169 citations as recorded by crossref.

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2. Reconstructing the 2015 Salgar flash flood using radar retrievals and a conceptual modeling framework in an ungauged basin N. Velásquez et al. https://doi.org/10.5194/hess-24-1367-2020
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
4. Riparian Zone Influence on Stream Water Dissolved Organic Carbon Concentrations at the Swedish Integrated Monitoring Sites M. Winterdahl et al. https://doi.org/10.1007/s13280-011-0199-4
5. Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020 E. Michael et al. https://doi.org/10.1186/s12916-017-0933-2
6. Stakeholder driven update and improvement of a national water resources model A. Højberg et al. https://doi.org/10.1016/j.envsoft.2012.09.010
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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10. Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. https://doi.org/10.1002/hyp.14288
11. Evaluation of SWAT models performance to simulate streamflow spatial origin. The case of a small forested watershed M. Meaurio et al. https://doi.org/10.1016/j.jhydrol.2015.03.050
12. Modelling catchment‐scale water storage dynamics: reconciling dynamic storage with tracer‐inferred passive storage C. Birkel et al. https://doi.org/10.1002/hyp.8201
13. Hydrological Response of Tropical Catchments to Climate Change as Modeled by the GR2M Model: A Case Study in Costa Rica M. Mendez et al. https://doi.org/10.3390/su142416938
14. Can we model the hydrological impacts of environmental change? T. Wagener https://doi.org/10.1002/hyp.6873
15. What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling T. Wagener & F. Pianosi https://doi.org/10.1016/j.earscirev.2019.04.006
16. Improving the theoretical underpinnings of process‐based hydrologic models M. Clark et al. https://doi.org/10.1002/2015WR017910
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
18. Uncertainty Analyses in Hydrologic/Hydraulic Modeling: Challenges and Proposed Resolutions C. Pathak et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001231
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
28. When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species? J. Kiesel et al. https://doi.org/10.1002/eco.2193
29. Analytical estimation of annual runoff distribution in ungauged seasonally dry basins based on a first order Taylor expansion of the Fu's equation D. Caracciolo et al. https://doi.org/10.1016/j.advwatres.2017.09.019
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
31. A decade of Predictions in Ungauged Basins (PUB)—a review M. Hrachowitz et al. https://doi.org/10.1080/02626667.2013.803183
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
34. Perceptual perplexity and parameter parsimony K. Beven & N. Chappell https://doi.org/10.1002/wat2.1530
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
38. Scientific geodata infrastructures: challenges, approaches and directions L. Bernard et al. https://doi.org/10.1080/17538947.2013.781244
39. A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo https://doi.org/10.1002/hyp.14569
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
47. Analysis of the influence of input data uncertainties on determining the reliability of reservoir storage capacity D. Marton et al. https://doi.org/10.1515/johh-2015-0036
48. Groundwater Visualisation System (GVS): A software framework for integrated display and interrogation of conceptual hydrogeological models, data and time-series animation M. Cox et al. https://doi.org/10.1016/j.jhydrol.2013.03.023
49. A feasible methodology for groundwater resource modelling for sustainable use in sparse-data drylands: Application to the Amtoudi Oasis in the northern Sahara F. Alcalá et al. https://doi.org/10.1016/j.scitotenv.2018.02.294
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. Beyond behavioural models: equifinality and overparameterisation undermine confidence in predictions by soil organic matter models M. Van de Broek & J. Six https://doi.org/10.5194/gmd-19-8651-2026
60. A mixing model to incorporate uncertainty in sediment fingerprinting K. Nosrati et al. https://doi.org/10.1016/j.geoderma.2013.12.002
61. 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
62. 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
63. Extending daily river discharge records across China using satellite-derived river widths Y. Wang et al. https://doi.org/10.5194/essd-18-6973-2026
64. Web-Based Environmental Simulation: Bridging the Gap between Scientific Modeling and Decision-Making W. Buytaert et al. https://doi.org/10.1021/es2031278
65. ArcE: A GIS tool for modelling actual evapotranspiration S. España et al. https://doi.org/10.1016/j.cageo.2011.03.008
66. 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
67. 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
68. Deep learning, hydrological processes and the uniqueness of place K. Beven https://doi.org/10.1002/hyp.13805
69. 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
70. 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
71. 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
72. Gauging the ungauged basin: how many discharge measurements are needed? J. Seibert & K. Beven https://doi.org/10.5194/hess-13-883-2009
73. 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
74. GRACE storage-runoff hystereses reveal the dynamics of regional watersheds E. Sproles et al. https://doi.org/10.5194/hess-19-3253-2015
75. 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
76. 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
77. 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
78. Editorial to the Special Issue “Recent Advances in Hydrological Modeling” M. He et al. https://doi.org/10.3390/hydrology11070108
79. 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
80. 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
81. Integrated environmental modeling: A vision and roadmap for the future G. Laniak et al. https://doi.org/10.1016/j.envsoft.2012.09.006
82. 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
83. A call for more fundamental science in regional hydrogeology R. Barthel https://doi.org/10.1007/s10040-014-1101-9
84. 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
85. 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
86. 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
87. 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
88. 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
89. Bias correcting instantaneous peak flows generated using a continuous, semi‐distributed hydrologic model P. Spellman et al. https://doi.org/10.1111/jfr3.12342
90. 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
91. 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
92. 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
93. 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
94. 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
95. 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
96. Evaluation of the Tank Model Optimized Parameter for Watershed Modeling K. Kim et al. https://doi.org/10.5389/KSAE.2014.56.4.009
97. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation A. Bahremand https://doi.org/10.5194/hess-20-1433-2016
98. 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
99. 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
100. HESS Opinions "Crash tests for a standardized evaluation of hydrological models" V. Andréassian et al. https://doi.org/10.5194/hess-13-1757-2009
101. Scale appropriate modelling of diffuse microbial pollution from agriculture D. Oliver et al. https://doi.org/10.1177/0309133309342647
102. On virtual observatories and modelled realities (or why discharge must be treated as a virtual variable) K. Beven et al. https://doi.org/10.1002/hyp.9261
103. Improving the representation of hydrologic processes in Earth System Models M. Clark et al. https://doi.org/10.1002/2015WR017096
104. The value of simplified models for spin up of complex models with an application to subsurface hydrology D. Erdal et al. https://doi.org/10.1016/j.cageo.2019.01.014
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107. The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord G. Blair et al. https://doi.org/10.1016/j.patter.2020.100156
108. Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water” by Eric F. Wood et al. K. Beven & H. Cloke https://doi.org/10.1029/2011WR010982
109. Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas J. Sundell et al. https://doi.org/10.1111/risa.12890
110. The role of input and hydrological parameters uncertainties in extreme hydrological simulations H. Meresa et al. https://doi.org/10.1111/nrm.12320
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112. 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
113. HESS Opinions "The art of hydrology"* H. Savenije https://doi.org/10.5194/hess-13-157-2009
114. 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
115. Challenges of Operational River Forecasting T. Pagano et al. https://doi.org/10.1175/JHM-D-13-0188.1
116. Validation of biophysical models: issues and methodologies. A review G. Bellocchi et al. https://doi.org/10.1051/agro/2009001
117. 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
118. 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
119. Advice to a young hydrologist K. Beven https://doi.org/10.1002/hyp.10879
120. 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
121. Barriers to progress in distributed hydrological modelling O. Semenova & K. Beven https://doi.org/10.1002/hyp.10434
122. 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
123. 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
124. Historical development of rainfall‐runoff modeling M. Peel & T. McMahon https://doi.org/10.1002/wat2.1471
125. Multi-method global sensitivity analysis of flood inundation models F. Pappenberger et al. https://doi.org/10.1016/j.advwatres.2007.04.009
126. So just why would a modeller choose to be incoherent? K. Beven et al. https://doi.org/10.1016/j.jhydrol.2008.02.007
127. Contrasting Uncertainties in Estimating Floods and Low Flow Extremes H. Meresa & Y. Zhang https://doi.org/10.1007/s11269-021-02809-3
128. 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
129. Incremental model breakdown to assess the multi-hypotheses problem F. Jehn et al. https://doi.org/10.5194/hess-22-4565-2018
130. 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
131. 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
132. How to make advances in hydrological modelling K. Beven https://doi.org/10.2166/nh.2019.134
133. 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
134. 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
135. 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
136. 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
137. 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
138. 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
139. 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
140. 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
141. The critical role of uncertainty in projections of hydrological extremes H. Meresa & R. Romanowicz https://doi.org/10.5194/hess-21-4245-2017
142. OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
143. 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
144. Do we need a Community Hydrological Model? M. Weiler & K. Beven https://doi.org/10.1002/2014WR016731
145. 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
146. 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
147. Evaluating uncertainty in integrated environmental models: A review of concepts and tools L. Matott et al. https://doi.org/10.1029/2008WR007301
148. 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
149. Preferential flows and travel time distributions: defining adequate hypothesis tests for hydrological process models K. Beven https://doi.org/10.1002/hyp.7718
150. 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
151. 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
152. Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model M. Buechel et al. https://doi.org/10.1088/1748-9326/ad4bf6
153. Digital catchment observatories: A platform for engagement and knowledge exchange between catchment scientists, policy makers, and local communities E. Mackay et al. https://doi.org/10.1002/2014WR016824
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156. Progress in socio‐hydrology: a meta‐analysis of challenges and opportunities S. Pande & M. Sivapalan https://doi.org/10.1002/wat2.1193
157. Models of everywhere revisited: A technological perspective G. Blair et al. https://doi.org/10.1016/j.envsoft.2019.104521
158. Evaluation of rainfall–runoff model performance under non-stationary hydroclimatic conditions P. Deb & A. Kiem https://doi.org/10.1080/02626667.2020.1754420
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160. Why can't we do better than Topmodel? W. Buytaert et al. https://doi.org/10.1002/hyp.7125
161. Understanding the role of hydrologic model structures on evapotranspiration-driven sensitivity D. Jayathilake & T. Smith https://doi.org/10.1080/02626667.2020.1754421
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