57 citations as recorded by crossref.

1. Temporal dynamics of streamflow: application of complex networks X. Han et al. https://doi.org/10.1186/s40562-018-0109-8
2. Stream gauge network grouping analysis using community detection H. Joo et al. https://doi.org/10.1007/s00477-020-01916-8
3. A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain R. Rodríguez-Alarcón & S. Lozano https://doi.org/10.1016/j.jhydrol.2024.132079
4. Influence of individual streamflow gauging stations: a hybrid approach based on complex networks and copulas D. Muthuvel et al. https://doi.org/10.1016/j.jhydrol.2025.134164
5. Modeling the dynamics of total suspended solids in a mountain basin using network theory J. García‐Usuga et al. https://doi.org/10.1002/rra.3828
6. A network-based analysis of spatial rainfall connections B. Sivakumar & F. Woldemeskel https://doi.org/10.1016/j.envsoft.2015.02.020
7. On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data K. Kim et al. https://doi.org/10.3390/w11081578
8. Optimal design of hydrometric station networks based on complex network analysis A. Agarwal et al. https://doi.org/10.5194/hess-24-2235-2020
9. Shortest path length for evaluating general circulation models for rainfall simulation B. Deepthi & B. Sivakumar https://doi.org/10.1007/s00382-023-06713-x
10. Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method D. Kim et al. https://doi.org/10.3390/w14030466
11. Quantifying the roles of single stations within homogeneous regions using complex network analysis A. Agarwal et al. https://doi.org/10.1016/j.jhydrol.2018.06.050
12. Exploring the Clustering Property and Network Structure of a Large-Scale Basin’s Precipitation Network: A Complex Network Approach Y. Xu et al. https://doi.org/10.3390/w12061739
13. Study of temporal streamflow dynamics with complex networks: network construction and clustering N. Yasmin & B. Sivakumar https://doi.org/10.1007/s00477-020-01931-9
14. Towards assessing the importance of individual stations in hydrometric networks: application of complex networks B. Deepthi & B. Sivakumar https://doi.org/10.1007/s00477-022-02340-w
15. Streamflow Hydrology Estimate Using Machine Learning (SHEM) T. Petty & P. Dhingra https://doi.org/10.1111/1752-1688.12555
16. Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework J. Chacon-Hurtado et al. https://doi.org/10.5194/hess-21-3071-2017
17. Complex network theory, streamflow, and hydrometric monitoring system design M. Halverson & S. Fleming https://doi.org/10.5194/hess-19-3301-2015
18. Complex networks for tracking extreme rainfall during typhoons U. Ozturk et al. https://doi.org/10.1063/1.5004480
19. Complex Networks Unveiling Spatial Patterns in Turbulence S. Scarsoglio et al. https://doi.org/10.1142/S0218127416502230
20. Spatial coherence patterns of extreme winter precipitation in the U.S. A. Banerjee et al. https://doi.org/10.1007/s00704-023-04393-5
21. Analysing the Hydro-Meteorological synchronization of reference evapotranspiration across Indian Mainland using cross recurrence approach S. Adarsh et al. https://doi.org/10.1007/s00704-026-06018-z
22. Revealing joint evolutions and causal interactions in complex ecohydrological systems by a network-based framework L. Wang et al. https://doi.org/10.5194/hess-29-361-2025
23. Transfer entropy coupled directed–weighted complex network analysis of rainfall dynamics H. Tongal & B. Sivakumar https://doi.org/10.1007/s00477-021-02091-0
24. Collective dynamics analysis based on the multiplex network method to unravel the backbone of fluctuations in groundwater level data L. Naghipour et al. https://doi.org/10.1016/j.cageo.2023.105310
25. Complex network modeling of a river basin: an application to the Guadalquivir River in Southern Spain R. Rodríguez-Alarcón & S. Lozano https://doi.org/10.2166/hydro.2022.148
26. A Canberra distance-based complex network classification framework using lumped catchment characteristics P. Istalkar et al. https://doi.org/10.1007/s00477-020-01952-4
27. Regional flood frequency analysis using complex networks T. Drissia et al. https://doi.org/10.1007/s00477-021-02074-1
28. Selection of representative indicators for flood risk assessment using marginal entropy and mutual information H. Joo et al. https://doi.org/10.1111/jfr3.12976
29. Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA G. Konapala & A. Mishra https://doi.org/10.1016/j.jhydrol.2017.10.033
30. A Nonlinear Local Approximation Approach for Catchment Classification S. Khan & B. Sivakumar https://doi.org/10.3390/e26030218
31. Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory S. Huang et al. https://doi.org/10.1016/j.jhydrol.2024.131577
32. A complex network theory based approach to better understand the infiltration-excess runoff generation thresholds A. Nanda & S. Sen https://doi.org/10.1016/j.jhydrol.2021.127038
33. Customized sea‐surface temperature indicators linking to streamflow at different timescales A. Ganapathy & A. Agarwal https://doi.org/10.1002/joc.7853
34. Spatial propagation of different drought types and their concurrent societal risks: A complex networks-based analysis D. Muthuvel & B. Sivakumar https://doi.org/10.1016/j.jhydrol.2024.131247
35. Complex Networks Reveal Heatwave Patterns and Propagations Over the USA S. Mondal & A. Mishra https://doi.org/10.1029/2020GL090411
36. Network theory and spatial rainfall connections: An interpretation S. Jha et al. https://doi.org/10.1016/j.jhydrol.2015.04.035
37. Forecasting rainfall using transfer entropy coupled directed–weighted complex networks H. Tongal & B. Sivakumar https://doi.org/10.1016/j.atmosres.2021.105531
38. Streamflow prediction using LASSO-FCM-DBN approach based on hydro-meteorological condition classification H. Chu et al. https://doi.org/10.1016/j.jhydrol.2019.124253
39. Complex networks, community structure, and catchment classification in a large-scale river basin K. Fang et al. https://doi.org/10.1016/j.jhydrol.2016.11.056
40. Complex networks and integrated centrality measure to assess the importance of streamflow stations in a River basin H. Joo et al. https://doi.org/10.1016/j.jhydrol.2021.126280
41. A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis X. Han et al. https://doi.org/10.1029/2019WR025910
42. Streamflow Prediction Using Complex Networks A. Farhat et al. https://doi.org/10.3390/e26070609
43. Snow-influenced floods are more strongly connected in space than purely rainfall-driven floods M. Brunner & S. Fischer https://doi.org/10.1088/1748-9326/ac948f
44. Characterizing the spatial correlation of daily streamflows A. Betterle et al. https://doi.org/10.1002/2016WR019195
45. Complex High‐ and Low‐Flow Networks Differ in Their Spatial Correlation Characteristics, Drivers, and Changes M. Brunner & E. Gilleland https://doi.org/10.1029/2021WR030049
46. A complex network analysis of Spanish river basins R. Rodríguez-Alarcón & S. Lozano https://doi.org/10.1016/j.jhydrol.2019.124065
47. Spatio-temporal connections in streamflow: a complex networks-based approach N. Yasmin & B. Sivakumar https://doi.org/10.1007/s00477-021-02022-z
48. A spatial model for coastal flood susceptibility assessment using the 2D-SPR method with complex network theory: A case study of a reclamation island in Zhoushan, China X. Fang et al. https://doi.org/10.1016/j.eiar.2022.106953
49. Hydrologic regionalization using wavelet-based multiscale entropy method A. Agarwal et al. https://doi.org/10.1016/j.jhydrol.2016.03.023
50. Canonical correlation and visual analytics for water resources analysis A. Bybordi et al. https://doi.org/10.1007/s11042-023-16926-1
51. Stream gauge clustering and analysis for non-stationary time series through complex networks R. Rocha & F. Souza Filho https://doi.org/10.1016/j.jhydrol.2022.128773
52. Temporal connections in reconstructed monthly rainfall time series in different rainfall regimes of Turkey M. Ghorbani et al. https://doi.org/10.1007/s12517-022-10271-7
53. Multifractal characterization of meteorological droughts in Türkiye’s mediterranean region using visibility graph approaches O. Simsek et al. https://doi.org/10.1007/s00477-026-03176-4
54. Testing protocols for smoothing datasets of hydraulic variables acquired during unsteady flows Ö. Baydaroğlu et al. https://doi.org/10.1080/02626667.2024.2394169
55. A kriging and entropy-based approach to raingauge network design P. Xu et al. https://doi.org/10.1016/j.envres.2017.10.038
56. Entropy analysis for spatiotemporal variability of seasonal, low, and high streamflows H. Tongal & B. Sivakumar https://doi.org/10.1007/s00477-018-1615-0
57. Identifying complex networks and operating scenarios for cascade water reservoirs for mitigating drought and flood impacts K. Ren et al. https://doi.org/10.1016/j.jhydrol.2020.125946