Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3595-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3595-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jun 2021
Research article |
| 24 Jun 2021
| 24 Jun 2021
Statistical modelling and climate variability of compound surge and precipitation events in a managed water system: a case study in the Netherlands
Víctor M. Santos
CORRESPONDING AUTHOR
Department of Civil, Environmental and Construction Engineering, National Center for Integrated Coastal Research, University of Central Florida, Orlando, Florida, USA
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine & Delta Systems, P.O. Box 140, 4400 AC Yerseke, the Netherlands
Mercè Casas-Prat
Climate Research Division, Science and Technology Directorate, Environment and Climate Change Canada, Toronto, Ontario, Canada
Benjamin Poschlod
Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
Elisa Ragno
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Bart van den Hurk
Deltares, Delft, the Netherlands
Zengchao Hao
College of Water Science, Beijing Normal University, Beijing, China
Tímea Kalmár
Department of Meteorology, Faculty of Science, Institute of Geography and Earth Sciences, Eötvös Loránd University, Budapest, Hungary
Lianhua Zhu
Key Laboratory of Meteorological Disaster, Ministry of Education; Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science & Technology, Nanjing, China
Husain Najafi
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
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Manuscript not accepted for further review
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Short summary
We present an application of multivariate statistical models to assess compound flooding events in a managed reservoir. Data (from a previous study) were obtained from a physical-based hydrological model driven by a regional climate model large ensemble, providing a time series expanding up to 800 years in length that ensures stable statistics. The length of the data set allows for a sensitivity assessment of the proposed statistical framework to natural climate variability.
We present an application of multivariate statistical models to assess compound flooding events...
