56 citations as recorded by crossref.

1. Long-term inflow forecast using meteorological data based on long short-term memory neural networks H. Zhao et al. https://doi.org/10.2166/hydro.2024.196
2. An intercomparison of approaches for improving operational seasonal streamflow forecasts P. Mendoza et al. https://doi.org/10.5194/hess-21-3915-2017
3. Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency D. Lucatero et al. https://doi.org/10.5194/hess-22-3601-2018
4. Streamflow forecasting for operational water management in the Incomati River Basin, Southern Africa R. Sunday et al. https://doi.org/10.1016/j.pce.2014.09.002
5. 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
6. Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand S. Singh https://doi.org/10.1007/s11269-016-1289-7
7. State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application M. Gibbs et al. https://doi.org/10.5194/hess-22-871-2018
8. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. https://doi.org/10.1029/2020WR028392
9. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network G. Humphrey et al. https://doi.org/10.1016/j.jhydrol.2016.06.026
10. An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin Y. Su et al. https://doi.org/10.1002/joc.7026
11. Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. https://doi.org/10.5194/hess-22-6257-2018
12. A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation S. Wang et al. https://doi.org/10.2166/nh.2024.035
13. Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees S. Liao et al. https://doi.org/10.5194/hess-24-2343-2020
14. 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
15. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. https://doi.org/10.1016/j.ejrh.2023.101584
16. 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
17. Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data W. Ren et al. https://doi.org/10.3390/rs15184527
18. The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment A. Tarpanelli et al. https://doi.org/10.1007/s10712-026-09935-w
19. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models M. Demirel et al. https://doi.org/10.5194/hess-19-275-2015
20. Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian neural network W. Ren et al. https://doi.org/10.1007/s00477-018-1553-x
21. Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques H. Ba et al. https://doi.org/10.2166/nh.2017.048
22. Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System S. Harrigan et al. https://doi.org/10.5194/hess-27-1-2023
23. Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) K. Förster et al. https://doi.org/10.5194/hess-22-1157-2018
24. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts L. Crochemore et al. https://doi.org/10.5194/hess-20-3601-2016
25. Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters C. Penn et al. https://doi.org/10.1111/1752-1688.12863
26. Runoff projection in the Tibetan Plateau using a long short-term memory network-based framework under various climate scenarios H. Chu et al. https://doi.org/10.1016/j.jhydrol.2024.130914
27. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model J. Bennett et al. https://doi.org/10.1002/2016WR019193
28. Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach S. Fleming et al. https://doi.org/10.1016/j.jhydrol.2021.126327
29. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia P. Pokhrel et al. https://doi.org/10.1002/wrcr.20449
30. Understanding regional streamflow trend magnitudes in the Southern Murray-Darling Basin, Australia Z. Gao et al. https://doi.org/10.1080/13241583.2022.2074942
31. The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers K. Foster et al. https://doi.org/10.5194/hess-22-2953-2018
32. Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting K. Sittichok et al. https://doi.org/10.1080/02626667.2018.1445854
33. Long-term variability of the leading seasonal modes of rainfall in south-eastern Australia M. Montazerolghaem et al. https://doi.org/10.1016/j.wace.2016.04.001
34. A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West S. Fleming & A. Goodbody https://doi.org/10.1109/ACCESS.2019.2936989
35. Hybrid physical–statistical framework for seasonal streamflow forecasting in the Upper Feather River Basin, California Z. Ozcan et al. https://doi.org/10.1038/s41598-025-15932-7
36. Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling U. Okkan et al. https://doi.org/10.1016/j.jhydrol.2021.126433
37. 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
38. Assessing the new Natural Resources Conservation Service water supply forecast model for the American West: A challenging test of explainable, automated, ensemble artificial intelligence S. Fleming et al. https://doi.org/10.1016/j.jhydrol.2021.126782
39. A comparison between numerical, neural network, and hybrid modelling approaches to simulate spring flow from a karst catchment in northwest Ireland using long-term hydrological data N. Chowdhury et al. https://doi.org/10.1016/j.ejrh.2025.102723
40. Towards Interpreting Machine‐Learning Models for Multi‐Step Ahead Daily Streamflow Forecasting R. Hao & H. Yan https://doi.org/10.1002/hyp.70163
41. Seasonal Drought Prediction: Advances, Challenges, and Future Prospects Z. Hao et al. https://doi.org/10.1002/2016RG000549
42. A new approach of coupled long-range forecasts for streamflow and groundwater level D. Robertson et al. https://doi.org/10.1016/j.jhydrol.2024.130837
43. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China H. Li et al. https://doi.org/10.3390/su13094627
44. Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al. https://doi.org/10.1029/2021WR031557
45. Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe D. Meißner et al. https://doi.org/10.5194/hess-21-6401-2017
46. 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
47. Untangling hybrid hydrological models with explainable artificial intelligence D. Althoff et al. https://doi.org/10.2166/h2oj.2021.066
48. Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques N. Daif & A. Hebal https://doi.org/10.1007/s40808-024-02067-3
49. A stepwise-cluster forecasting approach for monthly streamflows based on climate teleconnections Y. Fan et al. https://doi.org/10.1007/s00477-015-1048-y
50. A machine learning-based water supply forecasting model to quantify the impact of snow water equivalent on seasonal streamflow variability over the western U.S H. Yue et al. https://doi.org/10.1016/j.jhydrol.2025.133465
51. Investigating the Role of Snow Water Equivalent on Streamflow Predictability during Drought P. Modi et al. https://doi.org/10.1175/JHM-D-21-0229.1
52. Streamflow forecasting using Kolmogorov-Arnold network V. Varma & J. Patel https://doi.org/10.1088/2631-8695/addd66
53. Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia A. Schepen & Q. Wang https://doi.org/10.1002/2014WR016163
54. Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach L. Slater & G. Villarini https://doi.org/10.1029/2018GL077945
55. Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods S. Charles et al. https://doi.org/10.5194/hess-22-3533-2018
56. Seasonal Forecasts of Unregulated Inflows into the Murray River, Australia D. Robertson & Q. Wang https://doi.org/10.1007/s11269-013-0313-4