110 citations as recorded by crossref.

1. Regime-related regularities in river flow revealed by Aksentijevic-Gibson complexity A. Aksentijevic et al. 10.1016/j.jhydrol.2021.126364
2. Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent D. Mihailović et al. 10.1016/j.physa.2019.03.041
3. Complexity of hydrologic basins: A chaotic dynamics perspective M. Ombadi et al. 10.1016/j.jhydrol.2021.126222
4. A Process‐Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany L. Tarasova et al. 10.1029/2019WR026951
5. Complexity analysis of the turbulent environmental fluid flow time series D. Mihailović et al. 10.1016/j.physa.2013.09.062
6. Probabilistic Prediction for Monthly Streamflow through Coupling Stepwise Cluster Analysis and Quantile Regression Methods Y. Fan et al. 10.1007/s11269-016-1489-1
7. Temporal streamflow analysis: Coupling nonlinear dynamics with complex networks N. Yasmin & B. Sivakumar 10.1016/j.jhydrol.2018.06.072
8. Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance E. Azmi et al. 10.5194/hess-25-1103-2021
9. Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States A. Agarwal et al. 10.1007/s11269-016-1428-1
10. Complex networks, community structure, and catchment classification in a large-scale river basin K. Fang et al. 10.1016/j.jhydrol.2016.11.056
11. An inter-comparison of similarity-based methods for organisation and classification of groundwater hydrographs E. Haaf & R. Barthel 10.1016/j.jhydrol.2018.02.035
12. Is there an underestimation of long-term variability of streamflow across the continental United States? Y. Sang et al. 10.1016/j.jhydrol.2019.124365
13. Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil A. Costa et al. 10.1007/s40333-021-0097-y
14. Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters A. Rajib et al. 10.1016/j.jhydrol.2018.10.024
15. Streamflow Hydrology Estimate Using Machine Learning (SHEM) T. Petty & P. Dhingra 10.1111/1752-1688.12555
16. Dry spells in a semi-arid region of Brazil and their influence on maize productivity D. Nogueira et al. 10.1016/j.jaridenv.2022.104892
17. Simultaneous calibration of hydrological models in geographical space A. Bárdossy et al. 10.5194/hess-20-2913-2016
18. Complexity of streamflows in the west-flowing rivers of India C. Srivalli et al. 10.1007/s00477-019-01665-3
19. Use of Cokriging and Map Correlation to Study Hydrological Response Patterns and Select Reference Stream Gauges for Ungauged Catchments M. Zelelew & K. Alfredsen 10.1061/(ASCE)HE.1943-5584.0000803
20. Contrasting storage-flux-age interactions revealed by catchment inter-comparison using a tracer-aided runoff model T. Piovano et al. 10.1016/j.jhydrol.2020.125226
21. Spatio-temporal connections in streamflow: a complex networks-based approach N. Yasmin & B. Sivakumar 10.1007/s00477-021-02022-z
22. A simplified approach for the hydrological simulation of urban drainage systems with SWMM A. Farina et al. 10.1016/j.jhydrol.2023.129757
23. Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes S. Kim et al. 10.1155/2015/195940
24. Fully nonlinear statistical and machine‐learning approaches for hydrological frequency estimation at ungauged sites D. Ouali et al. 10.1002/2016MS000830
25. Streamflow variability and classification using false nearest neighbor method R. Vignesh et al. 10.1016/j.jhydrol.2015.10.056
26. Chaos arising from the hydrological behaviour of a floodplain river during the last century F. Giri & M. Devercelli 10.1002/rra.4080
27. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
28. Encoding daily rainfall records via adaptations of the fractal multifractal method M. Maskey et al. 10.1007/s00477-015-1201-7
29. 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
30. Examining the pluvial to nival river regime spectrum using nonlinear methods: Minimum delay embedding dimension N. Aksamit & P. Whitfield 10.1016/j.jhydrol.2019.03.058
31. Complexity of hydrometeorological variables: false nearest neighbour algorithm N. Saji et al. 10.2166/hydro.2024.178
32. Study of the Spatiotemporal Characteristics of Meltwater Contribution to the Total Runoff in the Upper Changjiang River Basin Y. Fang et al. 10.3390/w9030165
33. Detrended fluctuation analysis and entropy-complexity causality analysis of temperatures in an urbanized mountain stream C. Thaxton et al. 10.1007/s00477-017-1434-8
34. Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India V. Sharma & S. Regonda 10.3390/w13091224
35. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
36. The coupling impact of climate change on streamflow complexity in the headwater area of the northeastern Tibetan Plateau across multiple timescales S. Shen et al. 10.1016/j.jhydrol.2020.124996
37. Complex adaptive systems-based framework for modeling the health impacts of climate change B. Talukder et al. 10.1016/j.joclim.2023.100292
38. Shannon Entropy for Measuring Spatial Complexity Associated with Mean Annual Runoff of Tertiary Catchments of the Middle Vaal Basin in South Africa M. Ilunga 10.3390/e21040366
39. Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models H. Tongal & R. Berndtsson 10.1007/s00477-016-1236-4
40. Changes in streamflow statistical structure across the United States due to recent climate change A. Gupta et al. 10.1016/j.jhydrol.2023.129474
41. Velocities, celerities and the basin of attraction in catchment response K. Beven & J. Davies 10.1002/hyp.10699
42. Hydrologic regionalization using wavelet-based multiscale entropy method A. Agarwal et al. 10.1016/j.jhydrol.2016.03.023
43. Modeling of flood extremes using regional frequency analysis of sites of Khyber Pakhtunkhwa, Pakistan M. Khan et al. 10.1111/jfr3.12751
44. Conceptualizing catchment storage dynamics and nonlinearities M. Maneta et al. 10.1002/hyp.13262
45. Analysis of Drought Length using the BDS statistic and Close Returns Test S. Kim et al. 10.1007/s12205-014-0587-y
46. Regional Frequency Analysis of Precipitation Using Time Series Clustering Approaches H. Zaifoglu et al. 10.1061/(ASCE)HE.1943-5584.0001659
47. Investigating the Spatiotemporal Complexity of Rainfall from a Chaotic Perspective: Case Study in the Jinsha River Basin, China S. Yu et al. 10.1061/JHYEFF.HEENG-6038
48. Regional hydrological frequency analysis at ungauged sites with random forest regression S. Desai & T. Ouarda 10.1016/j.jhydrol.2020.125861
49. Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China Y. Fan et al. 10.1016/j.asoc.2016.11.039
50. Assessing the degree of hydrologic stress due to climate change R. Nathan et al. 10.1007/s10584-019-02497-4
51. A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations T. Lan et al. 10.1029/2018WR023160
52. Catchment classification using community structure concept: application to two large regions S. Tumiran & B. Sivakumar 10.1007/s00477-020-01936-4
53. Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques S. Kim et al. 10.1155/2014/517571
54. Consequences of Continuous Zero Values and Constant Values in Time Series Modeling: Understanding through Chaotic Approach T. Fathima & V. Jothiprakash 10.1061/(ASCE)HE.1943-5584.0001367
55. Analysis of hydrological processes with non-linear methods M. Kędra 10.1680/jwama.14.00122
56. Nonlinear analysis of rainfall variability in Australia B. Sivakumar et al. 10.1007/s00477-013-0689-y
57. Nonlinear Riparian Interactions Drive Changes in Headwater Streamflow S. Newcomb & S. Godsey 10.1029/2023WR034870
58. Networks: a generic theory for hydrology? B. Sivakumar 10.1007/s00477-014-0902-7
59. Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia N. Afrin et al. 10.1007/s11069-024-06669-z
60. Identifying Hydrologic Regimes and Drivers in Nova Scotia, Canada: Catchment Classification Efforts for a Data-Limited Region L. Johnston et al. 10.1061/(ASCE)HE.1943-5584.0002200
61. Incorporating climate model similarities and hydrologic error models to quantify climate change impacts on future riverine flood risk K. Ahn & Y. Kim 10.1016/j.jhydrol.2018.12.061
62. Flood‐type classification in mountainous catchments using crisp and fuzzy decision trees A. Sikorska et al. 10.1002/2015WR017326
63. Towards simplification of hydrologic modeling: identification of dominant processes S. Markstrom et al. 10.5194/hess-20-4655-2016
64. A Nonlinear Local Approximation Approach for Catchment Classification S. Khan & B. Sivakumar 10.3390/e26030218
65. On the complexity of model complexity: Viewpoints across the geosciences J. Baartman et al. 10.1016/j.catena.2019.104261
66. Investigating variation characteristics and driving forces of lake water level complexity in a complex river–lake system F. Huang et al. 10.1007/s00477-020-01907-9
67. Study of temporal streamflow dynamics with complex networks: network construction and clustering N. Yasmin & B. Sivakumar 10.1007/s00477-020-01931-9
68. Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain G. Astray et al. 10.2166/wst.2016.002
69. Evidence for deterministic chaos in long-term high-resolution karstic streamflow time series D. Labat et al. 10.1007/s00477-015-1175-5
70. Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability M. Herman et al. 10.1016/j.jhydrol.2017.11.009
71. Tsallis Entropy for Assessing Spatial Uncertainty Associated with Mean Annual Runoff of Quaternary Catchments of the Middle Vaal Basin in South Africa M. Ilunga 10.3390/e22091050
72. Spectral analysis of temporal variability of nonlinear and nonstationary rainfall-runoff processes C. Chang et al. 10.1016/j.jhydrol.2019.05.086
73. Catchment Classification Framework in Hydrology: Challenges and Directions B. Sivakumar et al. 10.1061/(ASCE)HE.1943-5584.0000837
74. A calibration framework for distributed hydrological models considering spatiotemporal parameter variations Y. Liu et al. 10.1016/j.jhydrol.2024.132273
75. Complexity–entropy analysis of daily stream flow time series in the continental United States F. Serinaldi et al. 10.1007/s00477-013-0825-8
76. Temporal dynamics of streamflow: application of complex networks X. Han et al. 10.1186/s40562-018-0109-8
77. Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins H. Ren et al. 10.1016/j.jhydrol.2016.02.042
78. Performance Analysis of Artificial Neural Network Models in Water Level Forecasting L. Velasco et al. 10.1016/j.procs.2024.02.154
79. Entropy analysis for spatiotemporal variability of seasonal, low, and high streamflows H. Tongal & B. Sivakumar 10.1007/s00477-018-1615-0
80. Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: A case study T. Stosic et al. 10.1016/j.jhydrol.2016.07.034
81. Parallelization of a two-dimensional time-area watershed routing Y. Her et al. 10.1016/j.envsoft.2021.105222
82. Land use/land cover changes and its impact on runoff using SWAT model in the upper Prek Thnot watershed in Cambodia N. Khorn et al. 10.1007/s12665-022-10583-7
83. Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping G. Bonotto et al. 10.1029/2021WR030231
84. Watershed classification by remote sensing indices: A fuzzy c-means clustering approach B. Choubin et al. 10.1007/s11629-017-4357-4
85. Technical note: An improved Grassberger–Procaccia algorithm for analysis of climate system complexity C. Di et al. 10.5194/hess-22-5069-2018
86. Assessment and Combination of SMAP and Sentinel-1A/B-Derived Soil Moisture Estimates With Land Surface Model Outputs in the Mid-Atlantic Coastal Plain, USA H. Kim et al. 10.1109/TGRS.2020.2991665
87. A hybrid wavelet analysis–cloud model data‐extending approach for meteorologic and hydrologic time series D. Wang et al. 10.1002/2015JD023192
88. Zoning technology for the management of ecological and clean small-watersheds via k-means clustering and entropy-weighted TOPSIS: A case study in Beijing X. Li et al. 10.1016/j.jclepro.2023.136449
89. A Canberra distance-based complex network classification framework using lumped catchment characteristics P. Istalkar et al. 10.1007/s00477-020-01952-4
90. Daily Streamflow of Argentine Rivers Analysis Using Information Theory Quantifiers M. Suriano et al. 10.3390/e26010056
91. Ordinal pattern and statistical complexity analysis of daily stream flow time series H. Lange et al. 10.1140/epjst/e2013-01858-3
92. Cross-entropy clustering framework for catchment classification H. Tongal & B. Sivakumar 10.1016/j.jhydrol.2017.07.005
93. Deterministic chaotic dynamics in soil moisture across Nebraska C. Di et al. 10.1016/j.jhydrol.2019.124048
94. Long-term river water temperature reconstruction and investigation: A case study of the Dongting Lake Basin, China F. Huang et al. 10.1016/j.jhydrol.2022.128857
95. Exploring spatiotemporal chaos in hydrological data: evidence from Ceará, Brazil L. Rolim & F. de Souza Filho 10.1007/s00477-023-02501-5
96. Fisher Shannon analysis of drought/wetness episodes along a rainfall gradient in Northeast Brazil A. da Silva et al. 10.1002/joc.6834
97. Technical note: Long-term persistence loss of urban streams as a metric for catchment classification D. Jovanovic et al. 10.5194/hess-22-3551-2018
98. Detecting causal relationship of non-floodplain wetland hydrologic connectivity using convergent cross mapping S. Lee et al. 10.1038/s41598-023-44071-0
99. Beyond the SCS‐CN method: A theoretical framework for spatially lumped rainfall‐runoff response M. Bartlett et al. 10.1002/2015WR018439
100. Complex networks for streamflow dynamics B. Sivakumar & F. Woldemeskel 10.5194/hess-18-4565-2014
101. Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis E. Haaf et al. 10.1029/2022WR033470
102. Hysteresis in streamflow‐water table relation provides a new classification system of rainfall‐runoff events Y. Gelmini et al. 10.1002/hyp.14685
103. Application of Nonlinear Time Series and Machine Learning Algorithms for Forecasting Groundwater Flooding in a Lowland Karst Area B. Basu et al. 10.1029/2021WR029576
104. Monthly streamflow forecasting for the Hunza River Basin using machine learning techniques S. Khan et al. 10.2166/wpt.2023.124
105. Selection of representative indicators for flood risk assessment using marginal entropy and mutual information H. Joo et al. 10.1111/jfr3.12976
106. Implementation of Chaotic Analysis on River Discharge Time Series A. Albostan & B. Önöz 10.4236/epe.2015.73008
107. Complexity and Predictability of Daily Actual Evapotranspiration Across Climate Regimes C. Di et al. 10.1029/2022WR032811
108. Meta‐analysis of flow modeling performances—to build a matching system between catchment complexity and model types L. Zhuo et al. 10.1002/hyp.10371
109. Investigating chaos in river stage and discharge time series R. Khatibi et al. 10.1016/j.jhydrol.2011.10.026
110. Variability in Canadian Seasonal Streamflow Information and Its Implication for Hydrometric Network Design A. Mishra & P. Coulibaly 10.1061/(ASCE)HE.1943-5584.0000971