31 citations as recorded by crossref.

1. Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation H. Abdelmoaty et al. https://doi.org/10.1029/2021EF002196
2. Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling R. Garna et al. https://doi.org/10.1002/hyp.15013
3. Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. https://doi.org/10.1016/j.advwatres.2025.104955
4. Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran H. Salehi et al. https://doi.org/10.3390/w16071028
5. Probabilistic Evaluation of Drought in CMIP6 Simulations S. Papalexiou et al. https://doi.org/10.1029/2021EF002150
6. Impact of precipitation on parameter sensitivity and identifiability in the variable infiltration capacity (VIC) model M. Noorbeh et al. https://doi.org/10.1080/02626667.2025.2569674
7. An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates J. Hu et al. https://doi.org/10.5194/essd-17-3987-2025
8. Modeling lane-level travel time reliability with uncertainty quantification A. Moeinaddini et al. https://doi.org/10.1080/19427867.2026.2632963
9. Value of various elements of the hydrological forecasting chain: Is there a successful pathway for improving the overall performance? J. Davidson-Chaput et al. https://doi.org/10.1016/j.jhydrol.2025.133186
10. Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets K. Mankin et al. https://doi.org/10.5194/hess-29-85-2025
11. Evaluating Operational Risk in Environmental Modeling: Assessment of Reliability and Sharpness for Ensemble Selection S. Pokorny et al. https://doi.org/10.1061/JHYEFF.HEENG-5833
12. Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science J. Janssen et al. https://doi.org/10.1002/env.70018
13. HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool E. Moges et al. https://doi.org/10.3389/feart.2022.884766
14. Intercomparison of global ERA reanalysis products for streamflow simulations at the high-resolution continental scale R. Bain et al. https://doi.org/10.1016/j.jhydrol.2022.128624
15. Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging X. Lu et al. https://doi.org/10.1007/s11629-021-6901-5
16. Representativeness of the Precipitation Observing Network for Monitoring Precipitation Change and Variability in Canada H. Wan et al. https://doi.org/10.1080/07055900.2022.2144111
17. High-resolution ensemble precipitation and temperature datasets for CONUS based on a probabilistic geospatial estimation approach G. Tang et al. https://doi.org/10.1016/j.jhydrol.2025.134761
18. Introducing IQR–DEMO for precipitation network design optimization A. Hailu & P. Coulibaly https://doi.org/10.1016/j.jhydrol.2026.136125
19. QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests Y. Zhang et al. https://doi.org/10.1029/2022WR032117
20. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. https://doi.org/10.1029/2020WR029001
21. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. https://doi.org/10.3389/fenvs.2023.1207508
22. GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications G. Tang et al. https://doi.org/10.5194/gmd-17-1153-2024
23. Evaluation of IMERG and ERA5 Precipitation-Phase Partitioning on the Global Scale W. Xiong et al. https://doi.org/10.3390/w14071122
24. Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient U. Sepúlveda et al. https://doi.org/10.5194/hess-26-3419-2022
25. Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. https://doi.org/10.1002/joc.7926
26. On the importance of the reference data: Uncertainty partitioning of bias-adjusted climate simulations over eastern Canada J. Lavoie et al. https://doi.org/10.1016/j.cliser.2025.100619
27. Hydro-climatological changes and mine water management challenges in Canada under climate change W. Palash et al. https://doi.org/10.1088/1748-9326/adf762
28. A station-based 0.1-degree daily gridded ensemble precipitation dataset for India A. Peringiyil et al. https://doi.org/10.1038/s41597-025-04474-2
29. The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins G. Tang et al. https://doi.org/10.1029/2022WR033767
30. Detailed investigation of discrepancies in Köppen-Geiger climate classification using seven global gridded products S. Hobbi et al. https://doi.org/10.1016/j.jhydrol.2022.128121
31. Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. https://doi.org/10.1111/jfr3.12895