122 citations as recorded by crossref.

1. Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas M. Rogelis & M. Werner https://doi.org/10.5194/hess-22-853-2018
2. Statistical refinement of the North American Multi-Model Ensemble precipitation forecasts over Karoon basin, Iran F. Yazdandoost et al. https://doi.org/10.2166/wcc.2023.277
3. Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China Y. Li et al. https://doi.org/10.2166/hydro.2021.176
4. Generating Calibrated Ensembles of Physically Realistic, High-Resolution Precipitation Forecast Fields Based on GEFS Model Output M. Scheuerer & T. Hamill https://doi.org/10.1175/JHM-D-18-0067.1
5. A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations A. Ghazikhani et al. https://doi.org/10.3390/su14116624
6. Reliability of Ensemble Climatological Forecasts Z. Huang et al. https://doi.org/10.1029/2023WR034942
7. A Nonhomogeneous Regression-Based Statistical Postprocessing Scheme for Generating Probabilistic Quantitative Precipitation Forecast M. Ghazvinian et al. https://doi.org/10.1175/JHM-D-20-0019.1
8. Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts W. Li et al. https://doi.org/10.1016/j.jhydrol.2021.127301
9. Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model T. Zhao et al. https://doi.org/10.1016/j.jhydrol.2015.06.043
10. Pyenspp: A Python package for ensemble precipitation forecast post-processing using a hybrid KAN–CSGD model F. Jiang et al. https://doi.org/10.1016/j.envsoft.2026.107012
11. A Similarity-Based Implementation of the Schaake Shuffle R. Schefzik https://doi.org/10.1175/MWR-D-15-0227.1
12. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes C. Alvarez-Garreton et al. https://doi.org/10.5194/hess-19-1659-2015
13. Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran M. Akbarian et al. https://doi.org/10.1016/j.jhydrol.2023.129480
14. A hybrid framework for sub-seasonal to seasonal streamflow prediction: integrating numerical and statistical models L. Li et al. https://doi.org/10.1038/s41612-025-01273-9
15. Insights to key operational questions in forecast-informed dam release operation: case of Hume Dam T. Ng & D. Robertson https://doi.org/10.1080/13241583.2024.2392312
16. Impacts of Hydrometeor Drift on Orographic Precipitation: Two Case Studies of Landfalling Atmospheric Rivers in British Columbia, Canada R. Mo et al. https://doi.org/10.1175/WAF-D-18-0176.1
17. A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau Y. Ma et al. https://doi.org/10.5194/hess-25-359-2021
18. Assessing Entropy-based Bayesian Model Averaging Method for Probabilistic Precipitation Forecasting https://doi.org/10.1175/JHM-D-21-0086.1
19. Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871 L. Caillouet et al. https://doi.org/10.5194/hess-21-2923-2017
20. Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada F. Aguilar Andrade et al. https://doi.org/10.1016/j.jhydrol.2024.131861
21. A five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2021.126893
22. Improving the outputs of regional heavy rainfall forecasting models using an adaptive real-time approach Z. Alizadeh et al. https://doi.org/10.1080/02626667.2022.2027951
23. Efficient River Management using Stochastic MPC and Ensemble Forecast of Uncertain In-flows ⁎ ⁎The first and the third authors acknowledge the financial support from the Australian Research Council Linkage Project (LP130100605) and the Brescia Smart Living Project (MIURSCN00416) respectively. H. Nasir et al. https://doi.org/10.1016/j.ifacol.2018.06.196
24. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting M. Li et al. https://doi.org/10.5194/hess-20-3561-2016
25. An evaluation of a methodology for seasonal soil water forecasting for Australian dry land cropping systems A. Western et al. https://doi.org/10.1016/j.agrformet.2018.02.012
26. An evaluation of post-processed TIGGE multimodel ensemble precipitation forecast in the Huai river basin Y. Tao et al. https://doi.org/10.1016/j.jhydrol.2014.04.040
27. Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model N. Samal et al. https://doi.org/10.1080/15715124.2023.2218094
28. Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous U.S. using machine learning models L. Zhang et al. https://doi.org/10.1016/j.atmosres.2022.106502
29. Improving Precipitation Forecasts by Generating Ensembles through Postprocessing D. Shrestha et al. https://doi.org/10.1175/MWR-D-14-00329.1
30. Hierarchical Temporal-Scale Framework for Real-Time Streamflow Prediction in Reservoir-Regulated Basins J. Chang et al. https://doi.org/10.3390/su17094046
31. Power transformation of variables for post-processing precipitation forecasts: Regionally versus locally optimized parameter values Y. Du et al. https://doi.org/10.1016/j.jhydrol.2022.127912
32. A real‐time pluvial flood forecasting system for Castries, St. Lucia J. René et al. https://doi.org/10.1111/jfr3.12205
33. A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data J. Lerat et al. https://doi.org/10.1016/j.jhydrol.2020.125129
34. pyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts Z. Huang & T. Zhao https://doi.org/10.1016/j.envsoft.2023.105732
35. An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia S. Acharya et al. https://doi.org/10.5194/hess-23-3387-2019
36. Analyzing the uncertainty of ensemble-based gridded observations in land surface simulations and drought assessment A. Ahmadalipour & H. Moradkhani https://doi.org/10.1016/j.jhydrol.2017.10.059
37. OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia G. Ayzel https://doi.org/10.3390/hydrology8010003
38. A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2023.129347
39. A novel deep learning model for post-processing of short- and medium-term daily precipitation forecasts C. Zhao et al. https://doi.org/10.1016/j.atmosres.2025.108319
40. A dialogue‐based weather forecast: adapting language to end‐users to improve communication A. Sivle & T. Aamodt https://doi.org/10.1002/wea.3439
41. Calibration of precipitation forecasts from NWP models for ungauged locations Y. Du et al. https://doi.org/10.1016/j.jhydrol.2025.133733
42. Machine Learning Approaches to Improve North American Precipitation Forecasts C. Sengoz et al. https://doi.org/10.1109/ACCESS.2023.3309054
43. Using the Schaake shuffle when calibrating ensemble means can be problematic D. Shrestha et al. https://doi.org/10.1016/j.jhydrol.2020.124991
44. Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks A. Akbari Asanjan et al. https://doi.org/10.1029/2018JD028375
45. Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction A. Jabbari & D. Bae https://doi.org/10.3390/atmos11030300
46. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn https://doi.org/10.1016/j.jenvman.2024.122114
47. A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs T. Zhao et al. https://doi.org/10.1016/j.agrformet.2019.02.003
48. Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea R. Torcasio et al. https://doi.org/10.3390/rs13040682
49. The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia J. Bennett et al. https://doi.org/10.5194/nhess-14-219-2014
50. Probabilistic Forecast of Ecological Drought in Rivers Based on Numerical Weather Forecast from S2S Dataset C. Cai et al. https://doi.org/10.3390/w16040579
51. Improving Weather Forecasting in Remote Regions Through Machine Learning K. Yadav et al. https://doi.org/10.3390/atmos16050587
52. Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach T. Zhao et al. https://doi.org/10.5194/hess-25-5717-2021
53. A physics informed deep learning framework for rainfall forecasting in diverse climatic regions S. Inam et al. https://doi.org/10.1007/s44163-026-00833-z
54. Downscaled numerical weather predictions can improve forecasts of sugarcane irrigation indices A. Schepen et al. https://doi.org/10.1016/j.compag.2024.109009
55. Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin H. Ouyang et al. https://doi.org/10.3390/atmos12101253
56. A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2022.127896
57. Improving Probabilistic Quantitative Precipitation Forecasts Using Short Training Data through Artificial Neural Networks M. Ghazvinian et al. https://doi.org/10.1175/JHM-D-22-0021.1
58. A method for preferential selection of dates in the Schaake shuffle approach to constructing spatiotemporal forecast fields of temperature and precipitation M. Scheuerer et al. https://doi.org/10.1002/2016WR020133
59. Statistical post-processing of numerical weather prediction data using distribution-based scaling for wind energy A. Rangaraj et al. https://doi.org/10.1177/0309524X241238353
60. Improving the heavy rainfall forecasting using a weighted deep learning model Y. Chen et al. https://doi.org/10.3389/fenvs.2023.1116672
61. Inter-comparison between retrospective ensemble streamflow forecasts using meteorological inputs from ECMWF and NOAA/ESRL in the Hudson River sub-basins during Hurricane Irene (2011) F. Saleh et al. https://doi.org/10.2166/nh.2018.182
62. Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. https://doi.org/10.5194/hess-22-6257-2018
63. Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* M. Vrac & P. Friederichs https://doi.org/10.1175/JCLI-D-14-00059.1
64. Differing roles of base and fast flow in ensemble seasonal streamflow forecasting: An experimental investigation Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2020.125272
65. Sub-daily temporal reconstruction of extreme precipitation events using NWP model simulations V. Bližňák et al. https://doi.org/10.1016/j.atmosres.2019.03.019
66. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. https://doi.org/10.1002/wat2.1246
67. Estimating daily precipitation climatology by postprocessing high‐resolution reanalysis data Y. Du et al. https://doi.org/10.1002/joc.8079
68. Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models P. Zhao et al. https://doi.org/10.1016/j.jhydrol.2021.127285
69. An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation W. Li et al. https://doi.org/10.1016/j.jhydrol.2019.04.073
70. A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days J. Bennett et al. https://doi.org/10.1016/j.jhydrol.2014.08.010
71. A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction A. Wright et al. https://doi.org/10.5194/hess-21-3827-2017
72. Describing the impacts of the extreme weather event on plants communities in Cibodas Botanic Gardens I. Hidayat https://doi.org/10.1088/1755-1315/1092/1/012004
73. Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale X. Li et al. https://doi.org/10.3390/rs15071743
74. A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: an application to quantitative precipitation forecasts for civil protection purposes D. Biondi et al. https://doi.org/10.2166/nh.2021.045
75. Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models P. Zhao et al. https://doi.org/10.1016/j.jhydrol.2022.128432
76. 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. https://doi.org/10.1016/j.jhydrol.2024.130643
77. How do I know if I’ve improved my continental scale flood early warning system? H. Cloke et al. https://doi.org/10.1088/1748-9326/aa625a
78. Probabilistic Conditioning and Recalibration of an Event-Based Flood Forecasting Model Using Real-Time Streamflow Observations K. Bahramian et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002236
79. Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia M. Bari et al. https://doi.org/10.3390/w16101438
80. Uncertainty estimation and error correction for multi-gauge water resource models S. Kim et al. https://doi.org/10.1016/j.jhydrol.2026.135797
81. Evaluating the Performance of the EcPoint Post-Processing Method for Ensemble Rainfall Forecasts over south China T. Mwira & S. Stejik https://doi.org/10.4236/acs.2025.152022
82. A national flood awareness system for ungauged catchments in complex topography: The case of development, communication and evaluation in New Zealand C. Cattoën et al. https://doi.org/10.1111/jfr3.12864
83. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting Y. Li et al. https://doi.org/10.1016/j.jhydrol.2014.08.009
84. Reliable hourly streamflow forecasting with emphasis on ephemeral rivers M. Li et al. https://doi.org/10.1016/j.jhydrol.2020.125739
85. Achieving effective calibration of precipitation forecasts over a continental scale Q. Yang et al. https://doi.org/10.1016/j.ejrh.2021.100818
86. OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
87. Post-processing of hydrological model simulations using the convolutional neural network and support vector regression S. Liu et al. https://doi.org/10.2166/nh.2022.004
88. Bayesian hierarchical network model for forecasting daily river stage for rainfed river networks N. Rastogi et al. https://doi.org/10.1016/j.jhydrol.2025.132894
89. Hydrological responses to climate change in Yarlung Zangbo River basin, Southwest China W. Xuan et al. https://doi.org/10.1016/j.jhydrol.2020.125761
90. An approach for improving the capability of a coupled meteorological and hydrological model for rainfall and flood forecasts H. Nguyen & D. Bae https://doi.org/10.1016/j.jhydrol.2019.124014
91. Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts W. Li et al. https://doi.org/10.1175/WAF-D-19-0121.1
92. Calibration and quantifying uncertainty of daily water quality forecasts for large lakes with a Bayesian joint probability modelling approach Z. Peng et al. https://doi.org/10.1016/j.watres.2020.116162
93. On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs A. Schepen et al. https://doi.org/10.1175/MWR-D-19-0046.1
94. Calibrating Hourly Precipitation Forecasts with Daily Observations C. Cattoën et al. https://doi.org/10.1175/JHM-D-19-0246.1
95. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. https://doi.org/10.1016/j.jhydrol.2023.129199
96. How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts? T. Zhao et al. https://doi.org/10.1175/JCLI-D-16-0652.1
97. Multivariate ensemble Model Output Statistics using empirical copulas D. Wilks https://doi.org/10.1002/qj.2414
98. A new scheme for multivariate, multisite weather generator with inter-variable, inter-site dependence and inter-annual variability based on empirical copula approach X. Li & V. Babovic https://doi.org/10.1007/s00382-018-4249-5
99. The enhanced integration of proven techniques to quantify the uncertainty of forecasting extreme flood events based on numerical weather prediction models M. Tanhapour et al. https://doi.org/10.1016/j.wace.2025.100767
100. Evaluation of ERA5 and ERA5-Land reanalysis precipitation datasets over Spain (1951–2020) J. Gomis-Cebolla et al. https://doi.org/10.1016/j.atmosres.2023.106606
101. Estimating rainfall time series and model parameter distributions using model data reduction and inversion techniques A. Wright et al. https://doi.org/10.1002/2017WR020442
102. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach T. Zhao et al. https://doi.org/10.1016/j.jhydrol.2016.07.040
103. Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution L. Li et al. https://doi.org/10.1016/j.atmosres.2023.107133
104. Improved error modelling for streamflow forecasting at hourly time steps by splitting hydrographs into rising and falling limbs M. Li et al. https://doi.org/10.1016/j.jhydrol.2017.10.057
105. Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings D. Robertson et al. https://doi.org/10.1016/j.jhydrol.2023.129322
106. Comparative Evaluation of Three Schaake Shuffle Schemes in Postprocessing GEFS Precipitation Ensemble Forecasts L. Wu et al. https://doi.org/10.1175/JHM-D-17-0054.1
107. Prediction of daily rainfall by a hybrid wavelet-season-neuro technique A. Altunkaynak & T. Nigussie https://doi.org/10.1016/j.jhydrol.2015.07.046
108. Simplified Approach to Mixed-Integer Chance-Constrained Optimization with Ensemble Streamflow Forecasts for Risk-Based Dam Operation T. Ng et al. https://doi.org/10.1061/JWRMD5.WRENG-5885
109. Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China T. Zhao et al. https://doi.org/10.1002/2017JD027018
110. Assessment and modelling of uncertainty in precipitation forecasts from TIGGE using fuzzy probability and Bayesian theory C. Cai et al. https://doi.org/10.1016/j.jhydrol.2019.123995
111. Evaluation of TIGGE Daily Accumulated Precipitation Forecasts over the Qu River Basin, China L. Liu et al. https://doi.org/10.1007/s13351-019-8096-z
112. A New Hybrid Framework for Error Correction and Uncertainty Analysis of Precipitation Forecasts with Combined Postprocessors C. Cai et al. https://doi.org/10.3390/w14193072
113. Bias-targeted deep learning enhances short-range heavy rainfall forecasts T. Tang et al. https://doi.org/10.1038/s41612-026-01366-z
114. Ensemble forecasting of short‐term system scale irrigation demands using real‐time flow data and numerical weather predictions K. Perera et al. https://doi.org/10.1002/2015WR018532
115. Development of a national 7-day ensemble streamflow forecasting service for Australia H. Hapuarachchi et al. https://doi.org/10.5194/hess-26-4801-2022
116. Deep-learning post-processing of short-term station precipitation based on NWP forecasts Q. Liu et al. https://doi.org/10.1016/j.atmosres.2023.107032
117. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments A. Schepen et al. https://doi.org/10.5194/hess-22-1615-2018
118. Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment S. Jha et al. https://doi.org/10.5194/hess-22-1957-2018
119. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. https://doi.org/10.1029/2021WR030138
120. A Seasonally Coherent Calibration (SCC) Model for Postprocessing Numerical Weather Predictions Q. Wang et al. https://doi.org/10.1175/MWR-D-19-0108.1
121. A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts W. Li et al. https://doi.org/10.1175/MWR-D-19-0258.1
122. Evaluation and Statistical Post‐Processing of Two Precipitation Reforecast Products During Summer in the Mainland of China W. Li et al. https://doi.org/10.1029/2022JD036606