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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.


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Swiss federal authoritiesSwiss federal authorities

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 2024

Spring vegetation developed in 2024 as early as only in one year since phenological observations began. It was 10 days ahead of the long-term average from 1991-2020. The first hazel bushes flowered at the beginning of January. However, hazel blossomed widely from 24 January, 3 weeks ahead of the average. Coltsfoot, wood anemones, dandelions and fruit trees have never bloomed as early as this year. Only in 1961 a similarly early flowering of cherry and apple trees was observed. The fruit tree blossom began in the last decade of March and was 14 - 15 days ahead of the 1991-2020 average and even 22 - 25 days ahead of the 1961-1990 average. Leaf unfolding was slowed down by low temperatures in the second half of April. The leaf unfolding of horse chestnut and hazel and the needle sprouting of larch were 7 - 9 days ahead of the mean, while beech sprouted only 3 days before the mean date.

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.