Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

Abstract

Abstract. The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-)processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.

Effect of hydroclimatological teleconnections on the watershed‐scale drought predictability in the southeastern United States

“…In order to address the inherent nonlinearity in the relationship of the climate indices and hydrologic variables, several studies have used nonlinear approaches like mutual information (Knuth et al ., ; Yoon and Lee, ), cross‐wavelet analysis (Labat, ; Agarwal et al ., ) or PC analysis (Bethere et al ., ), etc. However, the objective of this study is only to highlight the spatial variability in the strength of interrelationship between watershed‐scale drought and climate indices; the inferences are derived from linear correlation analysis and R ‐square of the least‐square model fit between hydrologic variables and DIs.…”

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

“…In last decade, various modification has been proposed in the basic algorithm, citing various issues such as boundary effect (Rheinwalt et al, 2016)., and bias toward number of events (Donges et al, 2009a), etc. thus modified basic algorithm proposed 15 by (Agarwal et al, 2017a;Rheinwalt et al, 2016), can be explained as, let us say an event above threshold percentile occurs in the signal ( ) and ( ) at time and where = 1,2,3,4 … , = 1,2,3,4 … … and within a time lag ± which is defined as following = { +1 − , − −1 , +1 − , − −1 } 2 ⁄…”

“…This implies = 1 for perfect synchronization between signal ( ) and ( ). (For detailed explanation refer Agarwal et al, 2017a).…”

“…In 20 general, (large) graphs of real-world entities with irregular topology are called complex networks. The links represent similar evolution or variability at different nodes and can be identified from data using a similarity measure such as Pearson correlation (Donges et al, 2009a;Jha et al, 2015), synchronization (Agarwal et al, 2017a;Conticello et al, 2017;Malik et al, 2012;Stolbova et al, 2016) or mutual information (Paluš, 2018).…”

“…The 95 th percentile is a good compromise between having a sufficient number of rainfall events at each location and a rather high threshold to study heavy precipitation. All rainfall event series are compared with each other using event synchronization (see Appendix A; Agarwal et al, 2017a;Malik et al, 2012;Stolbova et al, 2014). This results in the similarity matrix Q, whereas 10 the entry at index pair (i,j) defines synchronization in the occurrence of heavy rainfall events at station i and station j.…”