Simulating the spatiotemporal variations in aboveground biomass in Inner Mongolian grasslands under environmental changes

Abstract

Grassland aboveground biomass (AGB) is a critical component of the global carbon cycle and reflects ecosystem productivity. Although it is widely acknowledged that dynamics of grassland biomass is significantly regulated by climate change, in situ evidence at meaningfully large spatiotemporal scales is limited. Here, we combine biomass measurements from six long-term (> 30 years) experiments and data in existing literatures to explore the spatiotemporal changes in AGB in Inner Mongolian temperate grasslands. We show that, on average, annual AGB over the past 4 decades is 2561, 1496 and 835 kg ha−1, respectively, in meadow steppe, typical steppe and desert steppe in Inner Mongolia. The spatiotemporal changes of AGB are regulated by interactions of climatic attributes, edaphic properties, grassland type and livestock. Using a machine-learning-based approach, we map annual AGB (from 1981 to 2100) across the Inner Mongolian grasslands at the spatial resolution of 1 km. We find that on the regional scale, meadow steppe has the highest annual AGB, followed by typical and desert steppe. Future climate change characterized mainly by warming could lead to a general decrease in grassland AGB. Under climate change, on average, compared with the historical AGB (i.e. average of 1981-2019), the AGB at the end of this century (i.e. average of 2080-2100) would decrease by 14 % under Representative Concentration Pathway (RCP) 4.5 and 28 % under RCP8.5. If the carbon dioxide (CO2) enrichment effect on AGB is considered, however, the estimated decreases in future AGB can be reversed due to the growing atmospheric CO2 concentrations under both RCP4.5 and RCP8.5. The projected changes in AGB show large spatial and temporal disparities across different grassland types and RCP scenarios. Our study demonstrates the accuracy of predictions in AGB using a modelling approach driven by several readily obtainable environmental variables and provides new data at a large scale and fine resolution extrapolated from field measurements.