Quantifying the roles of single stations within homogeneous regions using complex network analysis
- 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 rainfallRegionalization 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.…
Author details: | Ankit AgarwalORCiDGND, Norbert MarwanORCiDGND, Rathinasamy MaheswaranORCiD, Bruno MerzORCiDGND, Jürgen KurthsORCiDGND |
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DOI: | https://doi.org/10.1016/j.jhydrol.2018.06.050 |
ISSN: | 0022-1694 |
ISSN: | 1879-2707 |
Title of parent work (English): | Journal of hydrology |
Publisher: | Elsevier |
Place of publishing: | Amsterdam |
Publication type: | Article |
Language: | English |
Year of first publication: | 2018 |
Publication year: | 2018 |
Release date: | 2021/10/20 |
Tag: | Complex network; Event synchronization; Rainfall network; Z-P approach |
Volume: | 563 |
Number of pages: | 9 |
First page: | 802 |
Last Page: | 810 |
Funding institution: | Deutsche Forschungsgemeinschaft (DFG) within the graduate research training group "Natural risk in a changing world (NatRiskChange)" at the University of PotsdamGerman Research Foundation (DFG) [GRK 2043/1]; Humboldt FoundationAlexander von Humboldt Foundation |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |