40 citations as recorded by crossref.

1. A back-fitting algorithm to improve real-time flood forecasting X. Zhang et al. 10.1016/j.jhydrol.2018.04.051
2. Theoretical analysis of non‐Gaussian heterogeneity effects on subsurface flow and transport M. Riva et al. 10.1002/2016WR019353
3. Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
4. Improving probabilistic prediction of daily streamflow by identifyingPareto optimal approaches for modeling heteroscedastic residual errors D. McInerney et al. 10.1002/2016WR019168
5. Enhancing the hydrologic system differential response method for flood forecasting correction X. Zhang et al. 10.1016/j.jhydrol.2020.125793
6. Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
7. Complex relationship between seasonal streamflow forecast skill and value in reservoir operations S. Turner et al. 10.5194/hess-21-4841-2017
8. An error model for long-range ensemble forecasts of ephemeral rivers J. Bennett et al. 10.1016/j.advwatres.2021.103891
9. New scaling model for variables and increments with heavy‐tailed distributions M. Riva et al. 10.1002/2015WR016998
10. A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network Á. Ossandón et al. 10.1175/JHM-D-21-0167.1
11. Water flow probabilistic predictions based on a rainfall–runoff simulator: a two-regime model with variable selection M. Courbariaux et al. 10.1007/s13253-017-0278-5
12. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
13. 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
14. Propagating reliable estimates of hydrological forecast uncertainty to many lead times J. Bennett et al. 10.1016/j.jhydrol.2021.126798
15. Study of teleconnection between hydrological variables and climatological variables in a headwater basin of the Maipo River for forecast model application J. Montalva et al. 10.24850/j-tyca-16-4-3
16. A long short-term components neural network model with data augmentation for daily runoff forecasting J. Zhang & H. Yan 10.1016/j.jhydrol.2022.128853
17. Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
18. Reliable hourly streamflow forecasting with emphasis on ephemeral rivers M. Li et al. 10.1016/j.jhydrol.2020.125739
19. Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs Y. Xie et al. 10.1016/j.eswa.2022.119469
20. Assessment of an ensemble seasonal streamflow forecasting system for Australia J. Bennett et al. 10.5194/hess-21-6007-2017
21. Quantifying the Risks that Propagate from the Inflow Forecast Uncertainty to the Reservoir Operations with Coupled Flood and Electricity Curtailment Risks Q. Ma et al. 10.3390/w13020173
22. 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
23. Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy A. Elshall et al. 10.5194/gmd-12-2009-2019
24. Combining two-stage decomposition based machine learning methods for annual runoff forecasting S. Chen et al. 10.1016/j.jhydrol.2021.126945
25. Multi-model integrated error correction for streamflow simulation based on Bayesian model averaging and dynamic system response curve J. Wang et al. 10.1016/j.jhydrol.2022.127518
26. Application of auto-regressive (AR) analysis to improve short-term prediction of water levels in the Yangtze estuary Y. Chen et al. 10.1016/j.jhydrol.2020.125386
27. Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework Á. Ossandón et al. 10.1029/2022WR033194
28. 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
29. Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions J. Hunter et al. 10.1016/j.jhydrol.2021.126578
30. 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
31. A Bayesian Hierarchical Model Combination Framework for Real‐Time Daily Ensemble Streamflow Forecasting Across a Rainfed River Basin Á. Ossandón et al. 10.1029/2022EF002958
32. A spatial distributed runoff correction approach based on differential response Z. Xiaoqin et al. 10.18307/2021.0624
33. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting M. Li et al. 10.5194/hess-20-3561-2016
34. A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers Q. Wang et al. 10.1029/2019WR026128
35. Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation R. Sun et al. 10.1016/j.jhydrol.2017.09.041
36. Error correction-based forecasting of reservoir water levels: Improving accuracy over multiple lead times X. Zhang et al. 10.1016/j.envsoft.2018.02.017
37. Groundwater level prediction using a SOM-aided stepwise cluster inference model J. Han et al. 10.1016/j.jenvman.2016.07.069
38. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
39. Objectivity verification experiment of the dynamic system response curve method for streamflow simulation J. Wang et al. 10.1016/j.jhydrol.2022.128969
40. A hybrid deep learning algorithm and its application to streamflow prediction Y. Lin et al. 10.1016/j.jhydrol.2021.126636