Skip to main content
SpringerLink
Log in

Streamflow Connectivity in a Large-Scale River Basin

  • Chapter
  • First Online:
Hydrology in a Changing World

Part of the book series: Springer Water ((SPWA))

Abstract

Large-scale river basins play a key role in the development of many regions around the world. However, their enormous sizes and the associated complexities also make streamflow modeling and, hence, water planning and management a tremendous challenge. The present study examines the spatial connections in streamflow in a large-scale river basin using the concepts of complex networks. The Mississippi River basin in the United States is considered as a representative large-scale river basin, and daily streamflow data from 1663 stations across the basin are analyzed. Three network-based methods are employed to examine the spatial streamflow connections: clustering coefficient, degree distribution, and shortest path length. The influence of streamflow correlation threshold (i.e., correlations in streamflow between stations) on the outcomes of these methods is also investigated by considering four different threshold levels: T = 0.70, 0.75, 0.80, and 0.85. The results indicate that: (1) streamflow stations in some regions (Missouri River, Upper Mississippi River, Ohio River, and Tennessee River regions) of the Mississippi River basin have generally higher clustering coefficients (i.e., high connectivity with the rest of the stations in the entire network) when compared to streamflow stations in some other regions (Arkansas-Red-White River and Lower Mississippi River regions); (2) the streamflow network is not a random graph, but one that exhibits a combination of scale-free and exponential distribution; and (3) the streamflow network is inefficient, with even the most-connected station is connected to only about 6% of the 1663 stations in the network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alexander JS, Wilson RC, Green WR (2012) A brief history and summary of the effects of river engineering and dams on the Mississippi River system and delta, p 43. US Geological Survey Circular 1375

    Google Scholar 

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

    Article  Google Scholar 

  3. Bouchaud J-P, Mézard M (2000) Wealth condensation in a simple model of economy. Phys A 282:536–540

    Article  Google Scholar 

  4. Braga AC, Alves LGA, Costa LS, Ribeiro AA, de Jesus MMA, Tateishi AA, Ribeiro HV (2016) Characterization of river flow fluctuations via horizontal visibility graphs. Phys A 444:1003–1011

    Article  Google Scholar 

  5. Chen J, Wu Y (2012) Advancing representation of hydrologic processes in the Soil and Water Assessment Tool (SWAT) through integration of the TOPographic MODEL (TOPMODEL) features. J Hydrol 420–421:319–328

    Article  Google Scholar 

  6. Costa LDF, Rodriguez FA, Traviesco G, Villas Boas PR (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242

    Google Scholar 

  7. Czuba JA, Foufoula-Georgiou E (2014) A network-based framework for identifying potential synchronizations and amplifications of sediment delivery in river basins. Water Resour Res 50:3826–3851

    Article  Google Scholar 

  8. Czuba JA, Foufoula-Georgiou E (2015) Dynamic connectivity in a fluvial network for identifying hotspots of geomorphic change. Water Resour Res 51:1401–1421

    Article  Google Scholar 

  9. Davis KF, D’Odorico P, Laio F, Ridolfi L (2013) Global spatio-temporal patterns in human migration: a complex network perspective. PLoS ONE 8(1):e53723. https://doi.org/10.1371/journal.pone.0053723

    Article  Google Scholar 

  10. Estrada E (2012) The structure of complex networks: theory and applications. Oxford University Press, UK

    Google Scholar 

  11. Falcone JA (2011) GAGES-II: geospatial attributes of gages for evaluating streamflow. US Geological Survey, Reston, Virginia. http://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml#Identification_Information

  12. Falcone JA, Carlisle DM, Wolock DM, Meador MR (2010) GAGES: a stream gage database for evaluating natural and altered flow conditions in the conterminous United States. Ecology 91(2):621. Data Paper in Ecological Archives E091-045-D1. http://esapubs.org/Archive/ecol/E091/045/metadata.htm

  13. Fang K, Sivakumar B, Woldemeskel FM (2017) Complex networks, community structure, and catchment classification in a large-scale river basin. J Hydrol 545:478–493

    Article  Google Scholar 

  14. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826

    Article  Google Scholar 

  15. Halverson MJ, Fleming SW (2015) Complex network theory, streamflow, and hydrometric monitoring system design. Hydrol Earth Syst Sci 19:3301–3318

    Article  Google Scholar 

  16. Han X, Sivakumar B, Woldemeskel FM, Guerra de Aguilar M (2018) Temporal dynamics of streamflow: application of complex networks. Geosci Lett 5:10

    Article  Google Scholar 

  17. 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 

  18. 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 

  19. Jothiprakash V, Kote SA (2011) Improving the performance of data driven techniques through data pre-processing for modelling daily reservoir inflow. Hydrol Sci J 56(1):168–186

    Article  Google Scholar 

  20. Kiang JE, Stewart DW, Archfield SA, Osborne EB, Eng K (2013) A national streamflow network gap analysis. US Geological Survey Scientific Investigations Report 2013–5013, Reston, Virginia, USA

    Google Scholar 

  21. Konapala G, Mishra AK (2017) Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA. J Hydrol 555:600–620

    Article  Google Scholar 

  22. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701

    Article  Google Scholar 

  23. Li T, Wang G, Chen J (2010) A modified binary tree codification of drainage networks to support complex hydrological models. Comput Geosci 36(11):1427–1435

    Article  Google Scholar 

  24. Liljeros F, Edling C, Amaral LN, Stanley HE, Åberg Y (2001) The web of human sexual contacts. Nature 411:907–908

    Article  Google Scholar 

  25. Miguens J, Mendes J (2008) Weighted and directed network on traveling patterns. Biowire 5151:145–154

    Google Scholar 

  26. Mishra AK, Coulibaly P (2009) Developments in hydrometric network design: a review. Rev Geophys 47, RG2001/2009

    Google Scholar 

  27. Naufan I, Sivakumar B, Woldemeskel FM, Raghavan SV, Vue MT, Liong S-Y (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 

  28. Rinaldo A, Rigon R, Banavar JR, Maritan A, Rodriguez-Iturbe I (2014) Evolution and selection of river networks: Statics, dynamics, and complexity. Proc Nat Acad Sci USA 111(7):2417–2424

    Article  Google Scholar 

  29. Rinaldo A, Banavar JR, Maritan A (2006) Trees, networks, and hydrology. Water Resour Res 42:W06D07. https://doi.org/10.1029/2005wr004108

  30. Salas JD, Delleur JW, Yevjevich V, Lane WL (1995) Applied modeling of hydrologic time series. Water Resources Publications, Littleton, Colorado, USA

    Google Scholar 

  31. Seaber PR, Kapinos FP, Knapp GL (1987) Hydrologic unit maps. US Geological Survey, Denver

    Google Scholar 

  32. Serinaldi F, Kilsby CG (2016) Irreversibility and complex network behavior of streamflow fluctuations. Phys A 450:485–600

    Article  Google Scholar 

  33. Singh VP (1997) The use of entropy in hydrology and water resources. Hydrol Process 11(6):587–626

    Article  Google Scholar 

  34. Sivakumar B (2003) Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach. Environ Modell Softw 18:721–728

    Article  Google Scholar 

  35. Sivakumar B (2015) Networks: a generic theory for hydrology? Stoch Environ Res Risk Assess 29:761–771

    Article  Google Scholar 

  36. Sivakumar B, Berndtsson R (2010) Advances in data-based approaches for hydrologic modeling and forecasting. World Scientific Publishing Company, Singapore

    Book  Google Scholar 

  37. Sivakumar B, Woldemeskel FM (2014) Complex networks for streamflow dynamics. Hydrol Earth Syst Sci 18:4565–4578

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Sivakumar B, Woldemeskel FM, Fang K, Singh VP (2017) Network theory. In: Singh VP (ed) Handbook of Applied Hydrology. McGraw-Hill Education, Chapter 35, pp. 35-1–35-10

    Google Scholar 

  40. Tang Q, Liu J, Liu H (2010) Comparison of different daily streamflow series in US and China, under a viewpoint of complex networks. Mod Phys Lett B 24(14):1541–1547

    Article  Google Scholar 

  41. Tongal H, Demirel MC, Booij MJ (2013) Seasonality of low flows and dominant processes in the Rhine River. Stoch Environ Res Risk Assess 27:489–503

    Article  Google Scholar 

  42. Tsonis AA, Roebber PJ (2004) The architecture of the climate network. Phys A 333:497–504

    Article  Google Scholar 

  43. Wang X, Hao G, Yang Z, Liang P, Cai Y, Li C, Sun L, Zhu J (2015) Variation analysis of streamflow and ecological flow for the twin rivers of the Miyun Reservoir Basin in northern China from 1963 to 2011. Sci Total Environ 536:739–749

    Article  Google Scholar 

  44. Wasserman S, Faust K (1994) Social network analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Zaliapin I, Foufoula‐Georgiou F, Ghil M (2010) Transport on river networks: a dynamic tree approach. J Geophys Res 115:F00A15. https://doi.org/10.1029/2009jf001281

  48. Zhao D, Shen F, Zeng J, Huang R, Yu Z, Wu QL (2016) Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton. Sci Total Environ 573:817–825

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Australian Research Council (ARC) Future Fellowship grant (FT110100328). Bellie Sivakumar acknowledges the financial support from ARC through this Future Fellowship grant.

Author information

Authors and Affiliations

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

    Koren Fang, Bellie Sivakumar & Fitsum M. Woldemeskel

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

    Bellie Sivakumar & Vinayakam Jothiprakash

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

    Bellie Sivakumar

Authors
  1. Koren Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bellie Sivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fitsum M. Woldemeskel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vinayakam Jothiprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bellie Sivakumar .

Editor information

Editors and Affiliations

  1. Hydrological Processes, National Institute of Water and Atmospheric Research, Christchurch, New Zealand

    Shailesh Kumar Singh

  2. Department of Civil Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, India

    C.T. Dhanya

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fang, K., Sivakumar, B., Woldemeskel, F.M., Jothiprakash, V. (2019). Streamflow Connectivity in a Large-Scale River Basin. In: Singh, S., Dhanya, C. (eds) Hydrology in a Changing World. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-030-02197-9_10

Download citation

Publish with us

Policies and ethics

Search

Navigation