New challenges in integrated water quality modelling

M. Rode, G. Arhonditsis, D. Balin, T. Kebede, V. Krysanova, A. Griensven, S.E.A.T.M. van der Zee

Research output: Contribution to journalArticleAcademicpeer-review

110 Citations (Scopus)


There is an increasing pressure for development of integrated water quality models that effectively couple catchment and in-stream biogeochemical processes. This need stems from increasing legislative requirements and emerging demands related to contemporary climate and land use changes. Modelling water quality and nutrient transport is challenging due a number of serious constraints associated with the input data as well as existing knowledge gaps related to the mathematical description of landscape and in-stream biogeochemical processes. The present paper summarizes the discussions held during the workshop on ‘Integrated water quality modelling: future demands and perspectives’ (Magdeburg, Germany, 23–24 June 2008). Our primary focus is placed on the current limitations and future challenges in water quality modelling. In particular, we evaluate the current state of integrated water quality modelling, we highlight major research needs to assess and reduce model uncertainties, and we examine opportunities to enhance model predictive capacity. To better account for the need of upscaling process knowledge, we advocate the adoption of combined process-oriented field and modelling studies at representative sites. In-stream nutrient metabolism investigations at the entire range of stream and river scales will enable the improvement of the mathematical representation of these processes and therefore the articulation level of coupled watershed-receiving waterbody models. Keeping the complexity of integrated water quality models in mind, the development of novel uncertainty analysis techniques for rigorous assessing parameter identification and model credibility is essential. In this regard, we recommend the use of Bayesian calibration frameworks that explicitly accommodate measurement errors, parameter uncertainties, and model structure errors. The Bayesian inference can be used to quantify the information the data contain about model inputs, to offer insights into the covariance structure among parameter estimates, to obtain predictions along with credible intervals for model outputs, and to effectively address the ‘change of support’ problems
Original languageEnglish
Pages (from-to)3447-3461
JournalHydrological Processes
Issue number24
Publication statusPublished - 2010


  • chain monte-carlo
  • stream nitrogen attenuation
  • hyporheic zone
  • uncertainty analysis
  • sensitivity-analysis
  • catchment models
  • eutrophication models
  • bayesian calibration
  • nutrient losses
  • organic-matter


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