Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python Xuesong Zhang a, * , Peter Beeson b , Robert Link a , David Manowitz a , Roberto C. Izaurralde a , Ali Sadeghi b , Allison M. Thomson a , Ritvik Sahajpal c , Raghavan Srinivasan d , Jeffrey G. Arnold e a Joint Global Change Research Institute, Pacific Northwest National Laboratory, University of Maryland, College Park, MD 20740, USA b Agricultural Research Service, United Stated Department of Agriculture, Beltsville, MD 20705, USA c Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA d Spatial Sciences Laboratory in the Department of Ecosystem Science and Management, Texas A&M University, College Stations, TX 77845, USA e Grassland, Soil & Water Research Laboratory USDA-ARS, Temple, TX 76502, USA article info Article history: Received 18 September 2012 Received in revised form 27 January 2013 Accepted 13 March 2013 Available online 28 April 2013 Keywords: Parallel processing Evolutionary multi-objective optimization High performance computer Soil and water assessment tool Parameter calibration abstract With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software to improve calibration efficiency has received growing attention of the watershed modeling community as it is very time demanding to run iteratively complex models for calibration. In this research, we introduce a Python-based parallel computing package, PP-SWAT, for efficient calibration of the Soil and Water Assessment Tool (SWAT) model. This software employs Python, MPI for Python (mpi4py) and OpenMPI to parallelize A Multi-method Genetically Adaptive Multi-objective Optimization Algorithm (AMALGAM), allowing for simultaneously addressing multiple objectives in calibrating SWAT. Test results on a Linux computer cluster showed that PP-SWAT can achieve a speedup of 45e109 depending on model complexity. Increasing the processor count beyond a certain threshold does not necessarily improve efficiency, because intensified resource competition may result in an I/O bottleneck. The efficiency achieved by PP-SWAT also makes it practical to implement multiple parameter adjustment schemes operating at different scales in affordable time, which helps provide SWAT users with a wider range of options of parameter sets to choose from for model(s) selection. PP-SWAT was not designed to address errors associated with other sources (e.g. model structure) and cautious supervision of its power should be exercised in order to attain physically meaningful calibration results. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction Watershed models have been widely used by researchers and decision makers to understand hydrological, ecological, and biogeochemical processes and to examine effects of human activ- ities and climate change/variability on water quantity and quality. These models, however, require careful calibration of a large number of parameters mostly due to measurement limitations and scaling issues (Beven, 2000). Manual and/or automatic parameter optimization has become a standard procedure by the researchers to reduce the disagreement between one or more model predicted and observed variables. With the advancement of powerful geographic information system (GIS) technology and availability of high spatial-resolution data, the last few decades have seen a substantial increase in process complexity and resolution (both Software availability Software: PP-SWAT Developer: Xuesong Zhang, Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, 5825 University Research Court Suite 1200, College Park, MD 20740, USA Operating systems: Linux Dependent software: Python 2.6 or above, mpi4py, and OpenMPI Availability: Free of charge; contact the first author to request a copy. * Corresponding author. E-mail address: xuesongzhang2004@gmail.com (X. Zhang). Contents lists available at SciVerse ScienceDirect Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft 1364-8152/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.envsoft.2013.03.013 Environmental Modelling & Software 46 (2013) 208e218