Extended Drought Scenarios
California has a highly variable climate and is susceptible to dry spells. Recent research suggests that extended drought occurrence (“mega-drought”) could become more pervasive in future decades. This tool explores data for two 20-year drought scenarios derived from LOCA downscaled meteorological and hydrological simulations: one for the earlier part of the 21st century, and one for the latter part.
Long Drought Scenarios
Projections for two 20-year drought scenarios derived from LOCA downscaled meteorological and hydrological simulations: one for the earlier part of the 21st century, and one for the latter part. Details are described in Pierce et al., 2018.
LOCA Downscaled CMIP5 Projections for HadGEM2-ES RCP 8.5
Daily climate projections for California at a resolution of 1/16° (about 6 km, or 3.7 miles) generated to support climate change impact studies for California’s Fourth Climate Change Assessment. The data, derived from 32 coarse-resolution (~100 km) global climate models from the CMIP5 archive, were bias corrected and downscaled using the Localized Constructed Analogues (LOCA) statistical method. The data cover 1950-2005 for the historical period and 2006-2100 (some models stop in 2099) for two future climate projections. Details are described in Pierce et al., 2018.
LOCA VIC Runs for HadGEM2-ES RCP 8.5
The LOCA meteorological data (daily minimum temperature, daily maximum temperature and precipitation) are used to force the Variable Infiltration Capacity (VIC) land surface model to provide high-resolution projections for a suite of hydrological parameters on the 16th degree LOCA grid. Details are described in Pierce et al., 2018.
Gridded Observed Meteorological Data
Historical observed daily temperature data from approximately 20,000 NOAA Cooperative Observer (COOP) stations form the basis of this gridded dataset from 1950–2013 at a spatial resolution of 1/16º (approximately 6 km). Observation-based meteorological data sets offer insights into changes to the hydro-climatic system by diagnosing spatio-temporal characteristics and providing a historical baseline for future projections. Details are described in Livneh et al., 2015.
In order to create data layers used in this visualization, we calculated annual averages of daily values of Maximum Temperature, Minimum Temperature, Precipitation (LOCA climate variables) and Evapotranspiration, Snow Water Equivalent, Runoff, Baseflow, Tair (VIC climate variables) for each calendar year and each water year. This process was done for each of the 32 LOCA downscaled climate models for the RCP 8.5 scenario.
An envelope of modeled variability for each climate variable were generated by selecting the highest and lowest values from annual averages of all 32 LOCA downscaled climate models.