Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model

Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model

Mazzoleni, M. (Uppsala University; Centre of Natural Hazards and Disaster Science (CNDS))
Amaranto, A. (Piazza Leonardo da Vinci)
Solomatine, D.P. (TU Delft Water Resources; IHE Delft Institute for Water Education; Water Problems Institute of Russian Academy of Sciences)

2019

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.

data assimilation
flood prediction
fuzzy theory
interacting multiple models
qualitative observations

http://resolver.tudelft.nl/uuid:d3bb2d60-deaf-4930-8c6a-244d0fee2aae

https://doi.org/10.1029/2018WR023768

2020-01-26

0043-1397

Water Resources Research, 55 (7), 6088-6108

Institutional Repository

journal article

© 2019 M. Mazzoleni, A. Amaranto, D.P. Solomatine