Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

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In this study, uncertainty in model input data (precipitation) and parameters is propagated through a physically based, spatially distributed hydrological model based on the MIKE SHE code. Precipitation uncertainty is accounted for using an ensemble of daily rainfall fields that incorporate four different sources of uncertainty, whereas parameter uncertainty is considered using Latin hypercube sampling. Model predictive uncertainty is assessed for multiple simulated hydrological variables (discharge, groundwater head, evapotranspiration, and soil moisture). Utilizing an extensive set of observational data, effective observational uncertainties for each hydrological variable are assessed. Considering not only model predictive uncertainty but also effective observational uncertainty leads to a notable increase in the number of instances, for which model simulation and observations are in good agreement (e.g., 47% vs. 91% for discharge and 0% vs. 98% for soil moisture). Effective observational uncertainty is in several cases larger than model predictive uncertainty. We conclude that the use of precipitation uncertainty with a realistic spatio-temporal correlation structure, analyses of multiple variables with different spatial support, and the consideration of observational uncertainty are crucial for adequately evaluating the performance of physically based, spatially distributed hydrological models.

Original languageEnglish
Pages (from-to)833-848
Number of pages16
JournalHydrological Processes
Issue number5
Publication statusPublished - 28 Feb 2019


  • distributed hydrological model
  • effective observational uncertainty
  • forward uncertainty analysis
  • multivariate uncertainty assessment
  • parameter uncertainty
  • precipitation uncertainty with realistic spatio-temporal correlation
  • DK-model

Programme Area

  • Programme Area 2: Water Resources


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