Abstract
With enhanced availability of high spatial resolution data, hydrologic models such as the Soil and Water Assessment Tool (SWAT) are increasingly used to investigate effects of management activities and climate change on water availability and quality. The advantages come at a price of greater computational demand and run time. This becomes challenging to model calibration and uncertainty analysis as these routines involve a large number of model runs. For efficient modelling, a cloud-based Calibration and Uncertainty analysis Tool for SWAT (CUT-SWAT) was implemented using Hadoop, an open source cloud platform, and the Generalized Likelihood Uncertainty Estimation method. Test results on an enterprise cloud showed that CUT-SWAT can significantly speedup the calibration and uncertainty analysis processes with a speedup of 21.7–26.6 depending on model complexity and provides a flexible and fault-tolerant model execution environment (it can gracefully and automatically handle partial failure), thus would be an ideal method to solve computational demand problems in hydrological modelling.
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