144 citations as recorded by crossref.

1. Complexity–entropy analysis of daily stream flow time series in the continental United States F. Serinaldi et al. https://doi.org/10.1007/s00477-013-0825-8
2. Generic and parsimonious stochastic modelling for hydrology and beyond D. Koutsoyiannis https://doi.org/10.1080/02626667.2015.1016950
3. Spatial Hurst–Kolmogorov Clustering P. Dimitriadis et al. https://doi.org/10.3390/encyclopedia1040077
4. Climatic Variability Over Time Scales Spanning Nine Orders of Magnitude: Connecting Milankovitch Cycles with Hurst–Kolmogorov Dynamics Y. Markonis & D. Koutsoyiannis https://doi.org/10.1007/s10712-012-9208-9
5. Why hydrological predictions should be evaluated using information theory S. Weijs et al. https://doi.org/10.5194/hess-14-2545-2010
6. Non-Stationary Amplification of Inter-Annual Discharge Deficits in Low-Memory Mountain Catchments: A Stochastic Diagnostic Framework F. Cervi https://doi.org/10.3390/w18172084
7. Stochastic Evaluation of the Investment Risk by the Scale of Water Infrastructures—Case Study: The Municipality of West Mani (Greece) D. Markantonis et al. https://doi.org/10.3390/world4010001
8. Water-energy-food nexus and its stochastic dynamics: case study Greece G. Sargentis & D. Markantonis https://doi.org/10.1007/s43621-024-00751-z
9. Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions A. Pizarro et al. https://doi.org/10.3390/hydrology9070126
10. Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
11. On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon E. O’Connell et al. https://doi.org/10.1029/2022WR033133
12. The influence of constrained fossil fuel emissions scenarios on climate and water resource projections J. Ward et al. https://doi.org/10.5194/hess-15-1879-2011
13. The predictability of global land dry-wet condition contributed by sea surface temperature of Pacific: A new perspective from nonlinear dynamical system Y. Zhao et al. https://doi.org/10.1016/j.jhydrol.2025.133536
14. The importance of hydrological uncertainty assessment methods in climate change impact studies M. Honti et al. https://doi.org/10.5194/hess-18-3301-2014
15. Concepts of Information Content and Likelihood in Parameter Calibration for Hydrological Simulation Models K. Beven & P. Smith https://doi.org/10.1061/(ASCE)HE.1943-5584.0000991
16. A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales J. Park et al. https://doi.org/10.5194/hess-23-989-2019
17. Probability Distribution of Waiting Time of the kth Extreme Event under Serial Dependence F. Serinaldi & F. Lombardo https://doi.org/10.1061/(ASCE)HE.1943-5584.0001923
18. Validation and forecasting accuracy in models of climate change R. Fildes & N. Kourentzes https://doi.org/10.1016/j.ijforecast.2011.03.008
19. Quantifying the relative impact of hydrological and hydraulic modelling parameterizations on uncertainty of inundation maps A. Annis et al. https://doi.org/10.1080/02626667.2019.1709640
20. Past, present, and future of the Hurst-Kolmogorov dynamics in Stochastics: A bibliometric analysis of the last 50 years in water resources A. Pizarro et al. https://doi.org/10.1016/j.jhydrol.2024.132008
21. Testing tests before testing data: an untold tale of compound events and binary dependence F. Serinaldi et al. https://doi.org/10.1007/s00477-022-02190-6
22. Complexity and Predictability of Daily Actual Evapotranspiration Across Climate Regimes C. Di et al. https://doi.org/10.1029/2022WR032811
23. Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene S. Thompson et al. https://doi.org/10.5194/hess-17-5013-2013
24. Revisiting causality using stochastics: 2. Applications D. Koutsoyiannis et al. https://doi.org/10.1098/rspa.2021.0836
25. Assessing future changes in hydroclimatic processes in the Metropolitan Region of Belo Horizonte, Brazil, with the expanded Bluecat framework L. Santos et al. https://doi.org/10.3389/frwa.2025.1541052
26. Regional patterns of streamflow persistence, complexity, and chaos indicators within the United States K. Raczyński et al. https://doi.org/10.1016/j.ejrh.2026.103184
27. Factors influencing long range dependence in streamflow of European rivers E. Szolgayova et al. https://doi.org/10.1002/hyp.9694
28. Aesthetical Issues of Leonardo Da Vinci’s and Pablo Picasso’s Paintings with Stochastic Evaluation G. Sargentis et al. https://doi.org/10.3390/heritage3020017
29. From deterministic to stochastic: A hybrid framework coupling machine learning with Bluecat for probabilistic satellite-based precipitation estimation over Greece N. Tepetidis et al. https://doi.org/10.1016/j.ejrh.2026.103904
30. Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology F. Lombardo et al. https://doi.org/10.5194/hess-18-243-2014
31. Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas K. Beven et al. https://doi.org/10.5194/nhess-18-2741-2018
32. The mode of the climacogram estimator for a Gaussian Hurst-Kolmogorov process P. Dimitriadis & D. Koutsoyiannis https://doi.org/10.2166/hydro.2019.038
33. Stochastic investigation of daily air temperature extremes from a global ground station network K. Glynis et al. https://doi.org/10.1007/s00477-021-02002-3
34. A blueprint for process‐based modeling of uncertain hydrological systems A. Montanari & D. Koutsoyiannis https://doi.org/10.1029/2011WR011412
35. Unsurprising Surprises: The Frequency of Record‐breaking and Overthreshold Hydrological Extremes Under Spatial and Temporal Dependence F. Serinaldi & C. Kilsby https://doi.org/10.1029/2018WR023055
36. Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records P. Kossieris et al. https://doi.org/10.3390/w13233429
37. Randomness: a property of the mathematical and physical systems A. Tsonis https://doi.org/10.1080/02626667.2014.992434
38. Spatiotemporal clustering of streamflow extremes and relevance to flood insurance claims: a stochastic investigation for the contiguous USA K. Papoulakos et al. https://doi.org/10.1007/s11069-024-06766-z
39. Understanding Persistence to Avoid Underestimation of Collective Flood Risk F. Serinaldi & C. Kilsby https://doi.org/10.3390/w8040152
40. A Stochastic View of Varying Styles in Art Paintings G. Sargentis et al. https://doi.org/10.3390/heritage4010021
41. Modern Use of Traditional Rainwater Harvesting Practices: An Assessment of Cisterns’ Water Supply Potential in West Mani, Greece T. Iliopoulou et al. https://doi.org/10.3390/heritage5040152
42. A decade of Predictions in Ungauged Basins (PUB)—a review M. Hrachowitz et al. https://doi.org/10.1080/02626667.2013.803183
43. So how much of your error is epistemic? Lessons from Japan and Italy K. Beven https://doi.org/10.1002/hyp.9648
44. An overview of current applications, challenges, and future trends in distributed process-based models in hydrology S. Fatichi et al. https://doi.org/10.1016/j.jhydrol.2016.03.026
45. Negligent killing of scientific concepts: the stationarity case D. Koutsoyiannis & A. Montanari https://doi.org/10.1080/02626667.2014.959959
46. A Multilayer Perceptron Model for Stochastic Synthesis E. Rozos et al. https://doi.org/10.3390/hydrology8020067
47. On the Sensitivity of Standardized-Precipitation-Evapotranspiration and Aridity Indexes Using Alternative Potential Evapotranspiration Models A. Tegos et al. https://doi.org/10.3390/hydrology10030064
48. Long‐Lead Drought Forecasting Across the Continental United States Using Burg Entropy Spectral Analysis With a Multiresolution Approach J. Han & V. Singh https://doi.org/10.1029/2022WR034188
49. Knowable moments for high-order stochastic characterization and modelling of hydrological processes D. Koutsoyiannis https://doi.org/10.1080/02626667.2018.1556794
50. Predictability in dice motion: how does it differ from hydro-meteorological processes? P. Dimitriadis et al. https://doi.org/10.1080/02626667.2015.1034128
51. Do probabilistic forecasts lead to better decisions? M. Ramos et al. https://doi.org/10.5194/hess-17-2219-2013
52. Climate Consensus and ‘Misinformation’: A Rejoinder to Agnotology, Scientific Consensus, and the Teaching and Learning of Climate Change D. Legates et al. https://doi.org/10.1007/s11191-013-9647-9
53. Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas G. Pouliasis et al. https://doi.org/10.3390/w13162156
54. Reconciling the signal and noise of atmospheric warming on decadal timescales R. Jones & J. Ricketts https://doi.org/10.5194/esd-8-177-2017
55. Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability G. Papacharalampous & H. Tyralis https://doi.org/10.1016/j.jhydrol.2020.125205
56. Preface—Special Issue: Facets of Uncertainty A. Carsteanu et al. https://doi.org/10.1080/02626667.2016.1186919
57. Investigation on the stochastic nature of the solar radiation process G. Koudouris et al. https://doi.org/10.1016/j.egypro.2017.08.076
58. Stochastic assessment of temperature–CO₂ causal relationship in climate from the Phanerozoic through modern times D. Koutsoyiannis https://doi.org/10.3934/mbe.2024287
59. Just a Single-Layer CNN for Stochastic Modeling: A Discriminator-Free Approach E. Rozos https://doi.org/10.3390/hydrology12070170
60. Revisiting the global hydrological cycle: is it intensifying? D. Koutsoyiannis https://doi.org/10.5194/hess-24-3899-2020
61. Stochastic Foundations of Atmospheric Thermodynamics: Deriving the Laws from Maximum Entropy and Implications for Earth’s Climate D. Koutsoyiannis https://doi.org/10.3390/geosciences16090371
62. On the long-range dependence properties of annual precipitation using a global network of instrumental measurements H. Tyralis et al. https://doi.org/10.1016/j.advwatres.2017.11.010
63. 3D stochastic modeling of flow and solute transport in karst vadose zone D. Zeqiraj https://doi.org/10.1016/j.cscee.2022.100217
64. Stochastic investigation of long-term persistence in two-dimensional images of rocks P. Dimitriadis et al. https://doi.org/10.1016/j.spasta.2018.11.002
65. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. https://doi.org/10.3390/w11102126
66. Characterizing the spatiotemporal variability of groundwater levels of alluvial aquifers in different settings using drought indices J. Haas & S. Birk https://doi.org/10.5194/hess-21-2421-2017
67. Global Sensitivity Analysis for Multiple Interpretive Models With Uncertain Parameters A. Dell'Oca et al. https://doi.org/10.1029/2019WR025754
68. Stochastic simulation of wind wave parameters for energy production K. Kardakaris et al. https://doi.org/10.1016/j.oceaneng.2023.114029
69. Knowable Moments in Stochastics: Knowing Their Advantages D. Koutsoyiannis https://doi.org/10.3390/axioms12060590
70. On Hens, Eggs, Temperatures and CO2: Causal Links in Earth’s Atmosphere D. Koutsoyiannis et al. https://doi.org/10.3390/sci5030035
71. Time’s arrow in stochastic characterization and simulation of atmospheric and hydrological processes D. Koutsoyiannis https://doi.org/10.1080/02626667.2019.1600700
72. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. https://doi.org/10.1016/j.gsf.2022.101349
73. Developing the Actual Precipitation Probability Distribution Based on the Complete Daily Series W. Zhai et al. https://doi.org/10.3390/su151713136
74. Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece) G. Sargentis et al. https://doi.org/10.3390/infrastructures6010012
75. Irreversibility and complex network behavior of stream flow fluctuations F. Serinaldi & C. Kilsby https://doi.org/10.1016/j.physa.2016.01.043
76. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. https://doi.org/10.1016/j.advwatres.2019.103470
77. Characterisation of hydroclimatological trends and variability in the Lake Naivasha basin, Kenya V. Odongo et al. https://doi.org/10.1002/hyp.10443
78. Can we tell more than we can know? The limits of bivariate drought analyses in the United States F. Serinaldi https://doi.org/10.1007/s00477-015-1124-3
79. Stochastic analysis and simulation of hydrometeorological processes associated with wind and solar energy G. Tsekouras & D. Koutsoyiannis https://doi.org/10.1016/j.renene.2013.10.018
80. Global Investigation of Double Periodicity οf Hourly Wind Speed for Stochastic Simulation; Application in Greece I. Deligiannis et al. https://doi.org/10.1016/j.egypro.2016.10.001
81. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes P. Dimitriadis et al. https://doi.org/10.3390/hydrology8020059
82. The tales that the distribution tails of non-Gaussian autocorrelated processes tell: efficient methods for the estimation of the k -length block-maxima distribution I. Tsoukalas https://doi.org/10.1080/02626667.2021.2014056
83. The Spatial Scale Dependence of The Hurst Coefficient in Global Annual Precipitation Data, and Its Role in Characterising Regional Precipitation Deficits within a Naturally Changing Climate E. O’Connell et al. https://doi.org/10.3390/hydrology9110199
84. A comparison of local and aggregated climate model outputs with observed data G. Anagnostopoulos et al. https://doi.org/10.1080/02626667.2010.513518
85. All in order: Distribution of serially correlated order statistics with applications to hydrological extremes F. Serinaldi et al. https://doi.org/10.1016/j.advwatres.2020.103686
86. Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication K. Beven https://doi.org/10.1080/02626667.2015.1031761
87. Projecting the future of rainfall extremes: Better classic than trendy T. Iliopoulou & D. Koutsoyiannis https://doi.org/10.1016/j.jhydrol.2020.125005
88. Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro https://doi.org/10.1002/hyp.15046
89. Comment on “Are soils overrated in hydrology?” by Gao et al. (2023) Y. Zhao et al. https://doi.org/10.5194/hess-28-4059-2024
90. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G. Papacharalampous et al. https://doi.org/10.1016/j.advwatres.2019.103471
91. Stochastic synthesis approximating any process dependence and distribution P. Dimitriadis & D. Koutsoyiannis https://doi.org/10.1007/s00477-018-1540-2
92. Time scale and influencing factor variations of water and heat fluxes in sandy land X. Li et al. https://doi.org/10.2166/nh.2022.020
93. Environmental Determinism vs. Social Dynamics: Prehistorical and Historical Examples G. Sargentis et al. https://doi.org/10.3390/world3020020
94. Rethinking Climate, Climate Change, and Their Relationship with Water D. Koutsoyiannis https://doi.org/10.3390/w13060849
95. Hydrological modelling in a changing world M. Peel & G. Blöschl https://doi.org/10.1177/0309133311402550
96. Hurst-Kolmogorov Dynamics and Uncertainty1 D. Koutsoyiannis https://doi.org/10.1111/j.1752-1688.2011.00543.x
97. Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes P. Dimitriadis & D. Koutsoyiannis https://doi.org/10.1007/s00477-015-1023-7
98. The seventh facet of uncertainty: wrong assumptions, unknowns and surprises in the dynamics of human–water systems G. Di Baldassarre et al. https://doi.org/10.1080/02626667.2015.1091460
99. Hydrology and change D. Koutsoyiannis https://doi.org/10.1080/02626667.2013.804626
100. On the statistical complexity of streamflow P. Dey & P. Mujumdar https://doi.org/10.1080/02626667.2021.2000991
101. An analysis of change in alpine annual maximum discharges: implications for the selection of design discharges A. Castellarin & A. Pistocchi https://doi.org/10.1002/hyp.8249
102. A stochastic model for the hourly solar radiation process for application in renewable resources management G. Koudouris et al. https://doi.org/10.5194/adgeo-45-139-2018
103. The uncertainty cascade in model fusion K. Beven & R. Lamb https://doi.org/10.1144/SP408.3
104. Hurst–Kolmogorov dynamics as a result of extremal entropy production D. Koutsoyiannis https://doi.org/10.1016/j.physa.2010.12.035
105. Revealing hidden persistence in maximum rainfall records T. Iliopoulou & D. Koutsoyiannis https://doi.org/10.1080/02626667.2019.1657578
106. Simulation of reactive solute transport in the critical zone: a Lagrangian model for transient flow and preferential transport A. Sternagel et al. https://doi.org/10.5194/hess-25-1483-2021
107. A precipitation shift from snow towards rain leads to a decrease in streamflow W. Berghuijs et al. https://doi.org/10.1038/nclimate2246
108. Imposed and Neglected Uncertainty in the Global Average Surface Air Temperature Index P. Frank https://doi.org/10.1260/0958-305X.22.4.407
109. Advancing catchment hydrology to deal with predictions under change U. Ehret et al. https://doi.org/10.5194/hess-18-649-2014
110. In Search of Climate Crisis in Greece Using Hydrological Data: 404 Not Found D. Koutsoyiannis et al. https://doi.org/10.3390/w15091711
111. A Monte Carlo Model for WWTP Effluent Flow Treatment through Enhanced Willow Evapotranspiration A. Tegos https://doi.org/10.3390/hydrology11090134
112. Ecohydrological particle model based on representative domains C. Jackisch & E. Zehe https://doi.org/10.5194/hess-22-3639-2018
113. Entropy and War, Toy Models G. Sargentis https://doi.org/10.21926/rpse.2502007
114. Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam's Digital Razor S. Weijs & B. Ruddell https://doi.org/10.1029/2019WR026471
115. Investigating the Performance of the Informer Model for Streamflow Forecasting N. Tepetidis et al. https://doi.org/10.3390/w16202882
116. Pyraingen: A python package for constrained continuous rainfall generation C. Dykman et al. https://doi.org/10.1016/j.envsoft.2024.105984
117. Low-frequency variability of European runoff L. Gudmundsson et al. https://doi.org/10.5194/hess-15-2853-2011
118. Simple stochastic simulation of time irreversible and reversible processes D. Koutsoyiannis https://doi.org/10.1080/02626667.2019.1705302
119. Robust informational entropy-based descriptors of flow in catchment hydrology I. Pechlivanidis et al. https://doi.org/10.1080/02626667.2014.983516
120. Stochastic Evaluation of Landscapes Transformed by Renewable Energy Installations and Civil Works G. Sargentis et al. https://doi.org/10.3390/en12142817
121. Data‐based comparison of frequency analysis methods: A general framework B. Renard et al. https://doi.org/10.1002/wrcr.20087
122. Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes G. Papacharalampous et al. https://doi.org/10.1007/s00477-018-1638-6
123. Towards Generic Simulation for Demanding Stochastic Processes D. Koutsoyiannis & P. Dimitriadis https://doi.org/10.3390/sci3030034
124. Estimation of Hydropower Potential Using Bayesian and Stochastic Approaches for Streamflow Simulation and Accounting for the Intermediate Storage Retention K. Spanoudaki et al. https://doi.org/10.3390/en15041413
125. Entropy Production in Stochastics D. Koutsoyiannis https://doi.org/10.3390/e19110581
126. Entropy and Wealth D. Koutsoyiannis & G. Sargentis https://doi.org/10.3390/e23101356
127. A philosophical basis for hydrological uncertainty G. Nearing et al. https://doi.org/10.1080/02626667.2016.1183009
128. Hydrology of the Po River: looking for changing patterns in river discharge A. Montanari https://doi.org/10.5194/hess-16-3739-2012
129. A new methodology to define homogeneous regions through an entropy based clustering method E. Ridolfi et al. https://doi.org/10.1016/j.advwatres.2016.07.007
130. Stochastic Analysis of Hourly to Monthly Potential Evapotranspiration with a Focus on the Long-Range Dependence and Application with Reanalysis and Ground-Station Data P. Dimitriadis et al. https://doi.org/10.3390/hydrology8040177
131. Scientific logic and spatio-temporal dependence in analyzing extreme-precipitation frequency: negligible or neglected? F. Serinaldi https://doi.org/10.5194/hess-28-3191-2024
132. Modeling and mitigating natural hazards: Stationarity is immortal! A. Montanari & D. Koutsoyiannis https://doi.org/10.1002/2014WR016092
133. Drought begets drought: self-exciting onsets, spell persistence, and network structure in hydroclimatic extremes M. Herrera-Marín et al. https://doi.org/10.1088/2515-7620/ae639a
134. Effects of levee cover strength on flood mapping in the case of levee breach due to overtopping M. Mazzoleni et al. https://doi.org/10.1080/02626667.2016.1246800
135. Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. https://doi.org/10.5194/hess-21-6219-2017
136. Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures G. Sargentis et al. https://doi.org/10.3390/su12197972
137. Spatial downscaling of precipitation using adaptable random forests X. He et al. https://doi.org/10.1002/2016WR019034
138. Scientific dialogue on climate: is it giving black eyes or opening closed eyes? Reply to “A black eye for theHydrological Sciences Journal” by D. Huard D. Koutsoyiannis et al. https://doi.org/10.1080/02626667.2011.610759
139. Simulating preferential soil water flow and tracer transport using the Lagrangian Soil Water and Solute Transport Model A. Sternagel et al. https://doi.org/10.5194/hess-23-4249-2019
140. Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology F. Serinaldi et al. https://doi.org/10.1016/j.advwatres.2017.10.015
141. Can a simple stochastic model generate rich patterns of rainfall events? S. Papalexiou et al. https://doi.org/10.1016/j.jhydrol.2011.10.008
142. Two-dimensional Hurst–Kolmogorov process and its application to rainfall fields D. Koutsoyiannis et al. https://doi.org/10.1016/j.jhydrol.2010.12.012
143. A guide to good practice in modeling semantics for authors and referees K. Beven & P. Young https://doi.org/10.1002/wrcr.20393
144. Perceptual models of uncertainty for socio-hydrological systems: a flood risk change example I. Westerberg et al. https://doi.org/10.1080/02626667.2017.1356926