TY  - JOUR
A1  - Agarwal, Ankit
A1  - Marwan, Norbert
A1  - Maheswaran, Rathinasamy
A1  - Merz, Bruno
A1  - Kurths, Jürgen
T1  - Quantifying the roles of single stations within homogeneous regions using complex network analysis
JF  - Journal of hydrology
N2  - Regionalization and pooling stations to form homogeneous regions or communities are essential for reliable parameter transfer, prediction in ungauged basins, and estimation of missing information. Over the years, several clustering methods have been proposed for regional analysis. Most of these methods are able to quantify the study region in terms of homogeneity but fail to provide microscopic information about the interaction between communities, as well as about each station within the communities. We propose a complex network-based approach to extract this valuable information and demonstrate the potential of our approach using a rainfall network constructed from the Indian gridded daily precipitation data. The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity. Further, the grid points (nodes) were classified into universal roles according to their pattern of within- and between-community connections. The method thus yields zoomed-in details of individual rainfall grids within each community.
KW  - Complex network
KW  - Event synchronization
KW  - Rainfall network
KW  - Z-P approach
Y1  - 2018
U6  - https://doi.org/10.1016/j.jhydrol.2018.06.050
SN  - 0022-1694
SN  - 1879-2707
VL  - 563
SP  - 802
EP  - 810
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Goswami, Bedartha
A1  - Boers, Niklas
A1  - Rheinwalt, Aljoscha
A1  - Marwan, Norbert
A1  - Heitzig, Jobst
A1  - Breitenbach, Sebastian Franz Martin
A1  - Kurths, Jürgen
T1  - Abrupt transitions in time series with uncertainties
JF  - Nature Communications
N2  - Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Niño-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an ‘uncertainty-aware’ framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon.
Y1  - 2018
U6  - https://doi.org/10.1038/s41467-017-02456-6
SN  - 2041-1723
VL  - 9
PB  - Nature Publ. Group
CY  - London
ER  -