Climate
WSRRI's spatial priorities were based, in part, on downscaled climate data. We used the software program ClimateNA (Wang et al. 2016) to produce a suite of 31 annual 30-year normal (i.e., the average value of the prior 30 years) bioclimatic variables from 1984 to present based on a 250m resolution digital elevation model (ESA Copernicus). ClimateNA statistically downscales PRISM gridded historical and projected future climate data (Daly et al. 2008) based on local lapse rates that govern the relationships between climate variables and elevation. These variables were used primarily as covariates in the TerrAdapt landcover and fractional rangeland cover models.
The historical 30-year normal annual variables include:
mean annual temperature (°C)
mean temperature of the warmest month (°C)
mean temperature of the coldest month (°C)
difference between mean coldest and mean warmest month temperature (°C)
winter (December to February) mean temperature (°C)
spring (March to May) mean temperature (°C)
summer (June to August) mean temperature (°C)
autumn (September to November) mean temperature (°C)
mean annual precipitation (mm)
mean summer (May to September) precipitation (mm)
winter (December to February) precipitation (mm)
spring (March to May) precipitation (mm)
summer (June to August) precipitation (mm)
autumn (September to November) precipitation (mm)
annual heat moisture index
summer heat moisture index
degree-days below 0°C (freezing degree days)
degree-days above 5°C (growing degree days)
the number of frost-free days
frost-free period
the Julian date on which the frost-free period begins
the Julian date on which the frost-free period ends
precipitation as snow (mm)
extreme minimum temperature over 30 years
extreme maximum temperature over 30 years
Hargreave's reference evaporation
Hargreave's climatic moisture index
mean annual solar radiation (MJ m-2 d-1)
mean annual relative humidity (%)
Hogg’s climate moisture index (mm)
degree-days above 10°C and below 40°C
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