Evaluation of uncertainty estimates in distributed hydrological modeling for micro watershed in Godavari River basin in India using SUFI-2, and ParaSol methods
Dattatray Regulwar, Sonali Nagargoje
Thursday 2 july 2015
17:15 - 17:30h
at Central America (level 0)
Themes: (T) Water resources and hydro informatics (WRHI), (ST) Catchment hydrology
Parallel session: 13H. Water resources - Catchment
Hydrological models always suffer from different sources of uncertainties. As the distributed hydrological models play vital role in water resource management, reliable quantification of uncertainty in hydrological modeling results is quite necessary. The objective of the present study is to apply two uncertainty analysis methods to a distributed hydrological modeling system, quantify the impact of parameter uncertainties, and examine their performance and capability. SWAT (Soil and Water Assessment Tool) is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. When calibrating a physically based model like SWAT, it is important to remember that all model input parameters must be kept within a realistic uncertainty range and that no automatic procedure can substitute for actual physical knowledge of the watershed. For sustainable development, best management practices should be implemented for the present case study which is under overexploited stage (According to report of Central Ground Water Board, India). This micro watershed lies in Godavari river basin which flows through Paithan, Khuldabad villages of Aurangabad district Maharashtra state India. The soil and water assessment tool (SWAT) model has been applied to estimate the surface runoff during 1985-2010 and validated by the observed data. Two uncertainty analysis methods were further conducted and compared within the same modeling framework: (1) the sequential uncertainty fitting algorithm (SUFI-2), and (3) the parameter solution (ParaSol) method. From the comparison of a set of proposed evaluation criteria for uncertainty analysis methods in this study, including R-factor, P-factor, computation efficiency, and performance of best estimates it is concluded that the SUFI-2 method was able to provide more reasonable and balanced predictive results.
