Glacial Regression in Switzerland Between 1850 and 2010

Making Sense of 160 Years of Glacial Data

Nico Tayao
gtayao@johncabot.edu

John Cabot University

Table of Contents

  • Intro: Glacial Regression From 1850 to 2010
  • Data Sources
  • Glacier Dense Areas
  • Reading the Data
  • Glacial Levels in 1850
  • Glacial Levels in 2010
  • Comparing the Data
  • Zermatt
  • Subtracting 2010 Glacial Levels from 1850 Levels
  • Conclusion
  • Visit While You Still Can

Intro: Glacial Regression From 1850 to 2010

Shrinking Glaciers - A phenomenon caused by the changing climate resulting in glaciers steadily melting.

Glaciers serve a vital role in the ecosystem as well as the alpine permafrost.

Melting glaciers mean a lower water reserve in the warmer months of the year.

Reports from ETH suggest minimal to no glaciers in Europe by 2050.

Data Sources

In order to map out the areas that have been affected by Glacial Regression, data has been taken from GLAMOS, a publicly funded entity operated directly by the University of Zurich and Fribourg as well as ETH Zurich. Official government websites, as well as GADM for country specific shape files. The data gathered spans one hundred and sixty years from 1850 to 2010 showing glacial changes throughout multiple centuries.

Glacier Dense Areas

For better visualization, here are some photographs in some of the most glacier dense regions in Switzerland during winter as well as summer.

Bachalpsee Frozen in Winter

Oeschinensee, Kandersteg

Oeschinensee, Kandersteg

Saas-Fee Glaciers (Leukerbad)

Calculating Glacial Levels in 1850 and 2010

Reading the Data

#Setting Directory
library("dplyr")

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(sf)
Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
setwd("/Users/gabriel_caffrey/Documents/FREEDOM/pl362/schweizergletschern/")
Show Code 
sf_use_s2(FALSE)
Spherical geometry (s2) switched off
Show Code 
library(ggplot2)
# Glacier Data Inventories
ch1850 <- st_read(dsn="./data/inventory_sgi1850_r1992/SGI_1850.shp", quiet = TRUE)
ch1931 <- st_read(dsn="./data/inventory_sgi1931_r2022/SGI_1931.shp", quiet = TRUE)
ch1973 <- st_read(dsn="./data/inventory_sgi1973_r1976/SGI_1973.shp", quiet = TRUE)
ch2010 <- st_read(dsn="./data/inventory_sgi2010_r2010/SGI_2010.shp", quiet = TRUE)

#Selecting Specific Parameters within the Shape Files 
ch1850 <- subset(ch1850, select = c(RivLevel0))
ch1931 <- subset(ch1931, select = c(RivLevel0))
ch1973 <- subset(ch1973, select = c(RivLevel0))
ch2010 <- subset(ch2010, select = c(RivLevel0))

# Shape File Switzerland Level 0
switzerland <- st_read(dsn="./data/gadm41_CHE_shp/gadm41_CHE_0.shp", quiet = TRUE)
switzerland<-st_simplify(switzerland, dTolerance = 0.005)
Show Code 
#Shape File Switzerland Level 3 
switzerland3 <- st_read(dsn="./data/gadm41_CHE_shp/gadm41_CHE_3.shp", quiet = TRUE)
#Simplifying 
switzerland3<-st_simplify(switzerland3, dTolerance = 0.005)
switzerland3<- subset(switzerland3, select = c(NAME_2, NAME_3))

Joining Repetitive Data and Simplifying Shape Files

ch1850b<-st_transform(ch1850, st_crs(switzerland3))
ch1850b<-st_simplify(ch1850b, dTolerance = 0.0005)
ch1850b$glacial_id<-rownames(ch1850b)
ch2010b<-st_transform(ch2010, st_crs(switzerland3))
ch2010b<-st_simplify(ch2010b, dTolerance = 0.0005)
ch2010b$glacial_id<-rownames(ch2010b)

Combining Counties with Glaciers

#Combining the Counties with Glaciers and Joining Repetitive Data
library(units)
udunits database from /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library/units/share/udunits/udunits2.xml
glacial_county_interesec1850<-st_intersection(switzerland3, ch1850b)
although coordinates are longitude/latitude, st_intersection assumes that they
are planar
glacial_county_interesec1850$area_glacier_1850<-set_units(st_area(glacial_county_interesec1850), 'km2')
#Combining the Counties with Glaciers and Joining Repetitive Data
library(units)
glacial_county_interesec2010<-st_intersection(switzerland3, ch2010b)
although coordinates are longitude/latitude, st_intersection assumes that they
are planar
glacial_county_interesec2010$area<-set_units(st_area(glacial_county_interesec2010), 'km2')

Glacier Levels in 1850

Grouping Data Together

glacial_county_interesec_1850df<-st_drop_geometry(glacial_county_interesec1850)
glacial_area_by_county1850<-glacial_county_interesec_1850df%>%
  group_by(NAME_3)%>%
  summarize(area_all_glacier_1850_by_county=sum(area_glacier_1850))

Converting the Data to Square Kilometers

#Calculate area county
switzerland3$area_county<-set_units(st_area(switzerland3), 'km2')
swiss_name3<-subset(switzerland3, select = c(NAME_3, area_county))

Merging the datasets by “NAME_3” and isolating the regions that have glaciers inside them

#Left join
merged_1850<-left_join(swiss_name3, glacial_area_by_county1850, by =c("NAME_3" = "NAME_3"))
merged_1850$area_all_glacier_1850_by_county[is.na(merged_1850$area_all_glacier_1850_by_county)]<-0
merged_1850$area_all_glacier_1850_by_county<-as.numeric(merged_1850$area_all_glacier_1850_by_county)

Plotting 1850 Levels

Show Code 
#Plotting 1850 Glacial Regions with Legend 
ggplot()+
  geom_sf(data=merged_1850, aes(fill=area_all_glacier_1850_by_county))+
   labs(title = "Glacial Levels in Swiss Regions for 1850") +  
  scale_fill_viridis_c(option = "turbo")+
  theme_void()

Plotting 1850 Levels Isolating Glaciers

Show Code 
#Plotting the Maps Together 
library(ggpubr)
fig1_1850<-ggplot()+
  geom_sf(data=merged_1850, aes(fill=area_all_glacier_1850_by_county))+
  scale_fill_viridis_c(option = "turbo")+ 
  guides(fill="none")+
  theme_void()

fig2_1850<-ggplot()+
  geom_sf(data=swiss_name3)+
  geom_sf(data=ch1850b, fill=alpha("Black", 0.9), color=NA)+
  theme_void()

ggarrange(fig1_1850, fig2_1850, ncol=2)

Glacier Levels in 2010

Grouping Data Together

glacial_county_interesec_2010df<-st_drop_geometry(glacial_county_interesec2010)  
glacial_area_by_county2010<-glacial_county_interesec_2010df%>%
  group_by(NAME_3)%>%
  summarize(area_glacial=sum(area))

Converting the Data to Square Kilometers

#Calculate area county
switzerland3$area_county<-set_units(st_area(switzerland3), 'km2')
swiss_name3<-subset(switzerland3, select = c(NAME_3, area_county))

Merging the datasets by “NAME_3” and isolating the regions that have glaciers inside them

#Left join
merged_2010<-left_join(swiss_name3, glacial_area_by_county2010, by =c("NAME_3" = "NAME_3"))
merged_2010$area_glacial[is.na(merged_2010$area_glacial)]<-0
merged_2010$area_glacial<-as.numeric(merged_2010$area_glacial)

Plotting 2010 Levels

Show Code 
#Plotting 2010 Glacial Levels in 2010
fig1_2010<-ggplot()+
  geom_sf(data=merged_2010, aes(fill=area_glacial))+
  labs(title = "Glacial Levels in Swiss Regions for 2010") + 
  scale_fill_viridis_c(option = "turbo")+
  theme_void()
fig1_2010

Plotting 2010 Levels Isolating Glaciers

Show Code 
#Plotting the Maps
library(ggpubr)
fig1_2010<-ggplot()+
  geom_sf(data=merged_2010, aes(fill=area_glacial))+
  scale_fill_viridis_c(option = "turbo")+
  guides(fill="none")+
  theme_void()

fig2_2010<-ggplot()+
  geom_sf(data=swiss_name3)+
  geom_sf(data=ch2010b, fill=alpha("black", 0.9), color=NA)+
  theme_void()

ggarrange(fig1_2010, fig2_2010, ncol=2)

Comparing the Data

Putting the two Maps side by side show a clear presence of Glacial Regression in Switzerland since data gathering began in 1850. The 2010 map shows clear differences especially in the southeast regions of the country with further substantial changes seen in the southwest, the area that boasts Switzerland’s largest mountains as well as the location of the famous Matterhorn.

1850 Levels vs 2010

Show Code 
fig1_1850<-ggplot()+
  geom_sf(data=merged_1850, aes(fill=area_all_glacier_1850_by_county))+
  labs(title = "Glacial Levels 1850") + 
  scale_fill_viridis_c(option = "turbo")+
  theme_void()

fig1_2010<-ggplot()+
  geom_sf(data=merged_2010, aes(fill=area_glacial))+
  labs(title = "Glacial Levels 2010") + 
  scale_fill_viridis_c(option = "turbo")+
  theme_void()

library(ggpubr)
ggarrange(fig1_1850, fig1_2010, common.legend = TRUE, legend="top", ncol = 2)

Focusing on a Specific Area

Taking a look at the dataframes

  • Together 2 dataframe

Mapping Zermatt

Lets Focus on Zermatt, one of Switzerland’s most famous destinations, and home to the famous “Matterhorn” (The Mountain you see on Toblerone Bars). This region is home to not only the largest mountains in Europe, but also some of the largest glaciers such as the famous Gorner Glacier being situated here. It is also a region heavily affected by melting glaciers.

The Matterhorn

## Selecting Specific Glacial Regions

Zermatt_1850<-subset(glacial_county_interesec1850, NAME_3=="Zermatt")
Zermatt_2010<-subset(glacial_county_interesec2010, NAME_3=="Zermatt")
Aletsch_1850<-subset(glacial_county_interesec1850, NAME_3 == "Fieschertal")
Aletsch_2010<-subset(glacial_county_interesec2010, NAME_3 == "Fieschertal")

Zermatt

Zermattdiff<-ggplot()+
  geom_sf(data=switzerland, fill = "White")+
  geom_sf(data=Zermatt_1850, fill = "Green")+
  geom_sf(data=Zermatt_2010, fill = "Blue")+
  labs(title = "Glacier Loss in Zermatt") + 
  theme_void()
Zermattdiff

Subtracting 2010 Glacier Levels in Comparison to 1850 Levels

By isolating the regions in Switzerland that actually have glaciers in them, as well as merging the glacier levels in 1850 and 2010 allow us to substract the glacial levels in 2010 to the clearly higher ones from centuries ago. The results show the most about of Glacial Regression to be situation in the southwest, home to some of the highest mountains in continental Europe.

Joining Data from Different Years Together

together<-merged_2010
together$diff<-merged_2010$area_glacial-merged_1850$area_all_glacier_1850_by_county

Plotting the Difference

together2<-together
together2$diff[together2$diff==0]<-NA
diff_pic<-ggplot()+
  geom_sf(data=together2, aes(fill=diff))+
  labs(title = "Regional Glacial Regression") + 
  scale_fill_viridis_c(option = "turbo", direction = -1)+
  theme_void()
diff_pic

Conclusion

There is a clear difference between glacier masses in Switzerland between 1850 and 2010 hitting some of the most prominent alpine destinations in the country. More and more glaciers are not only receding, they are diasappearing as well. There have been many studies suggestion that not only will this trend continue, but some estimates even point to the year 2050 onwards having no more glaciers in continental Europe.

Go Visit While You Still Can

Schweizer Gletschern