Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model

M. Mazzoleni, A. Amaranto, D. P. Solomatine

Research output: Contribution to journalArticleScientificpeer-review

10 Downloads (Pure)

Abstract

This study aims at proposing novel approaches for integrating qualitative flow observations in a lumped hydrologic routing model and assessing their usefulness for improving flood estimation. Routing is based on a three-parameter Muskingum model used to propagate streamflow in five different rivers in the United States. Qualitative flow observations, synthetically generated from observed flow, are converted into fuzzy observations using flow characteristic for defining fuzzy classes. A model states updating method and a model output correction technique are implemented. An innovative application of Interacting Multiple Models, which use was previously demonstrated on tracking in ballistic missile applications, is proposed as state updating method, together with the traditional Kalman filter. The output corrector approach is based on the fuzzy error corrector, which was previously used for robots navigation. This study demonstrates the usefulness of integrating qualitative flow observations for improving flood estimation. In particular, state updating methods outperform the output correction approach in terms of average improvement of model performances, while the latter is found to be less sensitive to biased observations and to the definition of fuzzy sets used to represent qualitative observations.

Original languageEnglish
Pages (from-to)6088-6108
Number of pages21
JournalWater Resources Research
Volume55
Issue number7
DOIs
Publication statusPublished - 2019

Keywords

  • data assimilation
  • flood prediction
  • fuzzy theory
  • interacting multiple models
  • qualitative observations

Fingerprint Dive into the research topics of 'Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model'. Together they form a unique fingerprint.

  • Cite this