93 citations as recorded by crossref.

1. How intensive revegetation affects runoff in semiarid watersheds: A case study based on an integrated modelling framework Y. Chang & H. Lei https://doi.org/10.1002/eco.2451
2. Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria M. Bertola et al. https://doi.org/10.1016/j.jhydrol.2019.123919
3. Identification of factors influencing hydrologic model performance using a top‐down approach in a large number of U.S. catchments C. Massmann https://doi.org/10.1002/hyp.13566
4. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. https://doi.org/10.1016/j.ejrh.2023.101478
5. Regionalization methods for PUB: a comprehensive review of progress after the PUB decade X. Yang et al. https://doi.org/10.2166/nh.2023.027
6. A comparative assessment of cluster-based regionalization approaches using conceptual rainfall–runoff models J. Ougahi & J. Rowan https://doi.org/10.1038/s41598-026-49424-z
7. Ordinary kriging as a tool to estimate historical daily streamflow records W. Farmer https://doi.org/10.5194/hess-20-2721-2016
8. Analytical estimation of annual runoff distribution in ungauged seasonally dry basins based on a first order Taylor expansion of the Fu's equation D. Caracciolo et al. https://doi.org/10.1016/j.advwatres.2017.09.019
9. Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures A. Bajracharya et al. https://doi.org/10.3390/w12040961
10. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. https://doi.org/10.1029/2019WR026987
11. Runoff prediction in ungauged catchments in Norway: comparison of regionalization approaches X. Yang et al. https://doi.org/10.2166/nh.2017.071
12. Complexity of hydrologic basins: A chaotic dynamics perspective M. Ombadi et al. https://doi.org/10.1016/j.jhydrol.2021.126222
13. A novel integrated modelling framework to assess the impacts of climate and socio-economic drivers on land use and water quality M. Zessner et al. https://doi.org/10.1016/j.scitotenv.2016.11.092
14. Predicting hydrological signatures in ungauged catchments using spatial interpolation, index model, and rainfall–runoff modelling Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2014.06.032
15. Factors controlling alterations in the performance of a runoff model in changing climate conditions P. Sleziak et al. https://doi.org/10.2478/johh-2018-0031
16. Optimizing hydrological consistency by incorporating hydrological signatures into model calibration objectives M. Shafii & B. Tolson https://doi.org/10.1002/2014WR016520
17. Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al. https://doi.org/10.5194/hess-29-4761-2025
18. 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. https://doi.org/10.1016/j.jhydrol.2019.04.070
19. Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions X. Yang et al. https://doi.org/10.1016/j.jhydrol.2019.124357
20. Uncertainty contributions to low-flow projections in Austria J. Parajka et al. https://doi.org/10.5194/hess-20-2085-2016
21. The role of land use and morphology representation in the setup and calibration of the conceptual TUW model M. Kubáň et al. https://doi.org/10.1088/1755-1315/906/1/012050
22. Prediction of Monthly Flow Regimes Using the Distance-Based Method Nested with Model Swapping M. Qamar et al. https://doi.org/10.1007/s11269-024-03923-8
23. The effect of the snow weighting on the temporal stability of hydrologic model efficiency and parameters P. Sleziak et al. https://doi.org/10.1016/j.jhydrol.2020.124639
24. Integrating Meteorological Forcing from Ground Observations and MSWX Dataset for Streamflow Prediction under Multiple Parameterization Scenarios H. Hafizi & A. Sorman https://doi.org/10.3390/w14172721
25. A Framework for Assessing Concentration‐Discharge Catchment Behavior From Low‐Frequency Water Quality Data I. Pohle et al. https://doi.org/10.1029/2021WR029692
26. Hydrological modeling of freshwater discharge into Hudson Bay using HYPE T. Stadnyk et al. https://doi.org/10.1525/elementa.439
27. Improved Grunsky method for streamflow prediction in ungauged catchments B. Marchezepe et al. https://doi.org/10.1080/02626667.2024.2446278
28. Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin H. Hafizi & A. Sorman https://doi.org/10.3390/atmos13010143
29. A new few-shot learning model for runoff prediction: Demonstration in two data scarce regions M. Yang et al. https://doi.org/10.1016/j.envsoft.2023.105659
30. Streamflow prediction in ungauged catchments by using the Grunsky method B. Marchezepe et al. https://doi.org/10.1016/j.iswcr.2023.06.004
31. Future flood and flood-on-drought peaks and volumes in the Northern Apennines through bias-correction and rainfall-runoff transformation of hourly climate forcings M. Neri et al. https://doi.org/10.1016/j.ejrh.2026.103864
32. Evaluation of regionalization parameters for stream flow prediction in ungauged catchments of Rift Valley Lakes Basin, Ethiopia Z. Mada et al. https://doi.org/10.1007/s40808-024-01977-6
33. Improvement of physics-based and data-driven model simulations based on multi-source soil moisture datasets X. Liang et al. https://doi.org/10.1016/j.ejrh.2025.102557
34. Modeling the Ecological Impact of Phosphorus in Catchments with Multiple Environmental Stressors M. Glendell et al. https://doi.org/10.2134/jeq2019.05.0195
35. Stepwise prediction of runoff using proxy data in a small agricultural catchment B. Széles et al. https://doi.org/10.2478/johh-2020-0029
36. Streamflow characteristics from modeled runoff time series – importance of calibration criteria selection S. Pool et al. https://doi.org/10.5194/hess-21-5443-2017
37. Impacts of post-Soviet land-use transformation on sediment dynamics in the Western Caucasus A. Tsyplenkov et al. https://doi.org/10.1016/j.jhydrol.2025.132965
38. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri https://doi.org/10.1029/2018WR022675
39. Developing stage-specific drought vulnerability curves for maize: The case study of the Po River basin B. Monteleone et al. https://doi.org/10.1016/j.agwat.2022.107713
40. Regional flood hydrology in a semi-arid catchment using a GLS regression model L. Mediero & T. Kjeldsen https://doi.org/10.1016/j.jhydrol.2014.04.007
41. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. https://doi.org/10.1017/eds.2024.14
42. Unravelling abiotic and biotic controls on the seasonal water balance using data-driven dimensionless diagnostics S. Seibert et al. https://doi.org/10.5194/hess-21-2817-2017
43. Why has catchment evaporation increased in the past 40 years? A data-based study in Austria D. Duethmann & G. Blöschl https://doi.org/10.5194/hess-22-5143-2018
44. TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks M. Müller & S. Thompson https://doi.org/10.5194/hess-19-2925-2015
45. Daily flow duration curve model for ungauged intermittent subbasins of gauged rivers H. Burgan & H. Aksoy https://doi.org/10.1016/j.jhydrol.2021.127249
46. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case I. Pechlivanidis & B. Arheimer https://doi.org/10.5194/hess-19-4559-2015
47. Assessing small hydro/solar power complementarity in ungauged mountainous areas: A crash test study for hydrological prediction methods B. François et al. https://doi.org/10.1016/j.energy.2017.03.090
48. Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches R. Dasgupta et al. https://doi.org/10.1016/j.hydres.2023.11.001
49. Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile P. Acuña & A. Pizarro https://doi.org/10.1016/j.jhydrol.2023.130118
50. The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites R. Tong et al. https://doi.org/10.5194/hess-26-1779-2022
51. Quantitative Classification of Spring Discharge Patterns: A Cluster Analysis Approach M. Seelig et al. https://doi.org/10.1002/hyp.15326
52. The role of station density for predicting daily runoff by top-kriging interpolation in Austria J. Parajka et al. https://doi.org/10.1515/johh-2015-0024
53. Variability in flow and tracer-based performance metric sensitivities reveal regional differences in dominant hydrological processes across the Athabasca River basin T. Holmes et al. https://doi.org/10.1016/j.ejrh.2022.101088
54. Improved Process Representation in the Simulation of the Hydrology of a Meso-Scale Semi-Arid Catchment A. Saraiva Okello et al. https://doi.org/10.3390/w10111549
55. The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model R. Tong et al. https://doi.org/10.5194/hess-25-1389-2021
56. From threat to opportunity: Hydrologic uncertainty regionalization across large domains S. Pokorny et al. https://doi.org/10.1016/j.ejrh.2024.101819
57. Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration S. Gharari et al. https://doi.org/10.5194/hess-18-4839-2014
58. Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case) R. Chérif & E. Gargouri-Ellouze https://doi.org/10.1038/s41598-023-38608-6
59. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. https://doi.org/10.1029/2021WR030437
60. Comparative assessment of predictions in ungauged basins – Part 2: Flood and low flow studies J. Salinas et al. https://doi.org/10.5194/hess-17-2637-2013
61. Transferability of regionalization methods under changing climate X. Yang et al. https://doi.org/10.1016/j.jhydrol.2018.10.030
62. Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review Y. Guo et al. https://doi.org/10.1002/wat2.1487
63. Nonparametric catchment clustering using the data depth function S. Singh et al. https://doi.org/10.1080/02626667.2016.1168927
64. Assessing Uncertainty in Multi-Source Precipitation for a Semi-Arid Mediterranean Catchment Using SWAT I. Gharnouki et al. https://doi.org/10.1007/s41748-026-01153-z
65. Transferring measured discharge time series: Large‐scale comparison of Top‐kriging to geomorphology‐based inverse modeling A. de Lavenne et al. https://doi.org/10.1002/2016WR018716
66. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. https://doi.org/10.3390/rs13030333
67. A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments D. Kim et al. https://doi.org/10.5194/hess-21-5647-2017
68. Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature M. Neri et al. https://doi.org/10.1016/j.jhydrol.2022.128555
69. A comprehensive intercomparison study between a lumped and a fully distributed hydrological model across a set of 50 catchments in the United Kingdom S. Sinha et al. https://doi.org/10.1002/hyp.14544
70. The effect of forcing and landscape distribution on performance and consistency of model structures T. Euser et al. https://doi.org/10.1002/hyp.10445
71. Unveiling hydrological dynamics in data-scarce regions: experiences from the Ethiopian Rift Valley Lakes Basin A. Ayalew et al. https://doi.org/10.5194/hess-28-1853-2024
72. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden M. Girons Lopez et al. https://doi.org/10.5194/hess-25-1189-2021
73. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. https://doi.org/10.5194/hess-27-139-2023
74. Guidance on large scale hydrologic model calibration with isotope tracers T. Holmes et al. https://doi.org/10.1016/j.jhydrol.2023.129604
75. Why does a conceptual hydrological model fail to correctly predict discharge changes in response to climate change? D. Duethmann et al. https://doi.org/10.5194/hess-24-3493-2020
76. Enhancing Understanding of Hydrologic Processes in the Shafe Watershed, Ethiopia Z. Mada et al. https://doi.org/10.1155/2023/5577851
77. Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density M. Neri et al. https://doi.org/10.5194/hess-24-5149-2020
78. A hydrologic and river ice modelling framework for assessing ungauged sub-basin streamflow impact in a large cold-region river basin Y. Chen & Y. She https://doi.org/10.1080/02626667.2023.2251957
79. Uncertainty in hydrological signatures for gauged and ungauged catchments I. Westerberg et al. https://doi.org/10.1002/2015WR017635
80. Accounting for the influence of vegetation and landscape improves model transferability in a tropical savannah region H. Gao et al. https://doi.org/10.1002/2016WR019574
81. Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions S. Narbondo et al. https://doi.org/10.3390/w12020528
82. Low-flow frequency analysis at ungauged sites based on regionally estimated streamflows A. Requena et al. https://doi.org/10.1016/j.jhydrol.2018.06.016
83. Robustness of hydroclimate metrics for climate change impact research M. Ekström et al. https://doi.org/10.1002/wat2.1288
84. A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records T. Roksvåg et al. https://doi.org/10.5194/hess-26-5391-2022
85. Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework T. Roksvåg et al. https://doi.org/10.5194/hess-24-4109-2020
86. Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades https://doi.org/10.5194/nhess-14-1641-2014
87. Management Influences on Stream-Flow Variability in the Past and Under Potential Climate Change in a Central European Mining Region I. Pohle et al. https://doi.org/10.1007/s11269-019-02432-3
88. Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval M. Vreugdenhil et al. https://doi.org/10.1109/TGRS.2016.2519842
89. A diagnostic approach to constraining flow partitioning in hydrologic models using a multiobjective optimization framework M. Shafii et al. https://doi.org/10.1002/2016WR019736
90. The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology A. de Lavenne et al. https://doi.org/10.1016/j.envsoft.2022.105562
91. Annual water, sediment, nutrient, and organic carbon fluxes in river basins: A global meta‐analysis as a function of scale M. Mutema et al. https://doi.org/10.1002/2014WR016668
92. The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model M. Kuban et al. https://doi.org/10.2478/johh-2022-0021
93. Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning C. Klingler et al. https://doi.org/10.1007/s00506-022-00891-4