Incorporating agricultural ecosystems into a dynamic global vegetation model for climate change studies.
Twine, Tracy*,1, Kucharik, Christopher2, 1 University of Illinois at Urbana-Champaign, Urbana, IL2 University of Wisconsin-Madison, Madison, WI
ABSTRACT- Traditionally, global climate models have simulated climate variability under an imposed increasing trend in atmospheric carbon dioxide with a fixed land surface cover. In reality, both anthropogenic carbon emissions and human modification of the land surface act in concert so that biophysical and biogeochemical processes interact to produce changes in energy, water, and carbon dynamics. One example of large-scale human modification of the land surface that acts at decadal to century time scales is conversion of the natural landscape to croplands and pastures. Together these two land cover classes cover about one-third of the global land area. By not including representation of managed ecosystems in climate model scenarios, scientists and policymakers are neglecting an important driver of the climate system. An updated version of a global dynamic vegetation model, IBIS, now integrates the representation of land surface and soil physics, canopy physiology and phenology, terrestrial carbon and nitrogen cycling, and solute transport of both natural vegetation and major Midwestern United States crops. From this model the exchange of energy, carbon, water, and momentum between the soil, vegetation, and atmosphere can be simulated at an hourly time step. Model validation has been performed at the individual plot scale (5 m), field experiment scale with Ameriflux data (100 m), and the continental scale with USDA county-level yield data and monthly-average satellite (AVHRR) observations of vegetation characteristics (0.5 degree). The IBIS model has been used to examine (1) the association between the El Niño-Southern Oscillation and water cycle variability, (2) the effects of conversion to present-day agriculture on the water cycle, (3) the impact of climate variability and nitrogen fertilizer management on nitrate export, and (4) trends in earlier crop planting dates and related increases in net primary productivity within the Mississippi River basin. ñ
Key words: agriculture, modeling, biophysical, biogeochemical
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