Abstract
The grassland of the Tibetan Plateau forms a globally significant biome, which represents 6% of the world’s grasslands and 44% of China’s grasslands. However, large uncertainties remain concerning the vegetation carbon storage and turnover time in this biome. In this study, we quantified the pool size of both the aboveground and belowground biomass and turnover time of belowground biomass across the Tibetan Plateau by combining systematic measurements taken from a substantial number of surveys (i.e. 1689 sites for aboveground biomass, 174 sites for belowground biomass) with a machine learning technique (i.e. random forest, RF). Our study demonstrated that the RF model is effective tool for upscaling local biomass observations to the regional scale, and for producing continuous biomass estimates of the Tibetan Plateau. On average, the models estimated 46.57 Tg (1 Tg = 1012g) C of aboveground biomass and 363.71 Tg C of belowground biomass in the Tibetan grasslands covering an area of 1.32 × 106 km2. The turnover time of belowground biomass demonstrated large spatial heterogeneity, with a median turnover time of 4.25 years. Our results also demonstrated large differences in the biomass simulations among the major ecosystem models used for the Tibetan Plateau, largely because of inadequate model parameterization and validation. This study provides a spatially continuous measure of vegetation carbon storage and turnover time, and provides useful information for advancing ecosystem models and improving their performance.
Highlights
The Tibetan Plateau is the highest and largest plateau in the world, with an average elevation that exceeds 4000 m above sea level and covering an area of about 2.5 million km2
Summer Normalized difference vegetation index (NDVI), and summer relative humidity were found to be the best combinations for simulating aboveground biomass (AGB), while summer NDVI, annual air temperature, and summer standardised precipitation– evapotranspiration index (SPEI) were selected for simulating belowground biomass (BGB)
The results show that the random forest (RF) enables the adequate retrieval of the regional pattern of biomass on the Tibetan Plateau
Summary
The Tibetan Plateau is the highest and largest plateau in the world, with an average elevation that exceeds 4000 m above sea level and covering an area of about 2.5 million km. During the past several decades, the Tibetan Plateau has become one of the most sensitive regions to rapid climate warming, in that its mean annual temperature has increased by 0.26 ◦C per decade from 1961–2005, which is twice the global mean temperature change (Lu and Liu 2010, Xu et al 2017). Climate warming has the potential to substantially impact the structure and function of ecosystems, and especially alter the carbon cycle processes (Zhuang et al 2010). The biomass turnover time substantially impacts the soil organic carbon on the Tibetan Plateau (Taghizadeh-Toosi et al 2016). This region contains a large amount of soil carbon due to low decomposition rates driven by low temperature (Ding et al 2017). Patterns and determinants of the variability in the turnover time of biomass on the Tibetan Plateau remain poorly understood
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