Making Sense of 160 Years of Glacial Data
The warming climate is a conerning change in the status quo, especially for alpine countries such as Switzerland. A country famed for its natural beauty and high altitude mountains, the country is especially vulnerable to the the changing climate given its potential implications for the environment. Switzerland also has a large amount of glaciers, one of the highest numbers of glaciers per capita in the entire world, these glaciers serve an integral role in the alpine environment such as a stable water supply, insulation for the permafrost in the mountains, as well as a stablizing factor for the wider microclimates in the mountainous regions in the country. Furthermore, in a problem that concerns not just Switzerland but the wider alpine regions of Europe, such as Austria, Northern Italy, the French Alps, as well as Southern Germany, glaciers serve a pivotal role in moderating the climate especially during warming times we see today. Perhaps one of the reasons why the climate in the alpine regions, despite the rising temperature, is still relatively stable is owed in no small part to the presence of these ancient glaciers.
The continued melting of the glaciers are a direct threat to the environment not just because of the climate but also the potential for the exposure of the permafrost. For the case of the permafrost in Switzerland, as well as other alpine areas beyond its borders, that permafrost is situated under the glaciers, meaning if the glaciers melt, it would expose this layer. Perhaps not a pressing concern at first, but the permafrost could be understood as a bit of a frozen time-bomb that trapped diseases from centuries past. The Grosser Aletsch Gletscher, by far the largest glacier in Switzerland, is by itself already more than a hundred thousand square kilometers in size. This particular glacier itself has already lost a large part of its mass over the years. The dissappearance of this glacier alone would expose hundreds of kilometers of permafrost to the elements, potentially exposing the larger population to diseases that they may not have immunity to. Given the recent experience the world had with the coronavirus pandemic just a few years ago, the introduction of old diseases the world has no immunity to must not be dismissed.
Between 1850 and 2010, which this project will focus on, has seen an accellerated increase in temperature in the alps, which has translated into the accellerated regression of the glaciers. Troubling still is that this process is not a linear phenomenon in which each year there is an expected figure of glacial regression, instead, this figure seems to be accelerating, especially in the last few decades. This project aims to map the glacial levels in Switzerland in 1850 as well as 2010, one hundred-sixty years of data obervations that would situate this study in three centuries. This scope is large enough to understand just how many glaciers were lost in these centuries, as well as compare the glacial levels between a year that is relatively stable in 1850, versus that of 2010 that there is a clear regression in climate quality.
This project takes data from GLAMOS, a publicly funded institution that is co-operated by the universities of Zurich, Fribourg, as well as ETH. Given that modern data mapping was not present in 1850, the data gathered to map the glaciers incorporated the use of historical maps of the glaciers as well as geomorphological evidence that are available to the public as shape files. For the glacial data in 2010 however, where modern tools are available, aerial imagery as well as sophistoicated geographic measuring coming fom experts using huts within the mountains themseleves were utilized. As reflected in the code in this document, four years among the one hundred – sixty year year period were downloaded, 1850, 1931, 1973, and 2010 in the interest of the wider message of glacial regression, the years of 1850 and 2010 were selected. However, the code will still be reflected in this project should an interest in further reproductions of this project arise.
Utilizing data from GADM in order to get a hold of the shape files of Switzerland, as well as its municipalities. This data, in connection to the larger files downloaded from GLAMOS, allows this project not only to map out glacial regression, but to combine this data with the regions as well. This means that this project is able to isolate the regions in Switzerland and look specifically in these regions to see how much glaciers were lost.
For better visualization, these are some drone videos and photos from parts of the Swiss Alps
Understanding the Glacial Regression means isolating the two years furthest apart in the available data for the difference to be most clearly seen, this case it would mean comparing 1850 to 2010 and to see how much has changed.
#Setting Directory
library("dplyr")
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(sf)Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUEsetwd("/Users/gabriel_caffrey/Documents/FREEDOM/pl362/schweizergletschern/")sf_use_s2(FALSE)Spherical geometry (s2) switched offlibrary(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.2# 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)Warning in st_simplify.sfc(st_geometry(x), preserveTopology, dTolerance):
st_simplify does not correctly simplify longitude/latitude data, dTolerance
needs to be in decimal degrees#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)Warning in st_simplify.sfc(st_geometry(x), preserveTopology, dTolerance):
st_simplify does not correctly simplify longitude/latitude data, dTolerance
needs to be in decimal degreesswitzerland3<- subset(switzerland3, select = c(NAME_2, NAME_3))ch1850b<-st_transform(ch1850, st_crs(switzerland3))
ch1850b<-st_simplify(ch1850b, dTolerance = 0.0005)Warning in st_simplify.sfc(st_geometry(x), preserveTopology, dTolerance):
st_simplify does not correctly simplify longitude/latitude data, dTolerance
needs to be in decimal degreesch1850b$glacial_id<-rownames(ch1850b)ch2010b<-st_transform(ch2010, st_crs(switzerland3))
ch2010b<-st_simplify(ch2010b, dTolerance = 0.0005)Warning in st_simplify.sfc(st_geometry(x), preserveTopology, dTolerance):
st_simplify does not correctly simplify longitude/latitude data, dTolerance
needs to be in decimal degreesch2010b$glacial_id<-rownames(ch2010b)#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.xmlglacial_county_interesec1850<-st_intersection(switzerland3, ch1850b)although coordinates are longitude/latitude, st_intersection assumes that they
are planarWarning: attribute variables are assumed to be spatially constant throughout
all geometriesglacial_county_interesec1850$area_glacier_1850<-set_units(st_area(glacial_county_interesec1850), 'km2')#Combining the Counties with Glaciers and Joining Repetitive Data
library(units)
glacial_county_interesec2010<-st_intersection(switzerland3, ch2010b)although coordinates are longitude/latitude, st_intersection assumes that they
are planarWarning: attribute variables are assumed to be spatially constant throughout
all geometriesglacial_county_interesec2010$area<-set_units(st_area(glacial_county_interesec2010), 'km2')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$area_all_glacier_1850_by_county)#Plotting 1850 Glacial Regions with Legend
ggplot()+
geom_sf(data=merged_1850, aes(fill=area_all_glacier_1850_by_county))+
labs(title = "Glacial Levels in Swiss Regions for 1850") +
scale_fill_viridis_c(option = "turbo")+
theme_void()
#Plotting the Maps Together
library(ggpubr)
fig1_1850<-ggplot()+
geom_sf(data=merged_1850, aes(fill=area_all_glacier_1850_by_county))+
scale_fill_viridis_c(option = "turbo")+
guides(fill="none")
fig2_1850<-ggplot()+
geom_sf(data=swiss_name3)+
geom_sf(data=ch1850b, fill=alpha("Black", 0.9), color=NA)
ggarrange(fig1_1850, fig2_1850, ncol=2)
glacial_county_interesec_2010df<-st_drop_geometry(glacial_county_interesec2010)
glacial_area_by_county2010<-glacial_county_interesec_2010df%>%
group_by(NAME_3)%>%
summarize(area_glacial=sum(area))Converting the Data to Square Kilometers
#Calculate area county
switzerland3$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 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 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")
fig2_2010<-ggplot()+
geom_sf(data=swiss_name3)+
geom_sf(data=ch2010b, fill=alpha("black", 0.9), color=NA)
ggarrange(fig1_2010, fig2_2010, ncol=2)
Combining the 1850 and 2010 maps show a clear difference between these two data sets, the 1850 shape file show clear signs of glaciers in the southeastern part of the country. This is in contrast with the 2010 data where the southeastern glaciers are almost completely gone. This data is significant because these glaciers are by the italian border, which means if there is a lot of glacial regression in this area for Switzerland, it would be a fair assumption the glaciers on the Italian side would certainly be affected as well.
Further still, when looking at the regions that still do have large amounts of glaciers, there are significant decreases of glacial levels as reflected in the legend. Interesting still is in the regions that experienced the most amout of glacial regression are also some of Switzerland’s natural crown jewels. The regions around the Matterhorn as well as the Aletsch Glacier seem to be particularly affected by this change, this has a direct impact not just in the environment, but in the tourism sector as well. In comparing the maps of 1850 and 2010 show a clear difference in glacial levels as well as the regions most affected.
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)
Lets Focus on Zermatt, one of Switzerland’s most famous destinations, and home to the famous “Matterhorn” (The Mountain you see on Toblerone Bars)
Perhaps the single most famous municipality in Switzerland, Zermatt is one of the crowning jewels not just just of the Swiss alps, but the entire alpine region of continental Europe given this is the municipality that the Matterhorn calls home. Because the richness of the data gathered, this gives us the capability of mapping out every single municipality, we can isolate the area around the Matterhorn and see how much glacier was lost from 1850 until 2010. As mapped below, the green area highlighted is the glacier masses that was lost since 1850. In fact, the dataframes show that in the 160 year scope of this project, 40 square kilometers of glacier was lost in Zermatt, the highest example of glacial regression in the alps.
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")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
If we isolate the municipalities in Switzerland that have glaciers, compare the glacier levels of 1850 and subtract the glacier levels from 2010, we can find out the municipalities that have experienced the most amount of glacial regression in the 160 year scope of this study. The results show that the Swiss German/French region of Valais/Wallis, as the region that has had the most amount of glaciers lost in terms of square kilometers. And the municipality that has lost the most amount of glacier mass is Zermatt, 40 square kilometers of glacier lost.
together<-merged_2010
together$diff<-merged_2010$area_glacial-merged_1850$area_all_glacier_1850_by_countytogether2<-together
together2$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
After mapping the glaciers present in 1850 and comparing them to those that remained in 2010 show the unfortunate reality of hundreds of kilometers of lost glacier mass in the almost two century scope of this project. The data shows the troubling, yet lesser talked about problem of glacial regression rampant within the Swiss alps. The findings of this project highlight the challenge the coming decades present in terms of not just preserving the safe water drinking levels of Switzerland as well as the general well being of the alpine environment, but also the threat of the melting permafrost that lay trapped underneath the ice. One can only imagine the potential diseases that the glaciers protected us from the frozen permafrost, unfortunately, it seems the day when we find out will come sooner rather than later.
The data presented in the visual maps speak for themselves, the environment of the alps is changing, and its changing fast. Given the alps are present not just in Switzerland but in Germany, France, Italy, as well as Austria, it is a reasonable assumption that this problem is not just a Swiss problem, but one that concerns this region of Europe. Unfortunately, given the continued warming of the climate all over the world, the phenomenon of melting glaciers seem unlikely to change. The message is clear, if you like glaciers and you want to see them in their full beauty in the alps, you would be well advised to book that trip sooner rather than later.
