects of climate change on the hydrology and water resources of the Colorado River basin," Climatic Change, vol. 62, 2004, pp. 337-363.
26. A.F. Hamlet and D.P. Lettenmaier, "Effects of climate change on hydrology and water resources in the Columbia River basin," Journal of the American Water Resources Association, vol. 35, 1999, pp. 1597-1623.
27. J.T. Payne, A.W. Wood, A.F. Hamlet, R.N. Palmer, and D.P. Lettenmaier, "Mitigating the Effects of Climate Change on the Water Resources of the Columbia River Basin," Climatic Change, vol. 62, 2004, pp. 233-256.
28. B.R. Bicknell, J.C. Imhoff, J.L. Kittle, A.S. Donigian and R.C. Johanson, R.C., 1997, Hydrological Simulation Program-Fortran, User's manual for version 11: U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, Ga., EPA/600/R-97/080, 755 p., 1997.
29. K. Eckhardt and J.G. Arnold. Automatic calibration of a distributed catchment model. Journal of Hydrology. Vol 251, issues 1-2,15 Sept 2001, pages 103-109.
30. C.-T. Cheng, Wu, X.-Y. Chau, K. W. Multiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computers. Hydrological Sciences Journal. 2005, Vol 50(6), pages 1069-1088.
31. T. Francke, A. Bronster, and C. A. Shoemaker. Heuristic optimization methods for run-time intensive models (Dynamically Dimensioned Search, Particle Swarm optimization, GA) - a comparison of performance and parallel implementation using R. Geophysical Research Abstracts. Vol 12. EGU2010-8741, 2010.
32. "Announcing New Windows Azure Services to Deliver 'Hybrid Cloud'". http://blogs.msdn.com/b/windowsazure/archive/2012/06/06/ announcing-new-windows-azure-services-to-deliver-hybrid-cloud.aspx
33. M. Mao, Jie Li and M. Humphrey. Cloud Auto-Scaling with Deadline and Budget Constraints. In Proceedings of 11th ACM/IEEE International Conference on Grid Computing (Grid 2010). Oct 25-28, 2010. Brussels, Belgium.
34. M. Mao and M. Humphrey. Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows. Proceedings of Supercomputing 2011, Seattle, WA, Nov 15-20, 2011.
35. Microsoft Corporation. Windows HPC Server 2008 R2 Suite. http://www.microsoft.com/hpc/en/us/product/cluster-computing.aspx