Abstract The impacts of climate change and extreme weather events (e.g. frost-, heat-, drought-, and heavy rainfall events) on the continuous phenological development over the entire seasonal cycle remained poorly understood. Previous studies mainly focused on modeling key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g. mean temperature, growing-degree days). We developed and evaluated a Bayesian Hierarchical Space-Time model for Land Surface Phenology (BHST-LSP) to synthesize remotely sensed vegetation greenness with climate covariates at a daily temporal scale from 1981 to 2014 across the entire conterminous United States. The BHST-LSP model incorporated both temporal and spatial information and exhibited high predictive power in simulating daily phenological development with an overall out-of-sample R2 of 0.80 ± 0.17 and 0.72 ± 0.20 for spring and fall phenology, respectively. The overall out-of-sample normalized root mean square errors were 9.3% ± 6.1% and 9.9% ± 5.2% between the observed and predicted vegetation greenness for spring and fall phenology, respectively. We found that a fast increase of temperature can accelerate the speed of spring green-up while a slow decrease of temperature can lead to a decelerated fall brown-down. Increasing accumulated precipitation can benefit daily phenological development over an entire growing season, while extreme rainfall events can have the opposite effects. More frequent frost events could slow spring leaf expansion and accelerate fall leaf senescence. Impacts of extreme heat events were complex and depended on water availability. Cropland in the Midwest as well as evergreen needleleaf forest along the coastal regions showed relatively strong resistance to drought events compared to other land cover types. The BHST-LSP model can be used to forecast vegetation phenology given future climate projection, thus providing valuable information for adopting climate change adaptation and mitigation measures.

中文翻译：

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使用贝叶斯分层时空模型了解每日时间步长的连续物候发展：气候变化和极端天气事件的影响

摘要 气候变化和极端天气事件（例如霜冻、高温、干旱和暴雨事件）对整个季节周期内持续物候发展的影响仍然知之甚少。以前的研究主要集中在基于聚合气候变量（例如平均温度、生长期天数）的关键物候转变日期（例如春季萌芽和秋季衰老的离散时间）建模。我们开发并评估了贝叶斯分层时空模型（BHST-LSP），以在整个美国本土从 1981 年到 2014 年在每日时间尺度上合成遥感植被绿度与气候协变量。BHST-LSP 模型结合了时间和空间信息，并在模拟日常物候发育方面表现出很高的预测能力，春季和秋季物候的总体样本外 R2 分别为 0.80 ± 0.17 和 0.72 ± 0.20。春季和秋季物候学观察到的和预测的植被绿度之间的总体样本外归一化均方根误差分别为 9.3% ± 6.1% 和 9.9% ± 5.2%。我们发现，温度的快速升高可以加快春季变绿的速度，而温度缓慢下降会导致秋季变黄的速度减慢。增加累积降水有利于整个生长季节的日常物候发展，而极端降雨事件可能会产生相反的影响。更频繁的霜冻事件可能会减缓春季叶片扩张并加速秋季叶片衰老。极端高温事件的影响很复杂，而且取决于可用水量。与其他土地覆盖类型相比，中西部的农田以及沿海地区的常绿针叶林对干旱事件的抵抗力相对较强。BHST-LSP 模型可用于预测未来气候预测的植被物候，从而为采取气候变化适应和减缓措施提供有价值的信息。