Watershed modeling using large-scale distributed computing in Condor and the Soil and Water Assessment Tool model

  • Authors:

  • Margaret W Gitau;Li-Chi Chiang;Mohamed Sayeed;Indrajeet Chaubey

  • Affiliations:

  • Biological and Agricultural Systems Engineering, and Center for Water and Air Quality, Florida A&M University, USA.;Department of Agricultural and Biological Engineering, Purdue University, USA.;Computing Research Institute, Rosen Center for Advanced Computing, USA.;Department of Agricultural and Biological Engineering, Department of Earth and Atmospheric Sciences, and Division of Environmental and Ecological Engineering, Purdue University, USA.

  • Venue:
  • Simulation
  • Year:
  • 2012

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Abstract

Models are increasingly being used to quantify the effects of best management practices (BMPs) on water quality. While these models offer the ability to study multiple BMP scenarios, and to analyze impacts of various management decisions on watershed response, associated analyses can be very computationally intensive due to a large number of runs needed to fully capture the various uncertainties in the model outputs. There is, thus, the need to develop suitable and efficient techniques to handle such comprehensive model evaluations. We demonstrate a novel approach to accomplish a large number of model runs with Condor, a distributed high-throughput computing framework for model runs with the Soil and Water Assessment Tool (SWAT) model. This application required more than 43,000 runs of the SWAT model to evaluate the impacts of 172 different watershed management decisions combined with weather uncertainty on water quality. The SWAT model was run in the Condor environment implemented on the TeraGrid. This framework significantly reduced the model run time from 2.5 years to 18 days and enabled us to perform comprehensive BMP analyses that may not have been possible with traditional model runs on a few desktop computers. The Condor system can be used effectively to make Monte Carlo analyses of complex watershed models requiring a large number of computational cycles.