Global sensitivity analyses for a complex hydrological model applied in an alpine watershed

C. Dobler*, F. Pappenberger

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

19 Citations (Scopus)


The increasing complexity of hydrological models results in a large number of parameters to be estimated. In order to better understand how these complex models work, efficient screening methods are required in order to identify the most important parameters. This is of particular importance for models that are used within an operational real-time forecasting chain such as HQsim. The objectives of this investigation are to (i) identify the most sensitive parameters of the complex HQsim model applied in the Alpine Lech catchment and (ii) compare model parameter sensitivity rankings attained from three global sensitivity analysis techniques. The techniques presented are the (i) regional sensitivity analysis, (ii) Morris analysis and (iii) state-dependent parameter modelling. The results indicate that parameters affecting snow melt as well as processes in the unsaturated soil zone reveal high significance in the analysed catchment. The snow melt parameters show clear temporal patterns in the sensitivity whereas most of the parameters affecting processes in the unsaturated soil zone do not vary in importance across the year. Overall, the maximum degree day factor (meltfunc_max) has been identified to play a key role within the HQsim model. Although the parameter sensitivity rankings are equivalent between methods for a number of parameters, for several key parameters differing results were obtained. An uncertainty analysis demonstrates that a parameter ranking attained from only one method is subjected to large uncertainty.

Original languageEnglish
Pages (from-to)3922-3940
Number of pages19
JournalHydrological Processes
Issue number26
Publication statusPublished - 30 Dec 2013


  • Alpine basin
  • Hydrological model
  • Morris analysis
  • Regional sensitivity analysis
  • Sensitivity analysis
  • State-dependent parameter modelling


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