A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

Abstract

The alpine meadow ecosystem accounts for 27 % of the total area of the Tibetan Plateau and is also one of the most important vegetation types. The Dangxiong alpine meadow ecosystem, located in the south-central part of the Tibetan Plateau, is a typical example. To understand the carbon and water fluxes, water use efficiency (WUE), and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area, two parameter estimation methods, the Model-independent Parameter Estimation (PEST) and the Dynamic Dimensions Search (DDS), were used to optimize the Biome-BGC model. Then, the gross primary productivity (GPP) and evapotranspiration (ET) were simulated. The results show that the DDS parameter calibration method has a better performance. The annual GPP and ET show an increasing trend, while the WUE shows a decreasing trend. Meanwhile, ET and GPP reach their peaks in July and August, respectively, and WUE shows a “dual-peak” pattern, reaching peaks in May and November. Furthermore, according to the simulation results for the next nearly 100 years, the ensemble average GPP and ET exhibit a significant increasing trend, and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario. WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario. This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.

摘要

全球气候变化对青藏高原生态系统产生了深远影响, 暖湿化背景下青藏高原植被碳, 水通量变化趋势值得关注. 高寒草甸是青藏高原最主要的植被类型之一, 为理解青藏高原当雄地区高寒草甸生态系统碳, 水通量, 水分利用效率及其对未来气候变化的响应, 本研究利用PEST和DDS两种参数率定方法优化Biome-BGC模型, 进而模拟2000–2019年当雄站的总初级生产力 (GPP) 和蒸散量 (ET) . 研究结果表明: DDS参数率定方法具有更优的性能. GPP和ET在研究时段内呈上升趋势, 而水分利用效率 (WUE) 则呈下降趋势. 同时, ET和GPP分别在7月和8月达到峰值, 而WUE则呈“双峰”变化, 分别于5月和11月达到峰值. 此外, 未来近百年的模拟表明GPP和ET的集合平均结果呈显著增加趋势, 其中在SSP5–8.5情景下的增速大于SSP2–4.5情景. WUE在SSP2–4.5情景下呈增加趋势, 而在SSP5–8.5情景下呈显著增加趋势. 本研究结果可为青藏高原碳, 水循环预测研究和植被生态保护的应用研究提供参考和借鉴.