Comparing the seasonal variation of parameter estimation of ecosystem carbon exchange between alpine meadow and cropland in Heihe River Basin, northwestern China.

Abstract

Grasslands and agro-ecosystems occupy one-third of the global terrestrial area.However,great uncertainty still exists about their contributions to the global carbon cycle.This study used various combinations of a simple ecosystem respiration model and a photosynthesis model to simulate the influence of different climate factors,specifically radiation,temperature,and moisture,on the ecosystem carbon exchange at two dissimilar study sites.Using a typical alpine meadow site in a cold region and a typical cropland site in an arid region as cases,we investigated the response characteristics of productivity of grasslands and croplands to different environmental factors,and analyzed the seasonal change patterns of different model parameters.Parameter estimations and uncertainty analyses were performed based on a Bayesian approach.Our results indicated that:(1) the net ecosystem exchange(NEE) of alpine meadow and seeded maize during the growing season presented obvious diurnal and seasonal variation patterns.On the whole,the alpine meadow and seeded maize ecosystems were both apparent sinks for atmospheric CO2;(2) in the daytime,the mean NEE of the two ecosystems had the largest values in July and the lowest values in October.However,overall carbon uptake in the cropland was greater than in the alpine meadow from June to September;(3) at the alpine meadow site,temperature was the main limiting factor influencing the ecosystem carbon exchange variations during the growing season,while the sensitivity to water limitation was relatively small since there is abundant of rainfall in this region;(4) at the cropland site,both temperature and moisture were the most important limiting factors for the variations of ecosystem carbon exchanges during the growing season; and(5) some parameters had an obvious characteristic of seasonal patterns,while others had only small seasonal variations.