319 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. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
4. 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
5. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
6. 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
7. Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
8. Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development 10.1175/JHM-D-21-0071.1
9. 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
10. 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
11. DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology G. Coxon et al. 10.5194/gmd-12-2285-2019
12. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
13. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
14. Hydrological Drought Simulations: How Climate and Model Structure Control Parameter Sensitivity L. Melsen & B. Guse 10.1029/2019WR025230
15. Improving Rainfall‐Runoff Model Reliability Under Nonstationarity of Model Parameters: A Hypothesis Testing Based Framework A. Vora & R. Singh 10.1029/2022WR032273
16. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. 10.5194/hess-26-1695-2022
17. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
18. How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
19. 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
20. 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
21. 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
22. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
23. 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
24. Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins K. Schlef et al. 10.1029/2019WR025861
25. 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
26. 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
27. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
28. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous 10.5194/adgeo-45-147-2018
29. 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
30. 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
31. 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
32. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. 10.5194/hess-21-6201-2017
33. 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
34. Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
35. Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty 10.1029/2022WR033249
36. An autoencoder-based snow drought index S. Rasiya Koya et al. 10.1038/s41598-023-47999-5
37. 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
38. 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
39. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
40. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
41. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
42. 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
43. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
44. Technical note: Diagnostic efficiency – specific evaluation of model performance R. Schwemmle et al. 10.5194/hess-25-2187-2021
45. Recent changes in extreme floods across multiple continents W. Berghuijs et al. 10.1088/1748-9326/aa8847
46. Effects of Snow Water Storage on Hydrologic Partitioning Across the Mountainous, Western United States K. Hale et al. 10.1029/2023WR034690
47. A water-energy complementary model for monthly runoff simulation Y. Zou et al. 10.1016/j.jhydrol.2024.131624
48. 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
49. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
50. The Seasonal Nature of Extreme Hydrological Events in the Northeastern United States A. Frei et al. 10.1175/JHM-D-14-0237.1
51. 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
52. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
53. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
54. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. 10.1029/2021WR030185
55. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
56. Most computational hydrology is not reproducible, so is it really science? C. Hutton et al. 10.1002/2016WR019285
57. Ensemble modeling of watershed‐scale hydrologic effects of short‐rotation woody crop production K. Vache et al. 10.1002/bbb.2247
58. Potential effects of landscape change on water supplies in the presence of reservoir storage A. Guswa et al. 10.1002/2016WR019691
59. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
60. Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. 10.1029/2022WR034092
61. Do Downscaled General Circulation Models Reliably Simulate Historical Climatic Conditions? A. Bock et al. 10.1175/EI-D-17-0018.1
62. CREST-VEC: a framework towards more accurate and realistic flood simulation across scales Z. Li et al. 10.5194/gmd-15-6181-2022
63. Insights on expected streamflow response to land-cover restoration P. James Dennedy-Frank & S. Gorelick 10.1016/j.jhydrol.2020.125121
64. 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
65. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
66. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
67. 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
68. 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
69. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. 10.3390/rs13030333
70. 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
71. 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
72. 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
73. Streamflow alteration and habitat ramifications for a threatened fish species in the Central United States K. Juracek et al. 10.1002/rra.3148
74. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
75. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt 10.1029/2022WR032263
76. Hypothesis Testing for the Difference between Two Nash–Sutcliffe Efficiencies for Comparing Hydrological Model Performance D. Liu 10.1061/JHYEFF.HEENG-6035
77. Parameter regionalization of a monthly water balance model for the conterminous United States A. Bock et al. 10.5194/hess-20-2861-2016
78. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
79. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
80. Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling S. Chen et al. 10.1038/s41598-024-77678-y
81. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
82. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. 10.1002/2015WR017498
83. 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
84. 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
85. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
86. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
87. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
88. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
89. 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
90. 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
91. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
92. Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
93. Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments H. Beck et al. 10.1029/2019JD031485
94. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
95. 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
96. 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
97. Role of forcing uncertainty and background model error characterization in snow data assimilation S. Kumar et al. 10.5194/hess-21-2637-2017
98. How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling L. Gu et al. 10.1016/j.jhydrol.2022.128920
99. Upper and lower benchmarks in hydrological modelling J. Seibert et al. 10.1002/hyp.11476
100. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. 10.1029/2020WR029001
101. NAC2H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al. 10.1029/2020WR027097
102. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
103. Modelling surface‐water depression storage in a Prairie Pothole Region L. Hay et al. 10.1002/hyp.11416
104. 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
105. Process‐based interpretation of conceptual hydrological model performance using a multinational catchment set C. Poncelet et al. 10.1002/2016WR019991
106. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
107. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
108. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
109. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. 10.1002/2016WR019430
110. 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
111. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
112. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
113. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
114. Timing the first emergence and disappearance of global water scarcity J. Liu et al. 10.1038/s41467-024-51302-z
115. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
116. 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
117. Scaling, similarity, and the fourth paradigm for hydrology C. Peters-Lidard et al. 10.5194/hess-21-3701-2017
118. Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data J. Lamontagne et al. 10.1029/2020WR027101
119. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri 10.1029/2018WR022675
120. pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms S. Rimer et al. 10.1016/j.envsoft.2023.105635
121. Impact of training data size on the LSTM performances for rainfall–runoff modeling T. Boulmaiz et al. 10.1007/s40808-020-00830-w
122. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
123. 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
124. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
125. Pseudo-Spatially-Distributed Modeling of Water Balance Components in the Free State of Saxony T. Luong et al. 10.3390/hydrology7040084
126. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
127. Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency S. Pool et al. 10.1080/02626667.2018.1552002
128. 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
129. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
130. 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
131. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
132. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
133. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al. 10.5194/hess-28-3597-2024
134. 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
135. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
136. Predictability and selection of hydrologic metrics in riverine ecohydrology K. Eng et al. 10.1086/694912
137. Towards simplification of hydrologic modeling: identification of dominant processes S. Markstrom et al. 10.5194/hess-20-4655-2016
138. 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
139. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
140. Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
141. 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
142. 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
143. 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
144. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton 10.1029/2021WR031575
145. 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
146. 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
147. 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
148. 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
149. 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
150. 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
151. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
152. 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
153. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. 10.5194/hess-26-1507-2022
154. Applications and interpretations of different machine learning models in runoff and sediment discharge simulations J. Miao et al. 10.1016/j.catena.2024.107848
155. Technical note: Do different projections matter for the Budyko framework? R. Nijzink & S. Schymanski 10.5194/hess-26-4575-2022
156. Benchmarking of a Physically Based Hydrologic Model A. Newman et al. 10.1175/JHM-D-16-0284.1
157. A Whittaker Biome‐Based Framework to Account for the Impact of Climate Change on Catchment Behavior A. Deshmukh & R. Singh 10.1029/2018WR023113
158. Snowmelt rate dictates streamflow T. Barnhart et al. 10.1002/2016GL069690
159. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767
160. Can model structure families be inferred from model output? J. Remmers et al. 10.1016/j.envsoft.2020.104817
161. 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
162. The sensitivity of simulated streamflow to individual hydrologic processes across North America J. Mai et al. 10.1038/s41467-022-28010-7
163. The hazards of split-sample validation in hydrological model calibration R. Arsenault et al. 10.1016/j.jhydrol.2018.09.027
164. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
165. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
166. 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
167. 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
168. 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
169. A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction L. Latifoğlu & M. Özger 10.3390/su151914046
170. 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
171. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
172. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
173. 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
174. 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
175. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
176. ADHI: the African Database of Hydrometric Indices (1950–2018) Y. Tramblay et al. 10.5194/essd-13-1547-2021
177. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
178. Towards seamless large‐domain parameter estimation for hydrologic models N. Mizukami et al. 10.1002/2017WR020401
179. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
180. 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
181. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
182. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
183. Reply to Comment by W. Knoben and M. Clark on “The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records” D. de Oliveira & J. Vrugt 10.1029/2023WR036550
184. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
185. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
186. 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
187. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
188. Information content of stream level class data for hydrological model calibration H. van Meerveld et al. 10.5194/hess-21-4895-2017
189. 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
190. Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow B. Gordon et al. 10.1029/2021WR030966
191. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
192. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
193. An Aridity Index‐Based Formulation of Streamflow Components A. Meira Neto et al. 10.1029/2020WR027123
194. A large dataset of fluvial hydraulic and geometry attributes derived from USGS field measurement records S. Erfani et al. 10.1016/j.envsoft.2024.106136
195. 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
196. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
197. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
198. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
199. Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Error Budget Analysis D. Woods et al. 10.1016/j.jhydrol.2023.130212
200. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
201. On the use of streamflow transformations for hydrological model calibration G. Thirel et al. 10.5194/hess-28-4837-2024
202. Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia C. Sezen & M. Šraj 10.1007/s00477-023-02607-w
203. Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive framework B. Gordon et al. 10.1088/1748-9326/ac64b4
204. Confidence intervals of the Kling-Gupta efficiency J. Vrugt & D. de Oliveira 10.1016/j.jhydrol.2022.127968
205. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
206. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
207. Evaluating stochastic rainfall models for hydrological modelling T. Nguyen et al. 10.1016/j.jhydrol.2023.130381
208. Uncertainties in measuring and estimating water‐budget components: Current state of the science S. Levin et al. 10.1002/wat2.1646
209. Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model A. Meyer Oliveira et al. 10.1029/2023WR034875
210. Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al. 10.1029/2023JD038534
211. Hydrological modelling of the Vistula and Odra river basins using SWAT M. Piniewski et al. 10.1080/02626667.2017.1321842
212. Understanding Flood Seasonality and Its Temporal Shifts within the Contiguous United States S. Ye et al. 10.1175/JHM-D-16-0207.1
213. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
214. 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
215. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
216. Spatiotemporal clustering of streamflow extremes and relevance to flood insurance claims: a stochastic investigation for the contiguous USA K. Papoulakos et al. 10.1007/s11069-024-06766-z
217. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
218. How informative are stream level observations in different geographic regions? J. Seibert & M. Vis 10.1002/hyp.10887
219. 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
220. Numerical daemons of hydrological models are summoned by extreme precipitation P. La Follette et al. 10.5194/hess-25-5425-2021
221. Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does S. Krogh et al. 10.5194/hess-26-3393-2022
222. 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
223. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al. 10.1016/j.xinn.2024.100617
224. Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets G. Papacharalampous & H. Tyralis 10.3390/w14101657
225. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn 10.1016/j.jhydrol.2024.130862
226. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
227. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
228. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
229. 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
230. 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
231. 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
232. Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales G. Abeshu et al. 10.1061/JHYEFF.HEENG-6163
233. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
234. 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
235. Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
236. Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation L. Melsen & B. Guse 10.5194/hess-25-1307-2021
237. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
238. Relationships between snowpack, low flows and stream temperature in mountain watersheds of the US west coast G. Boisramé et al. 10.1002/hyp.15157
239. 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
240. Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example Y. Liu et al. 10.5194/hess-26-5341-2022
241. FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications S. Sadeghi Tabas et al. 10.1016/j.envsoft.2023.105854
242. 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
243. Classification of watersheds in the conterminous United States using shape-based time-series clustering and Random Forests M. Yang & F. Olivera 10.1016/j.jhydrol.2023.129409
244. 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
245. Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill A. Wood et al. 10.1175/JHM-D-14-0213.1
246. Predicting dry‐season flows with a monthly rainfall–runoff model: Performance for gauged and ungauged catchments P. Hamel et al. 10.1002/hyp.11298
247. Increasing importance of temperature as a contributor to the spatial extent of streamflow drought M. Brunner et al. 10.1088/1748-9326/abd2f0
248. GeoAPEX-P, A web-based, spatial modeling tool for pesticide related environmental assessment F. Pan et al. 10.1016/j.envsoft.2023.105747
249. 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
250. Comprehensive assessment of baseflow responses to long-term meteorological droughts across the United States S. Lee & H. Ajami 10.1016/j.jhydrol.2023.130256
251. 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
252. 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
253. 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
254. Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling C. Massmann 10.1175/JHM-D-19-0113.1
255. 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
256. 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
257. Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach M. Brunner & E. Gilleland 10.5194/hess-24-3967-2020
258. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179
259. Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy D. Feng et al. 10.1029/2022WR032404
260. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
261. Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change H. Li et al. 10.1016/j.jag.2024.104101
262. Performance of the National Water Model in Iowa Using Independent Observations M. Rojas et al. 10.1111/1752-1688.12820
263. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
264. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
265. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
266. Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent J. Hoch et al. 10.5194/hess-27-1383-2023
267. Estimating river discharge from rainfall satellite data through simple statistical models P. Birocchi et al. 10.1007/s00704-023-04459-4
268. 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
269. Analytical Survey on the Sustainable Advancements in Water and Hydrology Resources with AI Implications for a Resilient Future A. Bhadauria et al. 10.1051/e3sconf/202455201074
270. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al. 10.1016/j.jhydrol.2022.128323
271. Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5) H. Yan et al. 10.1029/2022MS003312
272. Expectile-based hydrological modelling for uncertainty estimation: Life after mean H. Tyralis et al. 10.1016/j.jhydrol.2022.128986
273. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. 10.1016/j.advwatres.2024.104781
274. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
275. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
276. 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
277. Global‐scale regionalization of hydrologic model parameters H. Beck et al. 10.1002/2015WR018247
278. Vegetation optimality explains the convergence of catchments on the Budyko curve R. Nijzink & S. Schymanski 10.5194/hess-26-6289-2022
279. Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture S. Topp et al. 10.1029/2022WR033880
280. Soil drainage modulates climate effects to shape seasonal and mean annual water balances across the southeastern United States Z. Wang et al. 10.1002/hyp.15214
281. Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Effect of Spatial and Temporal Scales D. Woods et al. 10.1016/j.jhydrol.2024.131134
282. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan 10.1002/hyp.15215
283. Water Ages Explain Tradeoffs Between Long‐Term Evapotranspiration and Ecosystem Drought Resilience J. Knighton & W. Berghuijs 10.1029/2023GL103649
284. 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
285. An operational dynamical neuro-forecasting model for hydrological disasters G. de Lima et al. 10.1007/s40808-016-0145-3
286. A Ranking of Hydrological Signatures Based on Their Predictability in Space N. Addor et al. 10.1029/2018WR022606
287. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
288. Insights From Dayflow: A Historical Streamflow Reanalysis Dataset for the Conterminous United States G. Ghimire et al. 10.1029/2022WR032312
289. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
290. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
291. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
292. 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
293. Parameter's Controls of Distributed Catchment Models—How Much Information is in Conventional Catchment Descriptors? R. Merz et al. 10.1029/2019WR026008
294. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
295. 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
296. A two-stage partitioning monthly model and assessment of its performance on runoff modeling C. Deng & W. Wang 10.1016/j.jhydrol.2020.125829
297. The CAMELS data set: catchment attributes and meteorology for large-sample studies N. Addor et al. 10.5194/hess-21-5293-2017
298. Is There a Baseflow Budyko Curve? S. Gnann et al. 10.1029/2018WR024464
299. Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States H. Yan et al. 10.1038/s41597-023-02049-7
300. Curve Number Approach to Estimate Monthly and Annual Direct Runoff A. Guswa et al. 10.1061/(ASCE)HE.1943-5584.0001606
301. Improved Regionalization of the CN Method for Extreme Events at Ungauged Sites across the US T. Neelam et al. 10.1061/JHYEFF.HEENG-6180
302. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
303. Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States A. Newman et al. 10.1175/JHM-D-15-0026.1
304. 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
305. 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
306. Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning S. Chen et al. 10.1016/j.resenv.2024.100177
307. Toward an improved estimation of flood frequency statistics from simulated flows L. Hu et al. 10.1111/jfr3.12891
308. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
309. 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
310. Practice makes the model: A critical review of stormwater green infrastructure modelling practice V. Pons et al. 10.1016/j.watres.2023.119958
311. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
312. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
313. 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
314. Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS M. Shen & T. Chui 10.1016/j.jhydrol.2023.130255
315. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
316. 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
317. 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
318. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al. 10.1002/hyp.14410
319. Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models Y. Choi et al. 10.1016/j.envsoft.2024.106239