[Methods]
The data is a part of an extensive tree growth and drought monitoring network (www.treenet.info) in Switzerland. In this network, radial stem growth is derived from measurements of stem radius changes of trees with high-precision point dendrometers on mainly mature dominant trees. Tree radial growth was measured at about 1.3 m above the ground. The data was recorded and transmitted with Decentlab data transfer nodes (Decentlab GmbH, Duebendorf, Switzerland) with a logging resolution <1 µm at every 5-10 mins. The measurements were processed to a 10-min time aligned data set using the R-package ‘treenetproc’, including outlier removal, jump correction and linear interpolation of short gaps. Radial growth data, extracted with the zero-growth assumption were aggregated on a daily, weekly, monthly, and yearly sum basis. As growth strongly varies within individual trees, across years, species, sites and climatic regions, annual growth was standardized by converting it to relative annual growth. This was done by calculating the difference between each tree’s annual growth to its tree-specific long-term average, calculated over the available data from 2012 to 2022.
Air temperature (°C) data was obtained from weather stations www.meteoswiss.admin.ch, mean distance: 8 km, max: 15 km) or were measured at the site and aggregated to daily values. Air temperature and relative humidity (%) at the sites were measured at 2 m height within the forest stands. Precipitation was extracted from MeteoSwiss model generated CombiPrecip combining data from weather stations and precipitation radar. The vapour pressure deficit (VPD, in kPa) was calculated using temperature and relative humidity. Soil water potential (SWP) was measured with dielectric MPS-2/MPS-6 sensors (Decagon Devices, Pullman, US) at 10-20 cm soil depth at each site and the values were corrected for soil temperature fluctuations. All analyses, including statistical model runs and figures, were produced using the R statistical software.
