Application of complex networks for monthly rainfall dynamics over central Vietnam

Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainf...

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Veröffentlicht in:	Stochastic environmental research and risk assessment Jg. 35; H. 3; S. 535 - 548
Hauptverfasser:	Ghorbani, Mohammad Ali, Karimi, Vahid, Ruskeepää, Heikki, Sivakumar, Bellie, Pham, Quoc Bao, Mohammadi, Fatemeh, Yasmin, Nazly
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021 Springer Nature B.V
Schlagworte:	Annual rainfall Aquatic Pollution Chemistry and Earth Sciences Computational Intelligence Computer Science Delay time Earth and Environmental Science Earth Sciences Elevation Embedding Entropy Environment Hydrologic data Interpolation Math. Appl. in Environmental Science Mathematical analysis Networks Nodes Original Paper Physics Probability Theory and Stochastic Processes Rain gauges Rainfall Resource management River basins Stations Statistics for Engineering Time series Waste Water Technology Water Management Water Pollution Control Water resources Vietnam Rainfall Entropy Delay embedding method Phase space Network strength Complex networks
ISSN:	1436-3240, 1436-3259
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Abstract	Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensional phase space using delay embedding (i.e. phase-space reconstruction), where the appropriate delay time and optimal embedding dimension of the time series are determined by using average mutual information and false nearest neighbors methods, respectively. Then, this reconstructed phase space is treated as a ‘network,’ with the reconstructed vectors serving as ‘nodes’ and the connections between them serving as ‘links’. Finally, the strength of the nodes are calculated to identify some key properties of the temporal rainfall network. The approach is employed independently to monthly rainfall data observed over a period of 38 years (1979–2016) from 14 rain gauge stations in the Vu Gia Thu Bon River basin in central Vietnam. Moreover, entropy values of the original rainfall time series are calculated for obtaining additional information on the properties of the rainfall dynamics. The average node strengths are also examined in terms of the mean annual rainfall, entropy of the time series, and elevation of the rain gauge station. The results indicate that: (1) while some adjacent stations (i.e. networks) have somewhat similar strength (average node strength) values, several others that are geographically close show significantly different network strengths; (2) similar entropies for adjacent stations are found more frequently than similar average node strengths; (3) there is generally a positive and proportional relationship between average strengths of nodes and entropies; and (4) the average node strengths of different months have some distinct temporal patterns (3-month, 4-month, and 6-month patterns) in rainfall dynamics, depending upon the specific region of the study area. These results have important implications for prediction, interpolation, and extrapolation of rainfall data.
AbstractList	Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensional phase space using delay embedding (i.e. phase-space reconstruction), where the appropriate delay time and optimal embedding dimension of the time series are determined by using average mutual information and false nearest neighbors methods, respectively. Then, this reconstructed phase space is treated as a ‘network,’ with the reconstructed vectors serving as ‘nodes’ and the connections between them serving as ‘links’. Finally, the strength of the nodes are calculated to identify some key properties of the temporal rainfall network. The approach is employed independently to monthly rainfall data observed over a period of 38 years (1979–2016) from 14 rain gauge stations in the Vu Gia Thu Bon River basin in central Vietnam. Moreover, entropy values of the original rainfall time series are calculated for obtaining additional information on the properties of the rainfall dynamics. The average node strengths are also examined in terms of the mean annual rainfall, entropy of the time series, and elevation of the rain gauge station. The results indicate that: (1) while some adjacent stations (i.e. networks) have somewhat similar strength (average node strength) values, several others that are geographically close show significantly different network strengths; (2) similar entropies for adjacent stations are found more frequently than similar average node strengths; (3) there is generally a positive and proportional relationship between average strengths of nodes and entropies; and (4) the average node strengths of different months have some distinct temporal patterns (3-month, 4-month, and 6-month patterns) in rainfall dynamics, depending upon the specific region of the study area. These results have important implications for prediction, interpolation, and extrapolation of rainfall data. Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensional phase space using delay embedding (i.e. phase-space reconstruction), where the appropriate delay time and optimal embedding dimension of the time series are determined by using average mutual information and false nearest neighbors methods, respectively. Then, this reconstructed phase space is treated as a ‘network,’ with the reconstructed vectors serving as ‘nodes’ and the connections between them serving as ‘links’. Finally, the strength of the nodes are calculated to identify some key properties of the temporal rainfall network. The approach is employed independently to monthly rainfall data observed over a period of 38 years (1979–2016) from 14 rain gauge stations in the Vu Gia Thu Bon River basin in central Vietnam. Moreover, entropy values of the original rainfall time series are calculated for obtaining additional information on the properties of the rainfall dynamics. The average node strengths are also examined in terms of the mean annual rainfall, entropy of the time series, and elevation of the rain gauge station. The results indicate that: (1) while some adjacent stations (i.e. networks) have somewhat similar strength (average node strength) values, several others that are geographically close show significantly different network strengths; (2) similar entropies for adjacent stations are found more frequently than similar average node strengths; (3) there is generally a positive and proportional relationship between average strengths of nodes and entropies; and (4) the average node strengths of different months have some distinct temporal patterns (3-month, 4-month, and 6-month patterns) in rainfall dynamics, depending upon the specific region of the study area. These results have important implications for prediction, interpolation, and extrapolation of rainfall data.
Author	Karimi, Vahid Sivakumar, Bellie Mohammadi, Fatemeh Ruskeepää, Heikki Pham, Quoc Bao Yasmin, Nazly Ghorbani, Mohammad Ali
Author_xml	– sequence: 1 givenname: Mohammad Ali surname: Ghorbani fullname: Ghorbani, Mohammad Ali organization: Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Department of Civil Engineering, Istanbul Technical University – sequence: 2 givenname: Vahid orcidid: 0000-0002-5265-8395 surname: Karimi fullname: Karimi, Vahid email: vahid.karimi22@yahoo.com, vahid.karimi93@ms.tabrizu.ac.ir organization: Department of Water Engineering, Faculty of Agriculture, University of Tabriz – sequence: 3 givenname: Heikki surname: Ruskeepää fullname: Ruskeepää, Heikki organization: Department of Mathematics and Statistics, University of Turku – sequence: 4 givenname: Bellie surname: Sivakumar fullname: Sivakumar, Bellie organization: Department of Civil Engineering, Indian Institute of Technology Bombay, UNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, State Key Laboratory of Hydroscience and Engineering, Tsinghua University – sequence: 5 givenname: Quoc Bao surname: Pham fullname: Pham, Quoc Bao organization: Institute of Research and Development, Duy Tan University, Faculty of Environmental and Chemical Engineering, Duy Tan University – sequence: 6 givenname: Fatemeh surname: Mohammadi fullname: Mohammadi, Fatemeh organization: Department of Water Engineering, Faculty of Agriculture, University of Tabriz – sequence: 7 givenname: Nazly surname: Yasmin fullname: Yasmin, Nazly organization: UNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales
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Cites_doi	10.1007/s13762-018-1674-2 10.1016/S0022-1694(00)00344-9 10.1016/j.jastp.2010.03.007 10.1007/978-90-481-2552-4 10.1016/j.neucom.2012.10.043 10.1016/j.advwatres.2017.12.019 10.1371/journal.pone.0071129 10.1016/j.jhydrol.2018.06.072 10.1016/j.atmosres.2018.07.005 10.2166/hydro.2010.032 10.1016/j.jhydrol.2017.05.029 10.1126/science.286.5439.509 10.1016/S0022-1694(01)00355-9 10.1016/j.envsoft.2015.02.020 10.1080/02626667.2017.1346374 10.1007/s00500-002-0232-4 10.1016/j.jhydrol.2020.124647 10.1029/2000WR900196 10.1103/PhysRevA.45.3403 10.1016/j.jhydrol.2009.03.006 10.1103/PhysRevLett.45.712 10.1007/978-981-10-2624-9_10 10.1002/hyp.9693 10.1016/j.atmosres.2018.05.001 10.1016/j.jhydrol.2019.124185 10.1007/978-1-4612-0763-4 10.1038/30918 10.1007/s11269-019-02473-8 10.1103/PhysRevA.33.1134 10.1002/for.3980100104 10.1016/j.jhydrol.2015.04.035 10.1016/j.atmosres.2004.10.031 10.1029/WR016i001p00173 10.1007/BFb0091924 10.1016/0022-1694(92)90046-X 10.1016/j.jhydrol.2017.09.030
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SubjectTerms	Annual rainfall Aquatic Pollution Chemistry and Earth Sciences Computational Intelligence Computer Science Delay time Earth and Environmental Science Earth Sciences Elevation Embedding Entropy Environment Hydrologic data Interpolation Math. Appl. in Environmental Science Mathematical analysis Networks Nodes Original Paper Physics Probability Theory and Stochastic Processes Rain gauges Rainfall Resource management River basins Stations Statistics for Engineering Time series Waste Water Technology Water Management Water Pollution Control Water resources
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Title	Application of complex networks for monthly rainfall dynamics over central Vietnam
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