Department of Civil and Environmental Engineering, Imperial College London, London, UK.
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland.
Glob Chang Biol. 2020 Jun;26(6):3336-3355. doi: 10.1111/gcb.15024. Epub 2020 Mar 18.
Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.
降雨量和模式的变化已经被观察到,并预计在不久的将来会继续,这可能会对生态和社会产生重大影响。因此,模拟植被对降雨量变化的响应对于预测未来的水碳循环至关重要。在本研究中,我们提出了一个新的模型-数据对比项目的结果,在该项目中,我们测试了 10 个陆地生物圈模型复制观测到的生态系统生产力对全球 10 个地点的降雨量变化的敏感性的能力,其中 9 个地点进行了降雨排除和/或灌溉实验。主要结果如下:(a)模型间的差异通常很大,模型的一致性随时间尺度而变化。在严重缺水的地区,模型仅在蒸散量的年际变化上达成一致,在一定程度上也在总初级生产力上达成一致。在较湿润的地区,水和碳通量的模型一致性通常在较细的(日-月)时间尺度上较高,而在较长的(季节-年)时间尺度上降低。(b)模型平均高估了跨站点(空间)的生态系统生产力与平均降雨量之间的关系,并且在复制给定站点的植被生产力对年降雨量的时间(年际)敏感性方面能力较低,尽管观测不确定性与模型间变异性相当。(c)大多数模型复制了降雨干扰实验中生产力变化的观测模式,但在复制生产力变化的观测幅度方面能力较低。模型对降雨排除引起的生产力变化的复制效果优于降雨增加。(d)所有模型都将生态系统生产力变化归因于植被压力和最大叶面积的强度,而生长季节长度变化的影响可以忽略不计。模型之间最大叶面积和植被压力强度的相对贡献差异很大。