Preprints
https://doi.org/10.5194/hess-2024-58
https://doi.org/10.5194/hess-2024-58
26 Mar 2024
 | 26 Mar 2024
Status: a revised version of this preprint is currently under review for the journal HESS.

Review of Gridded Climate Products and Their Use in Hydrological Analyses Reveals Overlaps, Gaps, and Need for More Objective Approach to Model Forcings

Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard

Abstract. 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, windspeed, solar radiation) and considerations for climate product selection criteria for hydrologic modelling and analyses. All datasets summarized here span at least the coterminous U.S. (CONUS), and many are continental or global in extent. Gridded datasets built on ground-based observations (17), satellite imagery (20), and/or reanalysis products (23) are compiled and described, with focus on the characteristics that hydrologic investigators may find useful in discerning acceptable datasets (variables, coverage, resolution, accessibility, latency). Best-available-science recommendations for dataset selection are based on a review of 28 recent studies (past 10 years) that compared performance of various gridded climate datasets for hydrologic analyses. No single best source of gridded climate data exists, but several common themes arose. 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 as well as humid regions, reanalysis-based datasets generally performed better than ground-based when underlying data had low station density, but for higher station densities, there was no difference. Ground-based precipitation datasets generally performed better than satellite- or reanalysis-based datasets, though better precipitation and temperature datasets did not always translate into better streamflow modelling. Hydrologic analyses would benefit from improved gridded datasets that retain dependencies among climate variables and better represent small-scale spatial variability of climate variables in complex topography.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-58', Anonymous Referee #1, 22 Apr 2024
    • AC1: 'Reply on RC1', Kyle Mankin, 15 Jun 2024
  • RC2: 'Comment on hess-2024-58', Anonymous Referee #2, 21 May 2024
    • AC2: 'Reply on RC2', Kyle Mankin, 15 Jun 2024
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard

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Short summary
We assess 60 gridded climate datasets [ground- (G), satellite- (S), reanalysis-based (R)]. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; but R outperformed G when underlying data had low station density. G outperformed S or R datasets, though better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.