Soil respiration is a major process of carbon dioxide emission from terrestrial ecosystems to atmosphere. Thus, quantifying the spatiotemporal pattern of soil respiration (Rs) at the regional or continental scale is critical to establish an experimental and theoretical basis for accurate assessment of the global carbon budget.
Three major approaches are available for estimating Rs at the regional scale. The inventory method multiplies the mean Rs rates of each land cover type by its area. Then, the total of the CO2 emissions from different land covers is taken as the total Rs. The second method calculates the Rs and its spatiotemporal variability using a process-based model. The third uses geostatistical model of Rs, which is constructed based on the relationships between environmental variables and measured soil carbon flux.
Each method has weakness and uncertainties. Relatively, the geostatistical Rs model has simple structure, sound parameterization method, and reasonable results, though it cannot predict Rs changes with climate, nitrogen deposition, etc. It is usually built on the relationships between in situ Rs rates and environmental variables, such as temperature, precipitation, and leaf area index (LAI). In addition, the geostatistical model can validate the process based Rs model by providing independent data on soil CO2 emissions. Therefore, the geostatistical Rs model is most widely used in the quantification of spatiotemporal variability of Rs at regional scale. Progress has been made on the research of Rs quantification in China over the past decade.
The research group lead by professor Yu Guirui from Key Laboratory of Ecosystem Network Observation and Modeling, Institute Of Geographic Science and Natural Resource Research, Chinese Academy of Science published this article in <<Environ. Sci. Technol>> (2010, 44: 6074–6080), the article summarizes the Rs data measured in China from 1995 to 2004. Based on the data, a new region-scale geostatistical model of soil respiration (GSMSR) was developed by modifying a global scale statistical model. The GSMSR model, which is driven by monthly air temperature, monthly precipitation, and soil organic carbon (SOC) density, can capture 64% of the spatiotemporal variability of soil Rs.
The group evaluated the spatiotemporal pattern of Rs in China using the GSMSR model. The estimated results demonstrate that the annual Rs in China ranged from 3.77 to 4.00 Pg C yr-1 between 1995 and 2004, with an average value of 3.84 ± 0.07 Pg C yr-1, contributing 3.92%-4.87% to the global soil CO2 emission. Annual Rs rate of evergreen broadleaved forest ecosystem was 698 ±11 g C m-2 yr-1, significantly higher than that of grassland (439 ±7 g Cm-2 yr-1) and cropland (555±12gCm-2 yr-1). The contributions of grassland, cropland, and forestland ecosystems to the total Rs in China were 48.38±0.35%, 22.19±0.18%, and 20.84±0.13%, respectively.
