Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.2172/1226921 |
报告编号 | 1 |
来源ID | OSTI ID: 1226921 |
Improving models to predict phenological responses to global change | |
Richardson, Andrew D. | |
2015-11-25 | |
出版年 | 2015 |
页数 | 5 |
语种 | 英语 |
国家 | 美国 |
领域 | 地球科学 |
英文摘要 | The term phenology describes both the seasonal rhythms of plants and animals, and the study of these rhythms. Plant phenological processes, including, for example, when leaves emerge in the spring and change color in the autumn, are highly responsive to variation in weather (e.g. a warm vs. cold spring) as well as longer-term changes in climate (e.g. warming trends and changes in the timing and amount of rainfall). We conducted a study to investigate the phenological response of northern peatland communities to global change. Field work was conducted at the SPRUCE experiment in northern Minnesota, where we installed 10 digital cameras. Imagery from the cameras is being used to track shifts in plant phenology driven by elevated carbon dioxide and elevated temperature in the different SPRUCE experimental treatments. Camera imagery and derived products (âgreennessâ) is being posted in near-real time on a publicly available web page (http://phenocam.sr.unh.edu/webcam/gallery/). The images will provide a permanent visual record of the progression of the experiment over the next 10 years. Integrated with other measurements collected as part of the SPRUCE program, this study is providing insight into the degree to which phenology may mediate future shifts in carbon uptake and storage by peatland ecosystems. In the future, these data will be used to develop improved models of vegetation phenology, which will be tested against ground observations collected by a local collaborator. |
英文关键词 | SPRUCE experiment phenology models |
URL | 查看原文 |
来源平台 | US Department of Energy (DOE) |
引用统计 | |
文献类型 | 科技报告 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/6934 |
专题 | 地球科学 |
推荐引用方式 GB/T 7714 | Richardson, Andrew D.. Improving models to predict phenological responses to global change,2015. |
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