Comparison of Glacier Loss on Qori Kalis, Peru and Mt. Kilimanjaro, Tanzania Over The

Home , Freezing level, Quelccaya Ice Cap

Comparison of Glacier Loss on Qori Kalis, Peru and Mt. Kilimanjaro, Tanzania Over the

Last Decade Using Digital Photogrammetry and Stereo Analysis

Master’s Thesis

Presented in Partial Fulfillment of the Requirement for the Degree Master of Science in

the Graduate School of The Ohio State University

By Kara A. Lamantia

Graduate Program in Earth Sciences

The Ohio State University

2018

Thesis Committee:

Dr. Lonnie G. Thompson, Advisor

Dr. Ellen Mosley-Thompson

Dr. Rongjun Qin

Abstract

While there are only a handful of locations around the world today where tropical glaciers still exist, and given their high sensitivity to climate change they can be used as indicators of change and for interpretations of the mechanisms driving climate change.

Recent technological advances now provide an opportunity to modify the way glaciers are observed and measured. These are applied to the Qori Kalis glacier in Peru and the ice fields on Kilimanjaro in this work. New developments have opened doors for digital photogrammetry software such as the Leica photogrammetry suite and stereo analyst from ERDAS, which offer stereoscopic tools with the ability to plot the ice extent in a three dimensional image. The resulting three-dimensional digital content offers more flexibility in analysis, quantification, visualization, and improves the documentation of retreating glaciers. It is possible to produce both two-and three-dimensional area estimations and volume loss for glaciers such as Qori Kalis, the main outlet glacier of the

Quelccaya ice cap (Peru), and the Kilimanjaro ice fields, Tanzania. Satellite imagery was purchased for 2017 and used to acquire a more accurate measure of the ice loss than provided by the terrestrial or aerial imagery that was used previously. This new approach simplifies the measurement and calculation process and when measurements made using the digital method are compared with those from the earlier measurements they are found to be comparable.

The retreat of Qori Kalis is analyzed from 2004 to 2017 while the Kilimanjaro ice fields are analyzed from 2010 to 2017 and the resulting data are compared to past measurements from various studies. Both tropical locations show a decrease in the ice cover along a linear regression from 2004 until 2017. There is evidence for both a retreat

ii of the ice and a thinning of the surface at both sites. Kilimanjaro’s ice fields continue to separate into multiple smaller bodies and are losing ice from solar radiation induced melting as well as sublimation. This ice cap that covered 12 km2 just over a century ago

2 now covers a total area of only 1.01 km . The continued rise of the freezing level height, coupled with influences from the last ENSO event, results in the continuing upslope retreat and thinning of Qori Kalis. Over the thirteen year observational period Qori

Kalis’ areal extent decreased by 30%, its volume decreased by 43%, consistent with past studies and the behavior of the Quelccaya ice cap. Within one to two decades, the

Kilimanjaro ice fields and the Qori Kalis glacier are quite likely to disappear completely.

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Acknowledgments

I would like to thank my advisor Dr. Lonnie G. Thompson and my advisory committee of Drs. Ellen Mosley-Thompson and Rongjun Qin for their unwavering support and guidance. I am thankful for Dr. Thompson for sharing not only his knowledge but his endless enthusiasm for research and discovery. I would also like to thank Henry Brecher for sharing his knowledge of photogrammetry, past measurements, and providing me the opportunity to continue his past work. Past terrestrial and aerial imagery was provided through M.A.N. Mapping Services and the Byrd Polar and Climate

Research Center. The 2013 data of Mt. Kilimanjaro was acquired by Photomap Kenya

Limited. Satellite images from 2017 were acquired through Apollo Mapping Company.

I am grateful for the collaboration with the Civil Engineering Department at The Ohio

State University and Dr. Rongjun Qin for opening the doors to new technology and a new method for assessing ice loss. I also wish to thank fellow graduate student Xiaohu Lu in the Civil Engineering Department for running the satellite imagery with the RSP Stereo

Processor.

I am grateful for the School of Earth Sciences and all the faculty at The Ohio

State University for giving me the opportunity to not only learn but to teach in a wonderful program at a highly respected university. Those that work at the Byrd Polar and Climate Research Center not only welcomed me but encouraged my continued growth as a researcher and a scientist. This project could not have been accomplished without my parents, Mark and Lisa, for their continuous support and encouragement to follow my interests wherever they might take me.

Vita

February 9, 1994…………………………………….Born – Pittsburgh, PA

2016…………………………………………………B.S., Geology, Graduate Certificate, Geographic Information Systems, University of Dayton, Dayton, Ohio

2016 – Present………………………………………Graduate Teaching Assistant, and Graduate Research Associate, School of Earth Sciences, The Ohio State University, Columbus, Ohio

Fields of Study

Major Field: Earth Sciences

Table of Contents Abstract………………………………………………………………………………………...... ii Acknowledgements……………………………………………………………...…………….....iv Vita……………………………………………………………………………………………….v List of Figures………………………………………………………………...……………...…viii List of Tables………………………………………………………………………………....….xii

Chapters

1. Introduction...... ….....1

1.1 Concern for Natural Systems……………………………………………………...….1

1.2 Glacier Evidence for Climate Change………………………………………….….....1

1.3 Past Data Collection……………………………………………………………....….3

1.4 Expected Outcomes……………………………………………………………...... …4

2. The Study Areas: Qori Kalis Glacier, Peru and Mt. Kilimanjaro Ice Fields, Tanzani...... 6

2.1 Tropical Glaciers and their Environments……………………………...... ……...... 6

2.2 Importance of Tropical Glaciers…………………………………………………..…7

2.3 Regional Climate Overview…………………………………………………...…….10

2.4 Study Area Specifics…………………………………………………………...…….16

3. Methodology…....………………………………………………………………...……..18

3.1 Acquiring the Data………………………………………………………………..…18

3.2 Processing the Data……………………………………………………………...…..28

3.3 Error and Uncertainties…………………………………….…………………..……42

4. Results……………………………………………………………………………...……45

4.1 Horizontal Area and Surface Area Measurements……………………………...... …45

4.1.1 Qori Kalis………………………………………………………….….…..45

4.2.1 Kilimanjaro………………………...………………………………...……47

4.2 Retreat of Terminus, Qori Kalis……………………………………………..…..…..52

4.3 Volume Loss…………………………………………………………………..….…55

4.3.1 Qori Kalis…………………………………………………………….…..55

4.3.2 Kilimanjaro………………………………………………...……….……58

4.4 Contour Maps, Qori Kalis……………………………………………………..……60

5. Discussion………………………………………………………………………..……..64

5.1 Qori Kalis Area and Volume Loss…………………………………………….....…64

5.2 Qori Kalis Terminus Retreat…………………………………………………..……67

5.3 Qori Kalis Terrestrial to Satellite Transition, 2016-2017…………....…...…..…….68

5.4 Kilimanjaro Area and Volume Loss………………………………………..………70

5.5 Comparison of Kilimanjaro to Qori Kalis……………………………...……..……74

5.6 Indication of El Niño…………………………………...……………………..……77

6. Summary, Conclusions, and Suggestions for Future Research…………….…….…….79

6.1 Continuing Ice Loss………………………………………………….……….…….79

6.2 Implications for Ice Loss…………………………...... …………….……….……...80

6.3 Suggestions for Future Research………………………………….……….……….81

References……………………………………………………………….……………….……..82

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List of Figures

2.1 Figure 2.1: The Qori Kalis valley in July 2005 (a) and 2006 (b). An avalanche of ice from the glacier in March 2006 caused the proglacial lake to breach the moraine and flood the valley below. There are sediment deposits visible in (b) at the end of the lake that resulted from the flood (Thompson et al., 2011)…….…9

2.2 Map showing the location of the Qori Kalis glacier on the Quelccaya ice cap, Peru…………………………………………………….………………………..12

2.3 Aerial photo of the summit plateau of Kilimanjaro, showing the Kilimanjaro ice fields; the inset shows the location of Kilimanjaro on the border of Tanzania and Kenya, Africa (February 2010)..………………………………………………..13

2.4 World map showing the motion of the ITCZ with respect to the two study areas. The ITCZ passes over Mt. Kilimanjaro usually twice a year, while the Qori Kalis glacier is far enough south so that it only receives an annual passing of the ITCZ (Desonie, 2017)…………………………………………………………………15

3.1 The Qori Kalis glacier, Peru with the 5 GCPs used marked in blue. GCP #5 was not used due to difficulties with accurately locating the point on the images (June 27th, 2005)……………………………………………………………………....22

3.2 Kilimanjaro with six GCPs marked in red and numbered. The GCPs were originally placed in a circle around the crater and marked with targets but are not located on distinct features around the peak (February, 2010)………………....23

3.3 Satellite image of Kilimanjaro, taken June 22nd, 2017. Minor cloud cover is present around the mountain-top but does not interfere in measurements……...27

3.4 Coordinates from the stereo analyst program uploaded into the ArcMap program and will appear as points on the map space (example from 2004)..…………….28

3.5 ArcMap displays the ice surface points as a TIN file and creates an outline for the lake boundary, shown by the purple line (2004). Each elevation grouping (meters) is represented by an assigned color………………………....………....31

3.6 The TIN file is converted into a raster file, the elevation height is stored in each of the pixels and the raster clipped back to its original size. The lake is made into a polygon in order to measure the total area (2004). Elevation values are assigned at even intervals along a color gradient…………………………..…………….31

3.7 A final contour map of the measured area of Qori Kalis with 20 m contour intervals and the previously established limit line (2004). The overlay of ice and lake results in the earlier epoch measurements where the boundary between the two is hard to distinguish in the stereo analyst……………………………...…33

viii

3.8 The Qori Kalis glacier showing the limit line and the three transects drawn across its surface. The transects extract the Z values from each respective raster file as well as providing an X value for the distance away from the limit line. This X value can be used to assess horizontal extent for each year on Qori Kalis. The example year shown is 2016………….………………………………..………35

3.9 A) The “Cut and Fill” tool shades areas of loss with blue and areas of gain in red. This sample is the volume loss between 2004 and 2017 on Qori Kalis. The light blue line is the ice edge in 2017 while the green line is the ice edge in 2004. Note the full 2017 area is not shaded as it does not overlap exactly with the 2004 data, leaving blank spaces in the data. The purple line on the east side is the limit line. B) The attribute table that is produced with the output table provides data for assessing the volume change. The value numbers are arbitrary and serve no purpose except for locating each set of values. The count column shows the numbers of pixels that are similar in either loss or gain. The volume column shows a value in cubic meters for the volume loss or gain while the area column shows the area each section covers in square meters………………..………...38

3.10 An input-output functioning diagram of the RSP Stereo Processor (Qin, 2017)……………………………………………………………………..….....41

4.1 Kilimanjaro ice boundaries in 2013 and 2017. Ice bodies 5 and 8 could only be mapped in 2013 due to snow and cloud cover in 2017 that prevented the measurements. The numbers assigned to each body of ice and arbitrary and are used for labeling and analysis purposes to organize data. Some of the ice fields have been named and are labeled but as the ice continues to separate not every ice fields has a specific name……………………………………………………...51

4.2 Ice outlines from 2004 (green) and 2017 (blue), down glacier from the limit line (purple). There is a noticeable retreat of the horizontal ice extent over the thirteen-year span……………………………………………………………...54

4.3 Cut and Fill output for Qori Kalis 2004 compared to 2017. The green outline shows the ice extent in 2004, while the blue outline shows the ice extent in 2017. The blue surfaces are areas of net gain across the surface while the red areas show net loss. The outline of the 2004 ice extent was placed across the 2017 DSM and compared to the 2004 raster surface that was created. Since the tool only works with areas of overlap, the surface data outside the 2017 ice extent was required for comparison with 2004………………...……………………………………...... 56

4.4 The contours on Qori Kalis are spaced at 20 meter intervals with dark blue lines marking index contours every 100 meters. The purple line outlines the lake area. The green line shows the ice boundary and the red line is the limit line that was previously established in earlier studies. Each map is labeled with its respective year, and is in the coordinate system that was used throughout……………..61-63

5.1 Qori Kalis ice area loss. Data used from past measurements and this study. The data fits along a linear regression with an R2 value of 0.82. *Data from 1963 to 1993 are from Brecher and Thompson (1993), data from 1995 to 2003 are from Thompson et al (2006) and data from 2004 to 2017 are from this study and indicated with triangles…………………………………………………….….66

5.2 The jagged edges of the 2016 (green) ice outline verses the 2017 (blue) outline show the difference in the number of points that could be placed around the ice extent to allow the calculation of the area. The uncertainty on the northern edge (top left) of the ice in the stereo analyst could explain part of the small decrease in ice area, as a moraine blocks the exact edge of the ice in the 2016 and all the other terrestrial photos……………………………………………………………….69

5.3 A) Kilimanjaro ice area loss, data from past measurements and this study. The loss pattern is very close to a linear decline, with the 1912 estimate removed a linear decline is still apparent. B) Kilimanjaro ice area loss, with previous measurements except 1912 and the loss pattern is still close to a linear decline. *1912 and 2000 area calculations from Thompson et al (2002). 2003 area calculation obtained from Cullen et al (2006). 2010, 2013, and 2017 are obtained from this study and indicated with triangles…..………………………………72

5.4 Photograph of two sections from the Northern Ice Field (NIF) core 3. A) The top 0.65 m contained elongated bubbles and channels, and show characteristics of melting and refreezing. B) The rest of the 49 m ice core down to bedrock appears much different as it is glacial “bubbly” ice lacking the features associated with melting and refreezing (Thompson et al., 2009)……………………………….73

5.5 A) Ice area loss on Qori Kalis from 2000 to 2017. Measurements from 2000 to 2003 are from Thompson et al (2006) and measurements from 2004 to 2017 are from this study and indicated with triangles. B) Ice area loss on Kilimanjaro from 2000 to 2017. Measurements from 2000 are from Thompson et al (2002), measurements from 2003 are from Cullen et al (2006), and measurements from 2010 to 2017 are from this study and indicated with triangles…………...... 76

*All figures of Qori Kalis created in Arc Map use the information below  Coordinate System: Peru96 UTM Zone 19S  Projection: Transverse Mercator  Datum: Peru96  False Easting: 500,000.0  False Northing: 10,000,000.0  Central Meridian: -69.00  Latitude of Origin: 0.00  Units: Meter

*All figures of Mt. Kilimanjaro created in Arc Map use the information below  Coordinate System: Arc 1960 UTM Zone 37S  Projection: Transverse Mercator  Datum: Arc 1960  False Easting: 500,000.0  False Northing: 10,000,000.0  Central Meridian: 39.00  Latitude of Origin: 0.00  Units: Meter

List of Tables 3.1 List of photo collection dates on the Qori Kalis glacier, Peru from 2004 to 2017………………………………………………………………………….…20 3.2 Input values for Qori Kalis image parameters required in the photogrammetry extension of ERDAS Imagine………….…………………………………....…20 3.3 Input values for Mt. Kilimanjaro image parameters required in the photogrammetry extension of ERDAS Imagine……………..………………....20 3.4 Coordinates of the Ground Control Points (GCPs) for the Qori Kalis glacier and Mt. Kilimanjaro images……………………………………………………..…24 3.5 Qori Kalis (QK) stereo analyst RMSE triangulation errors in pixels from 2004 to 2016 and Kilimanjaro (K) stereo analyst RMSE triangulation error in pixels from 2010…………………………………………………………………….………44 4.1 The areas in both two and three dimensions for the ice extent on Qori Kalis as well as the proglacial lake. *Past Ice Area Measurements are from Henry Brecher…………………………………………………………………..……..46 4.2 A) Horizontal (2D) and B) Surface (3D) Areas for Kilimanjaro in 2010, 2013 and 2017. Ice fields 5 and 8 could not be measured in stereo in 2010 or 2017 and are omitted from the total area measurements in the last row of the table. Ice fields 6 and 7 are combined as 6 in 2010 and separated in 2013 and 201………….49 & 50 4.3 Terminus retreat for each transect on Qori Kalis for each epoch between 2004 and 2017 and overall retreat………………………………………………………….53 4.4 Volume loss calculated from the “Cut and Fill” tool in Arc Map for the Qori Kalis glacier…………………………………………….………………………………57 4.5 Volume loss between 2013 and 2017 on Mt. Kilimanjaro compared with the percent decrease in area from 2013 to 2017 and overall loss from 2010 to 2017. Ice fields 5 and 8 were omitted since they were only measured in 2013 and were not included in any totals……………………..………………………………….59 5.1 Percent area and volume loss and a volume/area loss ratio for each pair of epochs and over the entire thirteen-year time span……………………………….……...66 5.2 Table 5.2: Past Qori Kalis measurements compared to recent measurements from this study. The ratio used the retreat rate from 1963 to 1978 to compare different rates (Brecher and Thompson 1993). * “Data from” numbers are from sources listed below 1: Brecher and Thompson 1993 2: Thompson et al 2006 3: Hanshaw and Bookhagen 2014 4: This study……………………………………………………………………...68

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Chapter 1 Introduction

1.1 Concern for Natural Systems

Rising global temperatures are cause for concern, particularly among those who study the world’s glaciers. Evidence for the world’s rising temperatures can be seen among a variety of sources. The Intergovernmental Panel on Climate Change (IPCC) has noted that observational evidence indicates that regional changes in climate, especially temperature increases, have occurred and have affected a wide range of biological and physical systems in many locations around the world (IPCC, 2014). Systems that are used as resources such as agriculture, forestry, coastal, and marine, as well as energy providers and industries can also be affected by rising temperatures and climate change

(IPCC, 2014). Many natural systems such as glaciers, coral reefs, polar ecosystems, and wetlands are very vulnerable to climate change and often the damage can be irreversible.

The cryosphere, the part of Earth that is frozen, includes glacier and periglacial land environments, ice covered sea areas, and areas where ice, snow, or permafrost dominate.

While usually located at high elevations and Polar Regions, the loss of ice in these relatively remote areas has led to a rapid contraction of the cryosphere (Burkhart et al.,

2017).

1.2 Glacier Evidence for Climate Change

One of the most visible indicators of climate change is the increasing rate of glacier retreat over the past few decades. The Antarctic Peninsula has experienced the retreat of ten ice shelves during the latter part of the 20th century, and the loss of ice

1 shelves has caused an acceleration of the glaciers that feed them (Cook et al., 2005).

Fifty years ago John Mercer was the first scientist to predict the collapse of the Antarctic ice sheet. While East Antarctica is a mostly land-based ice sheet, West Antarctica is grounded almost 2,500 meters below sea level, making it more vulnerable and unstable

(Mercer, 1978). Mercer (1978) warned that with rising greenhouse gas a climatic warming above a critical level was likely and could remove all ice shelves, and all ice grounded below sea level, resulting in the deglaciation of most of West Antarctica. The future of the ice sheet is now at the top of the Antarctic research agenda, as the west

Antarctic ice sheet holds enough water to boost global sea levels by more than six meters.

Mercer is largely credited with sounding the alarm regarding the possibility that the West

Antarctic ice sheet could melt in response to the rising carbon dioxide concentrations.

(The Scientist, 5-6)

Twelve glaciers in Svalbard were investigated between 2000 and 2005 by monitoring their retreat rates and changes in ice thickness (Rachlewicz et al., 2007).

Most of the glaciers observed were found to be retreating at an increasing rate, presumably from climate warming, a main driver of ice retreat (Zemp et al., 2015). The ice fields on Mt. Kilimanjaro have also experienced rapid retreat, losing approximately

88.3% of their extent since 1912 (Burkhart et al., 2017). While the air temperatures are generally below freezing, the melt is usually induced by solar radiation (Cullen et al.,

2006; Thompson et al., 2006). Glaciers in the Andes mountain range have continued to experience accelerating retreat, since they reached their neoglacial maximum extent

(Rabatel et al., 2013). Both rapid and recent loss of ice in these locations is characteristic of most low and mid-latitude ice fields (Thompson et al.¸2006).

1.3 Past data collection

Measurements made at two low-latitude locations, the Qori Kalis outlet glacier on the Quelccaya ice cap in southern Peru (13°56’S, 70°50’W) and the ice fields on Mt.

Kilimanjaro in Tanzania (3°04’S, 37°21’E) have employed terrestrial or aerial photography to obtain the photos and were analyzed using various photogrammetric techniques. All of the photos of Qori Kalis were taken from a 370 m baseline about 900 m from the glacier terminus. A Hasselblad 500 EL metric camera was used in 1978 and

1983 and a Wild P32 camera has been used since 1991 (Brecher and Thompson, 1993).

Recent measurements of the Kilimanjaro ice fields were obtained through aerial photogrammetry. Due to difficult accessibility and the topography of Qori Kalis, a somewhat unsatisfactory stereoscopic model resulted from the relatively poor base to distance-away ratio (base/height ratio). This resulted in narrow intersection angles which adversely affected the precision in the “away from camera” direction, increasing as the square of the distance from the baseline.

Other more recent studies have also monitored Qori Kalis for area and volume loss. Salzmann et al (2013) observed the Cordillera Vilcanota range, including the

Quelccaya ice cap and found massive ice loss since 1985 (i.e. ~30% area loss and ~45% volume loss). The ice fields on Kilimanjaro were observed using satellite data from which their 20th century retreat rates were calculated. Cullen et al (2006) noted an aerial extent of 2.51 km2 in February of 2003 for the ice fields, and also concluded that the separation of both the Northern ice field and the Furtwängler glacier, each into two separate entities, would lead to the acceleration of the ice fields’ retreat rates. Recent technological advances, specifically “soft copy” photogrammetry, have provided the

3 opportunity to re-evaluate the retreating ice of both the Qori Kalis glacier and the Mt.

Kilimanjaro ice fields.

1.4 Expected Outcomes

A simple glance at photos of both Qori Kalis and Kilimanjaro shows an expected decrease of ice in both locations. In previous studies on Qori Kalis there was a noticeable relation of the decreasing ice area with the decreasing ice volume (~30% loss in area and

~45% loss in volume). It is expected this relation will continue over the next period of years that are assessed. The size of the rate of ice loss is expected to increase from previous years when the retreat of the terminus was approximately 60 m/yr between 1991 and 2005 (Hanshaw and Bookhagen, 2014). The lake at the terminus of Qori Kalis has been mostly unchanged in size since 2006 and is expected to remain the same size throughout the assessed years. An El Niño event such as the latest in 2015-2016 often causes higher temperatures and decreased precipitation. It is expected there will be more ice lost in both area and volume from 2016 to 2017, a response of the latest El Niño. The entire ice cap of Quelccaya has a dome-like shape, making it and its surrounding glaciers vulnerable to the rising altitude of the 0°C isotherm (Thompson et al., 2006). Strong El

Niños are also responsible for anomalous warming up to four degrees Celsius in the middle troposphere in the tropics, which would have a significant impact on the freezing level heights (Diaz et al., 2014).

The Kilimanjaro ice fields have lost 80% of their area from 1912 to 2000, ~12 km2 to ~2.6 km2 (Thompson et al., 2002). The ice was recorded in 2003 to have an aerial extent of 2.51 km2 (Cullen et al., 2006). Although the time span of this observation, 88

4 years, is rather lengthy, and this rate suggests that the ice on Kilimanjaro would be entirely gone between 2015 and 2020, recent satellite imagery from 2017 reveals that fourteen ice fields still remain. Many of the smaller ice fields will likely show an increase in their area loss from 2013 to 2017 relative to that from 2010 to 2013. As the remaining ice fields break into more but smaller ice fields their retreat rates will likely increase. It will not be possible to properly assess any impacts on Mt. Kilimanjaro’s ice fields that are expected from the 2015-2016 El Niño event. This is due to the lack of data from 2016 on Mt. Kilimanjaro, assessing any effects from an ENSO event would not be possible as the most recent data are from 2017 and 2013. However, southern Peru tends to experience a temperature increase and a precipitation decrease during an El Niño,

Kilimanjaro often sees an increase in both temperature and precipitation, which could potentially offset some of the warming impacts. Unlike Kilimanjaro, Qori Kalis imagery from 2016 and 2017 will facilitate an assessment of any potential impacts from the last El

Niño event.

Chapter 2 The Study Areas: Qori Kalis Glacier, Peru and Mt. Kilimanjaro Ice Fields, Tanzania

2.1 Tropical Glaciers and their Environments

In most parts of the world glaciers are exhibiting increasing retreat rates, especially in the tropics. The definition of a tropical environment tends to vary slightly.

Kaser (1999) defines the tropics as being located within 23.5 degrees north and south of the equator, an area where daily temperature variations exceed annual temperature variation, and within the oscillation of the Inter Tropical Convergence Zone (ITCZ).

Thompson (2004) classifies the tropics as the area between and including the subtropical high-pressure belts that are usually located between 30 to 35°N and S latitudes. The tropics hold half of the Earth’s surface area, and approximately seventy percent of the global population. As the world warms the tropical band is expanding, which could change the latitude boundaries that are considered tropical. Within the tropical region, there are both outer and inner tropical environments. The outer tropics have one wet and one dry season while the inner tropics have more or less continuous precipitation (Kaser,

1999). There are a handful of locations where tropical glaciers exist including Papua,

Indonesia (New Guinea), East African Mt. Kenya, Mt. Kilimanjaro and the Rwenzori

Mountains, and the South American Andes between Venezuela and Bolivia (Mercer,

1967). Unlike most glaciers, tropical glaciers can only exist at high elevations where temperatures are sufficiently cold that snow and ice can remain there year round (Kaser and Osmaton, 2002).

2.2 Importance of Tropical Glaciers

Tropical glaciers provide useful data for many climate change-related research projects (Houghton et al., 2001). There are only a handful of locations around the world today where tropical glaciers still exist, but given their high sensitivity to climate change they can be used as indicators of change and for interpretations of the mechanisms driving climate change. Generally, air temperature is considered to be the primary driving factor that causes a glacier to retreat (Kaser et al., 2003), but tropical glaciers seem to be slightly different. While air temperature plays a role, glaciers in a tropical environment may also be strongly affected by precipitation, cloud cover, humidity, and incoming shortwave radiation (Rabatel et al., 2013). However, tropical climate varies on decadal time scales and often the meteorological observations are too short or too scarce to make broad conclusions (Cullen et al., 2006).

There is also a possibility of elevation dependencies for observed temperature trends.

While there has been a slowing of warming in the Andes, this trend could be limited to lower elevations near coastal areas, and may not be affecting the highest elevations where the glaciers are located (Vuille et al., 2015). There is mounting evidence that the rate of tropospheric warming is increasing with elevation from factors such as atmospheric aerosol loading, latent heat release and feedbacks such as radiation fluxes and water vapor (Thompson et al., 2017). A more complete understanding of the drivers for the recent accelerated retreat is essential for interpreting their expected behavior in the future.

With the occurrence of El Niño in 2015-2016, a door has been opened to assess the impact of the El Niño Southern Oscillation (ENSO) circulation on low latitude glaciers.

The intensity of major El Niño events could also be a dominant forcing for the recent

7 glacier wasting (Thompson et al., 2017). A comparison between two glaciers, one in

South America and one in Africa could provide more clarity by documenting how tropical glaciers react to sudden changes in both temperature and precipitation across two different climate regimes.

The impact these retreating glaciers could have on the surrounding population is also a major concern. There are a large number of people who live on the western, arid side of the Andes Mountains who rely on glacial meltwater for agricultural, hydropower, and consumption purposes (Rabatel et al., 2013), especially during the dry season and in times of drought. If the glaciers in the Andes continue to retreat at their current rate, there is a high probability that many will disappear completely in the next few decades.

Thus, those on the dry western side of the Andes may be hard pressed to find reliable water sources. As the glaciers retreat further up the mountains much of the population must change their lifestyle, moving further up the mountains for a more constant source of water (Georges, 2004). With the reliability of a water source vanishing, there is another cause for concern created by the potential for flooding from proglacial lakes. In

2006, the lake at the terminus of the Qori Kalis glacier, the largest outlet glacier on the

Quelccaya ice cap in Peru, breached its moraine dam after an avalanche, flooding the valley below and drowned herds of alpaca (Figure 2.1) (Thompson et al., 2011). There is a similar concern for the population that resides around Mt. Kilimanjaro, although the density population is lower than in the Andes. They are also less dependent on water from the melting ice fields, and at a lower risk for avalanches and flooding cause by the melting ice (Kaser et al., 2004).

., 2011). 2011). .,

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ice from the glacier in in the glacier icefrom

An avalanche of of An avalanche

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2006

(a) (a) and

July 2005 2005 July

Qori Kalis valley in Kalis valley Qori

2006 caused the proglacial lake to breach the moraine and flood the valley below. There are below. are There and breach to the flood moraine the lake valley proglacial the caused 2006

Figure 2.1: The 2.1: Figure March the from of lake end the the resulted that deposits visible (Thompson flood at sediment (b) in

2.3 Regional Climate Overview

Glaciers in tropical regions have been retreating at an alarming rate since the second half of the 19th century (Kaser and Osmaston, 2002). While tropical glaciers in locations such as the Andes are not widespread, they play an important role in the communities around them by providing a continuous supply of water during the dry season and during times of drought for agriculture, consumption, and hydroelectricity. The Andes hold more than 99% of the world’s tropical glaciers (Rabatel et al., 2013). Glaciers in the

Andes act as temporary water storage for snow that falls at high elevations in the wet season (Schauwecker et al., 2014). The need for a water resource will become greater in the tropical Andes with the increasing speed and magnitude of many glaciers’ retreat.

Glacier retreat can also increase the potential for natural hazards, and could also result in negative impacts on the ecosystem composition in rivers downstream and other economic sectors such as tourism or mining (Vuille et al., 2017). Glaciers in equatorial latitudes are generally smaller, located in a warm temperature regime, are closer to the melting point, and seem to be more sensitive to climate change than polar glaciers that exist in high latitudes therefore these low latitudes glaciers tend to react more quickly to climate fluctuations (Georges, 2004).

The Qori Kalis glacier (Figure 2.2) and the glaciers on Mt. Kilimanjaro in Tanzania

(Figure 2.3) exist in a tropical environment. Under these tropical conditions, both locations experience a small range in annual temperatures and likewise both experience an annual cycle of wet and dry seasons. Andes Mountains have experienced an increase in temperature of 0.39°C per decade between 1951 and 1999 which is consistent with global climate trends (Vuille and Bradley, 2000). The Quelccaya ice cap has experienced

10 an increase in net accumulation that is consistent with the southward movement of the

Intertropical Convergence Zone (ITCZ). Data in the Andes suggest that the retreat of

Quelccaya is driven by warming temperatures (Stroup et al, 2014). Nearly annual field observations since 1978 confirm that Quelccaya has been retreating along its margins and the argument points to temperature as the dominant factor over longer timescales

(Thompson et al., 2013). Over the twentieth century, precipitation has not shown a consistent trend, and is not considered the primary factor driving the ice retreat (Rabatel et al., 2013). The dating of glacial moraines has also led to similar conclusions (Kelly et al., 2015; Stroup et al., 2014).

Figure 2.2 Map showing the location of the Qori Kalis glacier on the Quelccaya ice cap, Peru (Buffen et al., 2009)

Meters

Figure 2.3 Aerial photo of the summit plateau of Kilimanjaro, showing the Kilimanjaro ice fields; the inset shows the location of Kilimanjaro on the border of Tanzania and Kenya, Africa (February 2010).

Regardless of whether the location of a tropical glacier has experienced increased or decreased amounts of precipitation, all tropical glaciers have been retreating (Cullen et al., 2006). Temperatures have increased over Mt. Kilimanjaro although atmospheric moisture has declined since 1880 (Kaser et al., 2004). Since both locations lie close to the equator, the shifting of the ITCZ and the occurrence of El Niño every three to four years can impact the rates of glacier loss (Figure 2.4). El Niño events have a strong influence on moisture transport from the Amazon to the Andes, through the strengthened westerly air flow over the central Andes. This inhibits low and middle level easterly flow from the northern part of the Amazon Basin during the austral summer. Strong El Niño events can produce anomalous warming, up to 4°C in the middle troposphere of the

Tropics, and can have a significant impact on freezing level heights (Thompson et al.,

2017, Diaz et al., 2014).

The retreat of the Qori Kalis glacier has been assessed since 1978 with terrestrial photogrammetry (Thompson et al., 2006). Over the last few decades the size of Qori

Kalis has decreased noticeably; about one third in length and by an undetermined amount in ice thickness. With the changes in climate the world is experiencing on a global scale, glaciers that exist in low latitudes are experiencing an increase in their retreat rates.

Similarly, the ice fields that exist on the summit plateau of Mt. Kilimanjaro in Africa are also undergoing a rapid retreat. Between 1912 and 2013, the ice fields on Kilimanjaro have lost approximately 88% of their aerial extent (Thompson et al., 2009; Burkart et al.,

2017).

Mt. Kilimanjaro Mt.

2017). 2017).

2.4 World map showing the motion of the ITCZ with respect to the two study areas. The Mt. passes over to areas. two the respect with World 2.4 the motion the showing ITCZ study of map ITCZ

Qori Kalis Qori

Figure annual receives an far is Qori the enough it south while twice that so only glacier a Kalis usually Kilimanjaro year, (Desonie, the of passing ITCZ

2.4 Study Area Specifics

The Andes Mountains run along the western side of South America and extend through seven countries. They exhibit a wide range of environments from Ecuador all the way to the southernmost regions of Chile (Rabatel et al., 2013). The subequatorial northern Andes range from 3°S to 15°S where the Amazon side of the range receives more than 2 m/yr of precipitation while the Pacific side receives less than 0.2 m/yr

(Montgomery et al., 2001). The Qori Kalis glacier resides within this range at approximately 14°S in southern Peru, placing the glacier within an inner tropical environment. While there are not large temperature fluctuations throughout the year the area experiences a wet season from October to April and a dry “Andean Summer” from

May to September (Montgomery et al, 2001). Qori Kalis has been monitored by aerial photography in 1963 and by terrestrial photogrammetry since 1978 (Brecher and

Thompson, 1993). It is the largest outlet glacier on the Quelccaya ice cap and has been recently retreating at a rate ten times faster than the rate determined from the first measurements from 1963 to 1978. Between 1983 and 1991 a small proglacial lake formed at the terminus of the glacier and has continued to grow steadily in size since its first appearance (Thompson et al., 2006). By the summer of 2008, Qori Kalis had retreated noticeably, leaving behind a 34 hectare lake, 60 m in depth (Thompson et al.,

2010).

The glaciers on Mount Kilimanjaro in Tanzania exist even closer to the equator at approximately 3°S which is just 330 km south of the equator. Most of the precipitation the area receives is due to the passing of the Intertropical Convergence Zone (ITCZ) over

Kilimanjaro which leads to a bimodal pattern of rain occurring from March to May and

October to December (Thompson et al., 2002, Kaser et al., 2004). Between the rainy seasons, precipitation is much reduced with the seasonal fluctuation in temperature varying just slightly. Because of their surrounding environment and close proximity to the equator, the seasonality of the rainfall makes these glaciers very susceptible to changes in air humidity and cloudiness (Cullen et al., 2006). Lake Challa, a 97 m-deep crater lake 880 m above sea level on the eastern flank of Mt. Kilimanjaro, records

18 18 δ Odiatom that when correlated with ice core δ O records, show the seasonality of the climate at Kilimanjaro over the last 25 ky, and make the case that temperature and not precipitation or moisture balance drives the climate in this region (Barker et al., 2011).

The glaciers remaining on Kilimanjaro are believed to be the remnants of an ice cap that once covered the entire summit (Kaser et al., 2004). Of the three main peaks on

Kilimanjaro, only Kibo (5895m) still has ice fields remaining (Kaser et al., 2003).

During the height of the last glaciation of the Pleistocene, the ice cap is believed to have covered an area of approximately 150 km2 (Osmaston, 1989). The three remaining ice fields are losing volume vertically at a rate of 0.5 m/yr. A measurement of the ice in

2000 showed that the ice cover had decreased to approximately 2.6 km2. Should these climate conditions continue, the ice will likely disappear in the next five to ten years

(Burkhart et al., 2017).

Chapter 3 Methodology

3.1 Acquiring the Data

For any three dimensional measurements to be made, a stereo pair of images must be available. At the Qori Kalis glacier, two images for each epoch are available, taken from two ends of an established baseline. The photos considered here were collected between

2004 and 2016, taken during the summer months of their respective years. The 2017 images of Qori Kalis were a set of satellite images. Table 3.1 shows the dates each set of photos was collected. Since the Qori Kalis glacier extends approximately east-west each photo is labeled either “photo north” (PN) or “photo south” (PS) with corresponding coordinates of the camera stations. The photos were all taken with a Wild P32 camera with a 60 x 80 mm format and a 64 mm focal length lens from a 370 m baseline approximately 900 m from the terminus of the glacier (Brecher and Thompson, 1993).

The aerial photos from Mt. Kilimanjaro were taken in February of 2010 and consist of three exposures “10” “11” and “12” from west to east across the mountain peak. The

2013 data on Kilimanjaro were acquired from Photomap Kenya Limited and the 2017 images were acquired from Apollo Mapping.

The photo coordinate system in which the measurements are made, is established by fiducial marks on the photos so that measurements refer to the fiducial center of the photo. The fiducial marks are then corrected for the known offset from the principal point of the camera. Because the definition of the camera properties is based on the film camera parameters, a small MatLab program is required to convert such definition to digital camera representation for the ERDAS program. The Qori Kalis images from 2004

18 to 2016 have five fiducial marks and the Kilimanjaro images have four fiducials. It is necessary to measure the fiducials in order to properly work with the images. The program uses the fiducial marks on the photos to make the conversion, even if one of the fiducials cannot be located visually. From there, the photos can then be uploaded into the photogrammetry extension found in ERDAS and placed into a new block file, with the camera type chosen as a digital camera instead of a frame camera. The program requires a number of image specific parameters in order to properly process the images.

Coordinates are required for each of the camera locations as well as parameters such as focal length, pixel size, and flying height. In the case of Qori Kalis, the flying height is the distance from the camera to the center of the glacier, which is approximated using the available coordinates. The parameters for the Qori Kalis images from 2004 to 2016 can be found in Table

3.2.

The Kilimanjaro images were processed in a fashion similar to the Qori Kalis photos, but the MatLab script was altered to represent information from the Kilimanjaro photos, although the images were ultimately processed in the same manner. Unlike the

Qori Kalis images which have five fiducials and are not all needed for the MatLab script to successfully run, the Kilimanjaro images must have all four of its fiducials for the

MatLab script to run. The 2010 images of Kilimanjaro had clear fiducials in each image making it possible to process them through the MatLab script. The parameters for the

Kilimanjaro photos can be found in Table 3.3.

Year Month Day

2004 July 19 2005 June 27

2006 July 15 2008 June 25 2011 July 8 2015 June 5

2016 June 30 2017 July 20

Table 3.1: List of photo collection dates on the Qori Kalis glacier, Peru from 2004 to 2017.

Parameter Value Focal Length 63.94 mm

Principal point, Xo -0.004 mm Principal point, Yo 0.009 mm

Pixel Size (X and Y direction) 21 µm Flying Height ~1,600 m

Table 3.2: Input values for Qori Kalis image parameters required in the photogrammetry extension of ERDAS Imagine.

Parameter Value Focal Length 88.191 mm Principal point, Xo -0.004 mm Principal point, Yo -0.004 mm Pixel Size (X and Y direction) 22.3 µm Flying Height ~1,700 m

Table 3.3: Input values for Mt. Kilimanjaro image parameters required in the photogrammetry extension of ERDAS Imagine.

Once all parameters are entered, the ground control points (GCPs) for each stereo model must be located and their coordinates placed into the file. The Qori Kalis glacier had six GCPs, but number 5 was omitted due to difficulties that arose with locating the point on the images (Figure 3.1). Each GCP was placed on a feature that would be easily identifiable in future years, to be used in all the photos collected. Six GCPs for Mt.

Kilimanjaro were placed approximately in a circle on the mountain summit plateau

(Figure 3.2). The control points were intentionally placed on the summit plateau of

Kilimanjaro as that was the area of interest to be mapped. The GCPs were established by differential GPS measurements and marked with suitable target panels. Natural features were intentionally not used because of the difficulty of precise identification of any features on the surface of Kilimanjaro.

not used not to due

, 2005). 2005). ,

3 & 6 & 3

CPS

Figure 3.1: The Qori Kalis glacier, Peru with the 5 GCPs used marked in blue. GCP #5 was 5 GCP the with blue. in was #5 The 3.1: Kalis Peru GCPs Qori marked used glacier, Figure the on point the (June locating 27 images accurately with difficulties

Figure 3.2: Kilimanjaro with six GCPs marked in red and numbered. The GCPs were originally placed in a circle around the crater and marked with targets but are not located on distinct features around the peak (February, 2010).

The software used is intended to handle symmetrical marking of fiducials and since the Qori Kalis center, right, and left fiducials are not equidistant from the top and bottom edge of the photo it is necessary to transform the coordinates. In this case, terrestrial x coordinates become -Y values, y coordinates become Z values and z coordinates become

-X values. The Kilimanjaro images have four fiducials placed symmetrically on the image so their GCP ground coordinates do not have to be transformed. The GCPs for both locations are given below in Table 3.4, with their corresponding coordinates, before any coordinate transformation. The Qori Kalis images and measurements were made in the Peru 96 UTM Zone 19S coordinate system and the Kilimanjaro images used the Arc

1960 UTM Zone 37S coordinate system.

Qori Kalis Mt. Kilimanjaro

GCP X (m) Y (m) Z (m) GCP X (m) Y (m) Z (m)

1 300,681.1 8,462,370.7 4,922.6 1 316,264 9,661,801 5,744.1

2 302,573.6 8,462,099.3 5,304.6 2 317,244.5 9,662,317 5,736.8

3 301,796.7 8,462,792.3 5,121.5 3 318,119.4 9,661,895 5,722.3

4 304,670.1 8,462,345.8 4,921.6 4 318,394.5 9,661,099 5,687.3

6 301,580.6 8,462,757.0 5,079.1 5 317,389 9,660,226 5,719.0

6 316,505.9 9,660,796 5,726.2

Table 3.4: Coordinates of the Ground Control Points (GCPs) for the Qori Kalis glacier and Mt. Kilimanjaro images.

A triangulation must be run on the photos before they can be measured in the stereo analyst, requiring addition of tie points after the GCPs are in place. Using these common points and their lines of sight from the camera location, the intersection of the lines

24 between the two photos determines the three-dimensional location of each point. The program has the ability to run an automatic tie point generation between the images.

However, sometimes more tie points are necessary than the standard twenty-five created through the automatic tie generation process. Each tie point is manually checked for accuracy to ensure that the location is the same on both photos. More tie points can be added manually until each image has at least twenty-five tie points evenly spaced across the overlap between images. It is also possible to re-run the automatic tie generation multiple times to locate more tie points, but this is not a required procedure for each set of images. Coordinates for each tie point will be established and entered by the program during a triangulation run when the iteration is successfully completed.

The triangulation run will output a Root Mean Square Error (RMSE) value in pixels.

Once the error is low enough, preferably below one pixel, it is possible to proceed to the next step and measure features in the image. The product after all the GCPs are placed, the tie points generated, and a successful triangulation performed, will be a block file that is viewable in the stereo analyst. The stereo analyst in the ERDAS program can be used to measure the ice extent. The stereo analyst will enable measurements to be done in three dimensions. Points are collected for the boundary of the lake as well as the ice extent. Each point will have X, Y, and Z coordinate values. The number of points will vary with each epoch because of the changes in the ice boundary.

Points are also required for the surface of the ice, and at least seventy-five “mass points” are taken to ensure an even coverage across the surface of the Qori Kalis glacier.

The coordinates on Qori Kalis for the lake boundary, ice boundary, and ice surface can be exported into a text file and the coordinates transformed back to the original coordinates

25 system, which was necessary for Qori Kalis but not Kilimanjaro. The Kilimanjaro measurements are collected through the stereo analyst in the same manner for the boundaries of the different ice fields and across each surface. The number of points varies widely with the Kilimanjaro measurements as the fields of ice vary dramatically in surface area and overall size. The 2013 data for Mt. Kilimanjaro were provided by

Photomap Kenya Limited. The data were collected either through an analytical plotter or through soft copy techniques. The resulting data were then organized to correspond with previous measurements of the individual ice fields for comparison purposes resulting in a table of over 30,000 data points for the ice fields on the summit plateau and slopes of Mt.

Kilimanjaro.

The 2017 images of both Qori Kalis and Kilimanjaro were acquired in a different manner than any of the previously collected images. Two satellite images for each location were acquired from the Apollo Mapping Company. The images are from the

World View 3 satellite sensor (WV03) which operates at an altitude of about 617 km, has a 0.31 meter panchromatic resolution and a 1.24 meter multispectral resolution. Each acquired image came with a panchromatic image, a multispectral image, and various metadata files. The panchromatic image is one black and white band with a wide bandwidth (450-800 nm), giving the band the ability to have a high signal-to-noise ratio and the highest spatial resolution of the available bands. The multispectral image is comprised of eight separate bands, red, red edge, coastal, blue, green, yellow, near infrared 1 (NIR1), and near infrared 2 (NIR2) (Digital Globe, 2017). Each band is collected over a series of narrower bands, which provides a full color image, but at a lower resolution. The Qori Kalis images were both taken on August 20th, 2017. The Mt.

Kilimanjaro images were taken on June 22nd and June 29th, 2017. There is minor cloud cover in each of the Kilimanjaro images, but mostly around the perimeter of the image area not covering the ice on Mt. Kilimanjaro. Each image is approximately 25 square kilometers and completely covers the area of interest.

Figure 3.3: Satellite image of Kilimanjaro, taken June 22nd, 2017. Minor cloud cover is present around the mountain-top but does not interfere in measurements.

3.2 Processing the Data

The Qori Kalis text file containing the points collected from 2004 to 2016 can be uploaded, viewed, and analyzed within the ArcMap program. In order to properly upload the data into the ArcMap GIS program all the coordinates must be exported and saved as a “.csv” file. ArcMap provides a file importing option; “Add XY Data.” This enables the user to start with a file of coordinates, as in this instance, and process the data in a number of ways. Once the coordinates are uploaded they will appear as points within the program (Figure 3.4). The lake and ice boundary files can be connected using the “Points to Line” tool which then enables the “Feature to Polygon” tool for the lake area as well.

Since the lake does not have relief, ArcMap will compute an area for the lake, in this case hectares are used to compare to previous measurements. The ice area will be computed using a different method because of the point collection that was completed across the surface.

Figure 3.4: Coordinates from the stereo analyst program uploaded into the ArcMap program and will appear as points on the map space (example from 2004).

The ice measurements require a different set of processing steps. Once the points collected from the stereo analyst are uploaded into ArcMap they can be viewed in the

ArcScene extension, which allows the viewer to see the points in a three-dimensional space. Here, it is possible to visually detect any points that might have been measured incorrectly. A common error in the measured mass points is a point placed far above the surface of the glacier that should be on the glacier’s surface. This error results from the user improperly aligning the two images when taking the measurements in the stereo viewer. Any incorrect points can be adjusted and/or deleted within the ArcMap program.

Out of the 75-100 mass points collected for each year, there were approximately 3-5 points that needed adjusting. If more points are required, it will be necessary to repeat the measuring process and re-upload the points into ArcMap.

Once all the points have been adjusted or deleted as necessary, the process for measuring the glacier and creating a contour map of the ice area can begin. The first step is to move away from single points to the creation of a Triangular Integrated Network

(TIN) file. A TIN file uses a series of triangles to connect the surrounding points, making a three-dimensional surface that shows relief. The ice boundary points can be connected using the “points to line” tool to avoid the interpolation of a surface outside the ice boundary. Using the 3D Analyst toolbar available in ArcMap the “Create TIN” tool is selected (Figure 3.5). The ice boundary line is defined as a “hard line” and the mass points are defined as “mass points” within the TIN creating tool. While the TIN file is useful for visualization purposes it does not hold data that can be extracted and analyzed in the manner necessary.

Once the TIN file has been created it can be converted into a raster file. The 3D

Analyst has a number of conversion tools that make the creation of a raster file possible, from a variety of sources. In this case the “From TIN to Raster” option is chosen in the conversion toolbox and the output results in a two dimensional raster file, which stores elevations pixel by pixel. However, the creation of a raster often causes smoothing around the edges of an irregular shape, requiring an adjustment to shape the raster to the measured ice boundary. Within the Data Management toolbox the “Clip Raster” tool is used on the current raster file with the ice boundary coordinates to clip the edges of the raster back to the original measured shape (Figure 3.6). The raster file can be formatted to show the changing elevation on the surface of the glacier using a variety of available color schemes, made into a contour map, and measured for area, surface area and volume loss.

Figure 3.5: ArcMap displays the ice surface points as a TIN file and creates an outline for the lake boundary, shown by the purple line (2004). Each elevation grouping (meters) is represented by an assigned color.

Figure 3.6: The TIN file is converted into a raster file, the elevation height is stored in each of the pixels and the raster clipped back to its original size. The lake is made into a polygon in order to measure the total area (2004). Elevation values are assigned at even intervals along a color gradient.

From the raster the “Contours” tool is available in the 3D Analyst toolbar. The elevation contour lines are mapped with a 20 meter interval across the surface of the glacier with every 100 meter contour labeled as an index contour. As the contours are computer generated, the initial lines are often jagged and harsh, contrary to the final product of a hand plotted contour map. The “Smooth” tool is used to aid in the adjustment of the contour lines, in an effort to show a more realistic interpretation. A smoothing interval of 10 meters is used, half the value of the contour interval (Figure

3.7). In the past, the plots were created by the “Surfer” software from hand measured surface elevation points that were well distributed over the terrain. The “Surfer” software used these 30 randomly distributed points to create a regular grid of elevation from which the contour maps were generated. The measurements were taken across the surface of the Qori Kalis glacier to an “upper limit” line, a previously defined straight line created by connecting two easily identified topographic features, one on each side of the glacier.

This is approximately where the glacier meets the Quelccaya ice cap. For consistency and comparison to past measurements, the glacier was clipped along the limit line coordinates and area measurements were made from the clipped file. The “Surface

Volume” tool in the 3D Analyst toolbar in ArcMap is used to output a horizontal area measurement (2D) and a surface area measurement (3D), in square meters. For comparison to the previous measurements, all area measurements are divided by 10,000 to convert into hectares.

Figure 3.7: A final contour map of the measured area of Qori Kalis with 20 m contour intervals and the previously established limit line (2004). The overlay of ice and lake results in the earlier epoch measurements where the boundary between the two is hard to distinguish in the stereo analyst.

It is also possible to create a transect from the clipped raster file, to assess the changing horizontal extent of the glacier. Three lines were drawn across the surface of

Qori Kalis, one in the center, and one on each edge, from the limit line down to the terminus (Figure 3.8) labeled “Centerline,” “North Line,” and “South Line” with respect to Qori Kalis. The coordinates extracted from the “Stack Profile” tool at each transect can be used to assess horizontal extent of Qori Kalis. When viewing the data, the “X” value is the horizontal distance from the limit line, the largest number being the distance in meters from the limit line. This largest value shows how far the ice extent reaches from the limit line and serves to approximate the ice extent changes throughout the twelve year time span. Change will be compared from year to year as well as overall change between 2004 and 2017.

Figure 3.8: The Qori Kalis glacier showing the limit line and the three transects drawn across its surface. The transects extract the Z values from each respective raster file as well as providing an X value for the distance away from the limit line. This X value can be used to assess horizontal extent for each year on Qori Kalis. The example year shown is 2016.

To assess the volume change the “Cut and Fill” tool within ArcMap can be used.

Using the “Cut and Fill” tool it is possible to subtract two raster files from each other and calculate the changes between the files. However, the tool only works with areas of overlap between two files. The volume change to any areas from which the ice has retreated from the earlier year will not be assessed as part of the loss. This will underestimate the amount of ice loss since the volume lost from ice retreat will not be taken into account. It is possible to work around this using the 2017 digital surface model (DSM). Since the DSM for 2017 covers an area of about 25 km2 there are surface elevation values for the area outside the 2017 ice extent. This data are not available for the other years as the measurement within the stereo analyst is only done on Qori Kalis to capture the ice extent. There are also no features that would make it possible to trace the old ice extent in a later year (i.e. trace the 2005 ice in the 2015 photos). Each of the previous year’s ice extent can be extracted from the 2017 DSM and compared to their respective years. For example the 2004 raster file will be compared to the 2017 DSM, using the 2004 raster and the 2004 outline clipped from the 2017 DSM to properly quantify the amount of volume loss. The “Cut and Fill” tool is used to compute the amount of volume lost across the surface of Qori Kalis for each year relative to 2017.

The change in volume loss between each epoch is computed using the values resulting from the “Cut and Fill” tool.

The tool assigns each overlapping pixel a value of “net loss” or “net gain.” Areas where groups of pixels have all “net loss” or “net gain” are placed together and the overall loss for that area is determined by adding the change in Z value across each pixel in the total area. The end result is a Figure (Figure 3.9A) with an attribute Table (Figure

3.9B) which allows the visualization and summation of net loss and gain over the surface.

Figure 3.9 shows a calculation that underestimates the volume loss due to the lack of overlap between the two files and the necessity for the process previously described using the 2017 DSM compared to the previous epoch’s ice extent to calculate volume loss. The values for volume change are consolidated into categories, as neighboring pixels with the same direction of change are put into a single group. All the data are summarized into the output figure’s adjoining attribute table that enables the summation of total net loss or gain across the surfaces being compared. This way, each glacier surface that is compared across time can be classified as having overall volume loss or gain from year to year. An overall volume change was computed for the surface of Qori Kalis between 2004 and

2017 as well as individual assessments for each year of previously created raster files to view smaller fluctuations between specific years. Volume change for Kilimanjaro between 2013 and 2017 was computed in the same manner using the 2017 digital surface model, but 2010 could not be included due to the offset from the stereo analyst that resulted from a large RMSE error and difficulties locating the GCPs.

To ensure accuracy in the volume loss calculations the “Raster Calculator” is used in the ArcMap program. Upon selecting, the tool prompts the user to input an equation using file names. For example, to assess volume loss from 2004 to 2017 the equation would read “04_Raster” – “17_Raster.” In this case it subtracts each pixel value across the grid of values in the raster from the comparing raster file. The output values are a similar format to the “Cut and Fill” tool and can be summed to determine the amount of volume lost across each time span assessed. The “Raster Calculator” calculated the same values for volume loss as the “Cut and Fill” tool.

VALUE COUNT VOLUME AREA Figure 3.9: A) The “Cut and Fill” tool 1 7986 3865154.71 213134.24 shades areas of loss with blue and areas of 2 276 -38830.39 7366.02 gain in red. This sample is the volume loss 3 2 -14.56 53.38 between 2004 and 2017 on Qori Kalis. The 4 729 -70810.00 19455.91 light blue line is the ice edge in 2017 while 5 1 0.14 26.69 the green line is the ice edge in 2004. Note 6 1 -4.53 26.69 the full 2017 area is not shaded as it does 7 4 49.86 106.75 not overlap exactly with the 2004 data, 8 1 12.81 26.69 leaving blank spaces in the data. The purple 9 1 10.63 26.69 line on the east side is the limit line. B) The 10 3 30.70 80.07 11 1 8.51 26.69 attribute table that is produced with the 12 1 -8.86 26.69 output table provides data for assessing the 13 34 -1481.69 907.41 volume change. The value numbers are 14 1 -18.01 26.69 arbitrary and serve no purpose except for 15 9 -546.28 240.20 locating each set of values. The count 16 2 -40.67 53.38 column shows the numbers of pixels that 17 2 -322.99 53.38 are similar in either loss or gain. The 18 5 -266.14 133.44 volume column shows a value in cubic 19 4 -28.67 106.75 meters for the volume loss or gain while the 20 5 -206.74 133.44 area column shows the area each section covers in square meters.

The Kilimanjaro data required slightly different processing. The 2010 aerial images were measured through the stereo analysis program in the same manner as the Qori Kalis terrestrial images. However, due to an issue with the stereo analysis program, the data could not be exported as a text file and instead had to be exported as a shape file. All measurements occur as one shape file once they are in ArcMap and must be separated from each other in order to process properly. The ice boundaries were broken down from a single line to a series of points and each set of points for the different ice fields can be isolated and re-connected as the ice outline for each ice field. The surface points collected must also be separated into each individual ice field and coordinate values inserted for each point. The stereo analyst program will only save the exported shape files with “average Z” values. However, once the proper coordinate system is defined and the shape files are broken into a series of points it is possible to attach XYZ data for each point. The “Add XY” tool is selected within the Data Management toolbox, a coordinate system is specified producing an output with the same set of points, but with the proper coordinate values in their adjoining attribute Tables. Once completed the points and lines are then processed in the same manner as Qori Kalis, a TIN file is created, converted to a raster file, and the raster file is clipped back to its original area and measured for horizontal area (2D) and surface area (3D).

The 2013 data points for Kilimanjaro required separation into individual ice fields. In the ArcMap program each ice field was visible, but the coordinates were all saved as the same shape file. It became necessary to separate each individual ice field from the others. This was accomplished with the selection tool in ArcMap to export each ice field as a separate feature. Each ice field was assigned an arbitrary number for labeling and

39 visualization purposes, between zero and thirteen. In order to follow the same processing steps as before, a TIN file had to be created from the 2013 mass points. It was possible to isolate the boundary points of each ice field from the mass points as they were aligned in the attribute tables. The mass points and boundary points were then divided into separate features and used to create a TIN file for each field of ice on Kilimanjaro. From the TIN file a raster file was created and the horizontal area (2D) and surface area (3D) of each ice field was measured and recorded.

The Qori Kalis and Kilimanjaro 2017 images were processed in a different manner.

Since they are neither terrestrial nor aerial imagery, there are other options for computing the same results from sets of satellite imagery. Qin (2016) has created a rational polynomial stereo processor (RSP) which computes a Digital Surface Model (DSM), an orthophoto, and a point cloud. Since the terrain creates a slight offset in the imagery from relief displacement, orthorectification is required through georeferencing of the images, which can be done in the stereo processor. At least three GCP locations are defined with respect to pixels in the images (row, column), and are used in a first order affine transformation, a function that preserves points, straight lines, and planes to georeference the images (Qin, 2016). Once the images are georeferenced the RSP Stereo

Processor can be used to generate three different outputs (Figure 3.10) including a true orthophoto, a color/grey point cloud, and a digital surface model (DSM) raster.

Figure 3.10: An input-output functioning diagram of the RSP Stereo Processor (Qin, 2016).

Once all the outputs have been generated, they must be Peru 96 UTM Zone 19S while the Kilimanjaro data sets are projected into the Arc 1960 UTM Zone 37S coordinate system and opened in the ArcMap program. The DSM file can be clipped to the ice extent, whether it is the outline of Qori Kalis or the perimeter of Kilimanjaro’s multiple ice fields. The ice extent is obtained by viewing the orthophotos and accessing the drawing toolbar in ArcMap. A polygon will be drawn around the boundary of the ice extent, as well as the lake in the case of Qori Kalis. These polygons can be used as the boundary to clip the DSM file.

From there, the 2017 images can be processed in the same manner since the DSM files are equivalent to the raster created by the stereo analyst for previous data sets.

Contours will be created for Qori Kalis in the same manner as the 2004 to 2016 images.

The “Surface Volume” tool is used to compute the horizontal and surface area for both

Qori Kalis and Mt. Kilimanjaro. The “Cut and Fill” tool can be used with the Qori Kalis images to compare volume loss from the previous year (2016) and over the thirteen year span from 2004. The three transects previously drawn on Qori Kalis to compute

41 horizontal extent changes can be placed on the 2017 DSM file to obtain horizontal extent as done with the previous Qori Kalis files. Changes in area and volume across all time spans as well as percent decrease in area and volume were calculated for both Qori Kalis and Mt. Kilimanjaro. Loss of area and volume were assessed from 2016 to 2017 on Qori

Kalis to possibly note any influences that resulted from the last El Niño Southern

Oscillation in 2015 - 2016.

3.3 Error and Uncertainties

All of the data processing is computed through the ArcMap program, the

Kilimanjaro data in 2010 and the Qori Kalis data from 2004 to 2016 were collected through a stereo analyst in the ERDAS Imagine software. Within the stereo analyst, after a triangulation is run, each block file will have a Root Mean Square Error (RMSE) value in pixels. Table 3.5 lists the RMSE in pixels from each block file. The error must be noted because it affects any measurements that are taken in the stereo analyst. Most of the uncertainty results from the manual measurements that include tracing the ice extent, the lake boundary, and collecting mass points across the surface of the glacier. The

Kilimanjaro RMSE in 2010 is much higher than the Qori Kalis imagery from 2004 to

2016 that were processed the same way. It was difficult to locate the GCPs on

Kilimanjaro because the targets placed for the first photo coverage in 2000 were no longer present. Instead of the GCPs being placed on identifiable features, as is the case with the Qori Kalis photos, six points were placed, targeted, and measured by differential

GPS measurements around the crater of Mt. Kilimanjaro. The RMSE in the Kilimanjaro images is most likely the reason for the offset of the imagery in the 2010 images.

While all the Qori Kalis RMSEs were less than one pixel, an acceptable error margin, some difficulties arose with the manual measurements of the ice and lake margins in the 2004 to 2016 images. The north side of the ice and the lake, where the lake meets the ice, present some difficulties when viewing the mentioned section in the stereo analyst. Since the photos are terrestrial the perspective is from the baseline. There is a moraine on the north side of the glacier that prevents a clear view of the north ice edge, especially at the terminus where the ice meets the lake. All the area measurements from 2004 to 2016 have shown wide variability in that specific location and should be noted when assessing changes in area on Qori Kalis.

The 2017 images for both Qori Kalis and Kilimanjaro were acquired satellite imagery and run through a RPC Stereo Processor that ultimately creates a Digital Surface

Model of the area. While the resulting DSM is produced from an algorithm that usually has an error just larger than one pixel, the processor has been shown to have some difficulties with bright and complex roofs and therefore isn’t typically used on a glacier or snow surface (Qin, 2016). The ice outline is defined by a manual measurement, but the data from the DSM are used for the surface area and volume loss calculations and it should be noted that it is acquired differently than past imagery data. However, points across the photos used in 2017 to create the DSM are created through an algorithm in the stereo processor, which could also produce a small error.

Year RMSE error (pixels)

QK 2004 0.92

QK 2005 0.55

QK 2006 0.63

QK 2008 0.97 QK 2011 0.59

QK 2015 0.76

QK 2016 0.69

K 2010 4.14

Table 3.5: Qori Kalis (QK) stereo analyst RMSE triangulation errors in pixels from 2004 to 2016 and Kilimanjaro (K) stereo analyst RMSE triangulation error in pixels from 2010.

Chapter 4 Results

4.1 Horizontal Area and Surface Area Measurements

4.1.1 Qori Kalis

Area measurements for the Qori Kalis images were taken in the ArcMap program using the “Surface Volume” tool in the 3D Analyst toolbox. The tool outputs a horizontal area measurement (2D), and a surface area measurement that takes the Z values of each file into account (3D). The 2D area is projected to a horizontal reference plane while the 3D area accounts for surface topography. The output is originally in square meters, but is converted to hectares for comparison with the previous measurements (Table 4.1). The 2D measurements were compared to previous ice area measurements taken in 2006 and 2016. The 2D area measurements have the same decreasing trend as the 3D measurements, showing a decline in the ice area from 2004 to

2011, a slight increase in 2015, and a decrease in ice area again in 2016 and 2017. When viewing the glacier, the left edge (north side) is often covered by shadows and sometimes seasonal snow creates viewing difficulties when measuring the ice edge. The resulting stereo model covers a small portion of the lower left edge of the glacier, due to the terrain profile that is slightly in front of the ice edge. Over the thirteen-year span, the ice has lost over ten hectares of surface area. There was a total loss of 10.93 hectares in the 2D measurement and 10.90 hectares of loss in the 3D measurements. This is a 30.8% loss of area in just thirteen years. The lake area is mostly unchanged in size throughout the time span, it retains a general oblong oval shape, and was used both as a baseline against previous measurements and as a check throughout the process to ensure consistency with

45 the measured years. There is a similar possibility for error from the stereo model on the furthest left edge of the lake, where it meets the end of the ice.

2D Ice 3D Ice % Loss of Area Area Horizontal Ice Lake Past Ice Area Year (Horizontal (Surface Area from Area Measurements Area, ha) Area, ha) Previous Year 2004 35.54 39.24 30.23 2005 33.11 36.84 6.84 32.61 2006 31.07 34.30 6.16 31.97 31.1 2008 28.07 31.97 9.66 31.19 2011 27.33 30.53 2.64 31.53 2015 30.70 34.84 -12.33 31.13 2016 25.31 28.52 17.56 32.81 24.7 2017 24.61 28.34 2.77 32.12 2004- 30.8 2017

Table 4.1: The areas in both two and three dimensions for the ice extent on Qori Kalis as well as the proglacial lake. *Past Ice Area Measurements are from Henry Brecher.

4.1.2 Kilimanjaro

The Kilimanjaro ice fields were measured for area in the same manner as Qori

Kalis, with the “Surface Volume” tool in the 3D Analyst toolbox. While the 2013 images were processed from a set of data points, the 2010 images were acquired from the stereo analysis measurements and 2017 images were measured from the orthophoto with the

DSM clipped down to each respective area. All the ice fields were initially assigned numbers for organizational purposes, but most of the ice fields have also been named.

Some are listed in Table 4.2 with their corresponding numbers, but as the ice melts and the ice fields become separated multiple ice fields have the same original name. Because of the location of ice fields 5 and 8 (Figure 4.1), it was not possible to measure them in

2010. These two fields of ice are on a steep slope, creating difficulties in locating the boundary of the ice and the aerial images do not have enough overlap to allow the stereo analyst to measure around them. Seasonal snow prevented fields 5 and 8 from being measured in 2017 as well.

Ice fields 6 and 7 were separate entities in 2013 but in 2010 they were connected and measured as ice field 6 and were combined as part of the Southern ice field. Now ice field 6 is referred to as the Kersten glacier and ice field 7 is the Ratzel glacier. Ice field 0 is the Furtwängler glacier and ice field 13 was once part of Furtwängler but is no longer connected. Ice field 4 was named the Diamond glacier while fields 1, 2, and 3 were combined in the past and referred to as Little Penck. Ice fields 11 and 12 made up the northern ice field before they separated into Crendner and Drygalski, respectively. Ice fields 9 and 10 follow a similar pattern, and were part of the Eastern ice field, but have not been given specific names (Cullen et al., 2006). All the measurements for

Kilimanjaro horizontal ice areas are documented in Table 4.2A and the surface area measurements are shown in Table 4.2B. Due to a large error in the stereo analyst that resulted from difficulties locating the GCPs, an offset is present in 2010 when placed on a map in the proper coordinate system. Since the offset results from the stereo analyst error it is not the same across the entire study area and it cannot be adjusted or mapped with the other two data sets. However, it was still possible to measure the various ice fields and compute for horizontal and surface areas. The 2017 images had an offset that was created by relief displacement from the terrain, but can be corrected through georeferencing in the RSP stereo processor. The 2013 and 2017 ice areas are mapped together in Figure 4.1 and show a noticeable decrease in the amount of ice present on Mt.

Kilimanjaro.

Between 2010 and 2017 Kilimanjaro experienced a 30.7% loss in area across all of its ice fields. The individual percent loss for each separate ice field varies widely, from 97.5% loss for ice field 13 and 12.68% for ice field 4. Between 2010 and 2013 there was an overall 14.1% decrease in area, which is an average decrease per year of a

4.7%. This is slightly lower than the 19.3% decrease in area between 2013 and 2017 which is an average of 4.8% decrease per year.

4.2 A) Area Area % % Decrease Ice Field Area Total % (2D) (2D) Decrease in Area Number/ (2D) ha decrease ha ha in Area from 2013- Name 2017 in Area 2010 2013 2010-2013 2017 0/ 2.46 1.92 21.95 1.18 38.54 52.03 Furtwängler 1 1.39 0.43 69.06 0.382 11.16 72.52

2 0.99 0.63 36.36 0.273 56.67 72.42

3 3.65 1.76 51.78 1.73 1.70 52.60

4/ Diamond 7.02 6.41 8.69 6.13 4.37 12.68

5 NA 2.93 NA NA NA NA

6/ Kersten 30.11 23.93 20.52 15.96 33.31 46.99 See See 7/ Ratzel 2.49 See Above 1.52 38.96 above Above

8 NA 20.99 NA NA NA NA

9 4.41 3.62 17.91 1.7 53.04 61.45

10 3.98 2.87 27.89 1.49 48.08 62.56 11/ 34.68 28.43 18.02 23.2 18.40 33.10 Crendner 12/ 55.87 51.88 7.14 46.95 9.50 15.97 Drygalski 13 0.55 0.23 58.18 0.014 93.91 97.45 Total (without 5 145.1 124.6 14.1 100.53 19.3 30.7 and 8)

Table 4.2: A) Horizontal (2D) and B) Surface (3D) Areas for Kilimanjaro in 2010, 2013 and 2017. Ice fields 5 and 8 could not be measured in stereo in 2010 or 2017 and are omitted from the total area measurements in the last row of the table. Ice fields 6 and 7 are combined as 6 in 2010 and separated in 2013 and 2017.

4.2 B)

% % Surface Surface Surface Ice field Decrease Decrease Total % Area Area Area Number/ in Area in Area Decrease (3D) ha (3D) ha (3D) ha Name 2010- from in Area 2010 2013 2017 2013 2013-2017 0/ 2.69 2.07 23.05 1.33 35.75 50.56 Furtwängler

1 2.1 0.56 73.33 0.544 2.86 74.10

2 1.37 0.89 35.04 0.357 59.89 73.94

3 5.13 2.21 56.92 2.23 -0.90 56.53

4/ Diamond 9.86 8.1 17.85 10.19 -25.80 -3.35

5 NA 4.05 NA NA NA NA

6/ Kersten 37.43 31.24 16.54 24.64 21.13 34.17 See See 7/ Ratzel 3.2 See above 2.44 23.75 above above 8 NA 25.93 NA NA NA NA

9 6.55 4.64 29.16 2.87 38.15 56.18

10 4.56 3.87 15.13 2.26 41.60 50.44 11/ 39.59 31.75 19.80 31.87 -0.38 19.50 Crendner 12/ 62.93 57.06 9.33 63.53 -11.34 -0.95 Drygalski 13 0.63 0.37 41.27 0.015 95.95 97.62 Total (without 5 172.84 145.96 15.6 142.28 2.5 17.7 and 8)

Drygalski

Little Penck

Furtwängler

Kersten

Figure 4.1: Kilimanjaro ice boundaries in 2013 and 2017. Ice bodies 5 and 8 could only be mapped in 2013 due to seasonal snow in 2017 that prevented the measurements. The numbers assigned to each body of ice are arbitrary and are used for labeling and analysis purposes to organize data. Some of the ice fields have been named and are labeled but as the ice continues to separate not every ice field has a specific name.

4.2 Retreat of Terminus, Qori Kalis

The changes in ice extent were extracted from each of the three transects’ output data tables and the difference between each pair of years calculated. The centerline had an overall ice length loss of 209 meters, while the north line lost 54 meters of horizontal ice length and the south line showed a 297 meter loss over the thirteen-year span. This is an average of 187 meters of horizontal extent change over the past twelve years and an average of 14 meters of length loss from the terminus per year throughout the 13 years.

Table 4.3 A-C shows a breakdown for each transect as well as the changes recorded for each of the eight pairs of years with available data. The centerline has a retreat of approximately 12 m/yr between 2004 and 2016. The terminus retreat between 2004 and

2017 for the centerline increases from 12 to 16 m/yr when the 2017 data are added. The retreat of ice over the thirteen-year span from 2004 to 2017 is very noticeable from the outlines of the two epochs in Figure 4.2.

A) Centerline Furthest X Value Change in Horizontal Year (meters from Limit Extent (meters) line) 2004 667 2005 573 -94 2006 566 -7 2008 585 +19 2011 525 -60

2015 517 -8 2016 523 +6 2017 458 -65 Overall -209

B) North Line Furthest X Value Change in Horizontal Year (meters from Limit Extent (meters) line) 2004 603 2005 590 -13

2006 547 -43 2008 528 -19 2011 475 -53 2015 603 +128 2016 569 -34 2017 549 -20 Overall -54

C) South Line Furthest X Value Change in Horizontal Year (meters from Limit Extent (meters) line) 2004 711 2005 697 -14 2006 558 -139

2008 633 +75

2011 556 -77

2015 484 -72

2016 476 -8 2017 454 -22 Overall -297 Table 4.3: Terminus retreat for each transect on Qori Kalis for each epoch between 2004 and 2017 and overall retreat.

Figure 4.2: Ice outlines from 2004 (green) and 2017 (blue), down glacier from the limit line (purple). There is a noticeable retreat of the horizontal ice extent over the thirteen- year span.

4.3 Volume Loss

4.3.1 Qori Kalis

While the total volume of the ice on Qori Kalis at each epoch cannot be computed in the ArcMap program due to the lack of knowledge of the ice thickness, it is possible to assess volume loss from year to year using various tools in the ArcMap software. The

“Cut and Fill” tool subtracts two raster files from one another and creates a map that displays areas of loss and gain across the surface. Unfortunately, this process only works in areas of overlap, and thus limits the assessment to areas where the ice has not disappeared. A work around was implemented using the 2017 digital surface model to calculate volume loss from each of the previous years.

Each set of raster files was processed through the tool to calculate the amount of loss or gain across the surface. From 2004 to 2005, 2005 to 2006, 2008 to 2011, 2015 to

2016, and 2016 to 2017 the glacier surface experienced an overall volume loss while from 2006 to 2008 and 2011 to 2015 there appears to be an overall volume gain. When

2004 is compared with 2017 (Figure 4.3) there is a loss of approximately 5.13 million cubic meters of ice on Qori Kalis. Loss for each year is shown in Table 4.4. Between

2004 and 2016, 54% of the ice volume was lost, with the remaining 46% of the volume loss occurring between 2016 and 2017. Over the thirteen year span the ice loss averaged

5.8% during the years of volume loss and a 3.6% average volume loss when the years with surface gain are included. It should be noted that the area change between 2016 and

2017 was comparatively small, approximately 2.77 hectares, which would mean that most of the volume loss between 2016 and 2017 resulted from surface lowering and not from the retreat of the ice.

Figure 4.3: Cut and Fill output for Qori Kalis 2004 compared to 2017. The green outline shows the ice extent in 2004, while the blue outline shows the ice extent in 2017. The blue surfaces are areas of net gain across the surface while the red areas show net loss. The outline of the 2004 ice extent was placed across the 2017 DSM and compared to the 2004 raster surface that was created. Since the tool only works with areas of overlap, the surface data outside the 2017 ice extent was required for comparison with 2004.

% Decrease in Years Volume loss (m3) Loss/Gain Volume 2004-2005 514,663 Loss 20 2005-2006 3,892,618 Loss 11 2006-2008 -5,748,519 Gain -10 2008-2011 5,955,954 Loss 19 2011-2015 -2,880,323 Gain -17 2015-2016 1,038,944 Loss 11 2016-2017 2,352,389 Loss 13

2004-2016 2,773,336 Loss 34 2004-2017 5,125,726 Loss 43

Table 4.4: Volume loss calculated from the “Cut and Fill” tool in Arc Map for the Qori Kalis glacier.

4.3.2 Kilimanjaro

Volume loss for Kilimanjaro was computed in the same manner as Qori Kalis, by using the 2017 digital surface model of Kilimanjaro to compare to the 2013 surface.

2010 could not to be numerically quantified in the volume loss assessment due to the offset from the stereo analyst. It was possible to approximate percent loss using the

“Surface Volume” tool and comparing 2010 to 2017. The “Cut and Fill” output figures were not necessary as they showed only net loss on all of Kilimanjaro’s ice fields between 2013 and 2017. The volume loss between 2013 and 2017 is compared to the percent decrease in ice area for each field from 2013 to 2017 and overall from 2010 to

2017 (Table 4.5). The three largest ice fields (6, 11, and 12) make up 78% of the volume loss between 2013 and 2017 and 65% of the area loss over those four years. However, the four smallest ice fields (1, 2, 3, and 13) show the most rapid decrease in area although the amount of ice loss will be less than the larger fields since they are smaller in size. As the ice fields separate, they smaller ice fields become more susceptible to incoming solar radiation and show faster ice loss rates.

% Decrease in % Decrease in Ice Field Volume loss from 2013 Area from Area from Number to 2017 (m3) 2013 to 2017 2010 to 2017 0 496,767 38.54 52.03 1 277,594 11.16 72.52 2 23,908 56.67 72.42 3 784,084 1.70 52.60 4 4,986,867 4.37 12.68 6 6,569,222 34.89 48.26 7 1,227,479 38.96 See Above 9 804,738 53.04 61.45 10 631,284 48.43 62.81 11 10,160,213 19.31 33.85 12 16,291,759 10.79 17.16 13 64,355 97.39 98.91

Total 42,318,272 20.38 31.64

Table 4.5: Volume loss between 2013 and 2017 on Mt. Kilimanjaro compared with the percent decrease in area from 2013 to 2017 and overall loss from 2010 to 2017. Ice fields 5 and 8 were omitted since they were only measured in 2013 and were not included in any totals.

4.4 Contour Maps, Qori Kalis

Beginning in 1963, the product of the Qori Kalis measurements was contour maps of the glacier’s surface. From 1991 on, the proglacial lake was also mapped. The 1963 map was produced from aerial photography by direct contour line plotting on a mechanical stereo plotter. Previous maps were created from hand measured surface elevation points and the contours created from gridding of the data using the “Surfer” software. For comparison with previously measured years (i.e. 2006) Qori Kalis contour maps were created in a similar style for the eight years that were assessed over the thirteen-year span (Figure 4.4). The contour maps of Qori Kalis are prepared with a 20 meter interval with an index contour at every 100 meters. The limit line is drawn in red across each map, and aids in the visualization of the changing surface of Qori Kalis.

Contours were not created for Kilimanjaro, but the various ice fields around the crater have been mapped and their areas outlined, measured, and overlaid for visualization of the ice retreat.

Figure 4.4: The contours on Qori Kalis are spaced at 20 meter intervals with dark blue lines marking index contours every 100 meters. The purple line outlines the lake area. The green line shows the ice boundary and the red line is the limit line that was previously established in earlier studies. Each map is labeled with its respective year, and is in the coordinate system that was used throughout.

Chapter 5 Discussion

5.1 Qori Kalis Area and Volume Loss

The Qori Kalis glacier and the adjoining Quelccaya ice cap have been monitored for the relationship between their area loss and volume loss. Hanshaw and Bookhagen

(2014) have conducted studies to evaluate loss rates on Quelccaya. Their results used an automated method instead of manual delineation of the Quelccaya ice cap. However, their results were similar to previous studies (Brecher and Thompson 1993; Salzmann et al., 2013; Hanshaw and Bookhagen 2014) calculate a 31% decrease in area between 1980 and 2010. The rate of ice loss of both area and volume, for Qori Kalis shows similar trends to those measured for the whole Quelccaya ice cap. Since 1985 there has been a

30% decrease in the area of Quelccaya and a 45% decrease in volume (Salzmann et al.,

2013). The total glacier area of Quelccaya was reduced by 23% between 1985 and 2000 while Qori Kalis shows a similar decrease based on the available data (Hanshaw and

Bookhagen 2014). Most of the volume loss is thought to have taken place after 1985 and continued up to the last observed year in 2006 with a volume loss of about 40-45% or between 9.2 and 12.4 km3 (Salzmann et al., 2013).

This study calculated a 31% decrease in area and a 43% decrease in volume from

2004 to 2017 for Qori Kalis. These measurements are consistent with the decreasing trend noted from previous studies (Brecher and Thompson 1993, Salzmann et al., 2013,

Hanshaw and Bookhagen 2014). The measurements indicate the smallest decrease in area from 2016 to 2017 but a large decrease in volume. If the data from 2004 to 2016 are assessed, there was a 34% decrease in volume, whereas adding the 2017 data gives a total

64 of 42% decrease in volume but only changes the area loss from 28% to 31%. The larger change in volume was not expected with the relatively low change in area. The ratio between the volume loss and area loss from 2004 to 2017 is 1.39 over the thirteen-year span of years measured (Table 5.1). The ratio of volume to area loss varies widely from year to year and does not show a trend in the loss pattern.

The area decrease appears to follow the trend from previous data. Using previous measurements, from 1963 to 2003, and this study’s calculations from 2004 to 2017 a linear regression can be plotted and analyzed. The decrease in area on Qori Kalis fits a linear regression with a R2 value of 0.892. The data plots and trend line are shown in

Figure 5.1. Aside from an unusually high area measurement in 1988 and 2015 all the measurements follow a decreasing linear trend, until the last measurement in 2017 which gives the appearance of a slowing in the ice area loss although there was a large volume loss from 2016 to 2017. While the area total is less than in 2016, it has not declined as much as some of the previous year to year measurements. The transition from terrestrial to satellite imagery between 2016 and 2017 might account for some of the small loss and the location of the terminus that is now starting to move up a steeper slope toward the ice cap as opposed to the lower sloping area over which it retreated in previous years where the lake now exists.

Volume/Area % Loss Year % Area Loss % Volume Loss Ratio 2004-2005 6.84 20.16 2.95 2005-2006 6.16 11.47 1.86 2006-2008 9.66 -10.85 -1.12 2008-2011 2.64 19.25 7.29 2011-2015 -12.33 -17.56 -1.42 2015-2016 17.56 11.28 0.64 2016-2017 2.77 13.19 4.76 2004-2017 30.8 42.72 1.39

Table 5.1: Percent area and volume loss and a volume/area loss ratio for each pair of epochs and over the entire thirteen-year time span.

Qori Kalis Area Loss R² = 0.8924 120

100

60 Area Area (ha) 40

0 1960 1970 1980 1990 2000 2010 2020 Year

Figure 5.1: Qori Kalis ice area loss. Data used from past measurements and this study. The data fits along a linear regression with an R2 value of 0.82. *Data from 1963 to 1993 are from Brecher and Thompson (1993), data from 1995 to 2003 are from Thompson et al (2006) and data from 2004 to 2017 are from this study and indicated with triangles.

5.2 Qori Kalis Terminus Retreat

Over previous decades, monitoring the retreat of Qori Kalis’ terminus was a common approach to better understanding the glacier’s behavior. Since the first measurements in 1963 the retreat rate has noticeably increased. Brecher and Thompson

(1993) report a frontal retreat of 4.87 m/yr during the initial measurement period from

1963 to 1978. This rate increases over time up to a tenfold increase during the 14 years from 1991 to 2005 (Thompson et al., 2006). Hanshaw and Bookhagen (2014) use a satellite-based study and find retreat rates of 9-10 m/yr between 1980 and 1991 and a retreat rates of ~67 m/yr during the fourteen year period from 1991 to 2005 that are comparable to Thompson et al (2006). Between 2004 and 2016 this study calculated a retreat of about 14.4 m/yr, which is much lower than the rate for the previous fourteen years and closer to the rate between 1983 and 1991. However, between 2016 and 2017 the ice retreated 65 meters. The addition of the 2017 data also raises the retreat rate average by 2.4 m/yr to approximately 16 m/yr. While the rate is much lower than in previous studies (Table 5.2), the slope of the area around the ice edge is much different.

In past decades the ice retreat was over the low sloping area where the lake resides now.

As the ice has moved away from the lake edge, it is beginning to retreat up a much steeper slope toward the ice cap, possibly effecting the terminus retreat rate.

Rate of Time Period Data from retreat Ratio interval (yr) (m/yr)

1963-1978 1 15.09 4.87 1.00 1978-1983 1 5.06 8.22 1.69 1983-1991 1 8.17 13.82 2.84 1980-1991 3 11 9-10 1.85-2.05 1991-2005 2 14 60 12.32 1991-2005 3 14 67 13.76 2004-2017 4 13 14.36 2.95

Table 5.2: Past Qori Kalis measurements compared to recent measurements from this study. The ratio used the retreat rate from 1963 to 1978 to compare different rates (Brecher and Thompson 1993). * “Data from” numbers are from sources listed below 1: Brecher and Thompson 1993 2: Thompson et al 2006 3: Hanshaw and Bookhagen 2014 4: This study

5.3 Qori Kalis Terrestrial to Satellite Transition, 2016 to 2017

All of the Qori Kalis measurements were completed in the same manner except for the 2017 images. The images from 2004 to 2016 were terrestrial photos taken from a distance of about 900 m from the 1963 terminus of the glacier. The images were placed into a stereo analyst program and measured. The 2017 images were purchased as satellite images and processed into a digital surface model (DSM). Any issues with the stereo model, especially with visibility on the north side of Qori Kalis, were not present as the

2017 satellite imagery were from an aerial view, not from a terrestrial baseline. If the

2016 outline is compared to the 2017 outline of Qori Kalis (Figure 5.2) it is evident that fewer points were placed on the 2016 image, as seen by the jagged outline that traces the

68 ice. The higher resolution satellite images, as well as the aerial perspective, allow for more finite measurements of the ice extent. In order to properly compare the satellite measurement method to the terrestrial images, a satellite image of Qori Kalis from 2016 would be required for comparison with the 2016 terrestrial photos.

Figure 5.2: The jagged edges of the 2016 (green) ice outline verses the 2017 (blue) outline show the difference in the number of points that could be placed around the ice extent to allow the calculation of the area. The uncertainty on the northern edge (top left) of the ice in the stereo analyst could explain part of the small decrease in ice area, as a moraine blocks the exact edge of the ice in the 2016 and all the other terrestrial photos.

5.4 Kilimanjaro Area and Volume Loss

Mt. Kilimanjaro and its ice fields have been monitored for ice loss for over one hundred years using various techniques. The ice has been retreating since at least 1912 when its aerial extent was approximately 12 km2. By the year 2000 the ice had an aerial extent of approximately 2.6 km2, a loss of 80% (Thompson et al., 2002). Cullen et al

(2006) discuss the recession rates more in detail between 1912 and 2003, attaining a similar approximation of loss, with only 21% of the original ice extent in 1912 remaining in 2003 (2.51 km2). Thompson et al (2009) note the aerial extent of ice cover in 2007 as

1.85 km2. The total aerial extent of ice in 2017 was 1.01 km2, a 92% loss from the first measurement of 12 km2 ice in 1912. This is a nearly linear progression of decrease and when placed along a linear regression, an R2 value of 0.999 is obtained (Figure 5.3A).

When the 1912 estimate is removed and the data are compared between 2000 and 2017, the linear regression still yields an R2 value of 0.963 (Figure 5.3B).

The air temperature surrounding the ice fields of Kilimanjaro is almost always below freezing so most of the ice recession is due to the impact of solar radiation, especially on vertical walls of the ice masses (Cullen et al., 2006; Pepin et al., 2014).

The south-facing cliffs receive much more direct radiation input during the austral summer, making the ice fields located there more vulnerable to loss (Pepin et al. 2014).

A closer look at the receding ice on Kilimanjaro shows that most of the ice fields are breaking into smaller ice fields that show a faster loss of area than the larger ice fields.

Since most of the ice recession is due to solar radiation on the vertical walls, the smaller ice fields are more susceptible to ice loss as the walls make up a larger portion of their overall area. The process for the formation of the vertical walls of the ice fields on the

70 plateau is not well understood, but it is recognized as an irreversible process while evidence for sudden climate change is shown more in the slope glaciers (Cullen et al.,

2006). However, as explained by Thompson et al (2009) an energy balance study concluded that the upper surfaces of the ice fields are undergoing mass loss due to sublimation, and experienced physical evidence of melting. Ice cores drilled to the bedrock in 2000 showed the Furtwängler glacier to be completely saturated (Figure 5.4).

Thinning accounted for 43% of the volume loss on Furtwängler (Thompson et al., 2009).

Attributing the ice fields’ decreasing size to specific drivers is complicated in locations such as Kilimanjaro, due to the scarcity of ground-based meteorological observations in East Africa. Satellite-borne observations span only a few decades and in situ observations by automatic weather stations did not begin on Kilimanjaro until 2000

(Thompson et al., 2009). Drier conditions in East Africa during the 20th century have reduced overall precipitation and cloud cover, leading to an increase in insolation and net solar radiation (Cullen, 2006). Solar radiation driven melting has been shown to be primarily responsible for the retreat of the ice walls (Mölg et al., 2003) while sublimation is the more likely the cause of thinning from the uppermost surfaces (Thompson et al.,

2009).

Kilimanjaro Ice Area Decline R² = 0.9991 14

Area Area (square km) 4

0 1900 1920 1940 1960 1980 2000 2020 Year

Kilimanjaro Ice Area Decline R² = 0.963 3

2.5

1.5

1 Area Area (square km)

0.5

0 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Year

Figure 5.3: A) Kilimanjaro ice area loss, data from past measurements and this study. The loss pattern is very close to a linear decline, even though the 1912 measurement was an estimate. B) Kilimanjaro ice area loss, with the 1912 estimate removed a linear decline is still apparent. *1912 and 2000 area calculations from Thompson et al (2002). 2003 area calculation obtained from Cullen et al (2006). 2010, 2013, and 2017 are obtained from this study and indicated with triangles.

Figure 5.4: Photograph of two sections from the Northern Ice Field (NIF) core 3. A) The top 0.65 m contained elongated bubbles and channels, and show characteristics of melting and refreezing. B) The rest of the 49 m ice core down to bedrock appears much different as it is glacial “bubbly” ice lacking the features associated with melting and refreezing (Thompson et al., 2009).

5.5 Comparison of Kilimanjaro to Qori Kalis

While the Qori Kalis glacier and the ice fields on Mt. Kilimanjaro are on different continents, they are both tropical glaciers and are thus often subjected to the same conditions of tropical climate variability, such as that associated with ENSO. Over a thirteen-year time span Qori Kalis lost approximately 31% of its area while Kilimanjaro lost 31% loss of its area over the course of seven years. The loss of ice area over the fourteen years from 2003 to 2017 is approximately 60% for Kilimanjaro. Looking at the linear regression for each location’s ice decline, Qori Kalis has a lower R2 value (0.820) than Kilimanjaro’s (0.999). However, the time span for these regressions is different.

They can be compared from measurements between 2000 and 2017 (Figure 5.5).

2 Kilimanjaro’s ice loss has an R value of 0.963 from 2000 to 2017 that for Qori Kalis is

0.79. It should be noted that Kilimanjaro only has six measurements spanning that seventeen-year time span, while Qori Kalis has twelve measurements.

A different response between the ice fields on Kilimanjaro and the Qori Kalis glacier is expected as currently the Kilimanjaro ice fields are fourteen separate ice fields that all retreating at different rates while Qori Kalis is a single glacier flowing of off

Quelccaya. Qori Kalis’ retreat and response to climate is very different from the ice fields on Kilimanjaro that are not propagating off of a much larger higher elevation ice cap. While Qori Kalis is influenced by accumulation that falls on Quelccaya’s higher elevations, the Kilimanjaro ice fields only receive direct accumulation from seasonal snowfall. Most of the ice lost on Kilimanjaro’s vertical walls is from solar radiation as the temperatures are usually below freezing. Radiation driven surface melting can result from strong solar radiation even though the air temperature around the ice is not above

74 freezing. Comparing retreat rates and volume losses on Qori Kalis to those on

Kilimanjaro is not entirely possible as the response time of smaller ice fields compared to a glacier flowing from a larger ice cap varies widely but also because Kilimanjaro’s elevation (~5895 m) is significantly higher than Qori Kalis (~5000 m) and the summit of

Quelccaya (~5670 m) and will have a significantly lower annual mean temperature.

Qori Kalis Area Loss R² = 0.7904 50 45 40 35 30 25

Area Area (ha) 20 15 10 5 0 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Year

B) Kilimanjaro Ice Area Loss R² = 0.963 3

2.5

) 2

1.5 Area Area (km 1

0.5

0 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Year

Figure 5.5: A) Ice area loss on Qori Kalis from 2000 to 2017. Measurements from 2000 to 2003 are from Thompson et al (2006) and measurements from 2004 to 2017 are from this study and indicated with triangles. B) Ice area loss on Kilimanjaro from 2000 to 2017. Measurements from 2000 are from Thompson et al (2002), measurements from 2003 are from Cullen et al (2006), and measurements from 2010 to 2017 are from this study and indicated with triangles.

5.6 Indication of El Niño

With the onset of the 2015-2016 El Niño responses were anticipated to be evident in the 2017 measurements on Qori Kalis. While glaciers are considered a good indicator of climate change the response rate to changes in climate still remain uncertain. During the warm phase of El Niño in the tropical Andes an upper level westerly flow leads to a delay in the onset of the wet season, especially in southern Peru (Perry et al., 2014). This usually results in lower than normal precipitation, reductions in cloud cover and a drier environment in the Andes Mountains (Vuille and Keimig, 2004). Francou et al (2003) reported rapid glacier ablation and highly negative mass balance during strong El Niño events, such as that in 1997-1998. Strong El Niños can also be responsible for temperature anomalies up to +4°C, which significantly impacts the freezing level heights in these tropical environments (Diaz et al., 2014). Stroup et al (2014) conclude that the late Holocene fluctuations of Qori Kalis are temperature-driven while Bradley et al

(2009) suggest that the margin retreat is associated with seasonal elevation changes of the

0°C isotherm, which is in itself a function of temperature.

The ENSO circulation appears to have a more uniform and consistent effect in the southern parts of Peru, where Qori Kalis is located. Also, mean annual surface temperatures over the central Andes at 500 millibars have been rising with the freezing level height (Thompson et al., 2017). The changes observed for Qori Kalis between 2016 and 2017 were a small loss in area, just 2.77 hectares, but a large decrease in volume, 2.3 million cubic meters of ice. This indicates a substantial amount of surface lowering across Qori Kalis as most of the ice area remained intact. However, the transition from terrestrial to satellite imagery occurred from 2016 to 2017 and may account for a part of

77 the apparent surface lowering. Stereoscopic satellite imagery from a similar date in 2016 would be necessary to confirm if Qori Kalis lost a small amount of area but experienced a large amount of surface lowering during the 2015-2016 ENSO.

Chapter 6 Summary, Conclusions, and Suggestions for Future Research 6.1 Continuing Ice Loss

Measurements of both the Qori Kalis glacier and the Kilimanjaro ice fields show the continuing pattern of ice loss evident in past measurements as well as on most of the world’s glaciers. The retreat of Qori Kalis is similar to the recent retreat behavior of the

Quelccaya ice cap. The glacier has been shown to respond in a similar manner to the ice cap in the past as well (Salzmann et al., 2013). While the loss of area and volume are not consistent over time, the margins of the ice cap have continued to both retreat and thin from the surface. As precipitation is inconsistent over the passing years it is more likely that temperature is the main factor causing the ice retreat. The rising 0°C isotherm with seasonal fluctuations is more than likely responsible for at least part of the ice retreat.

The last El Niño event in 2015-2016 brought both warmer temperature and drier than usual conditions to the southern Andes which are the likely causes of the thinning on

Qori Kalis observed in 2017.

The Kilimanjaro ice fields are shown to be losing ice at a rate consistent with past studies. The ice has dwindled from its initial extent measured 12 km2 in 1912 to only

2 1.01 km in 2017, a 92% loss in just over 100 hundred years. The Kilimanjaro ice fields have been in existence for at least 11,700 years and a drought lasting approximately 300 years around 4,200 years ago was not sufficient to remove the Northern Ice Field

(Thompson et al., 2009). Over time the ice fields have transitioned from an ice cap to a total of fourteen different ice fields. While these vary widely in size, all of them have lost ice over the observational period from 2010 to 2017. Smaller ice bodies are more susceptible to ice loss as an increased surface area leads to more absorption of incoming

79 solar radiation which affects their vertical walls and sublimation. The greater number and smaller ice bodies lead to greater exposed surface areas which absorb more incoming radiation and allow for greater sublimation. Shifting gears, the linear regression of the ice extent and volume versus time suggest that a continued glacier retreat at the current rate would remove the glacier as early as 2030. A similar comparison for Kilimanjaro suggests that the mountain could lose all its ice as early as 2026.

6.2 Implications for Ice Loss

While the ice on Kilimanjaro is melting and the vertical walls are receding over time the impact of the ice loss is quite different from the loss of the Qori Kalis glacier that flows from the much larger Quelccaya ice cap as the surrounding population is not as heavily dependent on the glacial meltwater. Much of the population along the Andes

Mountains rely on glacial meltwater for a portion of their water used for consumption, hydroelectric power in larger cities such as Lima, and agriculture. Glacial meltwater is especially important during the dry season and in times of drought and as the ice retreats further up the mountain the surrounding population slowly loses access to the glacial meltwater and will eventually have to change their life style, find other reliable sources of water, or move to where water is available. In addition, glaciers such as Qori Kalis that have a lake at the terminus create the potential for geological hazards. As water eventually accumulates behind the terminal moraine, build up in the lake can burst through the natural moraine and may breach the moraine sending water down valley flooding grazing lands and the towns below.

6.3 Suggestions for future research

One of the uncertainties that arises with the assessment of changes in the Qori

Kalis glacier results from the transition from terrestrial to satellite imagery between 2016 and 2017. To eliminate this, satellite imagery of Qori Kalis would be needed from 2016 for comparison with the terrestrial measurements and/or both terrestrial and satellite imagery collected and compared in 2018. It would also be helpful to obtain satellite imagery for all the previously measured years and compare them. Although expensive, it would be ideal to obtain satellite imagery for the years in between that have not been measured to assess shorter term variability in the ice extent and volume. This would be ideal for both locations, as a more consistent documentation of the ice loss should advance our understanding of the drivers contributing to ice loss as well as the glacier response time to these drivers. Many of the complications with the Kilimanjaro ice field assessment originated from difficulties in locating the previously installed ground control points (GCPs). If possible, new GCPs should be established and targeted on the mountain using identifiable features for future measurements. With new advances in technology and more readily available and cost effective satellite imagery the need to travel to the study site is no longer a necessity except for establishing ground truth for the satellite imagery. Continued measurements of both locations would be necessary to further evaluate, better understand, and predict the ice loss over the coming years.

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