Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and need for more objective approach to selecting model forcing datasets

Authors: Mankin, Kyle; MEHAN, SUSHANT - South Dakota State University; Green, Timothy; Barnard, David

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/9/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Water resource analyses are built on precipitation, air temperature, humidity, windspeed, and solar radiation data. In the past several decades, datasets that present U.S. or global climate data as a checkerboard of gridded data values have become commonly available. Many studies have also been published that compare a small number of these datasets. We synthesized information from 29 recent studies from the past 10 years to provide best-available-science recommendations for climate dataset selection. Generally, datasets that were derived using higher ground-station density, covered less-mountainous terrain, and were adjusted using ground-based data improved the climate data, though this did not always translate into more accurate streamflow modeling. To improve hydrologic analyses, future gridded datasets need to better represent climate interdependencies among climate variables and perform better in mountain topography.

Technical 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 historical climate product selection criteria for hydrologic modelling and analyses based on a review and synthesis of previous studies conducting dataset intercomparisons. All datasets summarized here span at least the conterminous U.S. (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, 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 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: 1) Gridded daily temperature datasets improved when derived over regions with greater station density. Similarly, 2) 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. 3) In mountainous regions as well as humid regions, R precipitation datasets generally performed better than G when underlying data had low station density, but for higher station densities, there was no difference. 4) G datasets generally were more accurate in representing precipitation and temperature data than S or R datasets, though this did not always translate into better streamflow modelling. 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 of 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) having spatial and temporal resolutions that match modelling scales, (b) that are primarily (G) or secondarily (SG, 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.