Thursday, 11 January 2018: 4:15 PM
Room 18B (ACC) (Austin, Texas)
Though the statistical methods of Detection and Attribution (D&A) have been widely used in studies of physical climate variables (e.g., temperature, precipitation and extreme events), their applications on terrestrial ecosystem (e.g., vegetation dynamics, carbon fluxes, and hydrologic cycles) are limited, mainly owing to the lack of long-term observational records and credible model simulations. With the recent availability of long-term runoff observations (1950-present) and multiple factorial model simulations in the continental U.S., we are in a unique position to detect and attribute the multi-year changes of terrestrial runoff from local to continental scales. To disentangle the natural and anthropogenic drivers (e.g., climate change, elevated CO2 concentration, and land use/land cover change) underlying spatiotemporal changes in runoff, we carry out formal and modified D&A analysis using single-factor simulations from multiple offline land surface models. The importance of each natural and human drivers, however, is regionally and seasonally dependent. Focusing on the autumn season, we see a significant positive trend for the conterminous US in the observations and simulated climate change forcing for the study period. We find that in addition to climate change effects, particularly the precipitation, individual and combined human effects also show significant impacts.
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