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 - Mankin, Kyle R.; Mehan, Sushant; Green, Timothy R.; Barnard, David M. (Hydrology and earth system sciences, 2025)
Climate forcing data accuracy drives performance of hydrologic models and analyses, yet each investigator needs to select from among the numerous gridded climate dataset options and justify their selection for use in a particular hydrologic model or analysis. This study aims to provide a comprehensive compilation and overview of gridded datasets (precipitation, air temperature, humidity, wind speed, solar radiation) and considerations for historical climate product selection criteria for hydrologic modeling and analyses based on a review and synthesis of previous studies conducting dataset intercomparisons. All datasets summarized here span at least the conterminous US (CONUS), and many are continental or global in extent. Gridded datasets built on ground-based observations (G; n= 20), satellite imagery (S; n= 20), and/or reanalysis products (R; n= 23) are compiled and described, with focus on the characteristics that hydrologic investigators may find useful in discerning acceptable datasets (variables, coverage, resolution, accessibility, and latency). We provide best-available-science recommendations for dataset selection based on a thorough review, interpretation, and synthesis of 29 recent studies (past 10 years) that compared the performance of various gridded climate datasets for hydrologic analyses. No single best source of gridded climate data exists, but we identified several common themes that may help guide dataset selection in future studies: Gridded daily temperature datasets improved when derived over regions with greater station density. Similarly, gridded daily precipitation data were more accurate when derived over regions with higher-density station data, when used in spatially less-complex terrain, and when corrected using ground-based data. In mountainous regions and humid regions, R precipitation datasets generally performed better than G when underlying data had a low station density, but there was no difference for higher station densities. G datasets were generally more accurate in representing precipitation and temperature data than S or R datasets, although this did not always translate into better streamflow modeling. We conclude that hydrologic analyses would benefit from guided dataset selection by investigators, including justification for selecting a specific dataset, and improved gridded datasets that retain dependencies among climate variables and better represent small-scale spatial variability in climate variables in complex topography. Based on this study, the authors' overall recommendations to hydrologic modelers are to select the gridded dataset (from Tables 1, 2, and 3) (a) with spatial and temporal resolutions that match modeling scales, (b) that are primarily (G) or secondarily (SG and RG) derived from ground-based observations, (c) with sufficient spatial and temporal coverage for the analysis, (d) with adequate latency for analysis objectives, and (e) that includes all climate variables of interest (so as to better represent interdependencies).