Location: Range Management ResearchTitle: Critical soil water period for primary production in Chihuahuan Desert ecosystems) Author
|Peters, Debra - Deb|
Submitted to: Ecological Society of America (ESA)
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2013
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: In desert ecosystems where water is the main limiting factor, it is expected that net primary production (NPP) is largely determined by precipitation. However, precipitation alone often explains only a small portion of the variation in NPP, and the critical precipitation period for NPP varies by plant functional group, location, and ecosystem type. We expected that plant available water in soil (PAW) would be a better predictor of variation in NPP. We identified the critical soil water periods for NPP of perennial grasses and shrubs in five major types of Chihuahuan Desert ecosystems using field data of NPP and model-simulated daily PAW. Long-term data on aboveground NPP, other vegetation characteristics, daily climate, and soil texture were obtained from the Jornada USDA-LTER site in southern New Mexico. Critical soil water period was determined by a sliding-window method where water at various soil depths accumulated within “windows” of days was correlated to NPP. The “window” of days with the highest correlation was identified as the critical soil water period. Correlations between NPP and model-simulated PAW were generally higher than those between NPP and precipitation. Perennial grass NPP was highly correlated to PAW at soil depth <= 30 cm while shrub NPP was highly correlated to PAW at soil depths <= 30 cm and >30 cm, reflecting the difference in root distribution between the two functional groups. Critical soil water periods for perennial grass NPP occurred mainly in the monsoon season (Jul – Sep), and those for shrub NPP occurred in all three seasons. Determining differences in timing of soil water usage by perennial grasses and shrubs will improve understanding and prediction of state changes in desert ecosystems under global climate change.