168 citations as recorded by crossref.

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2. The sensitivity of simulated streamflow to individual hydrologic processes across North America J. Mai et al. 10.1038/s41467-022-28010-7
3. The hazards of split-sample validation in hydrological model calibration R. Arsenault et al. 10.1016/j.jhydrol.2018.09.027
4. Eleven years of mountain weather, snow, soil moisture and streamflow data from the rain–snow transition zone – the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA S. Godsey et al. 10.5194/essd-10-1207-2018
5. Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
6. Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development 10.1175/JHM-D-21-0071.1
7. Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates S. Saxe et al. 10.5194/hess-25-1529-2021
8. Prediction of hydrographs and flow-duration curves in almost ungauged catchments: Which runoff measurements are most informative for model calibration? S. Pool et al. 10.1016/j.jhydrol.2017.09.037
9. Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability J. Hecht et al. 10.1016/j.scitotenv.2021.151586
10. DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology G. Coxon et al. 10.5194/gmd-12-2285-2019
11. Hydrological Drought Simulations: How Climate and Model Structure Control Parameter Sensitivity L. Melsen & B. Guse 10.1029/2019WR025230
12. Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting G. Zuo et al. 10.1016/j.jhydrol.2020.124776
13. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
14. How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
15. Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales D. Feng et al. 10.1029/2019WR026793
16. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
17. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
18. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
19. ADHI: the African Database of Hydrometric Indices (1950–2018) Y. Tramblay et al. 10.5194/essd-13-1547-2021
20. Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins K. Schlef et al. 10.1029/2019WR025861
21. A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty M. Zink et al. 10.5194/hess-21-1769-2017
22. Precipitation Sensitivity to the Uncertainty of Terrestrial Water Flow in WRF-Hydro: An Ensemble Analysis for Central Europe J. Arnault et al. 10.1175/JHM-D-17-0042.1
23. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous 10.5194/adgeo-45-147-2018
24. Identification of uncertainty sources in quasi-global discharge and inundation simulations using satellite-based precipitation products Z. Wei et al. 10.1016/j.jhydrol.2020.125180
25. Towards seamless large-domain parameter estimation for hydrologic models N. Mizukami et al. 10.1002/2017WR020401
26. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. 10.1111/1752-1688.12964
27. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. 10.5194/hess-21-6201-2017
28. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al. 10.1016/j.jhydrol.2020.124897
29. Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
30. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
31. Value of a Limited Number of Discharge Observations for Improving Regionalization: A Large‐Sample Study Across the United States S. Pool et al. 10.1029/2018WR023855
32. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
33. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
34. Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States A. Van Beusekom et al. 10.1175/JHM-D-21-0111.1
35. Technical note: Diagnostic efficiency – specific evaluation of model performance R. Schwemmle et al. 10.5194/hess-25-2187-2021
36. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
37. Recent changes in extreme floods across multiple continents W. Berghuijs et al. 10.1088/1748-9326/aa8847
38. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al. 10.1016/j.jhydrol.2021.126455
39. The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff G. Ayzel et al. 10.1080/02626667.2020.1762886
40. The Seasonal Nature of Extreme Hydrological Events in the Northeastern United States A. Frei et al. 10.1175/JHM-D-14-0237.1
41. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
42. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
43. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. 10.1029/2021WR030185
44. Most computational hydrology is not reproducible, so is it really science? C. Hutton et al. 10.1002/2016WR019285
45. Information content of stream level class data for hydrological model calibration H. van Meerveld et al. 10.5194/hess-21-4895-2017
46. Ensemble modeling of watershed‐scale hydrologic effects of short‐rotation woody crop production K. Vache et al. 10.1002/bbb.2247
47. Potential effects of landscape change on water supplies in the presence of reservoir storage A. Guswa et al. 10.1002/2016WR019691
48. Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States B. Naz et al. 10.1016/j.jhydrol.2017.11.027
49. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
50. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
51. Do Downscaled General Circulation Models Reliably Simulate Historical Climatic Conditions? A. Bock et al. 10.1175/EI-D-17-0018.1
52. An Aridity Index‐Based Formulation of Streamflow Components A. Meira Neto et al. 10.1029/2020WR027123
53. 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
54. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
55. Insights on expected streamflow response to land-cover restoration P. James Dennedy-Frank & S. Gorelick 10.1016/j.jhydrol.2020.125121
56. Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models M. Brunner et al. 10.5194/hess-25-105-2021
57. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
58. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
59. 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
60. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. 10.3390/rs13030333
61. Implications of a Priori Parameters on Calibration in Conditions of Varying Terrain Characteristics: Case Study of the SAC-SMA Model in Eastern United States W. Chouaib et al. 10.3390/hydrology8020078
62. Hydrological modelling of the Vistula and Odra river basins using SWAT M. Piniewski et al. 10.1080/02626667.2017.1321842
63. Understanding Flood Seasonality and Its Temporal Shifts within the Contiguous United States S. Ye et al. 10.1175/JHM-D-16-0207.1
64. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
65. Streamflow alteration and habitat ramifications for a threatened fish species in the Central United States K. Juracek et al. 10.1002/rra.3148
66. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
67. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. 10.1021/acs.est.0c06783
68. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
69. Parameter regionalization of a monthly water balance model for the conterminous United States A. Bock et al. 10.5194/hess-20-2861-2016
70. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
71. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
72. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
73. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. 10.1002/2015WR017498
74. How informative are stream level observations in different geographic regions? J. Seibert & M. Vis 10.1002/hyp.10887
75. Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements S. Pool & J. Seibert 10.1016/j.jhydrol.2021.126448
76. The Utility of Information Flow in Formulating Discharge Forecast Models: A Case Study From an Arid Snow‐Dominated Catchment C. Tennant et al. 10.1029/2019WR024908
77. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
78. Numerical daemons of hydrological models are summoned by extreme precipitation P. La Follette et al. 10.5194/hess-25-5425-2021
79. Variability patterns of the annual frequency and timing of low streamflow days across the United States and their linkage to regional and large‐scale climate M. Pournasiri Poshtiri et al. 10.1002/hyp.13422
80. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
81. Advances in Quantifying Streamflow Variability Across Continental Scales: 2. Improved Model Regionalization and Prediction Uncertainties Using Hierarchical Bayesian Methods R. Alexander et al. 10.1029/2019WR025037
82. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
83. Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments H. Beck et al. 10.1029/2019JD031485
84. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
85. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
86. Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States C. Huang et al. 10.5194/hess-21-635-2017
87. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States H. Cai et al. 10.1016/j.ejrh.2021.100930
88. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data H. Beck et al. 10.5194/hess-21-589-2017
89. Role of forcing uncertainty and background model error characterization in snow data assimilation S. Kumar et al. 10.5194/hess-21-2637-2017
90. 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
91. Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
92. Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation L. Melsen & B. Guse 10.5194/hess-25-1307-2021
93. 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
94. 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
95. Upper and lower benchmarks in hydrological modelling J. Seibert et al. 10.1002/hyp.11476
96. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. 10.1029/2020WR029001
97. NAC 2 H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al. 10.1029/2020WR027097
98. Modelling surface-water depression storage in a Prairie Pothole Region L. Hay et al. 10.1002/hyp.11416
99. How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA L. Stein et al. 10.1029/2020WR028300
100. Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill A. Wood et al. 10.1175/JHM-D-14-0213.1
101. Predicting dry-season flows with a monthly rainfall-runoff model: Performance for gauged and ungauged catchments P. Hamel et al. 10.1002/hyp.11298
102. Increasing importance of temperature as a contributor to the spatial extent of streamflow drought M. Brunner et al. 10.1088/1748-9326/abd2f0
103. Process-based interpretation of conceptual hydrological model performance using a multinational catchment set C. Poncelet et al. 10.1002/2016WR019991
104. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
105. 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
106. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes P. Dimitriadis et al. 10.3390/hydrology8020059
107. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. 10.5194/hess-25-4947-2021
108. Potential effects of climate change on streamflow for seven watersheds in eastern and central Montana K. Chase et al. 10.1016/j.ejrh.2016.06.001
109. Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches P. Lin et al. 10.1029/2019WR025287
110. Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling C. Massmann 10.1175/JHM-D-19-0113.1
111. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
112. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. 10.1002/2016WR019430
113. Comprehensive comparison of artificial neural networks and long short-term memory networks for rainfall-runoff simulation G. Mao et al. 10.1016/j.pce.2021.103026
114. Progress on water data integration and distribution: a summary of select US Geological Survey data systems D. Blodgett et al. 10.2166/hydro.2015.067
115. 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
116. Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach M. Brunner & E. Gilleland 10.5194/hess-24-3967-2020
117. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
118. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179
119. Performance of the National Water Model in Iowa Using Independent Observations M. Rojas et al. 10.1111/1752-1688.12820
120. Scaling, similarity, and the fourth paradigm for hydrology C. Peters-Lidard et al. 10.5194/hess-21-3701-2017
121. Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data J. Lamontagne et al. 10.1029/2020WR027101
122. Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms H. Tyralis et al. 10.1007/s00521-020-05172-3
123. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri 10.1029/2018WR022675
124. Impact of training data size on the LSTM performances for rainfall–runoff modeling T. Boulmaiz et al. 10.1007/s40808-020-00830-w
125. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
126. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
127. 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
128. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
129. Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al. 10.1007/s11269-021-02770-1
130. Global-scale regionalization of hydrologic model parameters H. Beck et al. 10.1002/2015WR018247
131. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
132. Pseudo-Spatially-Distributed Modeling of Water Balance Components in the Free State of Saxony T. Luong et al. 10.3390/hydrology7040084
133. Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency S. Pool et al. 10.1080/02626667.2018.1552002
134. The Case for an Open Water Balance: Re‐envisioning Network Design and Data Analysis for a Complex, Uncertain World S. Kampf et al. 10.1029/2019WR026699
135. An operational dynamical neuro-forecasting model for hydrological disasters G. de Lima et al. 10.1007/s40808-016-0145-3
136. A Ranking of Hydrological Signatures Based on Their Predictability in Space N. Addor et al. 10.1029/2018WR022606
137. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
138. Examination and comparison of binary metaheuristic wrapper-based input variable selection for local and global climate information-driven one-step monthly streamflow forecasting K. Ren et al. 10.1016/j.jhydrol.2021.126152
139. Parameter's Controls of Distributed Catchment Models—How Much Information is in Conventional Catchment Descriptors? R. Merz et al. 10.1029/2019WR026008
140. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
141. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann 10.1016/j.cageo.2021.104708
142. Predictability and selection of hydrologic metrics in riverine ecohydrology K. Eng et al. 10.1086/694912
143. A two-stage partitioning monthly model and assessment of its performance on runoff modeling C. Deng & W. Wang 10.1016/j.jhydrol.2020.125829
144. Towards simplification of hydrologic modeling: identification of dominant processes S. Markstrom et al. 10.5194/hess-20-4655-2016
145. Understanding the 2011 Upper Missouri River Basin floods in the context of a changing climate A. Badger et al. 10.1016/j.ejrh.2018.08.004
146. The CAMELS data set: catchment attributes and meteorology for large-sample studies N. Addor et al. 10.5194/hess-21-5293-2017
147. Is There a Baseflow Budyko Curve? S. Gnann et al. 10.1029/2018WR024464
148. Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
149. Curve Number Approach to Estimate Monthly and Annual Direct Runoff A. Guswa et al. 10.1061/(ASCE)HE.1943-5584.0001606
150. 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
151. Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States A. Newman et al. 10.1175/JHM-D-15-0026.1
152. Regional variation of flow duration curves in the eastern United States: Process-based analyses of the interaction between climate and landscape properties W. Chouaib et al. 10.1016/j.jhydrol.2018.01.037
153. Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States W. Chouaib et al. 10.1016/j.jhydrol.2018.03.018
154. Twenty-First-Century Climate in CMIP5 Simulations: Implications for Snow and Water Yield across the Contiguous United States V. Mahat et al. 10.1175/JHM-D-16-0098.1
155. Streamflow regime of a lake‐stream system based on long‐term data from a high‐density hydrometric network D. Hudson et al. 10.1002/hyp.14396
156. A Water Balance–Based, Spatiotemporal Evaluation of Terrestrial Evapotranspiration Products across the Contiguous United States E. Carter et al. 10.1175/JHM-D-17-0186.1
157. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al. 10.1007/s00477-021-02057-2
158. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
159. Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil A. Getirana et al. 10.3390/rs12244095
160. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. 10.1016/j.jhydrol.2019.04.070
161. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
162. Key landscape and biotic indicators of watersheds sensitivity to forest disturbance identified using remote sensing and historical hydrography data B. Buma & B. Livneh 10.1088/1748-9326/aa7091
163. Benchmarking of a Physically Based Hydrologic Model A. Newman et al. 10.1175/JHM-D-16-0284.1
164. A Whittaker Biome‐Based Framework to Account for the Impact of Climate Change on Catchment Behavior A. Deshmukh & R. Singh 10.1029/2018WR023113
165. Quantifying uncertainty in simulated streamflow and runoff from a continental-scale monthly water balance model A. Bock et al. 10.1016/j.advwatres.2018.10.005
166. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al. 10.1002/hyp.14410
167. Snowmelt rate dictates streamflow T. Barnhart et al. 10.1002/2016GL069690
168. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767