Data Project (GIS + Excel): Analyzing Glacial Retreat in Chile
By: Cassidy Bruner
About Me:
I wrote this paper as a junior at the University of Pennsylvania for a final project in Dr. Whadcoat’s ENVS 326 course taught spring of 2021.
Intro:
This project analyzes glacial retreat from 1870 to 2016 in six different glaciers in Chile. Three of these glaciers are tidewater glaciers and three are mountain glaciers. The six glaciers are further classified by size so that we can compare a small tidewater glacier to a small mountain glacier, a medium tidewater glacier to a medium mountain glacier, and a large tidewater glacier to a large mountain glacier. Not only are we looking at rates of glacial retreat over different time intervals, we can also compare rates of retreat between glacier types and glacier sizes.
Glaciers are thickened ice masses made up of compacted and compressed snow over many many years. What differentiates glaciers from ice caps is their ability to flow, albeit very slowly. They are often thought of as “rivers of ice”. Glaciers cover around 10% of Earth’s land area mostly near the polar regions and come in a wide range of sizes (National Snow and Ice Data Center).
Glaciers have a dynamic system with various factors contributing both to their growth and their retreat. This is called the mass-balance system. In the accumulation zone, snow falls and adds to the glacier’s mass. The snow on the surface weighs on the ice snow, firn, and ice below creating more mass and pressure which causes the ice to deform and flow downslope. In the ablation zone, processes such as fracturing, evaporation, debris, and melting remove mass from the glacier. In between these two zones is
the equilibrium line where ablation equals accumulation. When the equilibrium is disturbed the glacier either retreats as the equilibrium line encroaches into the accumulation zone or the glacier advances as the
equilibrium line travels down into the ablation zone (Benn).
Ever since the last ice age, glaciers on Earth have been retreating, or losing mass, the rate of retreat varying based on a multitude of factors. Glacier size, elevation, climate, proximity to other glaciers, location near water, and so much more, all can contribute to a faster or slower rate of retreat. In
recent years, scientists have identified climate change as having a major influence on glacial retreat. In analyzing the retreat of the six glaciers in Chile, I predict that we will see an increase in rates of retreat during the more recent years than the earlier years.
The other major factor examined in this project is tide water glaciers versus mountain glaciers. There are other types of glaciers but this analysis will focus on just tidewater and mountain. A mountain glacier is solely on land. They can range from small masses to entire glacier systems filling a whole valley. A tidewater glacier is unique in that its terminus is in the ocean. This subjects the glacier to a lot more mass-balance instabilities due to the presence of water. For this reason, I predict the rate of retreat for tidewater glaciers overall will be faster than the rate of retreat for mountain glaciers (Benn).
The last major factor incorporated in this project is glacier size. Some glaciers are hundreds of kilometers long while others can be as small as just a few kilometers. Since large glaciers are much more established with a vast mass/balance system, one could imagine that small glaciers are more subject to imbalances in the system. I predict that small glaciers will have faster rates of retreat than large glaciers.
Data:
All of the data I obtained had units of meters and the GCS_WGS_1984 geographic coordinate system. One data set I obtained was from the ArcGis library. It is a shapefile of all the glaciers recorded in Chile as of 2015. The information for these shapefiles was gathered by satellite images carried out by the General Water Directorate attached to the Ministry of Public Works. The shapefiles show the location, size, name, and other characteristic data describing the glaciers.
From the GLIMS website and database, I obtained six shapefiles, one for each of the selected glaciers in Chile I plan to analyze. The GLIMS (Global Land Ice Measurements from Space) website gathers high quality and extensive data both quantitative and qualitative about the world’s glaciers using optical satellite instruments and makes it available to download. Some of the important features of the data I downloaded were polygons of the glacier’s boundary and area for the years 1870, 1986, 2000, 2007, and 2016 for each glacier. For some of the glaciers information about the maximum and mean elevation, internal rocks, source date, and more were captured in the shapefiles.
The glaciers I selected were Amalia Glacier, Jorge Montt Glacier, San Rafael Glacier, Exploradores Glacier, Tyndall Glacier, and Pio Xi Glacier. Amalia Glacier, also known as Skua Glacier, is a small tidewater glacier located in Bernardo O’Higgins National Park on the outside of the Sarmiento Channel. It has an area of 155 km2 . Jorge Montt Glacier is also a tidewater glacier located in Bernardo O’Higgins National Park in the Aisen Region of Chile South of Caleta Tortel. The Jorge Montt Glacier is 464 km2 which is medium sized. The San Rafael Glacier is the last tidewater glacier I chose for this project. The large San Rafael Glacier has an area around 760 km2 and is one of the major outlet glaciers of the Northern Patagonian Ice Field. Its terminus is in the Laguna San Rafael National Park. Exploradores Glacier is the smallest of the mountain glaciers at 95 km2 . The glacier is located on the northeastern slope of Monte San Valentin in the Aysen del General Carlos Ibanez del Campo region of Chile. The Tyndall Glacier, or the Geike Glacier, is a mountain glacier located in the Torres del Paine National Park. This glacier is medium-sized at 300 km2 . The Pio Xi Glacier, or Brüggen Glacier, is the largest of the mountain glaciers. It is 1245 km2 and is the largest glacier in the southern hemisphere outside of Antarctica.
The classifications for the six glaciers selected for this project have been summarized in the table below.
Workflow:
After obtaining the data from the GLIMS database, I first loaded in the shape-file for each individual glacier. I used the select by attribute to separate the polygon layers by source year and exported the feature layer for each of the years 1870, 2000, and 2016 to their own layer for every glacier. I then record the area of the polygon for each glacier each year to get the glacier size for that time period. Later I will use those numbers to calculate the rate of retreat. Since I know the glaciers are all retreating, to display this I put the layer for the year 1870 on the bottom, then the layer for the year 2000, then the layer for the year 2016 on the top. This way you can visualize how much area was lost over those time spans from 1870 to 2000 and 2000 to 2016. I did all of these steps for all 6 glaciers. I then added 5 more data frames so that each glacier could be shown next to one another for comparison in a side-by-side map. For each data frame I added a scale bar and north arrow.
I then added another data frame and loaded in the shapefiles for each glacier at the year 1870 over a basemap of Chile. This way we can see the relative location of each glacier to one another and the relative size of each glacier to one another. The glaciers on this map are color coded: Amalia is yellow, Jorge Montt is blue, San Rafael is lilac, Exploradores is turquoise, Tyndall is green, and Pio Xi is rose. These color schemes are the same for each glacier in their individual maps created earlier.
The last map I created before doing any analysis is the glacier density map. To start I converted the shapefile of all the glaciers in Chile into a raster file using ArcToolBox -> Conversion Tools -> To Raster -> Polygon to Raster. I used the glaciers in Chile shapefile as the input, ID as the value field, and exported the raster to my geodatabase. Then I added a field called “count” of type short integer and assigned a value of 1 to every unique glacier in the raster. Now that I have a raster file, with a count field, I can use some Spatial Analyst tools to measure the density of glaciers in any given area in Chile. I navigated to ArcToolBox -> Spatial Analyst Tools -> Neighborhood Tools -> Focal Statistics. Here I used my new raster layer of glaciers in Chile as the input, specified a circular neighborhood with radius 1 decimal degree, which is just over 11 km, selected sum as the statistics type to be calculated and the count value field, and left “ignore No Data in calculations” checked. The output was a raster file that shows which glaciers have a higher density of other glaciers near it and which are more isolated.
So for each cell in the glacier in the Chile raster file, the focal statistics for each input cell was summed. The input cells for any single cell’s calculations were determined by the specified circular neighborhood. So the other cells within that neighborhood of the processing cell were used in calculating that processing cell’s value. The way the processing cell’s value was calculated by summing the count values of the input cells in its neighborhood. To display the density I used a graduated color scale with the darker color corresponding to areas that have a higher density of glaciers. Lastly, I displayed a hollow outline of each of the 6 glaciers I examined over the density map so you can easily see the density of glaciers in the area of the glaciers of interest.
Outputs:
Conclusion:
The outputs above help answer the original question about glacial retreat over time, glacial retreat of tidewater versus mountain glaciers, and glacial retreat dependencies on size. In conclusion, while mountain glaciers on average over the past 146 years have been retreating at a faster rate than tidewater glaciers, it is tidewater glaciers we need to worry about as their rates of retreat are increasing rapidly. As for how much size plays a factor, we can say there is perhaps some correlation between larger glaciers retreating at faster rates than smaller glaciers over the past 146 years, but nothing indicates that the correlation is there today.
Looking at the table of the average rate of retreat from 1870 to 2016, it seems that mountain glaciers retreat faster than tidewater glaciers and the rate of retreat increases with increases in glacier size. The average rate of retreat for mountain glaciers is 506 m/yr versus the 240 m/yr of tidewater glaciers. The average rate of retreat for large glaciers is 531 m/yr versus 451 m/yr for medium glaciers, and 140 m/yr for small glaciers. But this isn’t the whole story. These averages are taken over 146 years. But we know that the climate has been changing rapidly and so those rates of retreat now are not the same as the rates in 1870.
So to better answer the question, we should look at rates of retreat from 1870 to 2000 and 2000 to 2016. In the second table, we can see that for mountain glaciers, the rate of retreat has decreased for one of the glaciers, stayed about the same for another, and increased for the last glacier. For the tidewater glaciers, it is extremely clear that the glacier rate of retreat has increased for all three of them. In fact, for one of the tidewater glaciers, the rate of retreat increased by over 1500% from the first time interval to the second.
We know that climate change and warming temperatures are likely the main culprits of increasing rates of retreat recently. But the question is, why are rates of retreat increasing so much faster for tidewater glaciers? According to this article by Robert M. DeConto & David Pollard, glaciers in contact with water are subject to all sorts of processes that mountain glaciers are not. First, we have downcutting. Downcutting is what happens when a glacier has a crack in it then the crack fills with water. The presence of water adds to the stress in the crack and pushes down “cutting” into the ice. So not only does the water have the ability to melt ice and form cracks but once there is a crack, the water increases the rate of that crack’s expansion. Similar to downcutting is hydrofracturing. Since water is more dense than ice, it is heavier. So, when a crack fills with water, the water forces the ice to rip open and fracture. These fractures cause major instabilities and can cause large chunks of ice to fall off glacier fronts.
Some stresses that can form these cracks in the first place are due to the undercutting of the ice. Because of undercutting, massive fracturing events can occur on the surface of tidewater glaciers usually parallel to the grounding line. The grounding line is where the base of the glacier changes from land to water. The fracturing relieves backstress created by the upward buoyant forces of the water under the front part of the ice contrasted with the downward weight of the ice above the land. Undercutting specifically is the process of erosion of ice at the base of the glacier when water melts the glacier and creeps toward the land, pushing that grounding line farther and farther backward.
The last process described in the paper is calving. Calving is when chunks of ice break off at the terminus. This can be due to ice cliff failure. At the front of a tidewater glacier that ends in the water, all sorts of instabilities can cause the collapse of the cliff into the water. Wind and water erosion, as well as melting all can enhance a calving event. But, the main factor triggering calving events today is warming ocean temperatures.
So, because of calving, undercutting, hydrofracturing, and downcutting, tidewater glaciers are experiencing faster rates of retreat than mountain glaciers that, for the most part, do not have these processes (DeConto and Pollard).
Directing attention to the glacier density map, going into the study one thought I had was that maybe proximity to other glaciers has some sort of influence on a glacier’s rate of retreat. For example: if one nearby glacier is extremely unstable, maybe that can affect the rate of retreat of bordering glaciers. Based on this article from the NASA Earth Data website, the collapse of the Larsen B. ice shelf in Antarctica did have an effect on surrounding glaciers. Specifically, the velocities of surrounding glaciers increased as their driving forces pushed them to occupy that space where the Larsen B ice shelf previously was. The increase in glacier velocity results in stretching forces acting on the glacier which causes thinning and weakening of the ice. This makes the ice more susceptible to deformations and crevassing. Two things which influence the rate of retreat (Naranjo). Directing attention to the density map, both the glacier with the fastest rate of retreat in 2016 (Jorge Montt) and the glacier with the slowest rate of retreat in 2016 (Pio Xi) are located in areas with relatively smaller densities of glaciers. Based on this output, there is no evidence suggesting that being surrounded by more or fewer glaciers has an effect on rate of retreat.
If I were to extend the study, it might be interesting to know if other glaciers in the area are affected in the same way. That is, utilizing a larger sample size and incorporating more glaciers, specifically glaciers of different sizes, might produce a more comprehensive analysis of glacial retreat. Additionally, other factors could be taken into account. Factors such as elevation, slope, location in the world, amount of precipitation received, average temperature, velocity, and debris cover could all contribute to the rates of retreat we are observing, in addition to the size and type of glacier (National Snow and Ice Data Center).
In the end, although size didn’t seem to have much of an effect on glacial retreat over time, the outputs very clearly indicate that type of glacier does have an effect. Especially in the recent past, tidewater glaciers are retreating at a much faster rate than mountain glaciers and should be the subjects of study in forecasting models of climate change and its effects on the cryosphere.
Sources:
Benn, D.I., and Evans, D.J.A. (2010) Glaciers & Glaciation, Routledge, 802 pp.; DeConto, Robert M., and David Pollard.
“Contribution of Antarctica to Past and Future Sea-Level Rise.” Nature, vol. 531, no. 7596, 2016, pp. 591–597., https://doi.org/10.1038/nature17145.
“Glims Glacier Database.” National Snow and Ice Database, GLIMS: Global Land Ice Measurements from Space, http://glims.colorado.edu/glacierdata/.
Naranjo, Laura. “After the Larsen B.” NASA, NASA, 27 Dec. 2020, https://earthdata.nasa.gov/learn/sensing-our-planet/after-the-larsen-b.
“National Snow and Ice Data Center.” What Are the Components of a Glacier? | National Snow and Ice Data Center, https://nsidc.org/cryosphere/glaciers/questions/components.html.
Salgado Baldó, Geog. Hungria. “Glaciers de Chile.” National Catalog of IDE Chile, Ministry of Public Works, General Water Directorate, 2015.
