Recent insights on uncertainties present in integrated catchment water quality modelling

Franz Tscheikner-Gratl*, Vasilis Bellos, Alma Schellart, Antonio Moreno-Rodenas, Manoranjan Muthusamy, Jeroen Langeveld, Francois Clemens, Lorenzo Benedetti, Miguel Angel Rico-Ramirez, Rita Fernandes de Carvalho, Lutz Breuer, James Shucksmith, Gerard B.M. Heuvelink, Simon Tait

*Corresponding author for this work

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

55 Citations (Scopus)
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This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.

Original languageEnglish
Pages (from-to)368-379
Number of pages12
JournalWater Research
Early online date5 Dec 2018
Publication statusPublished - 1 Mar 2019


  • Uncertainty
  • Integrated catchment modelling
  • Sub-models of integrated modelling
  • Water quality
  • Complexity management


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