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Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Ctr Nat Hazards & Disaster Sci, Uppsala, Sweden.
Dept Elect Informat & Bioengn, Piazza Leonardo da Vinci, Milan, Italy.
Delft Inst Water Educ, Hydroinformat Chair Grp, Delft, Netherlands;Delft Univ Technol, Water Resources Sect, Delft, Netherlands;RAS, Water Problems Inst, Moscow, Russia.
2019 (English)In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 55, no 7, p. 6088-6108Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
AMER GEOPHYSICAL UNION , 2019. Vol. 55, no 7, p. 6088-6108
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:uu:diva-393748DOI: 10.1029/2018WR023768ISI: 000481444700051OAI: oai:DiVA.org:uu-393748DiVA, id: diva2:1356770
Funder
EU, Horizon 2020, 689744Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2020-01-04

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Mazzoleni, Maurizio

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