Abstract
The present study applies the concept of community structure to classify catchments in two large regions: Australia and the United States. Specifically, the edge betweenness method is applied to monthly streamflow data from a network of 218 stations across Australia and from a network of 639 stations across the United States. The influence of streamflow correlation threshold (i.e. spatial correlation in streamflow between streamflow stations) on catchment classification is examined, through use of different thresholds, suitable for each region, as appropriate. The results reveal that, for both regions, a very small number of communities have a large number of catchments within them (for instance, considering both regions as small as 16–18% of the largest communities combine to represent as much as 70–75% of the catchments), and a significantly large number of communities have only a very few catchments within them (for instance, almost 70% of the communities have only one or two stations within them, and thus represent only about 20% and 10% of the catchments in Australia and the US, respectively). An interpretation of the identified catchment communities in terms of catchment characteristics (station drainage area, station stream length, and station elevation) and flow properties (mean and coefficient of variation) is also made. The catchment classification is also explained using the correlation–distance relationship between the stations.
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Agarwal A, Marwan N, Rathinasamy M, Merz B, Kurths J (2017) Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach. Nonlinear Process Geophys 24(4):599–611
Agarwal A, Marwan N, Maheswaran R, Merz B, Kurths J (2018) Quantifying the role of single stations within homogeneous regions using complex network analysis. J Hydrol 563:802–810
Agarwal A, Caesar L, Marwan N, Maheswaran R, Merz B, Kurths J (2019) Network-based identification and characterization of teleconnections on different time scales. Sci Rep. https://doi.org/10.1038/s41598-019-45423-5
Agarwal A, Marwan N, Maheswaran R, Ozturk U, Kurths J, Merz M (2020) Optimal design of hydrometric station networks based on complex network analysis. Hydrol Earth Syst Sci 24(5):2235–2251
Ali G et al (2012) A comparison of similarity indices for catchment classification using a cross-regional dataset. Adv Water Resour 40:11–22. https://doi.org/10.1016/j.advwatres.2012.01.008
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Boers N, Bookhagen B, Marwan N, Kurths J, Marengo J (2013) Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System. Geophys Res Lett 40:4386–4392
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 Stat Mech Appl 444:1003–1011. https://doi.org/10.1016/j.physa.2015.10.102
Dawdy DR (2007) Prediction versus understanding. J Hydrol Eng 12(1):1–3
Dooge JCI (1986) Looking for hydrologic laws. Water Resour Res 22(9):46S-58S
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. https://doi.org/10.1016/j.jhydrol.2016.11.056
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci United States Am 99(12):7821–7826. https://doi.org/10.1073/pnas.122653799
Gupta VK (2004) Emergence of statistical scaling in floods on channel networks from complex runoff dynamics. Chaos Solitons Fractals 19:357–365. https://doi.org/10.1016/S0960-0779(03)00048-1
Halverson MJ, Fleming SW (2015) Complex network theory, streamflow, and hydrometric monitoring system design. Hydrol Earth Syst Sci 19(7):3301–3318
Han X, Sivakumar B, Woldemeskel FM, Guerra de Aguilar M (2018) Temporal dynamics of streamflow: application of complex networks. Geosci Lett 5:10. https://doi.org/10.1186/s40562-018-0109-8
Han X, Ouarda TBMJ, Rahman A, Haddad K, Mehrotra R, Sharma A (2020) A network approach for delineating homogeneous regions in flood frequency analysis. Water Resour Res. https://doi.org/10.1029/2019WR025910
Isik S, Singh VP (2008) Hydrologic regionalization of watersheds in Turkey. J Hydrol Eng 13:824–834. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:9(824)
Jha SK, Sivakumar B (2017) Complex networks for rainfall modeling: spatial connections, temporal scale, and network size. J Hydrol 554:482–489
Jha SK et al (2015) Network theory and spatial rainfall connections: an interpretation. J Hydrol 527:13–19. https://doi.org/10.1016/j.jhydrol.2015.04.035
Kennard MJ, Pusey BJ, Olden JD, Mackay SJ, Stein JL, Marsh N (2010) Classification of natural flow regimes in Australia to support environmental flow management. Freshw Biol 55(1):171–193
Konapala G, Mishra A (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
Li Z, Zhang S, Wang RS, Zhang XS, Chen L (2008) Quantitative function for community detection. Phys Rev E Stat Nonlinear Soft Matter Phy 77(3):1–9. https://doi.org/10.1103/PhysRevE.77.036109
Lins HF (2012) USGS hydro-climatic data network 2009 (HCDN–2009). US geological survey fact sheet 2012–3047. US Geological Survey, Reston
Malik N, Bookhagen B, Marwan N, Kurths J (2012) Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks. Clim Dyn 39:971–987
McDonnell JJ, Woods R (2004) On the need for catchment classification. J Hydrol 299(1–2):2–3
Moliere DR, Lowry JBC, Humphrey CL (2009) Classifying the flow regime of data-limited streams in the wet-dry tropical region of Australia. J Hydrol 367(1–2):1–13
Naufan I, Sivakumar B, Woldemeskel FM, Raghavan SV, Vu MT, Liong SY (2018) Spatial connections in regional climate model rainfall outputs at different temporal scales: application of network theory. J Hydrol 556:1232–1243
Newman MEJ (2004) Detecting community structure in networks. Eur Phys J B Condens Matter Complex Syst 38(2):321–330. https://doi.org/10.1140/epjb/e2004-00124-y
Newman M, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):1–16. https://doi.org/10.1103/PhysRevE.69.026113
Nguyen TT et al (2015) ‘Clustering spatio-seasonal hydrogeochemical data using self-organizing maps for groundwater quality assessment in the Red River Delta Vietbam.’ J Hydrol 522:661–673. https://doi.org/10.1016/j.jhydrol.2015.01.023
Olden JD, Poff NL (2003) Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Res Appl 19(2):101–121
Olden JD, Kennard MJ, Pusey BJ (2012) A framework for hydrologic classification with a review of methodologies and applications in ecohydrology. Ecohydrology 5(4):503–518. https://doi.org/10.1002/eco.251
Razavi T, Coulibaly P (2013) Streamflow prediction in ungauged basins: review of regionalization methods. J Hydrol Eng 18(8):958–975. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000690
Rosvall M, Bergstrom CT (2007) An information-theoretic framework for resolving community structure in complex networks. Proc Natl Acad Sci USA 104:7327–7331
Saco P, Kumar P (2000) Coherent modes in multiscale variability of streamflow over the United States. Water Resour Res 36(4):1049–1067
Scarsoglio S, Laio F, Ridolfi L (2013) Climate dynamics: a network-based approach for the analysis of global precipitation. PLoS ONE 8(8):e71129
Serinaldi F, Kilsby CG (2016) Irreversibility and complex network behavior of stream flow fluctuations. Phys A Stat Mech Appl 450:585–600
Sivakumar B (2003) Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach. Environ Modell Softw 18(8–9):721–728
Sivakumar B (2004) Dominant processes concept in hydrology: moving forward. Hydrol Process 18(12):2349–2353. https://doi.org/10.1002/hyp.5606
Sivakumar B (2008) Dominant processes concept, model simplification and classification framework in catchment hydrology. Stoch Environ Res Risk Assess 22(6):737–748. https://doi.org/10.1007/s00477-007-0183-5
Sivakumar B (2015) Networks: a generic theory for hydrology. Stoch Environ Res Risk Assess 29:761–771.
Sivakumar B, Singh VP (2012) Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-16-4119-2012
Sivakumar B, Woldemeskel FM (2014) Complex networks for streamflow dynamics. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hessd-11-7255-2014
Sivakumar B, Woldemeskel FM (2015) A network-based analysis of spatial rainfall connections. Environ Model Softw 69:55–62. https://doi.org/10.1016/j.envsoft.2015.02.020
Sivakumar B, Singh VP, Berndtsson R, Khan SK (2015) Catchment classification framework in hydrology: challenges and directions. J Hydrol Eng 2:130426211354007. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000837
Slack JR, Landwehr VM (1992) Hydro-climatic data network (HCDN): a US Geological Survey streamflow data set for the United States for the study of climate variations, 1847–1988, US Geological Survey Open File Report 92–129. US Geological Survey, Reston
Snelder TH, Biggs BJF, Woods RA (2005) Improved eco-hydrological classification of rivers. River Res Appl 21(6):609–628. https://doi.org/10.1002/rra.826
Stolbova V, Martin P, Bookhagen B, Marwan N, Kurths J (2014) Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka. Nonlinear Process Geophys 21:901–917
Tan F, Xia Y, Zhu B (2014) Link prediction in complex networks: a mutual information perspective. PLOS One 9(9):e107056
Tang QA, Liu J, Liu HL (2010) Comparison of different daily streamflow series in US and China, under a viewpoint of complex networks. Mod Phys Lett B 24:1541–1547
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
Tongal H, Sivakumar B (2017) Cross-entropy clustering framework for catchment classification. J Hydrol 552:433–446. https://doi.org/10.1016/j.jhydrol.2017.07.005
Vignesh R, Jothiprakash V, Sivakumar B (2015) Streamflow variability and classification using false nearest neighbor method. J Hydrol 531:706–715
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442
Xu Y, Lu F, Zhu K, Song X, Dai Y (2020) Exploring the clustering property and network structure of a large-scale basin’s precipitation network: a complex network approach. Water 12:1739. https://doi.org/10.3390/w12061739
Yasmin N, Sivakumar B (2018) Temporal streamflow analysis: coupling nonlinear dynamics with complex networks. J Hydrol 564:59–67. https://doi.org/10.1016/j.jhydrol.2018.06.072
Young PC, Ratto M (2009) A unified approach to environmental systems modeling. Stoch Environ Res Risk Assess 23(7):1037–1057
Zhang XS et al (2016) How streamflow has changed across Australia since the 1950 s: evidence from the network of hydrologic reference stations. Hydrol Earth Syst Sci 20(9):3947
Acknowledgements
Siti Aisyah Tumiran acknowledges the financial support from the Ministry of Higher Education Malaysia and Universiti Malaysia Sabah. The authors would like to thank the two reviewers for their constructive comments and useful suggestions on an earlier version of the manuscript.
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Tumiran, S.A., Sivakumar, B. Catchment classification using community structure concept: application to two large regions. Stoch Environ Res Risk Assess 35, 561–578 (2021). https://doi.org/10.1007/s00477-020-01936-4
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DOI: https://doi.org/10.1007/s00477-020-01936-4