Simulated annual carbon fluxes of grassland ecosystems in extremely arid conditions

Abstract

AbstractIn order to understand how changes in climate and land cover affect carbon cycles and structure and function of regional grassland ecosystems, we developed a grassland landscape productivity model, proposed an approach that combined both process‐based modeling and spatial analysis with field measurements, and provided an example of semiarid region in Inner Mongolia, China, in extremely arid conditions. The modeled monthly mean and total net primary productivity, and monthly and annual mean respiration rates for Leymus chinensis steppes during the growing seasons in 2002 were mostly within the normal varying ranges of measured values, or similar to the field measurements, conducted in the similarly arid conditions. And the modeled total net ecosystem productivity (NEP) for L. chinensis steppes and Stipa grandis steppes were both between the lower and the higher measurements or within modeled multi‐annual data by the other model. The modeled total NEP was 1.91 g C/m2/year over the entire study region. It indicated that if human disturbances were not considered, carbon budget over the entire study region during the growing seasons was nearly in balance or weak carbon sink even under extremely arid conditions. However, the modeled NEP spatially greatly varied not only over the entire study region (−48.28–52.09 g C/m2/year), but also among different land cover types. The modeled results also showed that there were obvious seasonal variations in carbon fluxes, mainly caused by leaf area index; and annual precipitation was the major limiting factor for the obvious spatial patterns of carbon fluxes from the east to the west. The modeled results also revealed the influence of extreme drought on carbon fluxes. The study provides an effective approach to derive useful information about carbon fluxes for different land cover types, and thus can instruct regional land‐use planning and resource management based on carbon storage conditions.