Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size

We apply the concepts of complex networks to investigate the properties of rainfall. Specifically, we examine the rainfall properties in terms of spatial connections, temporal scale, and network size. We employ the clustering coefficient method to rainfall data at six different temporal scales (daily, 2-day, 4-day, 8-day, 16-day, and monthly) from a large number of stations in the Murray-Darling basin in Australia. We consider different correlation thresholds to identify the existence of links between stations. To account for the influence of network size (i.e. number of stations) and length of data, we consider three different networks: (1) 430 stations with 30 years of daily data; (2) 383 stations with 30 years of daily data; and (3) 383 stations with 64 years of daily data. The results indicate that the nature of spatial connections changes with correlation threshold, with changes occurring at different temporal scales for different thresholds. Identification of an appropriate threshold is key to understand the rainfall connectivity properties. (C) 2017 Elsevier B.V. All rights reserved.