  In high mountains, the effects of climate change are manifesting most
rapidly. This is especially critical for the high-altitude carbon cycle, for
which new feedbacks could be triggered. However, mountain carbon dynamics is
only partially known. In particular, models of the processes driving carbon
fluxes in high-altitude grasslands and Alpine tundra need to be improved. Here,
we propose a comparison of three empirical approaches using systematic
statistical analysis, to identify the environmental variables controlling
$CO_2$ fluxes. The methods were applied to a complete dataset of simultaneous
in situ measurements of the net $CO_2$ exchange, ecosystem respiration and
basic environmental variables in three sampling sites in the same catchment.
Large year-to-year variations in the gross primary production (GPP) and
ecosystem respiration (ER) dependences on solar irradiance and temperature were
observed,. We thus implemented a multi regression model in which additional
variables were introduced as perturbations of the standard exponential and
rectangular hyperbolic functions for ER and GPP, respectively. A comparison of
this model with other common modelling strategies, showed the benefits of this
approach, resulting in large explained variances (83% to 94%). The optimum
ensemble of variables explaining the inter- and intra-annual flux variability
included solar irradiance, soil moisture and day of the year for GPP, and air
temperature, soil moisture, air pressure and day of the year for the ER, in
agreement with other studies. The modelling approach discussed here provides a
basis for selecting drivers of carbon fluxes and understanding their role in
high-altitude Alpine ecosystems, also allowing for future short-range
assessments of local trends.
