Abstract
It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China’s grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R2 = 0.79, RMSE = 0.77 g C m−2 day−1) and temperate grasslands (R2 = 0.75, RMSE = 0.60 g C m−2 day−1). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.
Highlights
Grasslands are one of the most widespread vegetation types and store one-fifth of the total global carbon in its vegetation and soil [1,2]; they play an important role in the global carbon cycle
The variation of Reref in the Tibetan alpine grasslands is primarily attributed to the change in vegetation productivRiteyre(fE=VImpe1an+, Rp22 ∗=EV0.6Im12ea)n a+ndp3 ∗toLSthTemewana+terp4r∗egLiSmWe Im(LeSanW, Imean, R2 = 0.334()4.) Temperature plays a negligible role in the Reref (LSTmean, R2 = 0.128)
Multiple regression analyses between Reref and the remote-sensing data show that Reref has a significant dependence on EVImean, LSTmean and LSWImean, which can be approximated linearly (Figure 3)
Summary
Grasslands are one of the most widespread vegetation types and store one-fifth of the total global carbon in its vegetation and soil [1,2]; they play an important role in the global carbon cycle. Northern China’s grasslands located in the Tibetan Plateau and Inner Mongolian Plateau, in particular, constitute the majority (more than 70%) of the grasslands in China and represent two significant grassland types worldwide (i.e., alpine and temperate grasslands) [4]. They are sensitive to climate changes due to their unique plateau topography, the extreme cold, arid and semi-arid ecological environment and the high soil carbon density [5,6,7]. It is essential but challenging to accurately evaluate the regional RE in northern China’s grasslands for a quantitative assessment of the terrestrial ecosystem carbon budget and its response to future climate changes
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