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Semi-distributed parameter optimization and uncertainty assessment for an ungauged catchment of Deduru Oya Basin in Sri Lanka

Author(s): E. J. Warusavitharana

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Keywords: Hydrological modelling; Parameter optimization; Semi-distributed modelling; SWAT; SUFI-2; Deduru Oya basin

Abstract: Parameters are the key element in any hydrological model which govern the processes of water cycle. However, determining the most influential parameters and their ranges has certainly a challenging task for an ungauged river basin, where measurements related to hydrometeorological data and parameters are either too few or too poor in quality or not available at all. In this context, the aim of this research is to demonstrate a semi-distributed modelling approach and to optimize uncertainty assessment of model parameters in part of an ungauged river basin belongs to Deduru Oya Basin. Satellite retrieved weather data was used as input data and the model was run using SWAT water resources modelling tool. The parameters were optimized for the period of 2007–2011, using SUFI-2 inverse modelling approach. In local sensitivity analysis, out of the 43 parameters 14 were received as significant and out of them 4 were received as most sensitive with reference to the selected catchment. The model showed acceptable P values greater than 70% for 2007, 2008 and 2010 and lower R values that indicate lesser parameter uncertainty. The calibrated parameter ranges are also similar for the above 3 years. The key feature noticed in the final result was that the above 3 years have relatively similar seasonal rainfall pattern. Conversely, the model showed moderately acceptable P values, lesser than 60% for 2009 and 2011 where the annual rainfall pattern of the 2 years was comparatively different than its usual seasonal pattern. Moreover, the significant parameters identified by SUFI-2 have shown direct and indirect relationship with the physical properties and characteristics of the soil in the watershed.

DOI: https://doi.org/10.1080/15715124.2019.1656221

Year: 2020

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