Bern drought monitor

Overview

Drought conditions have intensified across parts of Switzerland during spring and early summer 2026. A combination of below-average precipitation and increasing atmospheric evaporative demand has lead to signals of increasing drought stress since April.

This drought monitor combines several complementary indicators to assess the current drought situation: - the potential cumulative water deficit (derived from local meteorological observations or ERA5-Land), - local soil water potential observations - tree water deficit observations - and satellite-based vegetation health metrics from swisstopo.

The combinations of these indicators provides insights into the severity of ongoing dry conditions with respect to the atmosphere, the soil, and the vegetation.

The figures below allow to monitor the current drought stress around Bern and across Switzerland as of today (see date in header).

Meteorological drought

To put current meteorological drought conditions in perspective, we compare potential cumulative water deficit (PCWD) in 2026 against past years at the MeteoSwiss station Bern/Zollikofen.

PCWD was calculated from daily precipitation minus potential evapotranspiration (PET) using the cwd::cwd() algorithm (described here). The estimate of PCWD uses Priestley–Taylor PET based on estimated net radiation. (The sensitivity of the results to the choice of PET calculation was assessed by additionally estimating PCWD with the Penman-Monteith (FAO56) and Thornthwaite methods. Results were consistent.)

Potential cumulative water deficit (PCWD)

NoteExplainer: potential cumulative water deficit

Potential cumulative water deficit (PCWD) describes the accumulated atmospheric water demand that is not compensated by precipitation. It is calculated from the daily water balance between precipitation and potential evapotranspiration (PET). Increasing PCWD values indicate increasing drought stress, while decreases indicate periods where precipitation partly or fully compensates the accumulated deficit.

Historic context of current PCWD

meteo_pcwd_PT <- meteo_pcwd |> 
  slice_head(n = -1) |> # drop last line which appears to be set to pcwd_mm = 0
  filter(!is.na(PCWD_PT)) # subset years from 2010 on

highlight_colours <- c(
  "2026" = "#D73027", 
  "2022" = "#4575B4", 
  "2018" = "#E69F00", 
  "2023" = "#009E73",
  "1947" = "#CC79A7", 
  "1976" = "#56B4E9", 
  "2003" = "#F0E442"
)

highlight_years <- as.integer(names(highlight_colours))

p_pt <- ggplot() +
  geom_line(
    data = meteo_pcwd_PT |> filter(!year %in% highlight_years),
    aes(x = doy, y = PCWD_PT, group = year),
    colour = "grey75",
    linewidth = 0.4,
    alpha = 0.5
  ) +
  geom_line(
    data = meteo_pcwd_PT |>  filter(year %in% highlight_years),
    aes(x = doy, y = PCWD_PT, colour = as.factor(year), group = year),
    linewidth = 0.5
  ) +
  geom_text(data = meteo_pcwd_PT |> filter(year %in% highlight_years) |>
      group_by(year) |>
      # keep only yearly maxima:
      filter(PCWD_PT == max(PCWD_PT)) |> ungroup() |>
      # keep only years among the top 5
      arrange(desc(PCWD_PT)) |> slice(1:5),
    aes(x = doy, y = PCWD_PT, colour = as.factor(year), label = year),
    vjust = -0.1, show.legend = FALSE
  ) +
  scale_colour_manual(
    values = highlight_colours,
    name = "Year"
  ) +
  labs(
    x = "Day of year",
    y = "PCWD (mm)",
    title = "Potential cumulative water deficit",
    subtitle = paste0(
      "Bern-Zollikofen, Priestley-Taylor PET (",
      min(meteo_pcwd_PT$year, na.rm = TRUE), "–", max(meteo_pcwd_PT$year, na.rm = TRUE), ")"
    )
  ) +
  general_plot_theme +
  theme(
    legend.position = "inside", legend.position.inside = c(0.01,1),  legend.justification = c(0,1),
    legend.direction = "vertical", legend.box = "vertical"
  )
p_pt

Figure 1: Potential cumulative water deficit Bern/Zollikofen.

The PCWD for the current year is substantially higher than observed at the same time of year in the last 17 years in Bern. This reflects the persistent lack of precipitation combined with exceptionally high evaporative demand caused by the recent warm and dry weather, highlighting the exceptional dryness of the current growing season.

The sensitivity analysis based on FAO56 PET indicated a very similar seasonal evolution of PCWD compared with the Priestley–Taylor approach. The current year clearly stands out from the previous 15 years in both methods, indicating that the exceptionally high water deficit is not sensitive to the choice of PET formulation.

For a larger context PCWD was calculated with the Thornthwaite method to calculate the PET and PCWD over the entire record.

meteo_pcwd_THORNTWAITE <- meteo_pcwd |> 
  slice_head(n = -1) |> # drop last line which appears to be set to pcwd_mm = 0
  filter(!is.na(PCWD_THORN)) # subset years from 2010 on

p_thorn <- ggplot() +
  geom_line(
    data = meteo_pcwd_THORNTWAITE |> filter(!year %in% highlight_years),
    aes(x = doy, y = PCWD_THORN, group = year),
    colour = "grey75",
    linewidth = 0.4,
    alpha = 0.5
  ) +
  geom_line(
    data = meteo_pcwd_THORNTWAITE |>  filter(year %in% highlight_years),
    aes(x = doy, y = PCWD_THORN, colour = as.factor(year), group = year),
    linewidth = 0.5
  ) +
  # add labels to maximum (first highlight years, then for 5 largest non-highlight years)
  geom_text(data = meteo_pcwd_THORNTWAITE |> filter(year %in% highlight_years) |>
      group_by(year) |>
      # keep only yearly maxima:
      filter(PCWD_THORN == max(PCWD_THORN)),
    aes(x = doy, y = PCWD_THORN, colour = as.factor(year), label = year),
    vjust = -0.1, show.legend = FALSE
  ) +
  geom_text(data = meteo_pcwd_THORNTWAITE |> filter(!year %in% highlight_years) |>
      group_by(year) |>
      # keep only yearly maxima:
      filter(PCWD_THORN == max(PCWD_THORN)) |>
      # keep only years among the top 5 maxima
       ungroup() |> arrange(desc(PCWD_THORN)) |> slice(1:5),
    aes(x = doy, y = PCWD_THORN, label = year),
    vjust = -0.1, show.legend = FALSE, color = "grey75"
  ) + 
  scale_colour_manual(
    values = highlight_colours,
    name = "Year"
  ) +
  labs(
    x = "Day of year",
    y = "PCWD (mm)",
    title = "Potential cumulative water deficit",
    subtitle = paste0(
      "Bern-Zollikofen, Thornthwaite PET (",
      min(meteo_pcwd_THORNTWAITE$year, na.rm = TRUE), "–", max(meteo_pcwd_THORNTWAITE$year, na.rm = TRUE), ")"
    )
  ) +
  general_plot_theme +
  theme(
    legend.position = "inside", legend.position.inside = c(0.01,1),  legend.justification = c(0,1),
    legend.direction = "vertical", legend.box = "vertical"
  )

p_thorn

Figure 2: Potential cumulative water deficit in Bern/Zollikofen based on Thornthwaite PET using the full available temperature record.

The Thornthwaite-based reconstruction allows to extend the PCWD record back to 1864 and thus provides a longer historical context for the current drought. Although the Thornthwaite method generally produces lower absolute PCWD values than the Priestley–Taylor method, the seasonal pattern is very similar. In 2026, the water deficit began to increase early and rose rapidly during spring and early summer. By the latest available date, PCWD was higher than at the same point in any other year in the 1864–2026 record. Previous drought years such as 2018, 2022 and 2023 also reached high deficits, but their strongest increases occurred later in the season.

Spatial visualization of current PCWD

To illustrate the current spatial extent, we additinally derived potential cumulative water deficit (PCWD) from the global ERA5-Land-T reanalysis dataset of the ongoing year. This approach uses also the Priestley–Taylor PET method.

Below map shows the spatial distribution of the PCWD across Switzerland at the indicated date and the temporal evolution of the region.

# laad data for ERA5-Land map and ERA5-Land timeseries
cwd_ERA5Land <- terra::rast(
  here("data-dynamic/ERA5LandCWD/data_derived_03_daily_pcwd_v2-doy_2026_r-generated_regionCH.nc") # or "regionBern.nc" or "regionEUROPE.nc"
)
cwd_ERA5Land_df <- dplyr::bind_rows(
  tidync::hyper_tibble(here("data-dynamic/ERA5LandCWD/data_derived_03_daily_pcwd_v2-doy_2026_r-generated_regionCH.nc")), # or "regionBern.nc" or "regionEUROPE.nc"
  tidync::hyper_tibble(here("data-dynamic/ERA5LandCWD/data_derived_03_daily_pcwd_v2-doy_2025_r-generated_regionCH.nc")), # or "regionBern.nc" or "regionEUROPE.nc"
) |> mutate(time = lubridate::ymd(time))

# plot ERA5-Land map
date_to_plot <- "2026-07-15"
raster_to_plot_ERA5LAND_PCWD <- cwd_ERA5Land[[time(cwd_ERA5Land) %in% date_to_plot]]

# Define similar palette to vhi:
# CONTINUOUS PALETTE:
# pal_ERA5LAND_PCWD <- colorNumeric(
#   #palette = "viridis",
#   palette = rev(vhi_cols),
#   domain = values(raster_to_plot_ERA5LAND_PCWD),
#   na.color = "transparent"
# )
# Discretized PALETTE:
pal_ERA5LAND_PCWD <- colorBin(
  palette = rev(vhi_cols),
  domain = values(raster_to_plot_ERA5LAND_PCWD),
  bins = 15,
  na.color = "transparent"
)

# Prepare the regional average time series plot (will be embedded into the map)
cwd_ERA5Land_df_mean <- cwd_ERA5Land_df |> 
  group_by(time) |> summarise(pcwd_mm = mean(pcwd_mm)) |> 
  slice_head(n = -1) # drop last line which appears to be set to pcwd_mm = 0
plt_ERA5Land_temp <- ggplot(cwd_ERA5Land_df_mean, aes(x = time, y=pcwd_mm)) +
  geom_line() + 
  theme_bw() + 
  labs(x=NULL, y= "Region average of PCWD (mm)") + 
  geom_vline(xintercept = ymd(date_to_plot), color = "red") +
  scale_x_date(NULL, date_breaks = "2 month", date_minor_breaks = "1 month", date_labels = "%b") + # \n%Y
  geom_text(data = \(df) slice(df, 1),  # this forces annotation only once
            x = ymd(date_to_plot), y = Inf, 
            label = "Map\ndate", color = "red", vjust=1.2, hjust = -0.5, angle = 270) + # vjust=-0.2
  general_plot_theme

# render the plot to a PNG and encode as data URI for embedding as inset into map
tmp_png <- tempfile(fileext = ".png")
ggsave(filename = tmp_png, plot = plt_ERA5Land_temp + labs(y=NULL),
       width = 600, height = 220, units = "px", dpi = 150)
img_uri <- base64enc::dataURI(file = tmp_png, mime = "image/png")

# generate map as leaflet
leaflet() |>
  addMapPane("basemap", zIndex = 1) |> # NOTE: pane is a fix to better control order
  addProviderTiles(providers$SwissFederalGeoportal.NationalMapGrey, group = "swisstopo Topo",       options = providerTileOptions(opacity = 1.0, pane = "basemap")) |>
  addProviderTiles(providers$SwissFederalGeoportal.SWISSIMAGE,      group = "swisstopo SWISSIMAGE", options = providerTileOptions(opacity = 1.0, pane = "basemap")) |>
  addProviderTiles(providers$Stadia.StamenTonerLite,                group = "Stamen Greyscale",     options = providerTileOptions(opacity = 1.0, pane = "basemap")) |>
  addLayersControl(
    baseGroups = c("swisstopo Topo", "swisstopo SWISSIMAGE", "Stamen Greyscale", "No background"),
    overlayGroups = c(sprintf("ERA5-Land PCWD (%s)", date_to_plot)),
    position = "topleft",
    options = layersControlOptions(collapsed = FALSE)
  ) |>
  # add inset image:
  addControl(
    position = "topleft",
    html = paste0("<style>
                  .leaflet-control-layers { width:220px !important; box-sizing:border-box; }
                  </style>", 
                  sprintf(
                    "<div style='width:%dpx;background:rgba(255,255,255,0.95);border-radius:4px;'><strong>Region average PCWD (mm)</strong><br/><img src='%s' style='width:100%%;height:auto;display:block;'/></div>",
      220-16, img_uri)) # 220px - 2*8px padding
  ) |>
  # add main raster image:
  addRasterImage(
    raster_to_plot_ERA5LAND_PCWD,
    colors = pal_ERA5LAND_PCWD,
    opacity = 0.75,
    group = sprintf("ERA5-Land PCWD (%s)", date_to_plot),
    attribution = paste(
      "\n&copy; Copernicus Climate Change Service information (2019). Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac (Accessed on 16-Jul-2026) Generated using or contains modified Copernicus Climate Change Service information <2019>. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains."
    )
  ) |>
  addLegend(
    position = "topright",
    pal = pal_ERA5LAND_PCWD, 
    values = values(raster_to_plot_ERA5LAND_PCWD), 
    opacity = 1,
    title = "PCWD (mm)"
  )

Figure 3: Potential cumulative water deficit across central Europe on 2026-07-15 from ERA5-Land reanalysis data (using Priestley Taylor PET).

Soil water

At the Bremgartenwald in Bern there is a collaboration project of the WSL and the University of Bern for measuring the dryness of the soil (soil water potential) at different landscape positions. The three positions (high, mid and low) indicate the vertical distance to the Aare river (or neareast drainage channel).

df_SWP_plot <- df_SWP |>
  mutate(
    year = factor(
      lubridate::year(date),
      levels = c(2025, 2026)
    ),

    # Map 2025 and 2026 onto the same reference year
    # so that the seasonal trajectories can be compared directly
    plot_date = as.Date(
      sprintf(
        "2000-%02d-%02d",
        lubridate::month(date),
        lubridate::day(date)
      )
    ),

    depth = factor(
      series_height,
      levels = c(-0.1, -0.4, -0.6, -1),
      labels = c("10 cm", "40 cm", "60 cm", "100 cm")
    ),

    # Keep the desired panel order
    plot = factor(
      tree_name,
      levels = c("P3", "P2", "P1", "G1"), labels = c("High","Mid","Low", "G1")
    )
  ) |>
  filter(
    year %in% c("2025", "2026"),
    plot %in% c("High","Mid","Low"),
    !is.na(SWP_mean),
    !is.na(depth)
  )

p1 <- 
  ggplot(
    df_SWP_plot,
    aes(
      x = plot_date,
      y = SWP_mean,
      colour = year,
    )
  ) +
  geom_line(
    linewidth = 0.7,
    alpha = 0.9,
    na.rm = TRUE
  ) +
  facet_grid(
    cols = vars(plot),
    rows = vars(depth),
    scales = "fixed",
    labeller = labeller(depth = label_both,
                        .default = label_value)
  ) +
  scale_colour_manual(
    values = c(highlight_colours, c("2025" = "darkgrey")),
    drop = FALSE
  ) +
  scale_x_date(
    date_breaks = "2 month",
    date_minor_breaks = "1 month",
    date_labels = "%b",
    limits = as.Date(c("2000-01-01", "2000-12-31")),
    expand = expansion(mult = c(0.01, 0.01))
  ) +
  scale_y_logModulus() + 
  labs(
    title = "Seasonal dynamics of soil water potential",
    x = NULL,
    y = "Soil water potential (kPa)",
    colour = NULL,
    caption = paste(
      "More negative values indicate drier soil conditions."
    )
  ) +
  general_plot_theme +
  theme(
    legend.position = "inside", legend.position.inside = c(0.01,0.01),  legend.justification = c(0,0),
    legend.direction = "vertical", legend.box = "vertical"
  )

p1

Figure 4: Seasonal dynamics of soil water potential at different landscape positions (above Aare river) and soil depths in 2025 and 2026.

In general, the further away from the Aare the drier the soil gets over the course of a summer. This is due to impact of the Aare on the groundwater table. Tree water use can also be different with soil depth depending on the root distributions. Typically, deeper soil layers remain wetter due to reduced root water uptake. However, in the above measurements the main dry-out of the soil in 2025 and 2026 was similar across all soil depths (except for the position close to the Aare).

In 2026, soil water dry-out started much earlier than in 2025.

Tree water deficit

As part of the collaboration project of the WSL and the University of Bern tree water deficit measurements were installed in the Bremgartenwald. The tree water deficit measurements are useful for understanding the impact of dry soils on the water availability in trees. Because soil water potential (shown above) describes how easily water can be extracted from the soil. As the soil becomes drier, trees increasingly rely on water stored in their stems. This causes the stem to shrink temporarily resulting in a higher tree water deficit.

df_TWD_blog <- df_TWD |>
  mutate(
    year = factor(
      lubridate::year(date),
      levels = c(2025, 2026)
    ),
    # Place both years on the same seasonal x-axis
    plot_date = as.Date(
      sprintf(
        "2000-%02d-%02d",
        lubridate::month(date),
        lubridate::day(date)
      )
    ),
    # Extract site identifier and define panel order
    site = stringr::str_sub(tree_name, -2, -1),

    plot = factor(
      site,
      levels = c("P3", "P2", "P1", "G1"), labels = c("High","Mid","Low", "G1")
    ),
    # Short common names for clearer facet labels
    species = factor(
      tree_genus_species,
      levels = c(
        "Abies alba",
        "Fagus sylvatica"
      ),
      labels = c(
        "Silver fir",
        "European beech"
      )
    )
  ) |>
  filter(
    year %in% c("2025", "2026"),
    plot %in% c("High","Mid","Low"),
    tree_genus_species %in% c(
      "Abies alba",
      "Fagus sylvatica"
    ),
    lubridate::month(date) >= 4,
    lubridate::month(date) <= 10
  ) |>
  group_by(
    plot_date,
    year,
    site,
    plot,
    species
  ) |>
  summarise(
    TWD_mean = mean(TWD_mean, na.rm = TRUE),
    .groups = "drop"
  ) |>
  filter(is.finite(TWD_mean))

p_twd_blog <- ggplot(
  df_TWD_blog,
  aes(
    x = plot_date,
    y = TWD_mean,
    colour = year,
    group = year
  )
) +
  geom_line(
    linewidth = 0.8,
    alpha = 0.95,
    na.rm = TRUE
  ) +
  facet_grid(
    cols = vars(plot),
    rows = vars(species),
    scales = "fixed"
  ) +
  scale_colour_manual(
    values = c(highlight_colours, c("2025" = "darkgrey")),
    drop = FALSE
  ) +
  scale_x_date(
    limits = as.Date(c("2000-04-01", "2000-10-31")),
    date_breaks = "1 month",
    date_labels = "%b",
    expand = expansion(mult = c(0.01, 0.01))
  ) +
  labs(
    title = "Tree water deficit across species",
    x = NULL,
    y = "Mean tree water deficit (\u00b5m)",
    colour = NULL,
    caption = paste(
      "Silver fir corresponds to Abies alba; European beech to Fagus sylvatica.\nHigher values indicate greater depletion of water stored in the elastic",
      "tissues of the stem."
    )
  ) +
  guides(
    colour = guide_legend(order = 1),
    linetype = guide_legend(order = 1)
  ) +
  general_plot_theme +
  theme(
    legend.position = "inside", legend.position.inside = c(0.01,1.0),  legend.justification = c(0,1),
    legend.direction = "vertical", legend.box = "vertical"
  )

p_twd_blog

Figure 5: Seasonal dynamics of tree water deficit at different landscape positions (above Aare river) in 2025 and 2026.

For the tree water deficit measurements a similar pattern to the soil water potential is visible with distance to the Aare. The location that is furthest away from the Aare shows the strongest tree water deficit.

In 2026, tree water deficit increases earlier than in 2025.

Vegetation Health Index (VHI)

The SwissEO Vegetation Health Index (VHI) combines Sentinel-2-derived Normalised Difference Vegetation Index (NDVI) observations with with land surface temperature data to provide an indication of vegetation stress. The index ranges from 0 to 100, where lower values indicate less healthy vegetation and a higher likelihood of drought-related impacts.

The map below shows the most recent VHI data available for the city of Bern region (2026-07-20 (latest valid available)). The median VHI across the study area was 29, with values ranging from 0 to 100.This suggests that vegetation is experiencing noticeable stress. With the warm current temperature the VHI is likely to further decrease over the coming days.

pal_vhi <- colorBin(
  palette = vhi_cols,
  domain = terra::values(vhi_rast_wgs84),
  bins = vhi_bins,
  na.color = "transparent",
  right = FALSE
)

raster_groups <- paste0("VHI ", time(vhi_rast_wgs84))


vhi_map <- leaflet() |>
  addMapPane("basemap", zIndex = 1) |> # NOTE: pane is a fix to better control order
  addProviderTiles(providers$SwissFederalGeoportal.NationalMapGrey, group = "swisstopo Topo",       options = providerTileOptions(opacity = 1.0, pane = "basemap")) |>
  addProviderTiles(providers$SwissFederalGeoportal.SWISSIMAGE,      group = "swisstopo SWISSIMAGE", options = providerTileOptions(opacity = 1.0, pane = "basemap")) |>
  addProviderTiles(providers$Stadia.StamenTonerLite,                group = "Stamen Greyscale",     options = providerTileOptions(opacity = 1.0, pane = "basemap")) |>
  addLayersControl(
    baseGroups = c("No background", "swisstopo Topo", "swisstopo SWISSIMAGE", "Stamen Greyscale"),
    overlayGroups = raster_groups,
    position = "topleft",
    options = layersControlOptions(collapsed = FALSE)
  ) 

for (i in seq_along(raster_groups)){
  vhi_map <- vhi_map |>
    addRasterImage(
      vhi_rast_wgs84[[i]],
      colors = pal_vhi,
      opacity = 1.0, # 0.75 # since VHI map is only forested areas we can easily use 1.0
      group = raster_groups[i],
      attribution = paste(
        "VHI layer: \n&copy; swisstopo, Contains modified Copernicus Sentinel data 2026"
      )
    )
}

vhi_map <- vhi_map |>
  hideGroup(raster_groups[-1]) |>
  showGroup(raster_groups[1]) |>
  addLegend(
    position = "bottomright",
    colors = vhi_cols,
    opacity = 1,
    labels = c(
      "0–9: Extremely stressed",
      "10–19: Very stressed",
      "20–29: Stressed",
      "30–39: Slightly stressed",
      "40–49: Normal",
      "50–59: Good",
      "60–100: Excellent"
    ),
    title = "VHI"
  )
vhi_map

Figure 6: Latest swissEO Vegetation Health Index around Bern (dating from 2026-07-20 (latest valid available) ).

Error sources such as clouds can potentially lead to anomalous values for certain dates. Note that the above map is automatically generated and has not necessarily been checked visually.

Current drought status summary

Taken together, the different indicators paint a consistent picture. Spring and early summer of 2026 were characterized by increasing atmospheric water demand and below-average moisture availability. Compared to 2025, these drought indicators increased earlier than 2025.

This signal is reflected in the satellite-based Vegetation Health Index, which shows widespread moderate vegetation stress around Bern. At the same time, the PCWD is currently as high as it has ever been at this point in the year.

Both indices indicate that the ecosystem is currently in a state of elevated drought stress. The evolution over the coming weeks will largely depend on the weather in the upcoming weeks. Currently, MeteoSwiss predicts warm temperatures and only little precipitation in the form of thunderstroms over the coming days, which will likely further increase drought stress.

Appendix: links for whole Switzerland

Looking beyond the Bern area, below appendices provide further resources on national level for Switzerland.

Appendix A - Soil water across Switzerland (SwissSMEX and SwissSPOT)

The SwissSMEX and SwissSPOT monitoring networks provide continuous observations of soil moisture at multiple depths across Switzerland. Current conditions can be explored through to official monitoring platform:

Open SwissSMEX Monitoring Dashboard

For the SwissSMEX site in Bern the soil moisture anomaly in the current year is slightly outside of the 5-95 percentile showing the current year is exceptional. It is comparable to some of the dry years of 2022 and 2023. With the warm and dry weather that is predicted for the next days soil moisture anomaly is likely to decrease even further.

Open SwissSPOT Monitoring Dashboard

Appendix B - Tree water deficit across Switzerland (TreeNet)

TreeNet provides near-real-time observations of tree water deficit (TWD), an indicator of the water status of individual trees. The data reveal substantial differences among species, but a common pattern emerges across Switzerland: many trees are already experiencing elevated water stress despite the relatively early stage of the summer season.

Currently, the highest proportions of trees classified as experiencing high or very high water deficit are found among Scots pine (Pinus sylvestris), silver fir (Abies alba), European ash (Fraxinus excelsior), and downy oak (Quercus pubescens). In contrast, species such as hazel (Corylus avellana) and Douglas fir (Pseudotsuga menziesii) still show a comparatively large share of individuals without measurable water stress.

Regional differences are also apparent. The southern Alps and the Pre-Alps currently exhibit the largest fractions of trees in the high and very high deficit categories, whereas the Jura and parts of the Swiss Plateau still contain a larger proportion of trees with little or no water deficit. Nevertheless, signs of water stress are present across all regions.

knitr::include_graphics("https://treenet.info/wp-content/uploads/Drought_Indicators_norm-1200x686.jpg")

Figure 7: TreeNet TWD Overview

On the following website there is a more detailed break down of the current tree water deficit and the growth performance per species across sites in Switzerland.

TreeNet TWD Overview per Species

Appendix C - Vegetation health index across Switzerland (swisstopo)

The full vegetation health index (VHI) across Switzerland can be checked at swisstopo: Open swisstopo VHI map viewer