Model identification for hydrological forecasting under uncertainty

Thorsten Wagener, Hoshin V. Gupta

Research output: Contribution to journalArticlepeer-review

225 Scopus citations

Abstract

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history ("The need for more POWER"), the current state ("Learning from other fields") and the future ("Towards a general framework") of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.

Original languageEnglish (US)
Pages (from-to)378-387
Number of pages10
JournalStochastic Environmental Research and Risk Assessment
Volume19
Issue number6
DOIs
StatePublished - Dec 1 2005

Keywords

  • Data assimilation
  • Flood forecasting
  • Hydrological models
  • Model identification
  • Model realism
  • Predictions in ungauged basins
  • Uncertainty

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology
  • Environmental Science(all)

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