Representativeness of point-wise phenological Betula data collected in different parts of Europe
Representativeness of point-wise phenological Betula data collected in different parts of Europe
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DOI：
10.1111/j.1466-8238.2008.00383.x
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发表时间：
2008-07-01
影响因子：
6.4
通讯作者：
Shalaboda, Valentina
中科院分区：
文献类型：
--
作者：
Siljamo, Pilvi;Sofiev, Mikhail;Shalaboda, Valentina
目的利用最新编制的欧洲桦树物候观测数据库来检验物候观测的空间和时间代表性的不确定性问题。定位欧洲。方法根据覆盖欧洲15个国家的国家物候观测建立一个新的数据库，其中一些站点的最长观测周期超过几十年。结果单站数据的使用存在着显著的不可约减的不确定性，这种不确定性随站点位置的不同而变化3~8天。在更多的大陆和北部气候带，不确定性较低，可能是因为春季天气发展较快。在温和的气候条件下，单站记录的物候期日期的不确定性超过1周。大量的数据使我们能够初步估计一些站点的特征，根据这些站点报告的日期和相应的区域平均值，将它们标记为“晚期”、“早期”、“代表性”或“随机”。主要结论在单点物候观测中发现的不确定性对几乎任何潜在的应用都具有重要意义。处理不确定性问题的可能方法是数据集的站点预平均和空间正则化、预选择(下采样)或将现象的描述从确定性改变为概率。
Aim We examine issues of uncertainty regarding the spatial and temporal representativeness of phenological observations using a newly compiled Europe-wide data base of phenological observations for Betula species.Location Europe.Methods A new data base was compiled from national phenological observations covering 15 European countries, with the longest observational periods exceeding several decades for some sites. From this, the spatial and temporal representativeness of phenological observations were evaluated via statistical analysis.Results The results showed that there was a significant and irreducible uncertainty related to the use of data of a single station, which varied from 3 to 8 days depending on the station location. In more continental and northern climatic zones the uncertainty was lower, probably due to faster spring-time weather developments. In mild climatic conditions, the uncertainty of dates of the phenological phases registered by a single station exceeded 1 week. The considerable number of data allowed us to preliminarily estimate the features of some stations, marking them as 'late', 'early', 'representative' or 'random', depending on the dates reported by these sites and the corresponding regional means.Main conclusions The uncertainties discovered in single-site phenological observations are significant for virtually any potential application. Possible approaches for handling the uncertainty problem are station pre-averaging and spatial regularization of the data set, pre-selection (down-sampling) or changing the description of the phenomena from deterministic to probabilistic.