31 citations as recorded by crossref.

1. The effects of pre‐season high flows, climate, and the Three Gorges Dam on low flow at the Three Gorges Region, China Z. Su et al. https://doi.org/10.1002/hyp.13714
2. AltEx: An open source web application and toolkit for accessing and exploring altimetry datasets K. Markert et al. https://doi.org/10.1016/j.envsoft.2019.03.021
3. 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
4. Comparison of nonstationary models in analyzing bivariate flood frequency at the Three Gorges Dam X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2019.124208
5. The impact of the Three Gorges Dam on summer streamflow in the Yangtze River Basin Z. Su et al. https://doi.org/10.1002/hyp.13619
6. Climate-informed clustering based nonstationary regional extreme flood events spatio-temporal evolution using hierarchical Bayesian modeling H. Zeng et al. https://doi.org/10.1016/j.ejrh.2024.102066
7. Challenges and Evolution of Water Level Monitoring towards a Comprehensive, World-Scale Coverage with Remote Sensing M. Machefer et al. https://doi.org/10.3390/rs14153513
8. Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework Z. Pan et al. https://doi.org/10.5194/hess-23-3405-2019
9. Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective Y. Li et al. https://doi.org/10.5194/hess-26-4975-2022
10. An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam, China Q. Dong et al. https://doi.org/10.1080/02626667.2019.1596274
11. Monthly drought prediction based on ensemble models M. Shaukat et al. https://doi.org/10.7717/peerj.9853
12. Spatially coherent trends of annual maximum daily precipitation in the United States X. Sun & U. Lall https://doi.org/10.1002/2015GL066483
13. Spatiotemporal patterns of precipitation regimes in the Huai River basin, China, and possible relations with ENSO events Y. Wang et al. https://doi.org/10.1007/s11069-016-2303-3
14. Quantifying the effect of climate variability on seasonal precipitation using Bayesian clustering approach in Kebir Rhumel Basin, Algeria L. Belkhiri & N. Krakauer https://doi.org/10.1007/s00477-023-02488-z
15. A hierarchical Bayesian model for decomposing the impacts of human activities and climate change on water resources in China X. Zhang et al. https://doi.org/10.1016/j.scitotenv.2019.02.189
16. Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment J. George & P. Athira https://doi.org/10.1007/s11269-024-03876-y
17. A review of methods for monitoring streamflow for sustainable water resource management P. Dobriyal et al. https://doi.org/10.1007/s13201-016-0488-y
18. 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
19. Impacts of various types of El Niño–Southern Oscillation (ENSO) and ENSO Modoki on the rainy season over the Huaihe River basin Q. Cao et al. https://doi.org/10.1002/joc.6002
20. Changing Seasonality of Annual Maximum Floods over the Conterminous US: Potential Drivers and Regional Synthesis B. Basu et al. https://doi.org/10.1061/JHYEFF.HEENG-5768
21. China’s socioeconomic risk from extreme events in a changing climate: a hierarchical Bayesian model X. Yuan et al. https://doi.org/10.1007/s10584-016-1749-3
22. A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information D. Tian et al. https://doi.org/10.1007/s00477-021-02023-y
23. Diagnosing the impacts of landscape characteristics on hydrologic signatures in the Krycklan catchment in Sweden using a flexible hydrological model R. Guo et al. https://doi.org/10.1016/j.pce.2024.103565
24. On the Predictability of Daily Rainfall during Rainy Season over the Huaihe River Basin Q. Cao et al. https://doi.org/10.3390/w11050916
25. Entropy‐based spatiotemporal patterns of precipitation regimes in the Huai River basin, China Q. Zhang et al. https://doi.org/10.1002/joc.4498
26. Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model P. Fabian et al. https://doi.org/10.1016/j.cej.2025.163074
27. Hierarchical Bayesian clustering for nonstationary flood frequency analysis: Application to trends of annual maximum flow in Germany X. Sun et al. https://doi.org/10.1002/2015WR017117
28. Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework Z. Pan et al. https://doi.org/10.1016/j.jhydrol.2019.123927
29. Forecasting China’s regional energy demand by 2030: A Bayesian approach X. Yuan et al. https://doi.org/10.1016/j.resconrec.2017.08.016
30. Comparison of ensemble models for drought prediction based on climate indexes X. Zhang et al. https://doi.org/10.1007/s00477-019-01650-w
31. Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes R. Das Bhowmik et al. https://doi.org/10.3390/w10070875