气候变化领域集成服务门户(CCPortal): Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events

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DOI	10.1016/j.rse.2020.111956
	Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events
	Qiu T.; Song C.; Clark J.S.; Seyednasrollah B.; Rathnayaka N.; Li J.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	Bayesian hierarchical model; Climate change; Continuous development; Extreme weather events; Land surface phenology; Vegetation index
语种	英语
scopus关键词	Climate models; Drought; Extreme weather; Mean square error; Rain; Vegetation; Weather information services; Climate change adaptation; Extreme weather events; Future climate projections; Land surface phenology; Root mean square errors; Temporal and spatial; Vegetation greenness; Vegetation phenology; Climate change; Bayesian analysis; climate change; extreme event; land cover; phenology; rainfall; remote sensing; spatiotemporal analysis; vegetation type; Midwest; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179235
作者单位	Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Nicholas School of the Environment, Duke University, Durham, NC 27708, United States; Department of Statistical Science, Duke University, Durham, NC 27708, United States; Northern Arizona University, School of Informatics, Computing, and Cyber Systems, Flagstaff, AZ 86011, United States; Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, United States; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China
推荐引用方式 GB/T 7714	Qiu T.,Song C.,Clark J.S.,et al. Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events[J],2020,247.
APA	Qiu T.,Song C.,Clark J.S.,Seyednasrollah B.,Rathnayaka N.,&Li J..(2020).Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events.Remote Sensing of Environment,247.
MLA	Qiu T.,et al."Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events".Remote Sensing of Environment 247(2020).