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Spring index

The spring index shows the time of vegetation development in the spring as deviation in days from the long-time average from 1981-2010. The phenological spring phases are summarized in the annually determined index. Since the temperature is vital for plant development, the spring index is a suitable parameter to measure the effects of climate change on vegetation.

 

The spring index is determined by means of the first ten phenological spring phases of the year and updated at the end of May. The observations documented by means of sufficiently long data series in the corresponding year at nearly 80 stations of the phenological measurement network are incorporated in it.

 

The spring of 2020

In 2020 the spring vegetation developed very early. From January to March, the vegetation had an advance of 3-4 weeks over the average of 1981-2010. The fruit tree flowering at the beginning of April belonged to the group of years with the earliest flowering and had an advance of 14-17 days. From 10 April onwards, the forests turned green very quickly. Already at the end of April, beginning of May, the high-altitude phenological stations also reported the leaf development of beech. The second warmest February together with the third warmest spring months (March to May) led to this very early vegetation development.

 

Calculating the spring index

The following ten phenological phases are used to characterise the phenological spring as a whole; they occur between January and May:

• Blooming of the hazel bush
• Blooming of the coltsfoot
• Blooming of the wood anemone
• Blooming of the cherry tree
• New leaf formation of the horse chestnut tree
• New leaf formation of the hazel bush
• New needle formation of the larch
• Blooming of the dandelion
• Blooming of the lady’s smock
• New leaf formation of the beech tree

The deviation from the average occurrence date is determined by means of a principal component analysis. This method is practical for structuring, simplifying and visualising complex data sets. In addition, it can be used to filter spatial and chronological dependencies. The result of the 1st principal component is finally transformed back into a deviation of number of days from the average.

 

 

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