Abstract
The application of fully distributed watershed models has the advantage of providing location-specific outputs. However, the calibration of these models is very challenging due to over-parameterization. A typical strategy is to aggregate parameters and screen out insensitive parameters in order to decrease the dimension of the problem for calibration. To ensure the validity of calibration, it is important to identify important physical processes and parameter interactions, and examine how different model setups affect model simulation. In this paper, a two-step multi-objective sensitivity analysis approach is applied to a distributed hydrologic model, the WetSpa (Water and Energy Transfer between Soil, Plant and Atmosphere), with case studies in the Chaohe Basin in China and the Margecany Basin in Slovakia respectively. This two-step global sensitivity analysis technique, incorporating the Morris method and the SDP (State Dependent Parameter) method, has proved to be effective in the two case studies. The results of two case studies show that (i) a warm-up period is essential for minimizing the impact of initial state variables to the model simulation, (ii) different objective functions lead to different sensitivity results, (iii) evapotranspiration is the most sensitive process to the model result in the two study watersheds followed by the groundwater and soil water process, and (iv) the sensitivity of snowmelt process is case dependent.
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Notes
Factors or input factors can be model parameters and/or input driving forces such as rainfall in hydrology, to be analyzed by the sensitivity analysis procedure. In this study, factors are equivalent to model parameters.
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Acknowledgment
The research is supported by the National Basic Research Program of China (973 Program:2010CB951003). The authors would like to thank Professor David Swayne from University of Guelph for his constructive comments.
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Yang, J., Liu, Y., Yang, W. et al. Multi-Objective Sensitivity Analysis of a Fully Distributed Hydrologic Model WetSpa. Water Resour Manage 26, 109–128 (2012). https://doi.org/10.1007/s11269-011-9908-9
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DOI: https://doi.org/10.1007/s11269-011-9908-9