Articles | Volume 29, issue 1
https://doi.org/10.5194/hess-29-85-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-85-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
10 Jan 2025
Review article |
| 10 Jan 2025
| 10 Jan 2025
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
Kyle R. Mankin
CORRESPONDING AUTHOR
Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, USA
Sushant Mehan
Agricultural and Biosystems Engineering Department, South Dakota State University, Brookings, SD, USA
Timothy R. Green
Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, USA
David M. Barnard
Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, USA
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA
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27 citations as recorded by crossref.
- Mexico's High Resolution Climate Database (MexHiResClimDB): a new daily high-resolution gridded climate dataset for Mexico covering 1951–2020 J. Carrera-Hernández https://doi.org/10.5194/essd-17-6911-2025
- Uncertainty propagation from gridded precipitation datasets to streamflow simulations: application to the Reno River basin (Italy) O. Cenobio-Cruz & G. Di Baldassarre https://doi.org/10.1080/02626667.2025.2590597
- Performance of gauge-based and reanalysis gridded temperature datasets in representing means and extremes across different climate zones of the Brazos River Basin, United States T. Tarkegn et al. https://doi.org/10.3389/fclim.2026.1731069
- Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin V. Akstinas et al. https://doi.org/10.3390/atmos16020229
- Integrated Soil Temperature Measurement at Multiple Depths for Building Energy Performance Assessment Under Climate Change Conditions E. Daniszewska et al. https://doi.org/10.3390/en18225881
- Analysis and prediction of monthly runoff evolution characteristics in Linqi reservoir (China) D. Ma et al. https://doi.org/10.3389/fenvs.2026.1785661
- From raw to reliable: machine learning bias correction of reanalysis data for improved drought severity classification P. Pandya et al. https://doi.org/10.1016/j.jhydrol.2025.134892
- Assessment of rainfall Spatio-temporal trends and climate variability for sustainable agriculture and environmental resilience in parts of Ekiti state, south West Nigeria O. Olaleye et al. https://doi.org/10.1016/j.ecofro.2025.12.020
- A comparative analysis of the gridded and observed precipitation dataset for drought detection using the Standardized Precipitation Index E. Mwanga et al. https://doi.org/10.1080/02626667.2026.2664062
- Multivariate bias correction of ERA5 using in-situ observations for planning and engineering D. Rasmussen https://doi.org/10.1088/2752-5295/ae63ee
- Evaluation of the performance of high-resolution global climate datasets over Malawi L. Nkhoma et al. https://doi.org/10.1016/j.pce.2025.104221
- Integrated Assessment of Climate-Driven Streamflow Changes in a Transboundary Lake Basin Using CMIP6-SWAT+-BMA: A Sustainability Perspective F. Xiao et al. https://doi.org/10.3390/su17177901
- Evaluation of daily gridded climate products using in situ FLUXNET data and tree growth modeling F. Wang et al. https://doi.org/10.1088/1748-9326/ae384b
- Containment lines, PODs and suppression success: a case study of the 2021 Schneider Springs Fire J. Young et al. https://doi.org/10.1071/WF25124
- Time-resolved moving‐window machine learning modeling for human‐caused wildfire ignitions from 2001 to 2020 in California Z. Cao et al. https://doi.org/10.1088/1748-9326/ae692d
- A National, Ecological Study on the Impact of Extreme Precipitation on Walking and Cycling to Work, 2005–2018 M. Wende et al. https://doi.org/10.3390/su18041874
- The evolution of drought characteristics in semi-arid Africa over the last four decades K. Afamondji et al. https://doi.org/10.1016/j.ejrh.2025.103087
- Temporal inhomogeneities in high-resolution gridded precipitation products for the southeastern United States J. Diem https://doi.org/10.5194/hess-30-1999-2026
- Rainfall Erosivity Main Features and Their Associated Synoptic Conditions in North-Eastern Romania R. Hrițac et al. https://doi.org/10.3390/app15126785
- Evaluating gridded climate datasets for hydrological assessment of data-scarce watersheds in the Philippines using the SWAT model K. Cabigao et al. https://doi.org/10.1080/02626667.2026.2676092
- A Climate-Network Framework For Seasonal Precipitation Regime Mapping In Arid and Semi-Arid Regions: Application to Saudi Arabia Y. Alabbad et al. https://doi.org/10.1007/s41748-026-01094-7
- Hydrological Assessment of Climate Change Impacts on Streamflow Using Downscaled CMIP6 GCM Ensemble H. Tayyeh & R. Mohammed https://doi.org/10.1007/s11269-026-04693-1
- In situ soil moisture data improve precipitation-based shallow landslide early warning through innovative machine learning methods T. Halter et al. https://doi.org/10.1007/s10346-025-02599-4
- Streamflow generation, hydroclimatic changes, and flood mechanisms in the Sheep River basin on the eastern slopes of the Canadian Rockies C. Vidrio-Sahagún et al. https://doi.org/10.1016/j.ejrh.2025.102562
- Assessing and optimizing high-resolution global river streamflow estimates with triple collocation analysis M. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135122
- Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco A. Chakri et al. https://doi.org/10.3390/atmos16111234
- Unseen damage: Bushfire, infant health and early child development Y. Zhang https://doi.org/10.1016/j.jeem.2026.103342
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets....
