概率模型预测中国西北沙漠生态系统中植被生物量的动态变化。
Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China.
机构信息
Shapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544.
出版信息
Proc Natl Acad Sci U S A. 2017 Jun 20;114(25):E4944-E4950. doi: 10.1073/pnas.1703684114. Epub 2017 Jun 5.
The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend-for each season-on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons.
植被生物量的时间动态对于评估干旱和半干旱生态系统的可持续性至关重要。在这些生态系统中，生物量和土壤水分是外部驱动的随机变量，主要由降雨动态驱动。基于中国西北地区（NW）的长期实地观测，我们测试了一种最近开发的分析方案，用于描述具有不同降雨特征的季节性循环中的叶片生物量动态。这种动态的概率特征与实地测量非常吻合，为预测干旱和半干旱生态系统在气候变化条件下预期的生物量变化提供了工具。这些变化将取决于每个季节的预测雨天率、雨天的平均降雨量和季节的持续时间。对于中国西北地区的站点，当前的情景是雨天率增加 10%、雨天的平均降雨量增加 10%、季节持续时间不变，这将导致两个季节的平均叶生物量预测增加近 25%。
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