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Publisher: Katlenburg-Lindau : European Geophysical Society × Subject: spatiotemporal analysis ×

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    Flood risk in a range of spatial perspectives – from global to local scales
    (Katlenburg-Lindau : European Geophysical Society, 2019) Kundzewicz, Zbigniew W.; Su, Buda; Wang, Yanjun; Wang, Guojie; Wang, Guofu; Huang, Jinlong; Jiang, Tong
    The present paper examines flood risk (composed of hazard, exposure, and vulnerability) in a range of spatial perspectives – from the global to the local scale. It deals with observed records, noting that flood damage has been increasing. It also tackles projections for the future, related to flood hazard and flood losses. There are multiple factors driving flood hazard and flood risk and there is a considerable uncertainty in our assessments, and particularly in projections for the future. Further, this paper analyses options for flood risk reduction in several spatial dimensions, from global framework to regional to local scales. It is necessary to continue examination of the updated records of flood-related indices, trying to search for changes that influence flood hazard and flood risk in river basins.
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    Recurrence analysis of extreme event-like data
    (Katlenburg-Lindau : European Geophysical Society, 2021) Banerjee, Abhirup; Goswami, Bedartha; Hirata, Yoshito; Eroglu, Deniz; Merz, Bruno; Kurths, Jürgen; Marwan, Norbert
    The identification of recurrences at various timescales in extreme event-like time series is challenging because of the rare occurrence of events which are separated by large temporal gaps. Most of the existing time series analysis techniques cannot be used to analyze an extreme event-like time series in its unaltered form. The study of the system dynamics by reconstruction of the phase space using the standard delay embedding method is not directly applicable to event-like time series as it assumes a Euclidean notion of distance between states in the phase space. The edit distance method is a novel approach that uses the point-process nature of events. We propose a modification of edit distance to analyze the dynamics of extreme event-like time series by incorporating a nonlinear function which takes into account the sparse distribution of extreme events and utilizes the physical significance of their temporal pattern. We apply the modified edit distance method to event-like data generated from point process as well as flood event series constructed from discharge data of the Mississippi River in the USA and compute their recurrence plots. From the recurrence analysis, we are able to quantify the deterministic properties of extreme event-like data. We also show that there is a significant serial dependency in the flood time series by using the random shuffle surrogate method.