90 citations as recorded by crossref.

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8. Sensitivity and Uncertainty of a Long‐Term, High‐Resolution, Global, Terrestrial Sensible Heat Flux Data Set A. Siemann et al. 10.1029/2017JD027785
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11. Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting Z. Shu et al. 10.1016/j.eng.2022.06.007
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21. Rainfall Uncertainty in Flood Forecasting: Belgian Case Study of Rivierbeek N. Van Steenbergen & P. Willems 10.1061/(ASCE)HE.1943-5584.0001004
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25. Towards Informed Water Resources Planning and Management P. Reggiani et al. 10.3390/hydrology9080136
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27. Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data K. Drakaki et al. 10.5194/adgeo-56-155-2022
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30. Uncertainty decomposition and reduction in river flood forecasting: Belgian case study N. Van Steenbergen & P. Willems 10.1111/jfr3.12093
31. A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems L. Vezzaro & M. Grum 10.1016/j.jhydrol.2014.05.019
32. Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts – A Hydrologic Model Output Statistics (HMOS) approach S. Regonda et al. 10.1016/j.jhydrol.2013.05.028
33. Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP) S. Alvisi & M. Franchini 10.1080/1573062X.2015.1057182
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44. Operational hydrological forecasting during the IPHEx-IOP campaign – Meet the challenge J. Tao et al. 10.1016/j.jhydrol.2016.02.019
45. Hydrologic post-processing of MOPEX streamflow simulations A. Ye et al. 10.1016/j.jhydrol.2013.10.055
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48. Hydrological post-processing based on approximate Bayesian computation (ABC) J. Romero-Cuellar et al. 10.1007/s00477-019-01694-y
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50. Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting D. Biondi & D. De Luca 10.1016/j.jhydrol.2012.11.019
51. Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor A. Anele et al. 10.3390/w10040475
52. Patterns of runoff and sediment production in response to land-use changes in an ungauged Mediterranean catchment X. Rodriguez-Lloveras et al. 10.1016/j.jhydrol.2015.11.014
53. Quantification of the forecast uncertainty using conditional probability and updating models H. Ba et al. 10.2166/nh.2019.094
54. Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. 10.5194/hess-21-4021-2017
55. Complementing near-real time satellite rainfall products with satellite soil moisture-derived rainfall through a Bayesian Inversion approach C. Massari et al. 10.1016/j.jhydrol.2019.03.038
56. Characterizing the uncertainty in river stage forecasts conditional on point forecast values J. Yan et al. 10.1029/2012WR011818
57. Performance evaluation of enterprises’ innovation capacity based on fuzzy system model and convolutional neural network A. Abudureheman et al. 10.3233/JIFS-179929
58. Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction A. Anele et al. 10.3390/w9110887
59. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
60. Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model T. Zhao et al. 10.1016/j.jhydrol.2015.06.043
61. Consideration of streamflow forecast uncertainty in the development of short-term hydropower station optimal operation schemes: A novel approach based on mean-variance theory Y. Liu et al. 10.1016/j.jclepro.2021.126929
62. Meteolakes: An operational online three-dimensional forecasting platform for lake hydrodynamics T. Baracchini et al. 10.1016/j.watres.2020.115529
63. Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS G. Coccia et al. 10.1016/j.rse.2015.09.010
64. Building a Multimodel Flood Prediction System with the TIGGE Archive E. Zsótér et al. 10.1175/JHM-D-15-0130.1
65. Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting F. Bourgin et al. 10.1016/j.jhydrol.2014.07.054
66. A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction M. Ghorbani et al. 10.1007/s12665-015-5096-x
67. Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram P. Reggiani et al. 10.3390/w8060263
68. Explore a Multivariate Bayesian Uncertainty Processor driven by artificial neural networks for probabilistic PM2.5 forecasting Y. Zhou et al. 10.1016/j.scitotenv.2019.134792
69. Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco E. El Khalki et al. 10.3390/w12020437
70. Does increased hydrochemical model complexity decrease robustness? C. Medici et al. 10.1016/j.jhydrol.2012.02.047
71. Predictive uncertainty assessment in flood forecasting using quantile regression A. M. K. & C. N. R 10.2166/h2oj.2023.040
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75. Addressing effective real-time forecasting inflows to dams through predictive uncertainty estimate S. Barbetta et al. 10.1016/j.jhydrol.2023.129512
76. Compound flood models in coastal areas: a review of methods and uncertainty analysis K. Xu et al. 10.1007/s11069-022-05683-3
77. The Science of NOAA's Operational Hydrologic Ensemble Forecast Service J. Demargne et al. 10.1175/BAMS-D-12-00081.1
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79. Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. 10.1080/02626667.2019.1610565
80. Discharge Interval method for uncertain flood forecasts using a flood model chain: city of Kulmbach M. Beg et al. 10.2166/hydro.2019.131
81. Hydrological uncertainty processor based on a copula function Z. Liu et al. 10.1080/02626667.2017.1410278
82. Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments N. Dogulu et al. 10.5194/hess-19-3181-2015
83. Enhancing real-time streamflow forecasts with wavelet-neural network based error-updating schemes and ECMWF meteorological predictions in Variable Infiltration Capacity model T. Nanda et al. 10.1016/j.jhydrol.2019.05.051
84. Determining the threshold of issuing flash flood warnings based on people's response process simulation R. Zhang et al. 10.5194/hess-28-5229-2024
85. Statistical Post-Processing to Improve Hydrometeorological Forecasts 青. 段 10.12677/JWRR.2012.14023
86. Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling L. Li et al. 10.1016/j.jhydrol.2013.02.002
87. A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication N. Van Steenbergen et al. 10.1016/j.envsoft.2012.01.013
88. Post‐processing hydrological ensemble predictions intercomparison experiment S. van Andel et al. 10.1002/hyp.9595
89. Uncertainty estimates by Bayesian method with likelihood of AR (1) plus Normal model and AR (1) plus Multi-Normal model in different time-scales hydrological models L. Li et al. 10.1016/j.jhydrol.2011.05.052
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