科研之友
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摘要
Sensitivity analyses are often carried out for the main model output and using a predefined evaluation period. It is possible, however, to get much more knowledge about how the system works when estimating the sensitivity for the output of individual modules for each time step (time-varying sensitivity analysis). This is shown here using a variance-based sensitivity analysis for a conceptual rainfall-runoff model applied to a mountainous catchment. The first-order and total sensitivities were computed using Sobol's method. Since the parameter ranges used in the sensitivity analysis were obtained through a Markov chain Monte Carlo (MCMC) sampling, the sensitivity indices reflect the parameter uncertainty and make a good use of the previous available information. As a first step, the variance of each flow component was calculated. The flow component with the highest variance at each time step can be regarded as the 'dominant physical control', which has been defined as the parameter to which the model output reacts in a highly sensitive way when the parameter varies within realistic ranges. This information about the dominant processes can be used for facilitating model calibration by identifying the periods on which to focus when calibrating different parameters. It also can be useful for estimating the amount of data available for calibrating each process. The second part presents the total sensitivity indices and interactions for individual flow components considering a 2-year period. The results show large differences in the time-varying sensitivity patterns of the flow components. It is concluded that such a high-resolution sensitivity analysis for each flow component is a good complement to a sensitivity analysis of the total discharge, increasing our understanding about the internal functioning of individual modules which can be helpful when comparing different model formulations.
- 出版日期2015-2
