Articles | Volume 18, issue 11
https://doi.org/10.5194/hess-18-4565-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-18-4565-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Complex networks for streamflow dynamics
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
F. M. Woldemeskel
School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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- A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain R. Rodríguez-Alarcón & S. Lozano 10.1016/j.jhydrol.2024.132079
- Modeling the dynamics of total suspended solids in a mountain basin using network theory J. García‐Usuga et al. 10.1002/rra.3828
- A network-based analysis of spatial rainfall connections B. Sivakumar & F. Woldemeskel 10.1016/j.envsoft.2015.02.020
- On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data K. Kim et al. 10.3390/w11081578
- Optimal design of hydrometric station networks based on complex network analysis A. Agarwal et al. 10.5194/hess-24-2235-2020
- Shortest path length for evaluating general circulation models for rainfall simulation B. Deepthi & B. Sivakumar 10.1007/s00382-023-06713-x
- Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method D. Kim et al. 10.3390/w14030466
- Quantifying the roles of single stations within homogeneous regions using complex network analysis A. Agarwal et al. 10.1016/j.jhydrol.2018.06.050
- Exploring the Clustering Property and Network Structure of a Large-Scale Basin’s Precipitation Network: A Complex Network Approach Y. Xu et al. 10.3390/w12061739
- Study of temporal streamflow dynamics with complex networks: network construction and clustering N. Yasmin & B. Sivakumar 10.1007/s00477-020-01931-9
- Towards assessing the importance of individual stations in hydrometric networks: application of complex networks B. Deepthi & B. Sivakumar 10.1007/s00477-022-02340-w
- Streamflow Hydrology Estimate Using Machine Learning (SHEM) T. Petty & P. Dhingra 10.1111/1752-1688.12555
- Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework J. Chacon-Hurtado et al. 10.5194/hess-21-3071-2017
- Complex network theory, streamflow, and hydrometric monitoring system design M. Halverson & S. Fleming 10.5194/hess-19-3301-2015
- Complex networks for tracking extreme rainfall during typhoons U. Ozturk et al. 10.1063/1.5004480
- Complex Networks Unveiling Spatial Patterns in Turbulence S. Scarsoglio et al. 10.1142/S0218127416502230
- Spatial coherence patterns of extreme winter precipitation in the U.S. A. Banerjee et al. 10.1007/s00704-023-04393-5
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- A Canberra distance-based complex network classification framework using lumped catchment characteristics P. Istalkar et al. 10.1007/s00477-020-01952-4
- Regional flood frequency analysis using complex networks T. Drissia et al. 10.1007/s00477-021-02074-1
- Selection of representative indicators for flood risk assessment using marginal entropy and mutual information H. Joo et al. 10.1111/jfr3.12976
- Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA G. Konapala & A. Mishra 10.1016/j.jhydrol.2017.10.033
- A Nonlinear Local Approximation Approach for Catchment Classification S. Khan & B. Sivakumar 10.3390/e26030218
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- Streamflow Prediction Using Complex Networks A. Farhat et al. 10.3390/e26070609
- Snow-influenced floods are more strongly connected in space than purely rainfall-driven floods M. Brunner & S. Fischer 10.1088/1748-9326/ac948f
- Characterizing the spatial correlation of daily streamflows A. Betterle et al. 10.1002/2016WR019195
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- Identifying complex networks and operating scenarios for cascade water reservoirs for mitigating drought and flood impacts K. Ren et al. 10.1016/j.jhydrol.2020.125946
8 citations as recorded by crossref.
- Characterization of river flow fluctuations via horizontal visibility graphs A. Braga et al. 10.1016/j.physa.2015.10.102
- Irreversibility and complex network behavior of stream flow fluctuations F. Serinaldi & C. Kilsby 10.1016/j.physa.2016.01.043
- Catchment classification using community structure concept: application to two large regions S. Tumiran & B. Sivakumar 10.1007/s00477-020-01936-4
- Identifying Potential Locations of Hydrologic Monitoring Stations Based on Topographical and Hydrological Information A. Singhal et al. 10.1007/s11269-023-03675-x
- Temporal streamflow analysis: Coupling nonlinear dynamics with complex networks N. Yasmin & B. Sivakumar 10.1016/j.jhydrol.2018.06.072
- Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size S. Jha & B. Sivakumar 10.1016/j.jhydrol.2017.09.030
- Streamflow variability and classification using false nearest neighbor method R. Vignesh et al. 10.1016/j.jhydrol.2015.10.056
- Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory I. Naufan et al. 10.1016/j.jhydrol.2017.05.029
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Short summary
This study introduces the theory of networks, and in particular complex networks, to examine connections in streamflow dynamics. Monthly streamflow data from a network of 639 stations in the United States are studied. The connections are examined primarily using the concept of clustering coefficient, which quantifies the network’s tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels based on correlations in streamflow between the stations.
This study introduces the theory of networks, and in particular complex networks, to examine...