Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions

• Published in: Journal of hydrometeorology Vol. 23; no. 3; p. 293
• Main Authors: Stern, M A, Flint, L E, Flint, A L, Boynton, R M, Stewart, J A E, Wright, J W, Thorne, J H
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.03.2022
• Subjects: Accuracy; Air temperature; Basins; Climate; Climate change; Climate models; Climate variability; Climatic data; Climatological charts; Datasets; Drought; Environmental protection; Environmental risk; Evapotranspiration; Future climates; Homogenization; Hydrologic models; Hydrology; Maximum temperatures; Minimum temperatures; Modelling; Mountains; Precipitation; Rain; Resolution; Risk assessment; Risk management; Runoff; Snow; Snow-water equivalent; Snowpack; Stream discharge; Stream flow; Temperature data; Variables; Water shortages; Water supply; Watershed management; Watersheds; Weather stations; United States > US; North America; California
• ISSN: 1525-755X; 1525-7541
• DOI: 10.1175/JHM-D-21-0045.1

Abstract High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1,231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-meter resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for Snow Water Equivalent (SWE), recharge and runoff.