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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #320519
Title: Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range

Author
item YEN, HAW - Texas Agrilife Research
item JEONG, JAEHAK - Texas Agrilife Research
item Smith, Douglas

Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/14/2015
Publication Date: 4/1/2016
Publication URL: http://handle.nal.usda.gov/10113/62883
Citation: Yen, H., Jeong, J., Smith, D.R. 2016. Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range. Journal of the American Water Resources Association. 52(2):443-455. doi:10.1111/1752-1688.12394.

Interpretive Summary: One of the most laborious procedures in modeling water quality in fields is calibrating the model to ensure output adequately represent observed data. Autocalibration of models allows a quicker way to accomplish this goal. This study was conducted to evaluate the Dynamically Dimensioned Search algorithm as one tool to autocalibrate the Soil and Water Assessment Tool. In this study, the Soil and Water Assessment Tool was calibrated for an agricultural watershed in Texas, USA. The Dynamically Dimensioned Search algorithm was tested using nine increments of the perturbation factor (0.05 to 1.0), and was also tested using assuming a normal distribution for resampling model parameters or a uniform distribution. The Dynamically Dimensioned Search algorithm worked better when the parameters were assumed to follow a normal distribution. It was also found that the ideal perturbation factor was the default setting of 0.2. The impact of this paper is to inform other researchers and modelers on the use of a tool to autocalibrate complex water quality models, such as the Soil and Water Assessment Tool.

Technical Abstract: The primary advantage of Dynamically Dimensioned Search algorithm (DDS) is that it outperforms many other optimization techniques in both convergence speed and the ability in searching for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation factor) in the optimization process. Conventionally, a default value of 0.2 is used as perturbation factor in DDS where normal distribution is applied as sampling distribution with zero mean and the variance of one. However, the sensitivity of the perturbation factor to the performance of DDS in applications of watershed modeling is still unknown. The fixed-form sampling distribution may result in finding optimal parameters at the local scale rather than global scale in the parameter sampling space. In this study, the efficiency of DDS was evaluated by altering the perturbation factor (from 0.05 to 1.00) and the selection of sampling distribution (normal and uniform distribution) on hydrologic and water quality predictions in a lowland agricultural watershed in Texas, USA. Results show that the use of altered perturbation factors may cause variations in convergence speed or the ability in finding better solutions. In addition, DDS results were found to be very sensitive to sampling distribution selections. It is concluded that DDS-Normal outperformed DDS-Uniform in all scenarios. The choice of sampling distributions could be the potential major factor to be consiered for the performance of auto-calibration techniques in calibration problems for watershed simulation models.