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Spring index

The spring index shows the difference (in days) in spring vegetation growth onset compared to the long-term average for the period 1991 to 2020. The phenological spring phases are summarized in the annually determined index. Since temperature is one of the central factors influencing vegetation growth onset, the spring index is an effective tool for measuring 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 each May. It draws on the observations documented in the corresponding year at the nearly 80 stations in the phenological measurement network, using datasets of adequate length.

 

The spring of 2022

In 2022, spring vegetation developed 4 days earlier than the long-term average from 1991-2020. The flowering of hazel began particularly early, as early as January, in Ticino and in some cases on the northern side of the Alps. Overall, hazel flowering was 13 days ahead of the average. Coltsfoot and wood anemone mainly flowered in March with an advance of 5 - 10 days. The fruit trees flowered from the end of March with an advance of 5-8 days. Dandelion and cuckoo flower flowered in the meadows with the same advance. From the end of March, the leaves of the deciduous trees sprouted. The leaf unfolding of the beech from mid-April was exactly on the average date of the period 1991-2020.

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:

  • Hazel bush flowering
  • Coltsfoot flowering
  • Wood anemone flowering
  • Cherry tree flowering
  • Leaf unfolding in horse chestnut
  • Leaf unfolding in hazel bush
  • Needle appearance in larch
  • Dandelion flowering
  • Lady’s smock flowering
  • Leaf unfolding in beech

The deviation from the average occurrence date is determined by means of a main component analysis. This method is practical for structuring, simplifying and visualising complex datasets. In addition, it can be used to filter spatial and chronological dependencies. The result of the first main component is then converted back into number of days’ deviation from the average.

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