Complex network analysis of climate change in the Tarim River Basin, Northwest China

Abstract

The complex network theory provides an approach for understanding the complexity of climate change from a new perspective. In this study, we used the coarse graining process to convert the data series of daily mean temperature and daily precipitation from 1961 to 2011 into symbol sequences consisting of five characteristic symbols ( i.e. , R , r , e , d and D ), and created the temperature fluctuation network (TFN) and precipitation fluctuation network (PFN) to discover the complex network characteristics of climate change in the Tarim River Basin of Northwest China. The results show that TFN and PEN both present characteristics of scale-free network and small-world network with short average path length and high clustering coefficient. The nodes with high degree in TFN are RRR , dRR and ReR while the nodes with high degree in PFN are rre , rrr , eee and err , which indicates that climate change modes represented by these nodes have large probability of occurrence. Symbol R and r are mostly included in the important nodes of TFN and PFN, which indicate that the fluctuating variation in temperature and precipitation in the Tarim River Basin mainly are rising over the past 50 years. The nodes RRR , DDD , ReR , RRd , DDd and Ree are the hub nodes in TFN, which undertake 19.71% betweenness centrality of the network. The nodes rre , rrr , eee and err are the hub nodes in PFN, which undertake 13.64% betweenness centrality of the network.