A network-based analysis of spatial rainfall connections. (July 2015)

• Record Type: Journal Article
• Authors: Sivakumar, Bellie; Woldemeskel, Fitsum M.
• Is Part Of: Environmental modelling & software. Volume 69(2015:Jul.)
• Page Start: 55
• Page End: 62
• Publication Date: 2015-07
• Subjects: Rainfall variability -- Spatial dynamics -- Connections -- Complex networks -- Clustering coefficient -- Degree distribution -- Australia; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.02.020
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study presents an analysis based on recent developments in network theory to examine the spatial dynamics of rainfall. The concepts of clustering coefficient and degree distribution are employed to study the spatial connections in a rainfall network across Australia. The clustering coefficient is a measure of local density, while the degree distribution is a measure of spread. Monthly rainfall data over a period of 68 years from a network of 230 gaging stations across Australia are analyzed, and different correlation thresholds are considered. The clustering coefficient results help identify actual neighbors and actual links in the network as well as stations/regions with high and low connectivities. The results from both methods also suggest that the network is not a purely random graph but may be an exponentially truncated power-law network. The connectivity and type of the raingage network are also influenced by the correlation threshold used for identifying connections. Highlights: A network-based approach for studying spatial variability of rainfall is presented. Clustering coefficient and degree distribution methods are employed to study connections in a rainfall monitoring network. The influence of correlation thresholds on network properties is examined. The outcomes have implications for rainfall interpolation and classification purposes.