OOS 88-6
Changes in precipitation intensity and frequency influence carbon dynamics in an arid grassland: Results from a multiannual experiment

Friday, August 14, 2015: 9:50 AM
328, Baltimore Convention Center
Rodrigo Vargas, Plant and Soil Sciences, University of Delaware, Newark, DE
Scott L. Collins, Department of Biology, University of New Mexico, Albuquerque, NM
Renee F. Brown, Department of Biology, University of New Mexico, Albuquerque, NM
Amaris L. Swann, Biology, University of New Mexico, Sevilleta LTER, Albuquerque, NM
John Mulhouse, University of New Mexico, Albuquerque, NM

Precipitation regimes in water-limited ecosystems of the Southwest of the United States are predicted to become more variable with more extreme rainfall events punctuated by longer intervening dry periods. These water-limited ecosystems are highly responsive to altered precipitation regimes and therefore changes will influence the biophysical drivers that regulate carbon dynamics. We present results from a rainfall manipulation experiment in a Chihuahuan Desert grassland. Rainfall was manipulated during the summer monsoon season (July–September) to vary both the size and frequency of events from 2007 to 2014 (8 years). This grassland experienced an intense lighting-caused wildfire during 2009. Treatments included (1) ambient rain, (2) ambient rain plus one 20mm rain event each month, and (3) ambient rain plus four 5mm rain events each month. Throughout the monsoon seasons, we measured soil temperature, soil moisture content, soil CO2 efflux, and seasonal aboveground and belowground net primary productivity of the dominant C4 grass, Bouteloua eriopoda


Our results show that water-limited ecosystems are highly sensitive to increased precipitation variability where carbon dynamics are enhanced by increased rainfall event size more than increased rainfall event frequency. However, the knowledge gained from control plots (ambient precipitation) does not provide a strong predictive capability of the responses to changes in precipitation regimes in the treatment plots. These results open new questions regarding our understanding of extreme events, the undelaying biophysical ecosystem processes that are modified, and the predictive capability of models.