EGU21-14831
https://doi.org/10.5194/egusphere-egu21-14831
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Recurrence based coupling analysis between event-like data and continuous data

Abhirup Banerjee1,2, Bedartha Goswami3, Norbert Marwan1,2, Bruno Merz1,4, and Juergen Kurths2,5
Abhirup Banerjee et al.
  • 1University of Potsdam, Germany (abhirupbanerjee@uni-potsdam.de)
  • 2Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
  • 3University of Tuebingen, Tuebingen, Germany
  • 4Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 5Institute of Physics, Humboldt University, Berlin, Germany

Extreme events such as earthquakes, tsunamis, heat weaves, droughts, floods, heavy precipitation, or tornados -- affect the human communities and cause tremendous loss of property and wealth, but can be related to multiple and complex sources. For example, a flood is a natural event caused by many drivers such as extreme precipitation, soil moisture, or temperature. We are interested in understanding the direct and indirect coupling between flood events with different climatological and hydrological drivers such as soil moisture and temperature.

We use multivariate recurrence plot and recurrence quantification analysis as a powerful framework to study the couplings between the different systems, especially the direction of coupling. The standard delay-embedding method is not a suitable for the recurrence analysis of event-like data. Therefore, we apply the novel edit-distance method to compute recurrence plots of time series of flood events and use the standard recurrence plot method for the continuous varying time series such as soil moisture and temperature. The coupling analysis is performed using the mean conditional probabilities of recurrence derived from the different recurrence plots. We demonstrate this approach on a prototype system and apply it on the hydrological data. Using this approach we are able to indicate the coupling direction and lag between the different coupled systems.

How to cite: Banerjee, A., Goswami, B., Marwan, N., Merz, B., and Kurths, J.: Recurrence based coupling analysis between event-like data and continuous data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14831, https://doi.org/10.5194/egusphere-egu21-14831, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.