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A generalized, bioclimatic index to predict foliar phenology in response to climate

机译:一个广义的生物气候指数,用于预测气候响应下的叶片物候

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摘要

The phenological state of vegetation significantly affects exchanges of heat, mass, and momentum between the Earth's surface and the atmosphere. Although current patterns can be estimated from satellites, we lack the ability to predict future trends in response to climate change. We searched the literature for a common set of variables that might be combined into an index to quantify the greenness of vegetation throughout the year. We selected as variables: daylength (photoperiod), evaporative demand (vapor pressure deficit), and suboptimal (minimum) temperatures. For each variable we set threshold limits, within which the relative phenological performance of the vegetation was assumed to vary from inactive (0) to unconstrained (1). A combined Growing Season Index (GSI) was derived as the product of the three indices. Ten-day mean GSI values for nine widely dispersed ecosystems showed good agreement (r > 0.8) with the satellite-derived Normalized Difference Vegetation Index (NDVI). We also tested the model at a temperate deciduous forest by comparing model estimates with average field observations of leaf flush and leaf coloration. The mean absolute error of predictions at this site was 3 days for average leaf flush dates and 2 days for leaf coloration dates. Finally, we used this model to produce a global map that distinguishes major differences in regional phenological controls. The model appears sufficiently robust to reconstruct historical variation as well as to forecast future phenological responses to changing climatic conditions.
机译:植被的物候态会显着影响地球表面与大气之间的热,质量和动量交换。尽管可以通过卫星估算当前模式,但我们缺乏预测应对气候变化的未来趋势的能力。我们在文献中搜索了一组常见的变量,这些变量可以组合为一个指标,以量化全年的植被绿色程度。我们选择了以下变量作为变量:日长(光周期),蒸发需求(蒸气压不足)和次优(最低)温度。对于每个变量,我们设置阈值限制,在该限制内,假定植被的相对物候性能从非活动状态(0)变为不受限制状态(1)。合并的生长季节指数(GSI)是这三个指数的乘积。 9个广泛分布的生态系统的10天平均GSI值与卫星衍生的归一化植被指数(NDVI)表现出良好的一致性(r> 0.8)。我们还通过将模型估计值与平均​​田间观察到的叶子潮红和叶子着色进行比较,在温带落叶林中测试了该模型。该站点预测的平均绝对误差为平均叶片冲洗日期为3天,叶片着色日期为2天。最后,我们使用此模型生成了一个全局图,该图可以区分区域物候控制中的主要差异。该模型似乎足够健壮,可以重建历史变异以及预测未来对气候条件变化的物候响应。

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