167 citations as recorded by crossref.

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3. Dynamic equifinality: The case of south-central Chile’s evolving forest landscape M. Patterson & N. Hoalst-Pullen https://doi.org/10.1016/j.apgeog.2010.12.004
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7. Effects of Urbanization and Climate Change on Peak Flows over the San Antonio River Basin, Texas G. Zhao et al. https://doi.org/10.1175/JHM-D-15-0216.1
8. Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data H. Haas et al. https://doi.org/10.3390/w13212947
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17. Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau Plain, Marlborough District, New Zealand M. Raiber et al. https://doi.org/10.1016/j.jhydrol.2012.01.045
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19. Revisiting the Relationship Between Data, Models, and Decision‐Making T. Ferré https://doi.org/10.1111/gwat.12574
20. Irrigation area, efficiency and water storage mediate the drought resilience of irrigated agriculture in a semi-arid catchment B. Lankford et al. https://doi.org/10.1016/j.scitotenv.2022.160263
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22. Probabilistic cost-benefit analysis for mitigating hydrogeological risks in underground construction J. Merisalu et al. https://doi.org/10.1016/j.tust.2022.104815
23. An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources J. Velázquez et al. https://doi.org/10.5194/hess-17-565-2013
24. Conceptualization in catchment modelling: simply learning? S. Dunn et al. https://doi.org/10.1002/hyp.7070
25. Mapping, Bayesian Geostatistical Analysis and Spatial Prediction of Lymphatic Filariasis Prevalence in Africa H. Slater et al. https://doi.org/10.1371/journal.pone.0071574
26. Comparing empirical models for sediment and phosphorus transfer from soils to water at field and catchment scale under data uncertainty T. Krueger et al. https://doi.org/10.1111/j.1365-2389.2011.01419.x
27. A hydrological–economic model for sustainable groundwater use in sparse-data drylands: Application to the Amtoudi Oasis in southern Morocco, northern Sahara F. Alcalá et al. https://doi.org/10.1016/j.scitotenv.2015.07.062
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30. Water Resources Criticality Due to Future Climate Change and Population Growth: Case of River Basins in Utah, USA K. Khatri et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000959
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32. The uncertainty cascade in model fusion K. Beven & R. Lamb https://doi.org/10.1144/SP408.3
33. An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools D. Viviroli et al. https://doi.org/10.1016/j.envsoft.2009.04.001
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35. Data Science of the Natural Environment: A Research Roadmap G. Blair et al. https://doi.org/10.3389/fenvs.2019.00121
36. How could managed aquifer recharge be feasible in the Coleambally Irrigation Area? N. Harvey et al. https://doi.org/10.1080/13241583.2023.2180830
37. A GLUE uncertainty analysis of a drying model of pharmaceutical granules S. Mortier et al. https://doi.org/10.1016/j.ejpb.2013.03.012
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40. Using Large Climate Ensembles to Plan for the Hydrological Impact of Climate Change in the Freshwater Environment F. Fung et al. https://doi.org/10.1007/s11269-012-0080-7
41. Assessing impacts of future potential climate change scenarios on aquifer recharge in continental Spain D. Pulido-Velazquez et al. https://doi.org/10.1016/j.jhydrol.2017.10.077
42. Regionalization of land‐use impacts on streamflow using a network of paired catchments B. Ochoa‐Tocachi et al. https://doi.org/10.1002/2016WR018596
43. On the future of hydroecological models of everywhere K. Beven https://doi.org/10.1016/j.envsoft.2025.106431
44. Implementing integrated and systems approaches to water quality management considering data uncertainty R. Hranova https://doi.org/10.1080/10286608.2013.853742
45. Using time domain and geographic source tracers to conceptualize streamflow generation processes in lumped rainfall‐runoff models C. Birkel et al. https://doi.org/10.1029/2010WR009547
46. Modeling hydrologic controls on denitrification: sensitivity to parameter uncertainty and landscape representation C. Tague https://doi.org/10.1007/s10533-008-9276-6
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50. Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow J. Senent-Aparicio et al. https://doi.org/10.3390/w12030657
51. Benchmarking hydrological models for an uncertain future K. Beven https://doi.org/10.1002/hyp.14882
52. Projecting climate change impacts on stream flow regimes with tracer-aided runoff models - preliminary assessment of heterogeneity at the mesoscale R. Capell et al. https://doi.org/10.1002/hyp.9612
53. A limits of acceptability approach to model evaluation and uncertainty estimation in flood frequency estimation by continuous simulation: Skalka catchment, Czech Republic S. Blazkova & K. Beven https://doi.org/10.1029/2007WR006726
54. Uncertainties associated with digital elevation models for hydrologic applications: a review S. Wechsler https://doi.org/10.5194/hess-11-1481-2007
55. High Expectations and Open Questions: Using High-Frequency Proxy Data to Calibrate a Catchment-Scale Phosphorus Model E. Lannergård et al. https://doi.org/10.1021/acsestwater.3c00775
56. Exploring groundwater-surface water interactions and recharge in fractured mountain systems: an integrated approach S. Ortenzi et al. https://doi.org/10.5194/hess-30-1755-2026
57. A three-dimensional hydrogeological–geophysical model of a multi-layered aquifer in the coastal alluvial plain of Sarno River (southern Italy) R. Di Maio et al. https://doi.org/10.1007/s10040-013-1087-8
58. A Perturbance Moment Point Estimate Method for uncertainty analysis of the hydrologic response S. Franceschini et al. https://doi.org/10.1016/j.advwatres.2012.02.007
59. A mixing model to incorporate uncertainty in sediment fingerprinting K. Nosrati et al. https://doi.org/10.1016/j.geoderma.2013.12.002
60. Re-shaping models of E. coli population dynamics in livestock faeces: Increased bacterial risk to humans? D. Oliver et al. https://doi.org/10.1016/j.envint.2009.08.006
61. Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin M. Morlot et al. https://doi.org/10.1016/j.jhydrol.2023.130587
62. Web-Based Environmental Simulation: Bridging the Gap between Scientific Modeling and Decision-Making W. Buytaert et al. https://doi.org/10.1021/es2031278
63. ArcE: A GIS tool for modelling actual evapotranspiration S. España et al. https://doi.org/10.1016/j.cageo.2011.03.008
64. On (in)validating environmental models. 1. Principles for formulating a Turing‐like Test for determining when a model is fit‐for purpose K. Beven & S. Lane https://doi.org/10.1002/hyp.14704
65. A massively parallel semi-Lagrangian algorithm for solving the transport equation J. Russell Manson et al. https://doi.org/10.1016/j.procs.2010.04.036
66. Deep learning, hydrological processes and the uniqueness of place K. Beven https://doi.org/10.1002/hyp.13805
67. Modelling everything everywhere: a new approach to decision‐making for water management under uncertainty K. BEVEN & R. ALCOCK https://doi.org/10.1111/j.1365-2427.2011.02592.x
68. Bayesian uncertainty analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed, China J. Han et al. https://doi.org/10.1007/s00477-013-0792-0
69. Uncertainty in computations of the spread of warm water in a river – lessons from Environmental Impact Assessment case study M. Kalinowska & P. Rowiński https://doi.org/10.5194/hess-16-4177-2012
70. Gauging the ungauged basin: how many discharge measurements are needed? J. Seibert & K. Beven https://doi.org/10.5194/hess-13-883-2009
71. The Water Quality of the River Enborne, UK: Observations from High-Frequency Monitoring in a Rural, Lowland River System S. Halliday et al. https://doi.org/10.3390/w6010150
72. GRACE storage-runoff hystereses reveal the dynamics of regional watersheds E. Sproles et al. https://doi.org/10.5194/hess-19-3253-2015
73. Emulator-enabled approximate Bayesian computation (ABC) and uncertainty analysis for computationally expensive groundwater models T. Cui et al. https://doi.org/10.1016/j.jhydrol.2018.07.005
74. Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling P. Döll et al. https://doi.org/10.5194/adgeo-18-51-2008
75. Impacts of the water framework directive on learning and knowledge practices in a Swedish catchment L. Dawson et al. https://doi.org/10.1016/j.jenvman.2018.06.054
76. Editorial to the Special Issue “Recent Advances in Hydrological Modeling” M. He et al. https://doi.org/10.3390/hydrology11070108
77. Large‐scale hydrology: advances in understanding processes, dynamics and models from beyond river basin to global scale H. Cloke & D. Hannah https://doi.org/10.1002/hyp.8059
78. Societal breakdown as an emergent property of large-scale behavioural models of land use change C. Brown et al. https://doi.org/10.5194/esd-10-809-2019
79. Integrated environmental modeling: A vision and roadmap for the future G. Laniak et al. https://doi.org/10.1016/j.envsoft.2012.09.006
80. Combining of MASW and GPR Imaging and Hydrogeological Surveys for the Groundwater Resource Evaluation in a Coastal Urban Area in Southern Spain F. Alcalá et al. https://doi.org/10.3390/app11073154
81. A call for more fundamental science in regional hydrogeology R. Barthel https://doi.org/10.1007/s10040-014-1101-9
82. The Impact of the Uncertain Input Data of Multi-Purpose Reservoir Volumes under Hydrological Extremes S. Paseka & D. Marton https://doi.org/10.3390/w13101389
83. Assessing certainty and uncertainty in riparian habitat suitability models by identifying parameters with extreme outputs B. Fu & J. Guillaume https://doi.org/10.1016/j.envsoft.2014.06.015
84. Representation of Hydrological Components under a Changing Climate—A Case Study of the Uruguay River Basin Using the New Version of the Soil and Water Assessment Tool Model (SWAT+) O. Barresi Armoa et al. https://doi.org/10.3390/w15142604
85. Using lumped conceptual rainfall–runoff models to simulate daily isotope variability with fractionation in a nested mesoscale catchment C. Birkel et al. https://doi.org/10.1016/j.advwatres.2010.12.006
86. Why is achieving good ecological outcomes in rivers so difficult? G. HARRIS & A. HEATHWAITE https://doi.org/10.1111/j.1365-2427.2011.02640.x
87. Bias correcting instantaneous peak flows generated using a continuous, semi‐distributed hydrologic model P. Spellman et al. https://doi.org/10.1111/jfr3.12342
88. Towards a simple dynamic process conceptualization in rainfall–runoff models using multi‐criteria calibration and tracers in temperate, upland catchments C. Birkel et al. https://doi.org/10.1002/hyp.7478
89. Comment on “The new assessment of soil loss by water erosion in Europe” by Panagos et al. (Environmental Science & Policy 54 (2015) 438–447) P. Fiener & K. Auerswald https://doi.org/10.1016/j.envsci.2015.12.012
90. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. https://doi.org/10.5194/hess-29-2361-2025
91. A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom H. Lewis & S. Dadson https://doi.org/10.1002/hyp.14438
92. Preface – special issue on “coupled atmosphere‐hydrological processes: Novel system developments and cross‐compartment evaluations” A. Senatore et al. https://doi.org/10.1002/hyp.14780
93. Hydrological regime of remote catchments with extreme gradients under accelerated change: the Baker basin in Patagonia A. Dussaillant J. et al. https://doi.org/10.1080/02626667.2012.726993
94. Evaluation of the Tank Model Optimized Parameter for Watershed Modeling K. Kim et al. https://doi.org/10.5389/KSAE.2014.56.4.009
95. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation A. Bahremand https://doi.org/10.5194/hess-20-1433-2016
96. Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model S. Lyon et al. https://doi.org/10.1111/jawr.12268
97. Reply to comment by Keith J. Beven and Hannah L. Cloke on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water” E. Wood et al. https://doi.org/10.1029/2011WR011202
98. HESS Opinions "Crash tests for a standardized evaluation of hydrological models" V. Andréassian et al. https://doi.org/10.5194/hess-13-1757-2009
99. Scale appropriate modelling of diffuse microbial pollution from agriculture D. Oliver et al. https://doi.org/10.1177/0309133309342647
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110. Using Multiple Watershed Models to Predict Water, Nitrogen, and Phosphorus Discharges to the Patuxent Estuary1 K. Boomer et al. https://doi.org/10.1111/j.1752-1688.2012.00689.x
111. HESS Opinions "The art of hydrology"* H. Savenije https://doi.org/10.5194/hess-13-157-2009
112. Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models H. Meresa et al. https://doi.org/10.1007/s11069-021-05152-3
113. Challenges of Operational River Forecasting T. Pagano et al. https://doi.org/10.1175/JHM-D-13-0188.1
114. Validation of biophysical models: issues and methodologies. A review G. Bellocchi et al. https://doi.org/10.1051/agro/2009001
115. Quasi-Monte Carlo based global uncertainty and sensitivity analysis in modeling free product migration and recovery from petroleum-contaminated aquifers L. He et al. https://doi.org/10.1016/j.jhazmat.2012.03.067
116. Hydrological drought across the world: impact of climate and physical catchment structure H. Van Lanen et al. https://doi.org/10.5194/hess-17-1715-2013
117. Advice to a young hydrologist K. Beven https://doi.org/10.1002/hyp.10879
118. Integrating different types of information into hydrological model parameter estimation: Application to ungauged catchments and land use scenario analysis N. Bulygina et al. https://doi.org/10.1029/2011WR011207
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120. On the importance of appropriate precipitation gauge catch correction for hydrological modelling at mid to high latitudes S. Stisen et al. https://doi.org/10.5194/hess-16-4157-2012
121. Conceptualising Scale in Regional Studies and Catchment Science – Towards an Integrated Characterisation of Spatial Units D. MacKinnon & D. Tetzlaff https://doi.org/10.1111/j.1749-8198.2009.00231.x
122. Historical development of rainfall‐runoff modeling M. Peel & T. McMahon https://doi.org/10.1002/wat2.1471
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124. So just why would a modeller choose to be incoherent? K. Beven et al. https://doi.org/10.1016/j.jhydrol.2008.02.007
125. Contrasting Uncertainties in Estimating Floods and Low Flow Extremes H. Meresa & Y. Zhang https://doi.org/10.1007/s11269-021-02809-3
126. An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales S. Halliday et al. https://doi.org/10.1016/j.scitotenv.2011.10.052
127. Incremental model breakdown to assess the multi-hypotheses problem F. Jehn et al. https://doi.org/10.5194/hess-22-4565-2018
128. Integrated MASW and ERT Imaging for Geological Definition of an Unconfined Alluvial Aquifer Sustaining a Coastal Groundwater-Dependent Ecosystem in Southwest Portugal M. Paz et al. https://doi.org/10.3390/app10175905
129. Detailed data is welcome, but with a pinch of salt: Accuracy, precision, and uncertainty in flood inundation modeling F. Dottori et al. https://doi.org/10.1002/wrcr.20406
130. How to make advances in hydrological modelling K. Beven https://doi.org/10.2166/nh.2019.134
131. Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software B. Dobson et al. https://doi.org/10.5194/gmd-17-4495-2024
132. Simulating hydrologic response of a pasture hillslope in North Alabama using the Hortonian Infiltration and Runoff/On model S. Sen et al. https://doi.org/10.2489/jswc.66.6.411
133. Transpiration inQuercus subertrees under shallow water table conditions: the role of soil and groundwater C. Pinto et al. https://doi.org/10.1002/hyp.10097
134. Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain M. Buechel et al. https://doi.org/10.5194/hess-28-2081-2024
135. Integration of land use, land cover, transportation, and environmental impact models: Expanding scenario analysis with multiple modules H. Shahumyan & R. Moeckel https://doi.org/10.1177/0265813516647062
136. Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales G. Baroni et al. https://doi.org/10.5194/hess-21-2301-2017
137. Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization S. Noh et al. https://doi.org/10.1016/j.envsoft.2018.02.008
138. A geospatial framework to support integrated biogeochemical modelling in the United Kingdom S. Greene et al. https://doi.org/10.1016/j.envsoft.2015.02.012
139. The critical role of uncertainty in projections of hydrological extremes H. Meresa & R. Romanowicz https://doi.org/10.5194/hess-21-4245-2017
140. OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
141. Estimation of groundwater recharge and drought severity with varying model complexity J. von Freyberg et al. https://doi.org/10.1016/j.jhydrol.2015.05.025
142. Do we need a Community Hydrological Model? M. Weiler & K. Beven https://doi.org/10.1002/2014WR016731
143. A cloud based tool for knowledge exchange on local scale flood risk M. Wilkinson et al. https://doi.org/10.1016/j.jenvman.2015.06.009
144. Managing the impacts of nutrient enrichment on river systems: dealing with complex uncertainties in risk analyses T. PAGE et al. https://doi.org/10.1111/j.1365-2427.2012.02756.x
145. Evaluating uncertainty in integrated environmental models: A review of concepts and tools L. Matott et al. https://doi.org/10.1029/2008WR007301
146. Evaluating the effects of soil erosion and productivity decline on soil carbon dynamics using a model-based approach S. Bouchoms et al. https://doi.org/10.5194/soil-5-367-2019
147. Preferential flows and travel time distributions: defining adequate hypothesis tests for hydrological process models K. Beven https://doi.org/10.1002/hyp.7718
148. Impact of hydraulic model resolution and loss of life model modification on flood fatality risk estimation: Case study of the Bommelerwaard, The Netherlands A. Brussee et al. https://doi.org/10.1111/jfr3.12713
149. Hydrodynamic Modeling of Inundation Patterns of a Large African Floodplain Indicates Sensitivity to Waterway Restoration T. Willis et al. https://doi.org/10.1029/2021WR030107
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154. Progress in socio‐hydrology: a meta‐analysis of challenges and opportunities S. Pande & M. Sivapalan https://doi.org/10.1002/wat2.1193
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158. Why can't we do better than Topmodel? W. Buytaert et al. https://doi.org/10.1002/hyp.7125
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