Search

The grassland ecosystems in Tibetan Plateau (TP) and Inner Mongolia (IM) of China play important roles in climate change mitigation and food and livestock production. These two regions have increasingly experienced higher temperatures and changing precipitation regimes over the past three decades. However, it remains uncertain to what extent rising temperature and varying precipitation regulate the water and carbon fluxes across alpine (TP) and temperate (IM) grasslands. Here, we first optimize a process-based model of carbon and water fluxes using eddy-covariance data (three sites in TP and six sites in IM), and analyze the simulated carbon and water fluxes based upon the optimized model exposed to a range of annual temperature and precipitation anomalies. We found that the changes in net ecosystem-atmosphere carbon exchange (NEE) of TP grassland are relatively small because the ecosystem respiration (R e ) and the gross primary productivity (GPP) increase at comparable rate with warming across multiple sites (R e : 22.1 ± 21.4 g C m −2 year −1 °C −1 , GPP: 22.43 ± 36.41 g C m −2 year −1 °C −1 ), which is due to the possibility that grasslands cannot respire more than the available supply of photosynthesis. The NEE of IM grassland increases (more carbon loss from ecosystem) with warming, which is mainly because GPP decreases faster than R e under warm-induced reduction in moisture availability, and the sensitivity of R e to warming (1.17 ± 3.56 g C m −2 year −1 °C −1 ) is much smaller than that of GPP (15.53 ± 15.91 g C m −2 year −1 °C −1 ). These results indicate that water is the major limiting factor in IM grasslands, but not in TP grasslands. In contrast to warming, we found an asymmetric response of water and carbon fluxes to drying and wetting in TP grasslands (i.e. a large decrease under the drying condition and a small increase under the wetting condition) but almost a linear response in IM grasslands. We therefore highlight that the underlying processes regulating the responses of water and carbon cycles to warming are fundamentally different between TP and IM grasslands, with the moisture being the major limiting factor in IM while grasslands in TP are much more limited by thermal conditions. Our results also imply that warming would significantly stimulate the net ecosystem carbon loss to atmosphere but not significantly enhance ET in IM grasslands, which may provide a positive feedback to accelerate climate change. Inversely, warming could not significantly affect the ecosystem carbon exchange but significantly enhance ET in TP grasslands, which may provide a negative feedback to mitigate climate change in alpine grasslands. [ABSTRACT FROM AUTHOR]

[Display omitted] • A bi-level joint-probabilistic programming approach is developed. • It can handle leader–follower issues and joint-probabilistic constraints. • The proposed approach is applied to China's multi-regional energy system model. • The optimal schemes during 2021–2050 under three mitigation scenarios are obtained. • Results are helpful to achieve low-carbon goals in multi-regional energy system. Climate change mitigation and renewable resources utilization are becoming particularly urgent for energy system management. In this study, a bi-level joint-probabilistic programming (BJPP) method is developed for planning multi-regional energy system under different mitigation policies and uncertainties. BJPP can handle leader–follower issues in decision-making process as well as examine the risk of violating joint-probabilistic constraints. Based on the BJPP method, a China's multi-regional energy system (named as BJPP_CMES) model is formulated to provide optimal scheme for energy system planning of China over a long-term horizon (2021–2050) by synergistically minimizing carbon dioxide (CO 2) emission and system cost. A series of scenarios associated with different carbon capture and storage (CCS) levels and violation risks of energy-demand constraints are examined. Results reveal that: (i) the share of non-fossil energy in China's energy supply would keep increasing in 2021–2050, and the highest growth of the renewable supply would occur in Ningxia (rising 47.7%); (ii) Sichuan, Inner Mongolia, and Gansu would be the top three suppliers of renewable electricity; (iii) the CO 2 emission of China would reach a peak of [44.3, 54.8] billion tonnes during the period of 2026–2030; Shandong, Inner Mongolia, and Shanxi would be main contributors of CO 2 emission in the future; (iv) compared with the single-level model, the CO 2 emission from the BJPP_CMES model would reduce by [2.7, 5.7]%; (v) among developed regions, the individual probability level of Jiangsu-Zhejiang-Shanghai is the most significant parameter for both CO 2 emission and system cost. The findings are helpful for decision makers to optimize multi-regional energy system (MES) with a low-carbon and cost-effective manner, as well as to provide useful information for renewable energy utilization and regional sustainable development. [ABSTRACT FROM AUTHOR]