Abstract
Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.
Highlights
A hydrological model is an effective tool for exploring complex hydrological processes and solving practical hydrological problems and is based on a combination of computer technology and system theory [1]
Sequential Uncertainty Fitting Version 2 (SUFI-2) was iterated four times and 500 simulations were performed for each iteration
In Generalized Likelihood Uncertainty Estimation (GLUE), the objective function ENS was set as 0.6, and was iterated twice; 3000 simulations were performed for each iteration
Summary
A hydrological model is an effective tool for exploring complex hydrological processes and solving practical hydrological problems and is based on a combination of computer technology and system theory [1]. As hydrological model research and application have developed, increasing attention has been paid to the uncertainty of these models. The main sources of uncertainty are the structure of the model itself [2], the input data of the model [3], and the uncertainty of model parameters [4]. The uncertainty caused by the model’s own structure needs to be solved inventively. The deviation and absence of model input data are mainly limited by external factors [5]. Research into model uncertainty at present mainly focuses on the uncertainty of model parameters
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