Are the driving forces of hydrological models really driving the model output?
Nossent, J.; Bauwens, W.; Pereira, F.; Verwaest, T.; Mostaert, F. (2014). Are the driving forces of hydrological models really driving the model output?, in: Ames, D.P. et al.Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, California, USA. pp. [1-6]
In: Ames, D.P. et al. (2014). Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, California, USA. [S.n.]: [s.l.]. ISBN 978-88-9035-744-2.
Rainfall is very often considered as the driving force of hydrological models. If the rainfall changes, the model output (i.e. flow) is also expected to change. In principal, as long as the values of the model parameters are fixed, rainfall will indeed determine the model output. Yet, uncertainties on the input, the model(parameters) and the output lead to variations of the actual values and can disrupt this rigid hypothesis. Hence, the question could be raised if it would be possible to maintain exactly the same initial model output when the model input changes, by varying the model parameter values? Or, in other words, how important is the rainfall/input and the related uncertainty for the model output with respect to the model parameter importance?
We assess the significance of the input uncertainty on the model output and compare it to the importance of the model parameters by applying a Sobol’ sensitivity analysis for 3 different hydrological models, considering 4 scenario’s for the parameters included in the analysis. Within these sensitivity analyses, the input uncertainty is handled in a probabilistic way by applying so called rainfall multipliers on hydrological independent storm events and treating them as regular model parameters.
This paper presents the main expected results of these sensitivity analyses. The results increase the awareness for research prioritization in view of the improvement of hydrological modelling. Besides, this study highlights the close relationship between uncertainty analysis and sensitivity analysis, and provides new opportunities to use sensitivity analysis for uncertainty importance assessment.
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