A constraint-based search algorithm for parameter identification of environmental models

S. Gharari, M. Shafiei, M. Hrachowitz, R. Kumar, F. Fenicia, H. V. Gupta, H. H.G. Savenije

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available (e.g., when making predictions in ungauged basins). In this study, we provide an alternative approach for parameter identification using constraints based on two types of restrictions derived from prior (or expert) knowledge. The first, called parameter constraints, restricts the solution space based on realistic relationships that must hold between the different model parameters while the second, called process constraints requires that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a search algorithm for finding parameter sets that simultaneously satisfy such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler's intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.

Original languageEnglish (US)
Pages (from-to)4861-4870
Number of pages10
JournalHydrology and Earth System Sciences
Issue number12
StatePublished - Dec 5 2014

ASJC Scopus subject areas

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)


Dive into the research topics of 'A constraint-based search algorithm for parameter identification of environmental models'. Together they form a unique fingerprint.

Cite this