22 citations as recorded by crossref.

1. Improved error modelling for streamflow forecasting at hourly time steps by splitting hydrographs into rising and falling limbs M. Li et al. 10.1016/j.jhydrol.2017.10.057
2. Potential applications of subseasonal‐to‐seasonal (S2S) predictions C. White et al. 10.1002/met.1654
3. Improving Precipitation Forecasts by Generating Ensembles through Postprocessing D. Shrestha et al. 10.1175/MWR-D-14-00329.1
4. Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities J. Das et al. 10.1080/02626667.2021.2023157
5. State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application M. Gibbs et al. 10.5194/hess-22-871-2018
6. Evaluation of ocean-atmospheric indices as predictors for summer streamflow of the Yangtze River based on ROC analysis R. He et al. 10.1007/s00477-018-1551-z
7. How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? L. Liu et al. 10.1002/joc.6867
8. Informing Stochastic Streamflow Generation by Large-Scale Climate Indices at Single and Multiple Sites M. Zaerpour et al. 10.1016/j.advwatres.2021.104037
9. Seasonal precipitation forecasts over China using monthly large-scale oceanic-atmospheric indices Z. Peng et al. 10.1016/j.jhydrol.2014.08.012
10. Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach A. Bradley et al. 10.1002/2014WR016811
11. Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China Z. Peng et al. 10.1002/2013JD021162
12. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model J. Bennett et al. 10.1002/2016WR019193
13. Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model T. Zhao et al. 10.1016/j.jhydrol.2015.06.043
14. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach T. Zhao et al. 10.1016/j.jhydrol.2016.07.040
15. Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning Y. Liu et al. 10.3390/w16070990
16. Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks J. Molina et al. 10.3390/w8110484
17. Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
18. Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2021.127285
19. Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia C. White et al. 10.5194/piahs-370-229-2015
20. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting M. Li et al. 10.5194/hess-20-3561-2016
21. Estimating daily inflows of large lakes using a water‐balance‐based runoff coefficient scaling approach Z. Peng et al. 10.1002/hyp.13486
22. A method to extend temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates Y. Li et al. 10.1016/j.jhydrol.2019.124355