32 citations as recorded by crossref.

1. A Satellite–UAV–USV Collaborative Monitoring Framework for Cross-Scale Assessment of River Restoration Effectiveness: A Case Study of the Nihe River Basin, China G. Chen et al. https://doi.org/10.3390/rs18121934
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. Monthly drought prediction based on ensemble models M. Shaukat et al. https://doi.org/10.7717/peerj.9853
13. Spatially coherent trends of annual maximum daily precipitation in the United States X. Sun & U. Lall https://doi.org/10.1002/2015GL066483
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. On the Predictability of Daily Rainfall during Rainy Season over the Huaihe River Basin Q. Cao et al. https://doi.org/10.3390/w11050916
26. Entropy‐based spatiotemporal patterns of precipitation regimes in the Huai River basin, China Q. Zhang et al. https://doi.org/10.1002/joc.4498
27. 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
28. 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
29. 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
30. 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
31. Comparison of ensemble models for drought prediction based on climate indexes X. Zhang et al. https://doi.org/10.1007/s00477-019-01650-w
32. Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes R. Das Bhowmik et al. https://doi.org/10.3390/w10070875