Abstract
In nature, rivers are always connected in various forms to constitute a specific type of river network. The identification and classification of river network types in watersheds is the premise of hydrological research. In this study, the Yellow River Basin, Huaihe River Basin, Haihe River Basin and Yangtze River Basin are divided into 71 sub-basins. According to the definition of river network types, the sub-basin river networks are qualitatively divided into 7 types. By comparing and analysing three river network characteristic parameters, which are river network density, river flow direction and river sinuosity, this study found that the types of river networks can be preliminarily determined according to the statistical data distribution of river sinuosity. The Cauchy distribution is used to fit the distribution characteristics of river sinuosity to further accurately determine the types of river networks. Except for the average R2 of the rectangular river network, which is 0.66, the R2 values of the fitting curves of the other river network types all range from 0.86 to 0.97. This method is applied to the four major watersheds, and the results are consistent with the hierarchical clustering analysis, with an accuracy of 82.86%. The method proposed in this study has application potential and can be applied to the automatic classification of river network types with high accuracy and efficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
The datasets generated during the current study are available from the corresponding author on reasonable request.
References
Argialas DP, Lyon JG, Mintzer OW (1988) Quantitative description and classification of drainage patterns. Photogrammetric Eng Remote Sens 54(4):505–509
Benda L, Andras K, Miller D, Bigelow P (2004a) Confluence effects in rivers: interactions of basin scale, network geometry, and disturbance regimes. Water Resour Res 40(5). https://doi.org/10.1029/2003wr002583
Benda L et al (2004b) The network dynamics hypothesis: how channel networks structure riverine habitats. Bioscience 54(5):413–427. https://doi.org/10.1641/0006-3568(2004)054[0413:Tndhhc]2.0.Co;2
Charlton R (2007) Fundamentals of Fluvial Geomorphology. Routledge, London
Clark MK et al (2004) Surface uplift, tectonics, and erosion of eastern Tibet from large-scale drainage patterns. Tectonics 23(1). ArtnTc100610.1029/2002tc001402
Deng Y et al (2022) Automatic extraction of river source region boundary based on multi-characteristic indexes and hierarchical cluster analysis. J Geo-information Sci 24(03):469–482
Fagan SD, Nanson GC (2004) The morphology and formation of floodplain-surface channels. Cooper Creek Australia Geomorphology 60(1–2):107–126. https://doi.org/10.1016/j.geomorph.2003.07.009
Gaucherel C et al (2017) Regional watershed characterization and classification with river network analyses. Earth Surf Proc Land 42(13):2068–2081. https://doi.org/10.1002/esp.4172
Ichoku C, Chorowicz J (1994) A numerical approach to the analysis and classification of channel network patterns. Water Resour Res 30(2):161–174. https://doi.org/10.1029/93WR02279
Jhong YD et al (2022) Real-time neural-network-based Ensemble Typhoon Flood forecasting model with self-organizing map cluster analysis: a Case Study on the Wu River Basin in Taiwan. Water Resour Manage 36:3221–3245. https://doi.org/10.1007/s11269-022-03197-y
Jung K, Marpu PR, Ouarda TBMJ (2017) Impact of river network type on the time of concentration. Arab J Geosci 10(24). https://doi.org/10.1007/s12517-017-3323-3
Jung K, Shin JY, Park D (2019) A new approach for river network classification based on the beta distribution of tributary junction angles. J Hydrol 572:66–74. https://doi.org/10.1016/j.jhydrol.2019.02.041
Jung K, Lee M, An H, Um M-J, Park D (2022) Characterization and classification of river networks in South Korea. ENVIRONMENTAL MODELLING & SOFTWARE, p 156. https://doi.org/10.1016/j.envsoft.2022.105495
Kassem Y, Gokcekus H, Gokcekus R (2021) Identification of the most suitable probability distribution models for monthly and annual rainfall series in Guzelyurt Region, Northern Cyprus. Desalination Water Treat 215:427–451. https://doi.org/10.5004/dwt.2021.26904
Knight J, Grab SW (2018) Drainage network morphometry and evolution in the eastern Lesotho highlands, southern Africa. Quatern Int 470:4–17. https://doi.org/10.1016/j.quaint.2017.07.024
Kong W, Zhang Y, Wang Y, Meng W (2013) River habitats classification in Taizi River based on spatial data. Res Environ Sci 26(5):487–493
Kwang JS, Langston AL, Parker G (2021) The role of lateral erosion in the evolution of nondendritic drainage networks to dendricity and the persistence of dynamic networks. P Natl Acad Sci USA 118(16). https://doi.org/10.1073/pnas.2015770118
Li ZW, Yu GA, Brierley G, Wang ZY (2016) Vegetative impacts upon bedload transport capacity and channel stability for differing alluvial planforms in the Yellow River source zone. Hydrol Earth Syst Sci 20(7):3013–3025. https://doi.org/10.5194/hess-20-3013-2016
Li MH, Wu BS, Chen Y, Li D (2022) Quantification of river network types based on hierarchical structures. CATENA 211. https://doi.org/10.1016/j.catena.2021.105986
Lu D, Wu H, Guo Q, Chen M (2016) Feature extraction and analysis of the Lijiang River water system form based on the Google Earth image. Remote Sens Nat Resour 28(02):161–167
Mejía AI, Niemann JD (2008) Identification and characterization of dendritic, parallel, pinnate, rectangular, and trellis networks based on deviations from planform self-similarity. J Phys Res 113(F2). https://doi.org/10.1029/2007jf000781
Meshkova LV, Carling PA (2013) Discrimination of alluvial and mixed bedrock-alluvial multichannel river networks. Earth Surf Proc Land 38(11):1299–1316. https://doi.org/10.1002/esp.3417
Niu HY, Wei JM, Chen YQ (2021) Optimal randomness for Stochastic Configuration Network (SCN) with heavy-tailed distributions. Entropy-Switz 23(1). https://doi.org/10.3390/e23010056
Pereira-Claren A et al (2019) Planform geometry and relief characterization of drainage networks in high-relief environments: an analysis of Chilean andean basins. Geomorphology 341:46–64. https://doi.org/10.1016/j.geomorph.2019.05.011
Perron JT, Richardson PW, Ferrier KL, Lapotre M (2012) The root of branching river networks. Nature 492(7427):100–103. https://doi.org/10.1038/nature11672
Soomro S et al (2022) River Flood susceptibility and Basin Maturity analyzed using a coupled Approach of Geo-morphometric parameters and SWAT model. Water Resour Manage 36:2131–2160. https://doi.org/10.1007/s11269-022-03127-y
Walley Y, Tunnicliffe J, Brierley G (2018) The influence of network structure upon sediment routing in two disturbed catchments, East Cape, New Zealand. Geomorphology 307:38–49. https://doi.org/10.1016/j.geomorph.2017.10.029
Walley Y, Henshaw AJ, Brasington J (2020) Topological structures of river networks and their regional-scale controls: a multivariate classification approach. Earth Surf Proc Land 45(12):2869–2883. https://doi.org/10.1002/esp.4936
Zhang L, Guilbert E (2013) Automatic drainage pattern recognition in river networks. Int J Geogr Inf Sci 27(12):2319–2342. https://doi.org/10.1080/13658816.2013.802794
Zhang Y et al (2020) Morphometrics of China’s Loess Plateau: The spatial legacy of tectonics, climate, and loess deposition history. Geomorphology 354. https://doi.org/10.1016/j.geomorph.2020.107043
Zhu J (2018) Study on the evolution of water network structure and its driving force in Haihe river basin since the Ming and Qing dynasty. Tianjin University
Funding
The authors would like to acknowledge the financial support for this work provided by the National Natural Science Foundation of China (Grant no. 52239004) and National Key R&D Program of China (Grant no. 2021YFC3200204).
Author information
Authors and Affiliations
Contributions
FL: Conceptualization, Methodology, Supervision, Reviewing and Editing, Funding acquisition. QL: Software, Formal analysis, Writing Visualization. YZ: Conceptualization Validation.
Corresponding authors
Ethics declarations
Ethical Approval
Not applicable.
Consent for publication
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no conflicts of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fawen, L., Qingyang, L. & Yong, Z. Characterization and Classification of River Network Types. Water Resour Manage 37, 6219–6236 (2023). https://doi.org/10.1007/s11269-023-03652-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-023-03652-4