189 citations as recorded by crossref.

1. Revisiting the Basis of Sensitivity Analysis for Dynamical Earth System Models H. Gupta & S. Razavi 10.1029/2018WR022668
2. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al. 10.1029/2021WR031860
3. Benchmarking and Process Diagnostics of Land Models G. Nearing et al. 10.1175/JHM-D-17-0209.1
4. Regional multi-objective calibration for distributed hydrological modelling: a decision tree based approach M. Pesce et al. 10.5194/piahs-385-65-2024
5. Event‐based classification for global study of river flood generating processes L. Stein et al. 10.1002/hyp.13678
6. Time‐Varying Sensitivity Analysis Reveals Relationships Between Watershed Climate and Variations in Annual Parameter Importance in Regions With Strong Interannual Variability R. Basijokaite & C. Kelleher 10.1029/2020WR028544
7. Typhoon Nina and the August 1975 Flood over Central China L. Yang et al. 10.1175/JHM-D-16-0152.1
8. Advancing catchment hydrology to deal with predictions under change U. Ehret et al. 10.5194/hess-18-649-2014
9. Simulating Runoff Under Changing Climatic Conditions: A Framework for Model Improvement K. Fowler et al. 10.1029/2018WR023989
10. EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. 10.5194/gmd-17-4561-2024
11. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton 10.1029/2021WR031575
12. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
13. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. 10.1016/j.scitotenv.2023.166422
14. Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics S. Zhu et al. 10.1016/j.jhydrol.2024.131586
15. On the ability of large-scale hydrological models to simulate land use and land cover change impacts in Amazonian basins D. Rodriguez & J. Tomasella 10.1080/02626667.2015.1051979
16. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
17. Robustness of a parsimonious subsurface drainage model at the French national scale A. Jeantet et al. 10.5194/hess-25-5447-2021
18. Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al. 10.1029/2023JD038534
19. Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall‐runoff models K. Fowler et al. 10.1002/2015WR018068
20. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation A. Bahremand 10.5194/hess-20-1433-2016
21. Challenges of Operational River Forecasting T. Pagano et al. 10.1175/JHM-D-13-0188.1
22. Using R in hydrology: a review of recent developments and future directions L. Slater et al. 10.5194/hess-23-2939-2019
23. A review of the current state of process-based and data-driven modelling: guidelines for Lake Erie managers and watershed modellers A. Neumann et al. 10.1139/er-2020-0070
24. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
25. Accounting for the influence of vegetation and landscape improves model transferability in a tropical savannah region H. Gao et al. 10.1002/2016WR019574
26. How does wildfire and climate variability affect streamflow in forested catchments? A regional study in eastern Australia D. Guo et al. 10.1016/j.jhydrol.2023.129979
27. Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions G. Nearing et al. 10.1175/JHM-D-15-0063.1
28. Seasonal streamflow forecasting by conditioning climatology with precipitation indices L. Crochemore et al. 10.5194/hess-21-1573-2017
29. A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
30. Quantifying Positive and Negative Human-Modified Droughts in the Anthropocene: Illustration with Two Iranian Catchments E. Kakaei et al. 10.3390/w11050884
31. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
32. Towards seamless large‐domain parameter estimation for hydrologic models N. Mizukami et al. 10.1002/2017WR020401
33. Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters J. Romero-Cuellar et al. 10.3390/w14081261
34. Spatial Combination Modeling Framework of Saturation-Excess and Infiltration-Excess Runoff for Semihumid Watersheds P. Huang et al. 10.1155/2016/5173984
35. Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
36. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case I. Pechlivanidis & B. Arheimer 10.5194/hess-19-4559-2015
37. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. 10.5194/hess-21-6201-2017
38. When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. 10.1080/02626667.2021.1923720
39. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance A. Newman et al. 10.5194/hess-19-209-2015
40. Diagnosis of insidious data disasters J. Lundquist et al. 10.1002/2014WR016585
41. Uncertainty in hydrological signatures for gauged and ungauged catchments I. Westerberg et al. 10.1002/2015WR017635
42. MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models R. Schweppe et al. 10.5194/gmd-15-859-2022
43. Ensemble Skill Gains Obtained From the Multi‐Physics Versus Multi‐Model Approaches for Continental‐Scale Hydrological Simulations W. Fei et al. 10.1029/2020WR028846
44. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
45. A national flood awareness system for ungauged catchments in complex topography: The case of development, communication and evaluation in New Zealand C. Cattoën et al. 10.1111/jfr3.12864
46. A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models F. Zheng et al. 10.1029/2022WR033703
47. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
48. On the need to test hydrological models under changing conditions G. Thirel et al. 10.1080/02626667.2015.1050027
49. Physically consistent conceptual rainfall–runoff model for urbanized catchments M. Saadi et al. 10.1016/j.jhydrol.2021.126394
50. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
51. FLOOD INUNDATION SIMULATION USING A LARGE-SCALE RAINFALL-RUNOFF-INUNDATION MODEL - EFFECTS OF TOPOGRAPHIC DATA CORRECTIONS - T. SAYAMA et al. 10.2208/jscejhe.78.2_I_565
52. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
53. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments P. Nicolle et al. 10.5194/hess-18-2829-2014
54. Étude de la sensibilité des paramètres d'un modèle «rural» sur des bassins versants urbanisés M. Saadi et al. 10.1051/lhb/2019013
55. Archetypes and Controls of Riverine Nutrient Export Across German Catchments P. Ebeling et al. 10.1029/2020WR028134
56. A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations G. Coxon et al. 10.1002/2014WR016532
57. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al. 10.5194/hess-23-2601-2019
58. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater T. Olarinoye et al. 10.1038/s41597-019-0346-5
59. Seasonal streamflow forecasting in South America’s largest rivers I. Petry et al. 10.1016/j.ejrh.2023.101487
60. Historical and future changes in global flood magnitude – evidence from a model–observation investigation H. Do et al. 10.5194/hess-24-1543-2020
61. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al. 10.5194/hess-23-5089-2019
62. Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models C. Prieto et al. 10.1029/2020WR028338
63. Using Natural Experiments and Counterfactuals for Causal Assessment: River Salinity and the Ganges Water Agreement G. Penny et al. 10.1029/2019WR026166
64. Performance assessment of methods to estimate initial hydrologic conditions for event-based rainfall-runoff modelling V. Manikanta & N. Umamahesh 10.2166/wcc.2023.043
65. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. 10.1002/hyp.13808
66. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al. 10.5194/hess-28-611-2024
67. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
68. Is Catchment Classification Possible by Means of Multiple Model Structures? A Case Study Based on 99 Catchments in Germany R. Ley et al. 10.3390/hydrology3020022
69. On the (im)possible validation of hydrogeological models V. Andréassian 10.5802/crgeos.142
70. Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe C. Donnelly et al. 10.1080/02626667.2015.1027710
71. Flood triggering in Switzerland: the role of daily to monthly preceding precipitation P. Froidevaux et al. 10.5194/hess-19-3903-2015
72. Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach F. Garavaglia et al. 10.5194/hess-21-3937-2017
73. Hydrology research articles are becoming more topically diverse M. Rahman et al. 10.1016/j.jhydrol.2022.128551
74. Exploding the myths: An introduction to artificial neural networks for prediction and forecasting H. Maier et al. 10.1016/j.envsoft.2023.105776
75. Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia S. Clark et al. 10.5194/hess-28-1191-2024
76. A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies S. Lindersson et al. 10.1002/wat2.1424
77. Panta Rhei 2013–2015: global perspectives on hydrology, society and change H. McMillan et al. 10.1080/02626667.2016.1159308
78. Toward seamless hydrologic predictions across spatial scales L. Samaniego et al. 10.5194/hess-21-4323-2017
79. Integrating remote sensing data in optimization of a national water resources model to improve the spatial pattern performance of evapotranspiration M. Soltani et al. 10.1016/j.jhydrol.2021.127026
80. Crash tests for forward-looking flood control in the city of Zürich (Switzerland) M. Zappa et al. 10.5194/piahs-370-235-2015
81. Identification of factors influencing hydrologic model performance using a top‐down approach in a large number of U.S. catchments C. Massmann 10.1002/hyp.13566
82. The hazards of split-sample validation in hydrological model calibration R. Arsenault et al. 10.1016/j.jhydrol.2018.09.027
83. Replication of ecologically relevant hydrological indicators following a modified covariance approach to hydrological model parameterization A. Visser-Quinn et al. 10.5194/hess-23-3279-2019
84. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
85. Improvement and evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach F. Quintero et al. 10.1016/j.jhydrol.2020.124686
86. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767
87. A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China S. Gu et al. 10.1109/TGRS.2024.3349548
88. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
89. Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent I. Pechlivanidis et al. 10.3390/w8050177
90. Process consistency in models: The importance of system signatures, expert knowledge, and process complexity M. Hrachowitz et al. 10.1002/2014WR015484
91. A wavelet-based approach to streamflow event identification and modeled timing error evaluation E. Towler & J. McCreight 10.5194/hess-25-2599-2021
92. CHOSEN: A synthesis of hydrometeorological data from intensively monitored catchments and comparative analysis of hydrologic extremes L. Zhang et al. 10.1002/hyp.14429
93. Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models? A. Piotrowski et al. 10.1080/02626667.2016.1234712
94. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell 10.1016/j.jhydrol.2021.127287
95. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. 10.1016/j.ejrh.2023.101478
96. Recession constants are non-stationary: Impacts of multi-annual drought on catchment recession behaviour and storage dynamics L. Trotter et al. 10.1016/j.jhydrol.2024.130707
97. Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds T. Mathevet et al. 10.5802/crgeos.189
98. Using hydrological and climatic catchment clusters to explore drivers of catchment behavior F. Jehn et al. 10.5194/hess-24-1081-2020
99. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds R. Arsenault et al. 10.1038/s41597-020-00583-2
100. A Systematic Evaluation of Noah‐MP in Simulating Land‐Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States N. Ma et al. 10.1002/2017JD027597
101. HESS Opinions: The complementary merits of competing modelling philosophies in hydrology M. Hrachowitz & M. Clark 10.5194/hess-21-3953-2017
102. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
103. Characteristics and controls of variability in soil moisture and groundwater in a headwater catchment H. McMillan & M. Srinivasan 10.5194/hess-19-1767-2015
104. Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments* H. Beck et al. 10.1175/JHM-D-14-0155.1
105. The CAMELS data set: catchment attributes and meteorology for large-sample studies N. Addor et al. 10.5194/hess-21-5293-2017
106. Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set L. Santos et al. 10.1080/02626667.2022.2035388
107. Global‐scale regionalization of hydrologic model parameters H. Beck et al. 10.1002/2015WR018247
108. Diagnostic calibration and cross‐catchment transferability of a simple process‐consistent hydrologic model T. Smith et al. 10.1002/hyp.10955
109. Event-based prediction of stream turbidity using a combined cluster analysis and classification tree approach A. Mather & R. Johnson 10.1016/j.jhydrol.2015.10.032
110. Computational approaches for annual maximum river flow series H. Tiwari et al. 10.1016/j.asej.2015.07.016
111. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al. 10.1029/2021WR030186
112. Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. 10.5194/hess-28-1539-2024
113. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
114. Benchmarking of a Physically Based Hydrologic Model A. Newman et al. 10.1175/JHM-D-16-0284.1
115. A process‐based approach to attribution of historical streamflow decline in a data‐scarce and human‐dominated watershed G. Penny et al. 10.1002/hyp.13707
116. Hydrological modelling of the Vistula and Odra river basins using SWAT M. Piniewski et al. 10.1080/02626667.2017.1321842
117. Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
118. The Quantile Solidarity approach for the parsimonious regionalization of flow duration curves C. Poncelet et al. 10.1080/02626667.2017.1335399
119. Accounting for dependencies in regionalized signatures for predictions in ungauged catchments S. Almeida et al. 10.5194/hess-20-887-2016
120. Are Model Transferability And Complexity Antithetical? Insights From Validation of a Variable‐Complexity Empirical Snow Model in Space and Time A. Lute & C. Luce 10.1002/2017WR020752
121. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. 10.1029/2019WR026987
122. Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins O. Rakovec et al. 10.1175/JHM-D-15-0054.1
123. Scaling, similarity, and the fourth paradigm for hydrology C. Peters-Lidard et al. 10.5194/hess-21-3701-2017
124. Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches B. Anderson et al. 10.1029/2021WR030742
125. Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning C. Talsma et al. 10.1029/2021EA002086
126. Global evaluation of runoff from 10 state-of-the-art hydrological models H. Beck et al. 10.5194/hess-21-2881-2017
127. Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. 10.1002/hyp.14288
128. Identifying the connective strength between model parameters and performance criteria B. Guse et al. 10.5194/hess-21-5663-2017
129. Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model R. Lane et al. 10.1029/2020WR028393
130. Parameter's Controls of Distributed Catchment Models—How Much Information is in Conventional Catchment Descriptors? R. Merz et al. 10.1029/2019WR026008
131. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
132. Heteroscedastic and symmetric efficiency for hydrological model evaluation criteria C. Zhan et al. 10.2166/nh.2019.121
133. Understanding the impacts of catchment characteristics on the shape of the storage capacity curve and its influence on flood flows H. Gao et al. 10.2166/nh.2017.245
134. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
135. Temporal interpolation of groundwater level hydrographs for regional drought analysis using mixed models B. Marchant et al. 10.1007/s10040-022-02528-y
136. Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models C. Thébault et al. 10.1080/02626667.2023.2234893
137. Virtual laboratories: new opportunities for collaborative water science S. Ceola et al. 10.5194/hess-19-2101-2015
138. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al. 10.5194/hess-22-5817-2018
139. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
140. Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method L. Ye et al. 10.1002/hyp.10799
141. On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation D. Guo et al. 10.1029/2019WR026752
142. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
143. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
144. Global-scale characterization of streamflow extremes S. Kuntla et al. 10.1016/j.jhydrol.2022.128668
145. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. 10.1002/2016WR019430
146. All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling A. Pedersen et al. 10.5194/hess-26-5879-2022
147. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
148. Bushfire‐Induced Water Balance Changes Detected by a Modified Paired Catchment Method Z. Xu et al. 10.1029/2021WR031013
149. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
150. On the visual detection of non-natural records in streamflow time series: challenges and impacts L. Strohmenger et al. 10.5194/hess-27-3375-2023
151. Improving the theoretical underpinnings of process‐based hydrologic models M. Clark et al. 10.1002/2015WR017910
152. Estimating a-priori kinematic wave model parameters based on regionalization for flash flood forecasting in the Conterminous United States H. Vergara et al. 10.1016/j.jhydrol.2016.06.011
153. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
154. Long‐Term Nitrate Trajectories Vary by Season in Western European Catchments P. Ebeling et al. 10.1029/2021GB007050
155. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
156. Pitfalls of space-time trading when parametrizing a land use dependent hydrological model L. Oudin & M. Lalonde 10.5802/crgeos.146
157. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
158. Land Use Change Increases Streamflow Across the Arc of Deforestation in Brazil M. Levy et al. 10.1002/2017GL076526
159. Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments B. Guse et al. 10.1016/j.jhydrol.2018.12.050
160. Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain R. Lane et al. 10.5194/hess-23-4011-2019
161. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation B. Arheimer et al. 10.5194/hess-24-535-2020
162. Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty E. Moges et al. 10.1002/2015WR018266
163. Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States E. Towler et al. 10.5194/hess-27-1809-2023
164. On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization H. Maier et al. 10.1016/j.envsoft.2023.105779
165. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
166. The Impacts of Native Forests and Forest Plantations on Water Supply in Chile C. Alvarez-Garreton et al. 10.3390/f10060473
167. How should a rainfall‐runoff model be parameterized in an almost ungauged catchment? A methodology tested on 609 catchments C. Rojas‐Serna et al. 10.1002/2015WR018549
168. What can we learn from the projections of changes of flow patterns? Results from Polish case studies M. Piniewski et al. 10.1007/s11600-017-0061-6
169. The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long‐Term Streamflow Components A. Ballarin et al. 10.1029/2022WR032413
170. Evaluating a finer resolution global hydrological model’s simulation of discharge in four West-African river basins T. Babalola et al. 10.1007/s40808-020-00948-x
171. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. 10.5194/hess-25-3105-2021
172. Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations S. Yatheendradas et al. 10.1007/s10596-018-9804-y
173. Moving sociohydrology forward: a synthesis across studies T. Troy et al. 10.5194/hess-19-3667-2015
174. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. 10.1002/2015WR017498
175. The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. 10.1016/j.jhydrol.2023.129666
176. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. 10.1016/j.jhydrol.2020.124698
177. Looking beyond general metrics for model comparison – lessons from an international model intercomparison study T. de Boer-Euser et al. 10.5194/hess-21-423-2017
178. Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US K. Fang & C. Shen 10.1002/2016WR020283
179. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
180. Why do we have so many different hydrological models? A review based on the case of Switzerland P. Horton et al. 10.1002/wat2.1574
181. Using paired catchments to quantify the human influence on hydrological droughts A. Van Loon et al. 10.5194/hess-23-1725-2019
182. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
183. Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. 10.1029/2019WR025001
184. Event-based hydrological modeling for detecting dominant hydrological process and suitable model strategy for semi-arid catchments P. Huang et al. 10.1016/j.jhydrol.2016.09.001
185. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
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