Abstract
Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol), Sequential Uncertainty Fitting (SUFI2), and Generalized Likelihood Uncertainty Estimation (GLUE)) to quantify the parameter sensitivity and uncertainty of the SWAT (Soil and Water Assessment Tool) model in a mountain-loess transitional watershed—Jingchuan River Basin (JCRB) on the Loess Plateau, China. The model was calibrated and validated using monthly observed streamflow at the Jingchuan gaging station and the modeling results showed that SWAT performed well in the study period in the JCRB. The parameter sensitivity results demonstrated that any of the three methods were capable for the parameter sensitivity analysis in this area. Among the parameters, CN2, SOL_K, and ALPHA_BF were more sensitive to the simulation of peak flow, average flow, and low flow, respectively, compared to others (e.g., ESCO, CH_K2, and SOL_AWC) in this basin. Although the ParaSol method was more efficient in capturing the most optimal parameter set, it showed limited ability in uncertainty analysis due to the narrower 95CI and poor P-factor and R-factor in this area. In contrast, the 95CIs in SUFI2 and GLUE were wider than ParaSol, indicating that these two methods can be promising in analyzing the model parameter uncertainty. However, for the model prediction uncertainty within the same parameter range, SUFI2 was proven to be slightly more superior to GLUE. Overall, through the comparisons of the proposed evaluation criteria for uncertainty analysis (e.g., P-factor, R-factor, NSE, and R2) and the computational efficiencies, SUFI2 can be a potentially efficient tool for the parameter optimization and uncertainty analysis. This study provides an insight into selecting uncertainty analysis method in the modeling field, especially for the hydrological modeling community.
Highlights
Watershed systems are complex due to multiple influencing factors, and an accurate prediction of the hydrological processesWater 2018, 10, 690; doi:10.3390/w10060690 www.mdpi.com/journal/waterWater 2018, 10, 690 is indispensable to watershed management [1,2]
The ranks of the sensitivity and relationships between the streamflow and each parameter yielded by Parameter Solution (ParaSol), SUFI2, and generalized likelihood uncertainty estimation (GLUE) demonstrated that all the three methods can be used for parameter sensitivity analysis
This study examined the capabilities of three uncertainty analysis methods through a distributed hydrological model—SWAT with a case study in the JCRB on the Chinese Loss Plateau
Summary
Watershed systems are complex due to multiple influencing factors (e.g., climate, land use, and other anthropogenic disturbances), and an accurate prediction of the hydrological processesWater 2018, 10, 690; doi:10.3390/w10060690 www.mdpi.com/journal/waterWater 2018, 10, 690 is indispensable to watershed management [1,2]. Watershed systems are complex due to multiple influencing factors (e.g., climate, land use, and other anthropogenic disturbances), and an accurate prediction of the hydrological processes. Uncertainties in hydrological modeling are associated with three possible sources: input data, such as the precipitation data, who can alter the hydrological modeling procedure and simulation results directly (e.g., surface runoff); model structure, which is mainly caused by the assumptions and simplification of the model; and model parameters [6,11,12,13]. Among these three sources, parameter uncertainty is the most common but relatively easy to control through appropriate calibrations [14]
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