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Application of complex networks for monthly rainfall dynamics over central Vietnam

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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.

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References

  • Abarbanel HDI (1996) Analysis of observed chaotic data. Springer, New York

    Book  Google Scholar 

  • Ali M, Deo RC, Downs NJ, Maraseni T (2018) Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting. Atmos Res 213:450–464

    Article  Google Scholar 

  • Ali M, Prasad R, Xiang Y, Yaseen ZM (2020) Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. J Hydrol 584:124647

    Article  Google Scholar 

  • Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 80(286):509–512

    Article  Google Scholar 

  • Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform 12:458–473

    Article  Google Scholar 

  • Danandeh Mehr A, Nourani V, Karimi Khosrowshahi V, Ghorbani MA (2019) A hybrid support vector regression–firefly model for monthly rainfall forecasting. Int J Environ Sci Technol 16:335–346. https://doi.org/10.1007/s13762-018-1674-2

    Article  Google Scholar 

  • De Michele C, Bernardara P (2005) Spectral analysis and modeling of space-time rainfall fields. Atmos Res 77:124–136

    Article  Google Scholar 

  • Diop L, Samadianfard S, Bodian A et al (2020) Annual rainfall forecasting using hybrid artificial intelligence model: integration of multilayer perceptron with whale optimization algorithm. Water Resour Manag 34:733–746

    Article  Google Scholar 

  • Folland C, Owen J, Ward MN, Colman A (1991) Prediction of seasonal rainfall in the Sahel region using empirical and dynamical methods. J Forecast 10:21–56

    Article  Google Scholar 

  • Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:1134–1140. https://doi.org/10.1103/PhysRevA.33.1134

    Article  CAS  Google Scholar 

  • French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using a neural network. J Hydrol 137:1–31

    Article  Google Scholar 

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2006) Void-filled seamless SRTM data V3, available from the CGIAR-CSI SRTM 90 m Database

  • Jha SK, Sivakumar B (2017) Complex networks for rainfall modeling: spatial connections, temporal scale, and network size. J Hydrol 554:482–489

    Article  Google Scholar 

  • Jha SK, Zhao H, Woldemeskel FM, Sivakumar B (2015) Network theory and spatial rainfall connections: an interpretation. J Hydrol 527:13–19

    Article  Google Scholar 

  • Johnson ER, Bras RL (1980) Multivariate short-term rainfall prediction. Water Resour Res 16:173–185

    Article  Google Scholar 

  • Kawachi T, Maruyama T, Singh VP (2001) Rainfall entropy for delineation of water resources zones in Japan. J Hydrol 246:36–44

    Article  Google Scholar 

  • Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45:3403–3411. https://doi.org/10.1103/PhysRevA.45.3403

    Article  CAS  Google Scholar 

  • Mishra AK, Özger M, Singh VP (2009) An entropy-based investigation into the variability of precipitation. J Hydrol 370:139–154

    Article  Google Scholar 

  • Naufan I, Sivakumar B, Woldemeskel FM et al (2018) Spatial connections in regional climate model rainfall outputs at different temporal scales: application of network theory. J Hydrol 556:1232–1243

    Article  Google Scholar 

  • Ouallouche F, Lazri M, Ameur S (2018) Improvement of rainfall estimation from MSG data using random forests classification and regression. Atmos Res 211:62–72

    Article  Google Scholar 

  • Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45:712–716

    Article  Google Scholar 

  • Remya R, Unnikrishnan K (2010) Chaotic behaviour of interplanetary magnetic field under various geomagnetic conditions. J Atmos Solar Terrestrial Phys 72:662–675

    Article  Google Scholar 

  • Ribbe L, Trinh VQ, Firoz ABM, Nguyen AT, Nguyen U, Nauditt A (2017) Integrated river basin management in the Vu Gia Thu Bon basin. In: Land use and climate change interactions in Central Vietnam. Springer, pp 153–170

  • Scarsoglio S, Laio F, Ridolfi L (2013) Climate dynamics: a network-based approach for the analysis of global precipitation. PLoS ONE 8:e71129

    Article  CAS  Google Scholar 

  • Sivakumar B (2017) Chaos in hydrology: bridging determinism and stochasticity. Springer, Dordrecht

    Book  Google Scholar 

  • Sivakumar B, Woldemeskel FM (2015) A network-based analysis of spatial rainfall connections. Environ Model Softw 69:55–62

    Article  Google Scholar 

  • Sivakumar B, Sorooshian S, Gupta HV, Gao X (2001) A chaotic approach to rainfall disaggregation. Water Resour Res 37:61–72

    Article  Google Scholar 

  • Souvignet M, Laux P, Freer J et al (2014) Recent climatic trends and linkages to river discharge in Central Vietnam. Hydrol Process 28:1587–1601

    Article  Google Scholar 

  • Sun AY, Xia Y, Caldwell TG, Hao Z (2018) Patterns of precipitation and soil moisture extremes in Texas, US: a complex network analysis. Adv Water Resour 112:203–213

    Article  Google Scholar 

  • Takens F (1981) Detecting strange attractors in turbulence. In: Rand D, Young LS (eds) Dynamical systems and turbulence, Warwick 1980: proceedings of a symposium held at the University of Warwick 1979/80, Springer, Berlin, pp 366–381

  • Tiwari S, Jha SK, Sivakumar B (2019) Reconstruction of daily rainfall data using the concepts of networks: accounting for spatial connections in neighborhood selection. J Hydrol 579:124185

    Article  Google Scholar 

  • Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239:132–147

    Article  Google Scholar 

  • Vu MT, Vo ND, Gourbesville P et al (2017) Hydro-meteorological drought assessment under climate change impact over the Vu Gia-Thu Bon river basin. Vietnam. Hydrol Sci J 62:1654–1668

    Article  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  CAS  Google Scholar 

  • Wong KW, Wong PM, Gedeon TD, Fung CC (2003) Rainfall prediction model using soft computing technique. Soft Comput 7:434–438

    Article  Google Scholar 

  • Wu J, Long J, Liu M (2015) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148:136–142

    Article  Google Scholar 

  • Yasmin N, Sivakumar B (2018) Temporal streamflow analysis: coupling nonlinear dynamics with complex networks. J Hydrol 564:59–67

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Mohammad Ali Ghorbani, Vahid Karimi & Fatemeh Mohammadi

  2. Department of Civil Engineering, Istanbul Technical University, Istanbul, Turkey

    Mohammad Ali Ghorbani

  3. Department of Mathematics and Statistics, University of Turku, 20014, Turku, Finland

    Heikki Ruskeepää

  4. Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India

    Bellie Sivakumar

  5. UNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Bellie Sivakumar & Nazly Yasmin

  6. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China

    Bellie Sivakumar

  7. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  8. Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

Authors
  1. Mohammad Ali Ghorbani
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  2. Vahid Karimi
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  3. Heikki Ruskeepää
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  4. Bellie Sivakumar
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  5. Quoc Bao Pham
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  6. Fatemeh Mohammadi
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  7. Nazly Yasmin
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Correspondence to Vahid Karimi.

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Ghorbani, M.A., Karimi, V., Ruskeepää, H. et al. Application of complex networks for monthly rainfall dynamics over central Vietnam. Stoch Environ Res Risk Assess 35, 535–548 (2021). https://doi.org/10.1007/s00477-020-01962-2

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  • DOI: https://doi.org/10.1007/s00477-020-01962-2

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