339 citations as recorded by crossref.

1. Probabilistic Sensitivity Analysis With Dependent Variables: Covariance‐Based Decomposition of Hydrologic Models Y. Gao et al. 10.1029/2022WR032834
2. Building cyberinfrastructure for the reuse and reproducibility of complex hydrologic modeling studies I. Maghami et al. 10.1016/j.envsoft.2023.105689
3. Analyzing Multi-Year Nitrate Concentration Evolution in Alabama Aquatic Systems Using a Machine Learning Model B. KarimiDermani et al. 10.3390/environments12030075
4. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
5. Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States K. Khand & G. Senay 10.1016/j.mlwa.2024.100551
6. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
7. 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
8. Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
9. Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development 10.1175/JHM-D-21-0071.1
10. Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna 10.1016/j.hydroa.2024.100198
11. 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
12. 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
13. DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology G. Coxon et al. 10.5194/gmd-12-2285-2019
14. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
15. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
16. Hydrological Drought Simulations: How Climate and Model Structure Control Parameter Sensitivity L. Melsen & B. Guse 10.1029/2019WR025230
17. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al. 10.1016/j.jhydrol.2025.132766
18. Improving Rainfall‐Runoff Model Reliability Under Nonstationarity of Model Parameters: A Hypothesis Testing Based Framework A. Vora & R. Singh 10.1029/2022WR032273
19. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. 10.5194/hess-26-1695-2022
20. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
21. How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
22. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
23. 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
24. 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
25. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
26. Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season K. Fang et al. 10.3389/frwa.2024.1456647
27. Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins K. Schlef et al. 10.1029/2019WR025861
28. 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
29. 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
30. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
31. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous 10.5194/adgeo-45-147-2018
32. 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
33. 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
34. 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
35. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. 10.5194/hess-21-6201-2017
36. 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
37. Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty 10.1029/2022WR033249
38. An autoencoder-based snow drought index S. Rasiya Koya et al. 10.1038/s41598-023-47999-5
39. Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning Y. Fang et al. 10.3390/rs14184609
40. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
41. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
42. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
43. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
44. 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
45. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
46. Technical note: Diagnostic efficiency – specific evaluation of model performance R. Schwemmle et al. 10.5194/hess-25-2187-2021
47. Recent changes in extreme floods across multiple continents W. Berghuijs et al. 10.1088/1748-9326/aa8847
48. Effects of Snow Water Storage on Hydrologic Partitioning Across the Mountainous, Western United States K. Hale et al. 10.1029/2023WR034690
49. A water-energy complementary model for monthly runoff simulation Y. Zou et al. 10.1016/j.jhydrol.2024.131624
50. 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
51. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
52. The Seasonal Nature of Extreme Hydrological Events in the Northeastern United States A. Frei et al. 10.1175/JHM-D-14-0237.1
53. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. 10.1016/j.jhydrol.2023.130574
54. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
55. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
56. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. 10.1029/2021WR030185
57. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
58. Most computational hydrology is not reproducible, so is it really science? C. Hutton et al. 10.1002/2016WR019285
59. Ensemble modeling of watershed‐scale hydrologic effects of short‐rotation woody crop production K. Vache et al. 10.1002/bbb.2247
60. Potential effects of landscape change on water supplies in the presence of reservoir storage A. Guswa et al. 10.1002/2016WR019691
61. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
62. Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. 10.1029/2022WR034092
63. Do Downscaled General Circulation Models Reliably Simulate Historical Climatic Conditions? A. Bock et al. 10.1175/EI-D-17-0018.1
64. CREST-VEC: a framework towards more accurate and realistic flood simulation across scales Z. Li et al. 10.5194/gmd-15-6181-2022
65. Insights on expected streamflow response to land-cover restoration P. James Dennedy-Frank & S. Gorelick 10.1016/j.jhydrol.2020.125121
66. 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
67. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
68. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
69. Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records M. Vlah et al. 10.5194/hess-28-545-2024
70. 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
71. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. 10.3390/rs13030333
72. A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion A. Sun et al. 10.5194/hess-26-5163-2022
73. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
74. 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
75. Streamflow alteration and habitat ramifications for a threatened fish species in the Central United States K. Juracek et al. 10.1002/rra.3148
76. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
77. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt 10.1029/2022WR032263
78. Hypothesis Testing for the Difference between Two Nash–Sutcliffe Efficiencies for Comparing Hydrological Model Performance D. Liu 10.1061/JHYEFF.HEENG-6035
79. Parameter regionalization of a monthly water balance model for the conterminous United States A. Bock et al. 10.5194/hess-20-2861-2016
80. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
81. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
82. Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling S. Chen et al. 10.1038/s41598-024-77678-y
83. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
84. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. 10.1002/2015WR017498
85. 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
86. Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach L. Zhang et al. 10.1016/j.jhydrol.2024.130643
87. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
88. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
89. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
90. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
91. 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
92. 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
93. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
94. Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
95. Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments H. Beck et al. 10.1029/2019JD031485
96. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
97. 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
98. On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking J. Vrugt et al. 10.1016/j.jhydrol.2022.128542
99. Role of forcing uncertainty and background model error characterization in snow data assimilation S. Kumar et al. 10.5194/hess-21-2637-2017
100. How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling L. Gu et al. 10.1016/j.jhydrol.2022.128920
101. Upper and lower benchmarks in hydrological modelling J. Seibert et al. 10.1002/hyp.11476
102. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. 10.1029/2020WR029001
103. NAC2H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al. 10.1029/2020WR027097
104. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
105. Modelling surface‐water depression storage in a Prairie Pothole Region L. Hay et al. 10.1002/hyp.11416
106. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
107. Process‐based interpretation of conceptual hydrological model performance using a multinational catchment set C. Poncelet et al. 10.1002/2016WR019991
108. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
109. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
110. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
111. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. 10.1002/2016WR019430
112. 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
113. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
114. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
115. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
116. Timing the first emergence and disappearance of global water scarcity J. Liu et al. 10.1038/s41467-024-51302-z
117. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
118. Hydrologic evaluation of the global precipitation measurement mission over the U.S.: Flood peak discharge and duration D. Woods et al. 10.1016/j.jhydrol.2023.129124
119. Scaling, similarity, and the fourth paradigm for hydrology C. Peters-Lidard et al. 10.5194/hess-21-3701-2017
120. Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data J. Lamontagne et al. 10.1029/2020WR027101
121. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri 10.1029/2018WR022675
122. pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms S. Rimer et al. 10.1016/j.envsoft.2023.105635
123. Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions? F. Boodoo et al. 10.1007/s40808-025-02316-z
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. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
126. 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
127. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
128. Pseudo-Spatially-Distributed Modeling of Water Balance Components in the Free State of Saxony T. Luong et al. 10.3390/hydrology7040084
129. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
130. Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency S. Pool et al. 10.1080/02626667.2018.1552002
131. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
132. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. 10.1016/j.jhydrol.2024.132440
133. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
134. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
135. 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
136. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
137. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
138. Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios M. He et al. 10.1038/s41598-025-89837-w
139. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al. 10.5194/hess-28-3597-2024
140. 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
141. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
142. Predictability and selection of hydrologic metrics in riverine ecohydrology K. Eng et al. 10.1086/694912
143. Towards simplification of hydrologic modeling: identification of dominant processes S. Markstrom et al. 10.5194/hess-20-4655-2016
144. 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
145. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
146. Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
147. 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
148. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
149. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
150. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton 10.1029/2021WR031575
151. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al. 10.1016/j.jhydrol.2023.129176
152. 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
153. 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
154. 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
155. 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
156. Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone? A. Todorović et al. 10.1016/j.jhydrol.2024.130829
157. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
158. Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model J. Li et al. 10.1016/j.jhydrol.2024.130623
159. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. 10.5194/hess-26-1507-2022
160. Applications and interpretations of different machine learning models in runoff and sediment discharge simulations J. Miao et al. 10.1016/j.catena.2024.107848
161. Technical note: Do different projections matter for the Budyko framework? R. Nijzink & S. Schymanski 10.5194/hess-26-4575-2022
162. Benchmarking of a Physically Based Hydrologic Model A. Newman et al. 10.1175/JHM-D-16-0284.1
163. A Whittaker Biome‐Based Framework to Account for the Impact of Climate Change on Catchment Behavior A. Deshmukh & R. Singh 10.1029/2018WR023113
164. Snowmelt rate dictates streamflow T. Barnhart et al. 10.1002/2016GL069690
165. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767
166. Can model structure families be inferred from model output? J. Remmers et al. 10.1016/j.envsoft.2020.104817
167. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
168. Exploring the influence of training sampling strategies on time-series deep learning model in hydrology S. Yoon & K. Ahn 10.1016/j.jhydrol.2025.132774
169. Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al. 10.1038/s43247-024-01891-w
170. The sensitivity of simulated streamflow to individual hydrologic processes across North America J. Mai et al. 10.1038/s41467-022-28010-7
171. The hazards of split-sample validation in hydrological model calibration R. Arsenault et al. 10.1016/j.jhydrol.2018.09.027
172. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
173. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
174. 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
175. 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
176. Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method H. Cai et al. 10.1016/j.jhydrol.2022.128495
177. A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction L. Latifoğlu & M. Özger 10.3390/su151914046
178. 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
179. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
180. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
181. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
182. Dive into transfer-learning for daily rainfall-runoff modeling in data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2025.133063
183. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
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