75 citations as recorded by crossref.

1. Strategies for smarter catchment hydrology models: incorporating scaling and better process representation R. Sidle https://doi.org/10.1186/s40562-021-00193-9
2. 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
3. Automatic Landform Recognition from the Perspective of Watershed Spatial Structure Based on Digital Elevation Models S. Lin et al. https://doi.org/10.3390/rs13193926
4. Exploring watershed structural variation during watershed evolution process under artificial rainfall experiment S. Lin & N. Chen https://doi.org/10.1007/s12145-023-01000-z
5. 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
6. Community structure concept for catchment classification: A modularity density-based edge betweenness (MDEB) method S. Tumiran & B. Sivakumar https://doi.org/10.1016/j.ecolind.2021.107346
7. Potentialities of Complex Network Theory Tools for Urban Drainage Networks Analysis A. Simone et al. https://doi.org/10.1029/2022WR032277
8. Transfer entropy coupled directed–weighted complex network analysis of rainfall dynamics H. Tongal & B. Sivakumar https://doi.org/10.1007/s00477-021-02091-0
9. HydroGRAF: Hybrid discharge reconstruction and basin-aware streamflow forecasting in the Himalayas A. Gul et al. https://doi.org/10.1016/j.jhydrol.2026.135479
10. Synchronized Structure and Teleconnection Patterns of Meteorological Drought Events over the Yangtze River Basin, China L. Liu et al. https://doi.org/10.3390/w15213707
11. Spatiotemporal patterns and propagation mechanism of meteorological droughts over Yangtze River Basin and Pearl River Basin based on complex network theory C. Gao et al. https://doi.org/10.1016/j.atmosres.2023.106874
12. 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
13. Analysis of the spatio-temporal propagation of drought over Eastern China using complex networks Y. Xu et al. https://doi.org/10.1051/e3sconf/202234601003
14. Wavelet-based multiscale similarity measure for complex networks A. Agarwal et al. https://doi.org/10.1140/epjb/e2018-90460-6
15. Using machine learning models to predict and choose meshes reordered by graph algorithms to improve execution times for hydrological modeling L. Leonard https://doi.org/10.1016/j.envsoft.2019.03.023
16. Forecasting rainfall using transfer entropy coupled directed–weighted complex networks H. Tongal & B. Sivakumar https://doi.org/10.1016/j.atmosres.2021.105531
17. 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
18. Canonical correlation and visual analytics for water resources analysis A. Bybordi et al. https://doi.org/10.1007/s11042-023-16926-1
19. Complex network approaches for identifying global drought teleconnection patterns W. Wang et al. https://doi.org/10.1016/j.gloplacha.2025.105093
20. Temporal streamflow analysis: Coupling nonlinear dynamics with complex networks N. Yasmin & B. Sivakumar https://doi.org/10.1016/j.jhydrol.2018.06.072
21. 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
22. Optimal design of hydrometric station networks based on complex network analysis A. Agarwal et al. https://doi.org/10.5194/hess-24-2235-2020
23. Temporal Networks‐Based Approach for Nonstationary Hydroclimatic Modeling and its Demonstration With Streamflow Prediction R. Dutta & R. Maity https://doi.org/10.1029/2020WR027086
24. Identification of homogeneous regions in terms of flood seasonality using a complex network approach W. Yang et al. https://doi.org/10.1016/j.jhydrol.2019.06.082
25. Spatio-temporal connections in streamflow: a complex networks-based approach N. Yasmin & B. Sivakumar https://doi.org/10.1007/s00477-021-02022-z
26. Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach J. Kurths et al. https://doi.org/10.5194/npg-26-251-2019
27. Landform classification based on landform geospatial structure – a case study on Loess Plateau of China S. Lin et al. https://doi.org/10.1080/17538947.2022.2088874
28. 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
29. Two different approaches for monitoring planning in sewer networks: topological vs. deterministic optimization A. Simone et al. https://doi.org/10.2166/hydro.2023.296
30. Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size S. Jha & B. Sivakumar https://doi.org/10.1016/j.jhydrol.2017.09.030
31. Informational analysis of the Canadian National Hydrometric program monitoring network J. Leach et al. https://doi.org/10.1080/07011784.2023.2242815
32. Integration of hydrological models with entropy and multi-objective optimization based methods for designing specific needs streamflow monitoring networks J. Ursulak & P. Coulibaly https://doi.org/10.1016/j.jhydrol.2020.125876
33. Multi-Objective Optimization and Allocation of Water Resources in Hancheng City Based on NSGA Algorithm and TOPSIS-CCDM Decision-Making Model H. Tian et al. https://doi.org/10.3390/su17104616
34. Complex networks for tracking extreme rainfall during typhoons U. Ozturk et al. https://doi.org/10.1063/1.5004480
35. 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
36. Structural Connectivity Analysis and Optimization of the River Network in the Baiyangdian Basin Using Complex Network Theory and MCR L. Zhang et al. https://doi.org/10.3390/su18094614
37. 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
38. A complex network analysis of Spanish river basins R. Rodríguez-Alarcón & S. Lozano https://doi.org/10.1016/j.jhydrol.2019.124065
39. Hydrometric network design in hyper-arid areas: example of Atacama Desert (North Chile) E. Lictevout & M. Gocht https://doi.org/10.2166/nh.2017.004
40. Network-based exploration of basin precipitation based on satellite and observed data M. Gadhawe et al. https://doi.org/10.1140/epjs/s11734-021-00017-z
41. 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
42. 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
43. The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window P. Whitfield et al. https://doi.org/10.5194/hess-25-2513-2021
44. Streamflow Prediction Using Complex Networks A. Farhat et al. https://doi.org/10.3390/e26070609
45. Temporal dynamics of streamflow: application of complex networks X. Han et al. https://doi.org/10.1186/s40562-018-0109-8
46. 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
47. 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
48. Self-organization and nonlinear dynamics in fluvial systems: a review J. Paredes https://doi.org/10.1016/j.jsames.2026.106101
49. 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
50. A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis X. Han et al. https://doi.org/10.1029/2019WR025910
51. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić https://doi.org/10.5194/hess-26-795-2022
52. Evaluation of Water System Connectivity Based on Node Centrality in the Tarim River Basin, Xinjiang, China J. Yu et al. https://doi.org/10.3390/w16213031
53. 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
54. Integrating multi-criteria decision analysis (MCDA) with kriging and entropy methods for optimising streamflow measurement in a scantly monitored river basin G. Weldearegay et al. https://doi.org/10.1080/15715124.2023.2286893
55. Information theory‐based decision support system for integrated design of multivariable hydrometric networks J. Keum & P. Coulibaly https://doi.org/10.1002/2016WR019981
56. The Ocean Carbon States Database: a proof-of-concept application of cluster analysis in the ocean carbon cycle R. Latto & A. Romanou https://doi.org/10.5194/essd-10-609-2018
57. Information theory-based multi-objective design of rainfall network for streamflow simulation W. Wang et al. https://doi.org/10.1016/j.advwatres.2019.103476
58. Spatiotemporal analysis of extreme precipitation events in the United States at mesoscale: Complex network theory T. Jamali et al. https://doi.org/10.1016/j.jhydrol.2023.130440
59. 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
60. Dependence of 25-MHz HF Radar Working Range on Near-Surface Conductivity, Sea State, and Tides M. Halverson et al. https://doi.org/10.1175/JTECH-D-16-0139.1
61. Clustering of small watersheds in hilly areas based on complex network theory and similarity analysis D. Li et al. https://doi.org/10.2166/ws.2024.089
62. Stream gauge network grouping analysis using community detection H. Joo et al. https://doi.org/10.1007/s00477-020-01916-8
63. A complex network-based framework for evaluating hydrometric monitoring networks S. Mondal & A. Mishra https://doi.org/10.1007/s00477-025-03045-6
64. Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa M. Ilunga https://doi.org/10.3390/e19050147
65. Identification of time points for LNG throughput: a complex network approach B. Liu et al. https://doi.org/10.3389/fphy.2025.1697310
66. 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
67. Catchment classification using community structure concept: application to two large regions S. Tumiran & B. Sivakumar https://doi.org/10.1007/s00477-020-01936-4
68. Strategic stream gauging network design for sustainable water management L. Andrews & T. Grantham https://doi.org/10.1038/s41893-024-01357-z
69. Regional flood frequency analysis using complex networks T. Drissia et al. https://doi.org/10.1007/s00477-021-02074-1
70. Small-world network analysis on fault propagation characteristics of water networks in eco-industrial parks Y. Xu et al. https://doi.org/10.1016/j.resconrec.2019.05.040
71. 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
72. Selection of Multiple Donor Gauges via Graphical Lasso for Estimation of Daily Streamflow Time Series G. Villalba et al. https://doi.org/10.1029/2020WR028936
73. Power Grid Partition Method for Black Start Based On Complex Network Theory F. Xu et al. https://doi.org/10.1088/1755-1315/192/1/012034
74. The physics of river prediction S. Fleming & H. Gupta https://doi.org/10.1063/PT.3.4523
75. Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity W. Zhang et al. https://doi.org/10.3390/w16071064