The spring index shows the time of vegetation development in the spring compared to 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 considered a statistical parameter. It 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 2019
In 2019 the spring vegetation developed early, for the sixth time in a row. Spring vegetation achieved its greatest advance in the first half of April. The cherry trees and the dandelion both flowered below 600 m on average on 7 April, 10 days earlier than on average for the period 1981-2010. The reason for this was the high temperatures in February and March, which were 3°C and 1.5°C respectively above the normals. Overall, the development of spring vegetation was the 14th earliest since 1951.
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. One result of this analysis, the 1st main component, is suitable to illustrate the year-over-year variability.