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Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model
Author(s) -
Mazzoleni M.,
Amaranto A.,
Solomatine D.P.
Publication year - 2019
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr023768
Subject(s) - routing (electronic design automation) , computer science , kalman filter , streamflow , fuzzy logic , flow (mathematics) , flow routing , state (computer science) , ballistic missile , hydrological modelling , filter (signal processing) , flood myth , extended kalman filter , data mining , control theory (sociology) , algorithm , artificial intelligence , mathematics , missile , engineering , control (management) , geology , aerospace engineering , geography , computer network , geometry , climatology , computer vision , drainage basin , cartography , geotechnical engineering , philosophy , theology
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.