Dickinson College Dickinson Scholar

Student Honors Theses By Year Student Honors Theses

5-22-2016

Short-term Climate Cycles, Recent Climate Changes, and Volcano- Ice Hazards: Nevado Coropuna, Arequipa, Peru

William Hardy Kochtitzky Dickinson College

Follow this and additional works at: https://scholar.dickinson.edu/student_honors

Part of the Climate Commons, Glaciology Commons, and the Volcanology Commons

Recommended Citation Kochtitzky, William Hardy, "Short-term Climate Cycles, Recent Climate Changes, and Volcano-Ice Hazards: Nevado Coropuna, Arequipa, Peru" (2016). Dickinson College Honors Theses. Paper 289.

This Honors Thesis is brought to you for free and open access by Dickinson Scholar. It has been accepted for inclusion by an authorized administrator. For more information, please contact [email protected].

Dickinson College

Department of Earth Sciences

Short-term Climate Cycles, Recent Climate Changes, and Volcano-Ice Hazards:

Nevado Coropuna, Arequipa, Peru

A Thesis in

Earth Sciences

By

William Hardy Kochtitzky

Submitted in Partial Fulfullment of the requirements for the Degree of

Bachelor of Science (with Honors)

May 2016h

Abstract

The glacier body atop Nevado Coropuna Peru is the largest body of ice in the tropics. The surrounding area is home to ~100,000 people and is vital for agricultural production in southern Peru. Not only does the ice cap provide a water resource to

Peruvians in the area, but it also poses a potential hazard if the volcano erupts. The location and physiography of Coropuna make it an excellent location to understand local climate over a variety of timescales in the ice and snow. Using 258 Landsat scenes, to measure snow and ice extent at Coropuna since 1980, this study has suggested a more accurate measure of glacial shrinking on Coropuna. During fieldwork in 2015, we collected nitrogen and diatom samples, made observations of millennial scale lava flows, and collected century scale lake sediment cores. In additional, this study uses photographs of ice retreat for historic comparison, and measured ice thickness at an outcrop.

An analysis of 20 Landsat images from 1980 to 2014 to measure aerial changes in the ice cap at the Nevado Coropuna volcanic complex, Peru suggests ice loss to be 0.41 km2 yr-1. Even though previous studies have reported ice loss rates of 1.4 km2/yr.

Analysis of 258 Landsat scenes determined annual snow minimums using the

Normalized Difference Snow Index. Field photographs estimate that a western outcrop of ice is 37 meters, and provide useful supplemental information to work by others including Peduzzi et al (2010) and Birkos (2009). While testing of N during the 2015 field season was inconclusive, preliminary diatom analysis is promising and in progress.

Although some predict that Coropuna will be a non-contributor to water supply by 2025, my results suggest that this will not be the case and that the ice-cap could survive at

ii present rates for the remainder of this century and beyond. My results have significant implications for hazard assessment and resource water planning in southern Peru, which relies heavily on glacial meltwater for year round agricultural production and domestic use.

iii

Table Contents List of Tables ...... vi List of Figures ...... vii Acknowledgements ...... viii Introduction ...... 1 The Climate System ...... 1 Tropical glaciers ...... 2 Volcano-Ice hazards in the tropics ...... 3 Glaciers in Peru ...... 4 Nevado Coropuna ...... 6 Geology of Nevado Coropuna ...... 7 Goals of this study ...... 8 Methods ...... 9 Landsat Re-Analysis of Glacial ice ...... 9 Glacier Classification...... 10 Error Assessment of Ice Classification ...... 10 Ice thickness measurements on Coropuna ...... 11 Snow Classification and El Niño Southern Oscillation ...... 11 The Landsat Archive ...... 13 Photographs from the early 1900s of Coropuna: Hiram Bingham ...... 14 Results ...... 14 Landsat Archive Record of Change to the Ice Cap Area ...... 14 Error assessment...... 15 Ice thickness measurements ...... 15 Comparison photographs from 1911 to 2015 ...... 16 El Niño Southern Oscillation ...... 16 Discussion ...... 17 Implications of Ice Area Shrinking Rates at Nevado Coropuna ...... 17 Previous studies of Nevado Coropuna ...... 18 de Silva and Francis, 1990 ...... 18 Ames et al., 1998 ...... 19 Nunez-Juarez and Valenzuela-Ortiz, 2001 ...... 19 Racoviteanu et al., 2007 ...... 19 Forget et al., 2008 ...... 20

iv

Peduzi et al., (2010) ...... 20 Úbeda, 2011...... 20 Silverio and Jaquet (2012) ...... 21 Veetil et al., 2015 ...... 21 This study ...... 22 Coropuna as a sentinel for climate change ...... 22 Studies of other tropical ice bodies ...... 22 Implications for volcanic hazards ...... 23 Selecting images for glacial extent analysis ...... 23 Ice volume estimate from Coropuna ...... 24 Ice thickness measurements ...... 25 El Niño Southern Oscillation ...... 25 Conclusions ...... 27 Appendix A. Peruvian volcanism ...... 28 Recent Peruvian Volcanism ...... 28 Cordillera Ampato ...... 29 Appendix B. Coropuna Volcanic history ...... 29 Ancient Volcanism: Pedestal to Coropuna Complex ...... 29 Appendix C. Nitrogen testing ...... 32 Test kit methodology ...... 32 Site selection ...... 33 Appendix D. El Niño Southern Oscillation (ENSO) in South America ...... 35 Appendix E. Diatoms on the ice Coropuna ...... 37 Appendix F. Methods of glacier classification via remote sensing ...... 38 Introduction to glacial remote sensing ...... 38 Digitization of ice outlines ...... 39 Tables ...... 53 Figures ...... 55

v

List of Tables

Table 1: Landsat Scene ID and ice area……………………………………………..53 Table 2: Nitrogen Samples from Coropuna…………………………………………54

vi

List of Figures

Figure 1: Location maps showing tropical glaciers in Central Peru………………..55

Figure 2: Little Ice Age moraine on the western flank of Coropuna.………………56

Figure 3: ENSO and snow area since 1987…………………………………………57

Figure 4: Model builder in ArcGIS to process Landsat scenes……………………..58

Figure 5: The carapace on the western flank of Coropuna…………………….……59

Figure 6: Comparison of ice-cover estimate as a function of month……………….60

Figure 7: Landsat Satellite ice-surface area change at Coropuna…………………..61

Figure 8: NDSI error assessment……………………………………………………62

Figure 9: Ice areas using two classification schemes, band 4/band 5 and NDSI……63

Figure 10: Comparison of Hiram Bingham photograph to modern………………...64

Figure 11: Comparison of Hiram Bingham photograph to modern…………………65

Figure 12: Satellite image with location of October 1911 photographs……………..66

Figure 13: ENSO and maximum snow area…………………………………………67

Figure 14: Glacierized area of Nevado Coropuna, Peru……………………………..68

Figure 15: Sabancaya eruption………………………………………………………69

Figure 16: The Cordillera Ampato…………………………………………………..70

Figure 17: Nitrogen sample locations………………………………………………..71

Figure 18: Algae on glacial ice………………………………………………………72

vii

Acknowledgements

I would like to thank Dan and Betty Churchill and the Dickinson College

Research and Development Committee for funding my 2015 field season in Peru. I would also like to thank the staff of the Observatorio Vulcanológico de INGEMMET for support throughtout the project. Especially Jersy Marino and Nelida Manrique for the constant communication and willingness to collaborate and support during the 2015 field season. I also thank James Ciarrocca at for his GIS advice and guidance in analyzing remote sensing data and cartography. I am especially thankful to Ronald Concha with the

GA51 Cryosphere and Climate Change project of INGEMMET for the SPOT image. I would like to thank Marcus Key, Pete Sak, Jeff Niemitz, Rob Dean, and Aly Thibodeau for their guidance and support during numerous logistical and research related questions and conversations. Finally, I am indebted to Ben Edwards for his advice, guidance, and constant support. I especially thank Ben for putting me in contact with our Peruvian collaborators, writing a grant to support my summer research through Dickinson R & D, and completing our 2015 field season. Without Ben this project would not have been possible.

viii

Introduction

The Climate System

Although waxing and waning of terrestrial ice has been the hallmark of Earth’s climate system since at least the Pleistocene, recent evidence that almost all terrestrial ice-bodies are presently losing mass is a concerning sign of anthropogenic climate change

(Kargel, 2014). This is particularly true in the tropics (Kaser, 1999), where large human populations depend on ice as a source of freshwater, especially during dry seasons (Vuille et al, 2008). In these areas, knowledge of ice loss rates is critical for long-term water resource planning and for assessing hazards from jökulhlaups and eruptions of ice-clad volcanoes.

Humans have clearly influenced the climate system leading to unequivocal global warming (IPCC, 2014). This warming is causing ice to melt at all latitudes (IPCC, 2014).

Because of warming, water supply in the tropics is changing with glacial mass balance decrease (IPCC, 2014; Magrin et al, 2014). This causes a short-term water subsidy, but will likely lead to an insufficient supply of fresh water in the near future when glaciers are gone, especially during dry seasons. While the impacts of climate change in polar regions are obvious (i.e., melting ice/glacial retreat), ecosystems at low latitudes are more sensitive to climate change (Deutsch et al, 2008). This is due to a small thermal tolerance window in low latitude organisms, while high latitude organisms are already adapted to shifts of more than 100˚F over the course of a year (Deutsch et al, 2008). As environments change in the Andes due to warming ecosystems, humans will have to adapt.

1

The Andean climate is principally made up of three distinct regions: the inner tropics, subtropics, and outer tropics. While each location has seasonal patterns and cycles, El Niño Southern Oscillation (ENSO) is the main cause of year to year variability in the Andean climate system. An El Niño occurs when trade winds over the Pacific weaken causing upwelling off the coast of South America to temporarily shut down and surface waters to warm (NASA, 2015). This has major economic consequences for many countries in the western hemisphere, including Peru. During strong ENSO events, Peru’s coastal fisheries shut down, the northern part of Peru receives torrential rainfall causing flooding and landslides, and southern Peru becomes even more arid due to diminished precipitation. These climate shifts have significant impacts on subsistence farmers and the economy, making local climate change important to understand in the context of a warming world.

Tropical glaciers

Increasing global temperatures are causing tropical glaciers to shrink, which has repercussions for future water access (Jomelli et al, 2009; Gilbert et al, 2010; Casassa et al, 2007; Kaser, 1999). Andean tropical glaciers, which make up the vast majority of glaciers in the tropics globally, are expected to disappear in the next twenty to fifty years

(Magrin et al, 2014). The inhabitants of the Andes rely on tropical glaciers to provide water for drinking, electricity, agriculture, and resource extraction, especially during the dry season when there is little or no precipitation (Vuille et al, 2008; Perez et al, 2010;

Casassa et al, 2007; Magrin et al, 2014). Additionally, some of the densest and poorest populations in the world are in these Andean regions (Salzmann et al, 2009; Bradley et al,

2006). As urban centers grow in the Andes, these scarce water resources are being further

2 strained by rising demand for water (Magrin et al, 2014). These compounding factors make the people of the Andes particularly vulnerable to warming and a loss of reliable water access during dry seasons.

Tropical glaciers have been shown to be especially sensitive to climatic changes, making them good records of climate fluctuations and viable study subjects for understanding global and local climate change (Jomelli et al, 2009; Gilbert et al, 2010;

Casassa et al, 2007; Kaser, 1999). Tropical glaciers are thought to be at the edge of a delicate balance with the climate system, such that small changes in temperature, humidity, or precipitation can drive significant glacial responses. Topographic highs are created in the Andes in response to the subducting Nazca plate. This occurs from reverse faulting and uplift due to injections of mantle magmas into the South American plate and the ensuing building of volcanic massifs. This not only creates high mountain ranges like the Cordillera Blanca in northern Peru, but also forms volcanic provinces in southern

Peru. As volcanoes build topographic height, they eventually cross the equilibrium line altitude where atmospheric temperature and humidity are right for snow to accumulate and remain year round. After multiple seasons of snow accumulation, glaciers can form on these high peaks.

Volcano-Ice hazards in the tropics

Although 1,204 volcanoes occur in the tropics (defined as the area between the

Tropic of Cancer and Capricorn), only 29 of those volcanoes currently host glaciers. As of 1989, over 40 locations around the world have had historical accounts of lahars and floods produced from volcano-ice interactions, but only a handful have occurred in the tropics (Major and Newhall, 1989). Studies in Iceland have shown that ice thinning from

3 warming temperatures is causing increased magma production from mantle decompression (Schmidt et al, 2013). As tropical glaciers disappear and mantle decompression occurs, Andean volcanism could increase. This would have major implications for the already small ice fields of the Andes, corresponding water supplies, and lahar hazards.

Although eruptions on ice-clad volcanoes are infrequent, they can be catastrophic.

The 1985 eruption of Nevado del Ruiz in Columbia killed over 23,000 people from volcanic mudflows (Pierson et al, 1990). Other volcanoes in South America have also produced lahars during the Holocene such as Cotopaxi (Major and Newhall, 1989;

Mothes, Hall and Janda, 1998), Chimborazo (Barba et al, 2008), Parinacota (Clavero R. et al, 2004), Cayambe (Samaniego et al, 1998), and Nevado del Huila (Worni et al,

2012). And still others have had other eruptions involving ice including Antisana,

Sangay, Santa Isabel, Guillatiri, Nevado del Tolima, Ampato-Sabancaya (Global

Volcanism Program, 2013) and Popocatépetl (Siebe et al, 1996). In the short term, volcano-ice interactions can destroy property and cause high casualties, but in the long term, can also contaminate and deplete critical water supplies (Stewart et al, 2006). This makes understanding tropical ice-clad volcanoes particularly important for 1) assessing,

2) planning, and 3) mitigating hazards.

Peru has and continues to experience wide ranging effects from volcanism

(Appendix A).

Glaciers in Peru

Peru hosts 70% of all tropical glaciers worldwide (Vuille et al, 2008). Thus, knowledge of rates of ice mass change is particularly critical there, as inhabitants of Peru

4 rely on glaciers to provide water for drinking, electricity, and agriculture, especially during dry seasons (Vuille et al, 2008). In 1988, the first published glacier inventory of

Peru found that 2,041 km2 of Peru was glacierized (Ames et al, 1988). Glaciers in Peru reached their most recent maximum extent during the Little Ice Age (LIA) between 1630 and 1680 CE and have been shrinking since that time (Jomelli et al, 2009). Looking into the future, climate models predict a warmer and wetter Andean climate with rapidly shrinking glaciers (Barnett et al., 2005). This makes Peru a critical location for studying tropical glaciers to unravel past climate histories and future impacts.

Glacial shrinking is having wide spread effects on many sectors of the Peruvian economy and ecosystems including agriculture, tourism, biodiversity (Vergara et al,

2007), wetland habitat (Buytaert et al., 2011), indigenous subsistence food production

(Carey et al., 2012), and industrial and domestic water supplies (Casassa et al, 2007;

Carey et al., 2012). Temperature rise will create short-term benefits through an increase of water resources, but could leave millions of people without a secure source of year round fresh water as glaciers disappear (Perez et al, 2010). As urban centers grow in the

Peruvian Andes, scarce water resources are being further strained by rising demand for water (Magrin et al, 2014). These compounding factors make the people of Peru particularly vulnerable to water shortages.

Shrinking tropical glaciers will leave Peruvians in an increasingly more water stressed environment within the next 20 to 50 years (Magrin et al, 2014). Many highland people inhabit an already fragile environment and the disappearance of glaciers will cause even greater vulnerability to water source fluctuations (Buytaert et al., 2011).

Because Peru receives approximately sixty percent of its electricity needs from renewable

5 sources, mainly hydroelectric energy (United States Energy Information Administration,

2012), a decrease in melt water will lead to a decrease in hydroelectric production. Some hydroelectric stations could see as much as a fifty percent decrease in water input during the dry season without glaciers present in their watersheds (Vergara et al, 2007). With shifting energy sources, large capital expenditures will become necessary to provide power to growing urban areas (Bradley et al, 2006). Peru has a lot to lose if their glaciers disappear.

Nevado Coropuna

Located in Southern Peru, El Nevado Coropuna (Figure 1; 15° 33’ S, 72° 38’ W,

6,425 m.a.s.l) is ~110 km from the Pacific Ocean and ~150 km northwest of Arequipa

City. The base area around Nevado Coropuna receives 100-230 mm of rain/year with a mean annual temperature of -2 to 6°C (Kuentz et al., 2012). Nevado Coropuna represents the largest fresh water reserves in the province of Arequipa, which has a population of

1.15 million (Instituto Nacional de Estadistica e Informatico, 2007). Tens of thousands of these people rely on snow and ice melt for agriculture, household use, and other economic activities (Silverio and Jaquet, 2012; Úbeda, Palacios and Vázquez-Selém,

2012).

Normal atmospheric circulation patterns at Coropuna are driven by easterly trade winds that dump precipitation from the eastern side during months from December to

April, as weather moves across the Amazon and up into the Andes (Birkos, 2009). The dry season at Coropuna starts in May when the westerlies take over and bring dry air off the coast of western Peru (Birkos, 2009). This shuts down precipitation patterns and the

6 glaciers ablate snowfall and ice. This causes a pronounced signal of snow accumulation at Coropuna during the early part of each calendar year in satellite imagery.

An eruption from Coropuna could produce lava flows, ash fall, pyroclastic density currents and/or lahars affecting populations in at least three nearby provinces. According to the 2007 Peruvian Census, 57,416 people live in the two provinces, Condesuyos and

Castilla, within which Coropuna is located (INEI, 2007). An additional 53,065 people live in Camaná (INEI, 2007), a province that receives meltwater from Coropuna via the

Ocoña and Majes Rivers (Figure 1). Coropuna poses as an icon on the landscape in southern Peru not only for its natural resources but also for tourism and spiritual reasons.

Meltwater from Coropuna does not flow through any known hydroelectric stations as of 2015. Thus, reduction in the Coropuna ice cap only has implications for water as an agricultural and domestic source, not for electricity production.

Geology of Nevado Coropuna

Volcanic Activity

While major volcanic activity may have ceased during the last glaciation on

Coropuna (Úbeda, 2013), deglaciation has been accompanied by volcanic activity at this site (Úbeda et al., 2012). Lavas flows on the west, northeast, and southeast sides of the volcano have surface exposure ages of 6 ka, 2 ka, and <1 ka respectively (Úbeda et al.,

2012). These flows are only eroded from the LIA advance on Coropuna, which is consistent with a Holocene age (Úbeda, 2013). Currently, the surrounding Coropuna area contains several hot springs between 25-51°C with pH as low as 3.5 (Steinmueller,

2001). The east side of the complex is potentially active (Jersy Mariño, personal communications, 2014) and poses a threat to inhabitants of both the Majes River and

7

Ocoña River canyon. Activity from any of the six high peaks, assumed to be vents, would produce flooding, which would drain on both the north and south sides.

The past volcanic history of Coropuna has been divided into 4 phases: general examination of these past phases may be a useful guide to future activity (Appendix B).

Ice on Coropuna

Coropuna currently hosts the largest amount of ice in the tropics with a glacierized area of ~45 km2 as of 2014. However, Coropuna has had a long history of ice cover. For at least the last 80,000 years Coropuna has hosted glaciers (Úbeda et al, 2012).

Two independent dates have been determined for the last glacial maximum (LGM) at

Coropuna. A group from the University of Maine determined that the LGM at Coropuna was between 24.5 – 25.3 ka (Bromley et al, 2009). Another group from Spain determined that the LGM was ~21 ka (Úbeda et al, 2012). The moraine exposure age estimates of both studies were found from samples in the moraines farthest away from the present day ice. In between these old moraines and the present day ice, lie moraines from a re- advance between 11.9 – 13.9 ka (Bromley et al, 2011). LIA moraines are present just down slope of the modern day ice (Figure 2) (Úbeda, 2011).

Goals of this study

This study primarily aims to understand how snow and ice flux has varied since the launch of the Landsat satellites and the implications for understanding ENSO variations. Additional goals of this study are to measure ice thickness, collect modern diatom samples, retake 1911 photographs from Hiram Bingham, and measure nitrogen in the glacial streams of Coropuna to measure the glacial runoff quality.

8

Methods

Landsat Re-Analysis of Glacial ice

I measured snow and ice extent using the normalized difference snow index

(NDSI; equation 1) in 258 Landsat 4, 5, and 7 scenes from 1980 to 2014 (Figure 3).

Previous workers have found the NDSI to be one of the few reliable methods for glacial ice classification (Hall et al, 1995; Albert, 2002), even though glacial mapping methods continue to improve, especially for debris covered glaciers (Smith et al, 2015). From this

I choose 19 Landsat scenes that present the least snow and ice area for a given year to obtain measurements of glacial aerial extents at Coropuna through time (Table 1; Figure

3). I also digitized one Landsat 2 scene to extend the record back to 1980. All 258 images were manually verified to be cloud free.

퐵푎푛푑 2 − 퐵푎푛푑 5 (1) 푁퐷푆퐼 = 퐵푎푛푑2 + 퐵푎푛푑 5

Images were downloaded from the USGS Earth Explorer in WGS UTM 18N.

Using model builder from ESRI ArcGIS, we extracted the glacierized area of Coropuna using the NDSI (NDSI; Dozier, 1989; Hall et al, 1995). We executed NDSI math, reclassified the image into 2 classes (ice and not ice), and calculated the area of the ice for all scenes.

The ArcGIS model builder (Figure 4) allows iterative processing of infinite raster images through a workflow to extract areas of snow and ice using the NDSI. By using the raster calculator to select only the pixels that are interpreted as glacial ice, based on their values, the glacierized area can be calculated. The model builder takes in Landsat scenes and outputs a table of snow and ice values.

9

The NDSI threshold value was determined from a SPOT image (6 m panchromatic, 1.5 m monochromatic) taken on 23 November 2013, and a Landsat 7 ETM

(30 m resolution) image taken 26 November 2013. Due to the temporal proximity of the images, they are considered equivalent in ice extent. The ice extent was digitized using the SPOT image, buffered by -42.43 m (the diagonal length of one Landsat 7 pixel), then used to clip the NDSI values of the 2013 Landsat 7 image. The SPOT image extent was buffered by -42.43 m to ensure that every pixel selected in the clip would theoretically be

100% snow or ice. Then the minimum value from the resulting clip was used, 0.5567, for the NDSI classification. This allows for more confidence that the values only represent ice, dirty ice, and snow-covered ice.

The band 4/band 5 threshold value was determined using the same methods as the

NDSI value. However, the band4/band 5 threshold value was determined to be 1.765.

Glacier Classification

Error Assessment of Ice Classification

Errors were assessed for ice classification in four distinct ways: 1) Pixel classification error, 2) sensitivity analysis of the NDSI, 3) comparison with band 4 to band 5 ratio, and 4) comparison of area to a digitized SPOT image. While many glacial remote sensing studies include error analysis, some authors still do not (Duran Alarcon et al, 2015; Kulkarni et al, 2007; Veettil et al, 2015).

For the pixel error assessment the area of the perimeter pixels for each Landsat scene (Table 1) was calculated to determine the maximum amount of error (Rivera et al,

2007; Silverio and Jaquet, 2005). Then the value of the NDSI threshold changed to understand sensitivity of the threshold. In the Landsat 7 user manual, NASA (1998) cites

10 that they aim for 5% error in the band data. As long as error in band measurement are consistent within the same scene, the ratioed nature of the NDSI, will cancel the errors.

However, in the sensitivity analysis it is assumed that the NDSI value is off by ±5%.

Then the 19 images used in the NDSI classification using the band 4 to band 5 ratio method are reanalyzed (Bayr et al, 1994; Paul, 2002; Paul et al, 2004; Albert, 2002).

Finally, a 2013 SPOT image was used to digitize the ice area of Coropuna and compared with a 2013 Landsat scene taken three days later (Paul, 2000; Paul, 2002).

Ice thickness measurements on Coropuna

Ice thickness measurements were made at Coropuna using GPS points from a handheld Garmin GPS, a single-lens reflex camera, and Photoscan software from

AgiSoft. The photograph locations were georectified in Photocan and a 3-D model of the western ice carapace was constructed using the photographs and known locations (Figure

5). The carapace ice thickness was then estimated by using markers on the bottom and top of the outcrop.

Snow Classification and El Niño Southern Oscillation

Snow area was calculated using the same methods for glacier classification because like ice, snow is highly reflective. Snow must be included in glacier classification because snow will remain in accumulation zones at high elevations year round. The complete Landsat archive available for Coropuna was utilized to measure snow area over the course of the study period from 1986 to 2014. The snow classification utilized the same NDSI thresholds and ArcGIS model in model builder (Figure 4) for all

Landsat scenes, but all available cloud-free Landsat scenes were iterated to obtain results.

11

In order to measure snow area, all cloud free-daytime Landsat scenes of Coropuna were downloaded from the USGS Earth Explorer. These scenes were analyzed using the same method as described for glacier classification. The NDSI, with a threshold of

0.5567, was used to distinguish between snow and not-snow in ArcGIS. Mean and maximum annual quantities were calculated to plot against the Multivariate ENSO Index

(MEI).

Two indices are commonly used to measure the relative strengths of El Niño and

La Niña. Values for these two indices are free and available to download from the

National Oceanic and Atmospheric Administration. One index used is the MEI, which takes into account 6 variables including atmospheric pressure at sea-level, zonal and meridional surface winds, sea surface temperature, air temperature, and total cloudiness fraction (Wolter and Timlin, 2015). El Nino is positive in the MEI and La Nina is negative. This index is the most widely used.

The other index that is frequently used is the Southern Oscillation Index (SOI), which is based on the observed surface air pressure difference between Tahiti and

Darwin, Australia (NOAA, 2015). This measures the fluctuation in air pressure across the

Pacific. El Nino is negative in the SOI while La Nina is positive (NOAA, 2015). When multiplied by negative one the SOI is essentially identical to the MEI. Since the two indices use many of the same indicators, their consistency is not suprising. For the purposes of this study, only the MEI was used due to the similarity between the two indices.

12

The Landsat Archive

In 2008, the Landsat archive became free in its entirety to any researcher around the globe via the USGS. This archive, which dates to back 1972, allows users to download remote sensing data for any location on earth to monitor global change. It can be utilized to measure changes such as deforestation, lake clarity, lake area, glacial areas, desertification, land use change, and almost any other process that changes the surface of the earth over annual to decadal time scales at a size greater than the resolution of the satellite (30 m for Landsat 4 to 8).

On 31 May 2003 the Scanning Line Corrector (SLC) failed on Landsat 7 (USGS,

2013). This compromised the ability of the satellite to collect data throughout the scene leaving data gaps in images. Fortunately, Nevado Coropuna is located exactly in the middle of the Landsat scene so that little to no error is present in the image. This means that Landsat 7 scenes can be used and processed without the need for correction or interpretation between data gaps. When all images were analyzed for snow extent, some

Landsat 7 images contained SLC interference. No “bare ice” images contained scanning line errors. While some data is missing in eight snow covered terrain scenes, this at most decreases the snow covered area by 2.2% (shown in Landsat scene

LE770040712013202CUB00), thus it is not addressed in this study because the error is not on an order of magnitude that would alter results.

For the year 2004, Landsat 7 was in orbit and was still collecting images even though the SLC was no longer operating. However, during this time period, Landsat 7 data is not available for download from USGS Earth Explorer with multiple bands, only a

13 single Landsat Look image of low quality is available. However, Landsat 5 data remains available for this time period and is utilized in this study.

Photographs from the early 1900s of Coropuna: Hiram Bingham

In 1911, Hiram Bingham, a Yale scholar, and his crew completed the first documented climb of Nevado Coropuna. Bingham is widely known as an archaeologist and is credited with the discovery of Machu Picchu. On his journey, he took limited pictures of the ice, which we can use to visually assess glacial area in 1911. These photographs were taken by H.L Tucker and currently reside in the Yale Peabody Museum of Natural History, New Haven, CT. While it is difficult to delineate ice cover from these photographs, I use them here to visually assess glacial shrinking since 1911.

Results

Landsat Archive Record of Change to the Ice Cap Area

Using satellite images taken farthest in time from the last snow of the wet season, but before the first snow of the next wet season, allows for the most accurate reconstruction of the glacierized area of Coropuna. Only those images that are cloud free and have present snow cover on the highest peaks are considered for image analysis. For example, Silverio and Jaquet (2012) used an image from 1 August 1985 and found a glacierized area of 96.4 km2. By using an image from just two years forward in time, 5

December 1987 instead of the August image, an area of 52.9 km2 is calculated. This 45% reduction in the estimated area of ice cover is impossible over this two year time span

(Figure 6). Logically, the more accurate outline will have the least amount of area, because annual snow and ice area minimum will be the greatest when the most ice is

14 exposed. Thus, the 5 December 1987 image provides for a more accurate representation of ice area.

The analysis of this present study estimates that from 1980 to 2014 Coropuna experienced a 24 percent reduction in glacierized area from 58.0 km2 to 44.1 km2 or 0.41 km2 yr-1 (Figure 7). However, smaller periods of time have seen varying rates of retreat.

From 1980 to 1992 Coropuna lost an average of 0.86 km2 yr-1 of ice. From 1992 to 2006 this rate slowed tremendously to an average of 0.00 km2 yr-1 of ice, but increased again from 2006 to 2008, to an average loss of 1.1 km2yr-1. For the most recent analysis time- period, 2008 to 2014, the average rate of ice loss has slowed again to 0.20 km2 yr-1.

Error assessment

Frist, the pixel classification error is the maximum amount of error possible in this study. We find that the error using this method is on average ~9% for Landsat 4, 5, and 7 scenes (Table 1; Figure 8). Second, the NDSI sensitivity shows that given a 5% change in the NDSI threshold value the total glacierized area changes by ~0.6% on average. Third, the band 4 to band 5 ratio has an average of -2.1% difference (Figure 9). Finally, the 2013

SPOT image and Landsat scene have an area of 44.95 km2 and 44.85 km2 respectively, a difference of 0.2%.

Ice thickness measurements

Ice thickness was measured using GPS points, photographs, and Photoscan at the

Coropuna ice cap carapace on the western flank. Using the photographs from varying ground control points, Photoscan was able to stitch together a mesh of the western carapace. This mesh (Figure 5) allowed for 4 different measurements of the ice in photoscan. These measurements were averaged to find a thickness of ~37 m at the

15 outcrop (Figure 5). This number should be considered as a ball park estimate and provides a useful starting point for estimating the volume of ice on Coropuna.

Comparison photographs from 1911 to 2015

Two photographs were recreated from the 1911 expedition of Hiram Bingham

(Figures 10 and 11). These photographs were taken from about 5 km south of the ice cap

(Figure 12). The location was determined using the photographs in the field and matching up rocks in the foreground and background of the 1911 photographs. The photographs were taken during the June 2015 field season. The photographs show quantitatively that the ice cap has not retreated tremendously. While ice cover loss is obvious in the images

(Figures 10 and 11), the shrinking does not appear to be more than a few hundred meters over a 104 year period.

El Niño Southern Oscillation

To test the hypothesis that snow cover and ENSO are related, I calculated the snow and ice cover of 258 Landsat scenes from 1986 to 2014 (Figure 3). Previous studies have suggested that southern high altitude Peru receives less precipitation during El Niño events and more precipitation during La Niña events (Jaksic, 2001). The results of this study are also consistent with a relationship between ENSO and snow cover (Figure 3).

ENSO index and the percent change per year in glacierized area (linear regression, R2 = 0.011, P = 0.687) nor the glacierized area calculated using the NDSI

(linear regression, R2 = 0.17, P = 0.075) appear to be correlated during the study period

(Figure 13). However, El Niño favors low snow years while La Niña conditions tend to favor greater snow fall. The mean maximum snow area when the MEI is less than 0.5

16

(95.3 km2) is significantly higher than that for mean snow area when MEI is greater than

0.5 (74.7 km2) (t-Test, P = 0.0486) (Figure 13c).

When MEI and maximum snow area of each year at Coropuna since 1988 are plotted against each other, they have a linear relationship with an R2 value of 0.1143

(Figure 13A). When the data is split into four quadrants (Figure 13A), nine our of eleven years when the MEI is less than zero, the snow cover is greater than 80 km2. Similarly, ten out of 16 years when the MEI is greater than zero, the snow cover is less than 80 km2.

To ensure that the data set is not biased by time of year, the MEI was compared to the maximum snow area as coded for time of year (Figure 13B). Because no clear pattern between time of year and snow area is evident, I think that that ENSO is the primary driver of snow cover.

Strong ENSO phases clearly cause changes in snow cover. During the six strongest El Niño years, snow cover area never exceeded 80 km2, while during the six strongest La Niña years, snow cover was always above 80 km2 (Figure 13C). The R2 value for this linear trend is 0.5748, showing a weak correlation between the MEI and maximum snow cover. However, the clear break at 80 km2 is a clear sign that ENSO and snow cover are related.

Discussion

Implications of Ice Area Shrinking Rates at Nevado Coropuna

Magrin et al (2014) suggest that within the next 20 to 50 years tropical glaciers and their meltwater will disappear, specifically citing evidence from Silverio and Jaquet

(2012) for the Coropuna ice cap. If true, this will cause significant economic stress for populations living in arid regions, such as Peru, who depend on snow and ice melt,

17 particularly during dry seasons. A report from the Peru Environmental Ministry indicates that they are currently preparing for the ice cap of Coropuna to be a non-contributor to water supply by 2025 (Ministerio del Ambiente del Perú, 2010). Dry season adaptation measures for water supply, such as dam construction and water storage, will strain the developing economies in this region and cause expenditure of scarce resources (Lasage et al, 2015). Lasage et al (2015) specifically identify Coropuna as an important source of water during September and October and they suggest that this source will disappear within the coming decades. But our results indicate that the rush to implement adaptation measures may be unnecessary in the coming decades. Using misleading data as a basis for long-term planning will cause unneeded resource expenditure on present adaptation measures for this developing economy.

Previous studies of Nevado Coropuna

Nine studies have previously attempted to quantify the glacierized area of

Coropuna (Figure 14; Silverio and Jaquet, 2012; Peduzi et al, 2010; Ames et al, 1988;

Racoviteanu et al, 2007; Forget et al, 2008; de Silva and Francis, 1990; Úbeda, 2011;

Nunez-Juarez and Valenzuela-Ortiz, 2001; Veettil et al, 2015). Data sources used for ice measurements include space shuttle photography, aircraft aerial photography, and imagery from a variety of satellites.

de Silva and Francis, 1990

de Silva and Francis (1990) used a space shuttle image to calculate the area of

Coropuna in 1990 to be 130 km2. They indicated that they did not observe any valley glaciers on Coropuna. Images from before 1990 clearly show valley glaciers and a lesser

18 glacial area. Thus we conclude that the shuttle image was taken at a time when Coropuna was blanketed in snow, thus it should not be used in constraining ice area.

Ames et al., 1998

In an inventory of all Peruvian glaciers, Ames et al (1988) found that Coropuna was covered by 82.6 km2 of ice in 1962 based on an aerial photograph. However, while the image was published in 1962, the orthophoto was actually taken in 1955 (Úbeda,

2011). This study is unable to verify their measurements, because we could not access the 1955 image. However, this ice area estimate is consistent with our results.

Nunez-Juarez and Valenzuela-Ortiz, 2001

Nunez-Juarez and Valenzuela-Ortiz (2001) wrote an INGEMMET bulletin about the volcanic potential of Coropuna and hazards from future eruptions. In their report they cite the 1955 air photograph with snow cover and a 1986 Landsat 5 scene for which they estimated an ice area of area 67.16 km2. This area is 9.16 km2 greater than the area calculated for 1980 in the present study, which suggests that this 1986 Landsat scene also presents significant snow cover.

Racoviteanu et al., 2007

Racoviteanu et al. (2007) found that the summit area has thickened between 25 m and 50 m between 1955 and 2000 using a 1955 topographic maps and a 2000 Shuttle

Radar Topography Mission DEM. Meanwhile the toes of the glaciers have thinned by 25 to 75 m over the same time period (Racoviteanu et al, 2007). They also used an October

2000 ASTER L1B scene to measure glacial extent. The 60.8 km2 glacial area is higher than expected, likely due to snow cover. The present study analyzed a 1998 Landsat scene and found it to have an area 48 km2, no measured Landsat scenes in the present

19 study had areas greater than 58 km2. Thus, it is highly unlikely that the area measured by

Racoviteanu et al. (2007) is accurate.

Forget et al., 2008

Forget et al. (2008) used a 2000 Landsat scene to measure ice area on Coropuna and found it to be 53.9 km2. This number is slightly higher than the present study’s estimates, likely due to snow cover. They do not provide a Landsat scene identification number for verification.

Peduzi et al., (2010)

Peduzi et al. (2010) completed ice radar studies of Coropuna in 2004 in addition to areal extent measurements and changes in thickness from elevation models. However, they were unable to come to a conclusion about change in thickness due to greater uncertainties than the magnitude of their results. The ice radar allowed them to constrain ice volume of Coropuna to be 4.62 km3 ± 0.94 km3 or 3.2 million tonnes of water. Peduzi et al. (2010) also contributed five different glacial extent measurements of Coropuna. The

1955 air photo they use to digitize the ice extent is an inappropriate tool to measure glacial area due to the high snow area surrounding Coropuna. Additionally, they use two scenes from June and May, which is at the end of the wet season in Southern Peru, this also casts doubt on the accuracy of their results due to likelihood of high snow cover.

Úbeda, 2011

In his PhD thesis, Jose Úbeda outlines the glacial area at three different time periods. He uses a 1955 aerial photograph to measure the area, under snow, of the glacier.

Given our analysis it seems unlikely that the ice cap had an area of only 56 km2 in 1955.

Additionally, Úbeda does not explain his methodology for extracting this area for the ice

20 cap. The areas he calculates from a 1986 orthophoto and a 2007 ASTER scene are consistent with measurements in this study, although the orthophoto appears to have snow cover beyond the ice extents.

Silverio and Jaquet (2012)

Silverio and Jaquet (2012) are arguably the most cited study of Coropuna in recent years. This study suffered from a sever overestimation of glacial area due to the presence of snow. Their 1985 scene is a good example of this because their misclassification of snow lead to a 45% overestimation of glacial area (Figure 6). Silverio and Jaquet (2012) however, provide a method to area calculate error, which no other

Coropuna study does.

Veetil et al., 2015

Veetil et al. (2015) is the most recent study of aerial ice extent at Coropuna. They use the Landsat archive to more accurately quantify ice on Coropuna. However, they show large amounts of variation between years; more variation than Coropuna has experienced since 1980. This suggests that Veettil et al (2015) are at best measuring end of year snow and ice cover. It is unlikely that satellite images are available for every year since 1980 due to temporal resolutions, cloud cover, and satellite timing, thus it is difficult to justify measuring ice extent every year even for locations prime for satellite remote sensing like Coropuna. Finally, they use an NDSI value that varies between 0.45 and 0.55. A varying NDSI value decreases the reproducibility of results and leads to inconsistency in measurements of glacial area. Measurement precision is maximized by maintaining a consistent NDSI value.

21

This study

For this present study we have carefully selected images where snow cover is at a minimum for a given year (Figure 3). This study does not use images for years were no appropriate image is available. Furthermore, by tracking the yearly snow fluctuations, this study has more confidence that ice minimums are being documents are accurately as possible. This further supports that previous authors have misclassified snow as glacial ice.

Coropuna as a sentinel for climate change

The Intergovernmental Panel on Climate Change (IPCC) has cited Coropuna as being one of eight sentinels for glacial shrinking in the tropics. The IPCC cites rates of shrinking from Silverio and Jaquet (2012) to show that Coropuna is the fourth fastest retreating glacier in the tropics, the first being Kilimanjaro (Magrin et al, 2014). With our revised estimate of ice shrinking, Coropuna would be the slowest shrinking tropical glacier cited in the IPCC report.

Studies of other tropical ice bodies

Studies of other high altitude tropical ice masses such as the Cordillera Blanca and Quelccaya ice cap indicate that they are losing areal ice extent at similar rates to those we have estimated for Coropuna (~0.7% area yr-1) (Racoviteanu et al, 2008;

Rabatel et al, 2013; Albert et al, 2014; Salzmann et al, 2013). However, Coropuna is retreating more slowly than lower altitude tropical glaciers (Rabatel et al, 2013). Having reliable rates of ice mass changes in tropical glaciers is critical for global policy makers because that data is being used as a basis for policy recommendations (Magrin et al,

2014).

22

Implications for volcanic hazards

Our results have mixed implications for hazard assessment from future eruptions at Nevado Coropuna. As long as ice persists on top of the volcano, lahar-generation via ice melting during eruptions will be a significant potential hazard; if the ice cap is melting more slowly than previously thought, that hazard will persist longer. On the other hand, it has been proposed that reduction in ice volumes overlying volcanic systems can be a triggering mechanism for eruptions from crustal magma chambers (e.g.

Sigmundsson et al, 2010). While the thickness of the Coropuna ice cap is poorly constrained, slower rates of ice loss will reduce rates of change of crustal stresses and potentially the overall risk of climate-change induced eruptions.

Selecting images for glacial extent analysis

When estimating shrinkage in ice aerial extent, care must be taken to select only those satellite images from times of least snow cover (Figure 7), which is dependent on not only regional but also local climate. Image analysis from snow covered scenes can lead to overestimation of the rates of ice melting by consistently selecting images with snow cover shrinking at the same rate of ice shrinking; we think is why previous estimates of ice-loss at Coropuna have been too high. This leads to overestimation of ice area (Figure 7). If images are inconsistently selected, large changes in snow cover can lead to large estimated shrinking rates by selecting images with and without snow. For the Coropuna area, the minimum snow cover occurs at the end of the austral winter, before the wet season begins in January. For example, using images from December 1986 instead of August 1985, leads to a 45% difference in area (Figure 7), which is highly unlikely that area could have changed by 45% in 17 months. But, Sajama Volcano, which

23 is located 500 km to the SE of Coropuna, receives precipitation starting in early

November (Hardy and Vuille, 1998). In contrast, Coropuna usually receives precipitation beginning in mid-December. Thus, images from varying times of year must be examined in order to ensure that ice minimums are used for analysis because ice will not necessarily reach a minimum at the end of the regional dry or warm season. This highlights the importance of understanding local climate conditions (e.g. precipitation and temperature) before analyzing satellite data for ice area measurements.

Our data is consistent with the ice cap on Coropuna shrinking at one-third the rate previously estimated. This suggests that the ice will not completely disappear until ~2140 assuming that the rate of shrinking is linear and no drastic changes in rate occur. Thus,

Coropuna is likely to be one of the last surviving glacierized peaks in the tropics during the next century. This will reduce the impact of climate change for the ~100,000 people in southwestern Peru who depend on Coropuna meltwater.

Ice volume estimate from Coropuna

Assuming that marginal ice is on average 1 m thick, previous estimates of ice loss at Coropuna (~1.4 km2 yr-1) translate into a reduction of ~12,600 l of water per person per year until the ice disappears; but the actual water loss is closer to 3,800 l per person per year based on losing only 0.41 km2 yr-1. More importantly, with climate change mitigation it is possible that ice bodies such as Coropuna will remain in the tropics for the foreseeable future.

Using the log-log relationship described by Bahr et al. (2015; equation 2) the estimate of ice at Nevado Coropuna in 2014 was 5.16 km3. Using this equation from Bahr

24 et al (2015), we estimate that on average Coropuna has an average net loss of ~88 billion liters of water each year from 1975-2014 in addition to annual snow melt (equation 2).

2) 푉 = 0.03 푆1.36

Where V is volume and S is surface area. Coropuna meltwater is currently providing a water subsidy to the region for agriculture and domestic uses.

Ice thickness measurements

Photogrammetry provides a useful ballpark value for ice thickness on the western carapace. While this work has never been done before in glacial environments, it should be further developed to improve the measurement accuracies. With more ice depths across the volcano and/or over time, we can tell how the ice is shrinking both laterally and vertically.

El Niño Southern Oscillation

Over the course of the 28 year study period, eight El Niños have occurred and eight La Niñas have occurred. Averaged over this time period an El Niño and La Niña occurs once every 3.5 years. The results of this study are consistent with a connection between snow cover and ENSO, with less snow cover during El Niño years and more snow cover during La Niña years (Figures 3 and 13).

The results are consistent with observations from other studies that high Andean regions receive less precipitation during El Niño years (Jaksic, 2001; Birkos, 2009;

Garreaud and Aceituno, 2001). Additionally, our results show that the biggest La Niña event during 1999 by annual average brought with it the third highest maximum annual snow cover recorded in this dataset. Meanwhile the largest El Niño event in magnitude, during 1997 to 1998, coincides with the smallest peak annual snow cover on Coropuna.

25

While it does not always hold true that El Niño brings less snow and La Niña brings more snow, they clearly are related. This finding is consistent with other studies of tropical ice cores (Birkos, 2009).

The connections between ENSO events and climate change remain unclear.

Although La Niña events appear to be decreasing in maximum snow cover towards present. This either means that La Niña events are weaker than in previous decades due to climate change or ENSO cycling, or that the snowline has risen and the elevation at which precipitation freezes is increasing faster than in the past due to global or regional warming. To test which of these are occurring, we would need high temporal resolution temperature and snow area data. This could be acquired from regional weather stations and by expanding the scope of satellites used in the study. No trend in the ENSO record is apparent in terms of snowline and progressing towards the present with warming.

The peak snow time of year at Coropuna has a wide range from the end of

January to the end of August. This shows that the end of the wet season does not have a consistent ending time as some have suggested (Birkos, 2009; Garreaud and Aceituno,

2001; Jaksic, 2001). Additionally, in his book Inca Land, Hiram Bingham (1922) writes about a snow storm in October. This clearly shows the potential to have large precipitation events during what is typically considered to be the dry season. In order to determine how climate varies over the course of a year at Coropuna, a weather station should be installed to monitor precipitation, humidity, and temperature over the course of decades.

26

Conclusions

Like all tropical glaciers, Coropuna is shrinking. The ice cap area has decreased

24% from 58.0 km2 in 1980 to 44.1 km2 in 2014 (Figure 7). But previous studies have suggested that Coropuna is shrinking at 1.4 km2yr-1, while our analysis suggests that

Coropuna is shrinking at one-third that rate (0.41 km2 yr-1). This means that ice on

Coropuna is likely to persist into the next century given steady, linear retreat rates. Our work shows that care must be taken when selecting satellite imagery for determining the extents of glacierized areas to ensure that snow cover, which can obscure ice boundaries, is at a minimum. By analyzing all available images for snow and ice area, the user can select the images with the least snow cover. Using this method, measurements of glacial ice area are likely to be more accurate. This has significant implications for water planning in Peru, which relies on glacial meltwater for domestic, industrial, and agricultural uses. Our study suggests that, with climate change mitigation strategy implementation, ice on Coropuna will persist into the foreseeable future.

Coropuna clearly receives a signal from ENSO in the maximum snow area.

During El Niño years, Coropuna receives less snow and during La Niña years, Coropuna receives high snow. This shows that the high Andes is directly impacted by ENSO and that planning is necessary for water management in regards to ENSO events.

27

Appendices Appendix A. Peruvian volcanism

Recent Peruvian Volcanism

Peru is home to 16 volcanoes, 7 of which have erupted during the Common Era.

Currently only four Peruvian volcanoes are glacierized including Neavdos Coropuna,

Sabancaya, Solimana and Firura. Of the modern day Peruvian ice-clad volcanoes,

Sabancaya is only one to have experienced eruptions during the Common Era. However, many of the Peruvian volcanoes host seasonal snow cover and permafrost, leading to seasonal lahar hazards. Even though ice-clad volcanoes have not displayed recent activity, they have the potential to endanger human safety.

The only account of volcano-ice interactions in Peru’s recorded history occurred in February of 1600 during an eruption of Huaynaputina that produced lahars on the Rio

Tambo, south of the city of Arequipa (Major and Newhall, 1989). At that time,

Huaynaputina had ice; however it is not known how much. The eruption is known to be the largest Andean eruption in historic times with an erupted volume of at least 10.2 km3

(Thouret et al., 1999). This eruption had wide spread effects from earthquakes as well as ash and pumice fallout (Thouret et al., 1999). A 2-3 cm ash layer from the Huaynaputina eruption was found in Lake Pallarcocha, near Coropuna. Another eruption of this size would significantly impact the Peruvian economy and civilian safety.

Ubinas volcano in southern Peru is the most recent sizable Peruvian eruption in the past decade. Ubinas erupted between 2006 – 2008 causing damage from ash fall out in 100 km2 surrounding the volcano (Rivera et al, 2010). This was the first long-lasting volcanic crisis the Peruvian government has faced, leading to new practices and warning

28 systems (Rivera et al, 2010). During the summer of 2015 Ubinas volcano was also erupting leading to ash hazards and evacuations in the region.

Misti Volcano also poses a threat to Peruvian safety because it is less than 20 km from Arequipa City, Peru’s second largest population. While Misti has had limited historic activity, it has the potential to wreak havoc on nearly one million inhabitants, including flooding from seasonal snow.

Sabancaya volcano in southern Peru, which hosts a small amount of ice, has erupted in the 1990s and during the last two years (Figure 15), although no significant damage has been documented to the ice or property.

Cordillera Ampato

The Cordillera Ampato includes 5 volcanoes: Coropuna, Ampato-Sabancaya-

Hualca Hualca, Solimina, Firura and Sara Sara (Úbeda, 2011) (Figure 16). Of these, only

Sara Sara does not currently have glaciers, although it has in the past and currently hosts seasonal snow. While Peru hosts many glaciers and volcanoes, all four of the ice-clad volcanoes in Peru reside in the Cordillera Ampato within 120 km of each other. Of these, the volcanic complex of Ampato-Sabancaya-Hualca Hualca has been the most active. As recently as June 2015, Sabancaya has erupted ash and gases (Figure 15).

Appendix B. Coropuna Volcanic history

Ancient Volcanism: Pedestal to Coropuna Complex

The compositions through each phase have varied somewhat, ranging from silicic

(up to rhyolite) to intermediate (trachyte), but have consistently produced dacite lavas and pyroclastic deposits.

29

The southeast and western sides of the volcano are covered by Oligocene-

Miocene dacite and rhyolite, accompanied by explosive pyroclastic and ignimbrites

(Nunez-Juarez and Valenzuela-Ortiz, 2001). There are pyroclastic flow deposits near

Machahuay-Viraco with 20 cm blocks and ash of dacite-rhyolite composition (Nunez-

Juarez and Valenzuela-Ortiz, 2001). In the Arma river, there are pyroclastics of early

Pliocene age are exposed (Senecca Formation) (Nunez-Juarez and Valenzuela-Ortiz,

2001).

Coropuna Phase 1

Lava of late Pliocene age (Carroso Group) circumscribes the volcano (Nunez-

Juarez and Valenzuela-Ortiz, 2001). The first phase began with an explosive stage, producing flows of ash and pumice with trachyandesite to dacite compositions (Nunez-

Juarez and Valenzuela-Ortiz, 2001). Next came an effusive phase of trachyandesite lava

(Nunez-Juarez and Valenzuela-Ortiz, 2001). An eight-meter thick unit with ash flows and half-centimeter pumice has been found in Machahuay-Viraco (Nunez-Juarez and

Valenzuela-Ortiz, 2001). This is covered by trachyandesite lava (Nunez-Juarez and

Valenzuela-Ortiz, 2001). A dome of this same period with trachyandesite and large plagioclase crystals is mapped in Lake Caracara (Nunez-Juarez and Valenzuela-Ortiz,

2001). On the road from Tipan to Pampacolca a 4-meter ash unit from this time have been identified (Nunez-Juarez and Valenzuela-Ortiz, 2001). In the Viraco-Tipan sector pyroclastic flow deposits are covered by lahar deposits and then trachyandesite lavas

(Nunez-Juarez and Valenzuela-Ortiz, 2001). The most distal lava from this time is ~25 km to the south of Coropuna (Nunez-Juarez and Valenzuela-Ortiz, 2001).

Coropuna Phase 2

30

The north, southeast and southwest sides of Coropuna contain lavas that override the Coropuna Phase 1 deposits (Nunez-Juarez and Valenzuela-Ortiz, 2001). These are aphanitic, vesiculated lavas with trachyandesite compositions, which contain 0.5 cm long plagioclase crystals (Nunez-Juarez and Valenzuela-Ortiz, 2001). This is associated with an explosive phase of the craters on Coropuna and fault scarps (Nunez-Juarez and

Valenzuela-Ortiz, 2001).

Coropuna Phase 3

Aphanitic trachyandesite to trachydacite lavas from this time period surround the entire volcano and override deposits from Coropuna Phase 2 (Nunez-Juarez and

Valenzuela-Ortiz, 2001).

Coropuna Phase 4: Recent volcanism

Nevado Coropuna is a volcanic complex with up to six coalescing vents but no historic eruptions. However, three Holocene age lava flows (Andagua group) are located on the western, northeast, and southeastern flanks of the volcano. Surface exposure dates suggest eruption ages of 6 ka, 2 ka, and <1 ka respectively (Úbeda et al, 2012).

The Andahua group (Holocene) comprises deposits from explosive and effusive style eruptions with some bombs as far as 7 km from the complex (Nunez-Juarez and

Valenzuela-Ortiz, 2001). These rocks have a dacite composition with 0.5 cm long plagioclase crystals (Nunez-Juarez and Valenzuela-Ortiz, 2001). The lavas were slow moving and viscous, and formed a’a’ flows up to 20 meters thick (Nunez-Juarez and

Valenzuela-Ortiz, 2001). The age of these lavas show that Coropuna is not extinct and must be considered as a potential threat to the surrounding area.

31

Appendix C. Nitrogen testing

Introduction to nitrogen in glacial environments

Recent studies have suggested that nitrogen inputs could be an important nutrient subsidy from glaciers to aquatic ecosystems (Barnes et al, 2014; Baron et al, 2009; Saros et al, 2010; Slemmons et al, 2013; Slemmons and Saros, 2012). If glacial melt from

Coropuna is laced with nitrogen it would provide a nutrient subsidy to agriculture downstream. This is important because decreasing or increasing ice melt would signify a change in not only water quantity but also quality. In the short run, agriculture would receive a boost from nitrogen-rich glacial melt, while the long term depletion of glacial ice and thus meltwater will decrease the output of nitrogen, if it is present in the ice.

Test kit methodology

During a 2015 field season, I collected six water samples and analyzed them for nitrogen content. All samples came from watersheds that currently host glacial ice

(Figure 17). The samples were processed using a nitrogen test kit from Hach.

Nitrogen content, as nitrate, was measured in 6 medial to distal water samples from the ice cap of Nevado Coropuna (Figure 17). All samples were collected during the

June 2015 field season and were analyzed within three days of collection (Table 2).

Samples were processed using the low range nitrate test kit for 0-1 and 0-10 mg/L as nitrate nitrogen using the Model NI-14 test kit from Hach. The samples were processed using the following protocol:

1. Samples were collected from running streams in plastic containers. Before collecting

the sample, the container was rinsed with stream water at least 3 times. This water

32

was returned to the stream after rinsing. Samples were taken from just below the

surface of moving water in the middle of sampled streams.

2. Glass tubes and a viewing kit from Hach were used to analyze the samples along

with NitraVer© 6 Reagent Powder Pillow and NitriVer© 3 Nitrite Reagent Powder

Pillow.

3. The glass tubes were each rinsed and shaken three times with sample water.

4. Enough water was added to fill the glass tube to the white ring, about 10 ml.

5. The NitraVer© 6 Reagent Powder Pillow was added to the water, stoppered, and

shaken for three minutes. The sample was then left undisturbed for 30 seconds (as

recommended) to allow un-oxidized cadmium particles to settle out. However, no

un-oxidized cadmium particles were observed.

6. Since no cadmium particles were present, the NitriVer© 3 Nitrite Reagent Powder

Pillow was added to the glass tube. The tube was stoppered and shaken for 30

seconds.

7. The tube sat still for at least 10 but no more than 20 minutes. Upon completing this

time requirement, the sample was analyzed for red color using the color comparator

from Hach. A blank of the water sample was also inserted in the color comparator to

ensure any red already in the water did not change the results. The color compactor

was held up to a light source to measure the red color, which corresponds to a

concentration of nitrate.

Site selection

Sites were selected based on ease of access and the potential presence of glacial meltwater. All sites are near a road, allowing for easy resampling during future

33 expeditions. Even though the Mauca Llacta East sample location does not appear to have any ice within the water shed, the discharge was high, roughly equal to that of the Mauca

Llacta sample location (Figure 17). This site is believed to be the output of a glacier fed spring that comes out through the lava.

Results of N

Only one of six samples had nitrogen present at detectable levels: the sample near

Tipan. That sample showed 0.64 mg/L of nitrate present in the stream. All other samples registered 0 mg/L of nitrate, for a detection limit of 0.1 mg/L nitrate.

Discussion of N results

The samples in this study show no conclusive evidence that Nevado Coropuna provides a nitrogen subsidy to the water supply. However, several potential explanations can be provided for this result. Due to high snow cover during June, the meltwater from

Coropuna could be sourced chiefly from snow. Saros et al (2010) show that snow packs produce little to no nitrate. Thus, if no ice from Coropuna was contributing to the meltwater from Coropuna, we would not expect any nitrate inputs. Additionally, if snow melt was driving melt discharge, the nitrate present in the meltwater could have been diluted below detectable levels. It is also possible that no nitrate is produced from the

Coropuna ice cap.

The sample near Tipan showed evidence of high nitrate levels. However, due to the enormity of the water shed and lack of nitrate in other samples, we cannot be certain that the nitrate originated from glacial meltwater. A great deal of agriculture takes place above the sample location in Tipan, leading to the possibility that the nitrate is from fertilizer runoff or cow manure. We would have more confidence in this sample if an

34 additional sample had been taken upstream, we if had examined the watershed for additional inputs of nitrogen, and if the location has reproducible results from future measurements.

To further test the hypothesis that Coropuna contributes to water quality in addition to quantity, samples should be retaken during late November or early December, when snow is at a minimum. This means that almost all of the meltwater should come from glacial ice, not snow, leading to higher concentrations of nitrate, and a better probability of detecting it using a color comparator. Additionally, more accurate measurements of nitrate could be made if sample timing and refrigeration was optimally planned so that samples could be analyzed for nitrate in a lab in the United States.

Conclusion on Nitrogen

Only one sample revealed nitrogen, however it is possible that the nitrogen could be from a non-glacial sources. The presence of algae on the ice suggests a possible mechanism to produce nitrogen in glacial meltwater (Figure 18); however, this cannot be substantiated from sample evidence. It appears that Coropuna did not influence water quality during June, 2015. These samples should be recollected during late November or early December of a future year.

Appendix D. El Niño Southern Oscillation (ENSO) in South America

El Niño Southern Oscillation (ENSO) accounts for most of the natural, short term climate variability across the world (Adams et al, 1999) and is the most significant source of annual precipitation variability in South America (Baker and Fritz, 2015). ENSO operates on a timescale of two to seven years, flipping between El Niño and La Niña phases. El Niño phases lead to anomalously warm years, while La Niña conditions

35 commonly produce colder years for air temperature and ocean surface waters. This causes changes in the structure of the thermocline in the Pacific Ocean and affects upwelling along the coast of western South America, including Peru, Chile, and Ecuador.

Additionally, the pressure differential between western South America and eastern

Australia changes with the jet stream. This leads to changes in precipitation patterns across the world, including the United States (Adams et al, 1999) and western South

America (Jaksic, 2001). These changes have economic and ecological consequences, especially in the tropics, as tropical organisms are accustomed to a narrow range of climatic conditions in comparison with higher latitude inhabitants (Deutsch et al, 2008).

During strong phases, ENSO has both costs and benefits across the world. ENSO adversely affects agricultural production in the United States, to the tune of several billion dollars, during both the El Niño and La Niña phases (Adams et al, 1999).

However, El Niño brings rain that can help to relieve California droughts. But, Peruvian fisheries have famously been known to collapse during El Niño events, thus crippling coastal Peruvian economies (Broad et al, 2002). Due to its economic impacts, forecasting

ENSO events has become particularly important to mitigate loss and damage; planning for forthcoming climate phenomena is key to preventing substantial economic loss.

The Andean regions of South America are typically anomalously dry during El

Niño events, while other lowland regions receive intense rainfall (Jaksic, 2001; Casimiro and Espinoza, 2014). This makes ENSO a particularly important phenomenon to understand as it has large geographic and economic impacts on the climate of South

America and beyond. However, ENSO does not have consistent impacts even if the magnitude of the event is the same (Baker and Fritz, 2015). At present the relationship

36 between ENSO and climate is unclear, as strong ENSO signals do not always signify the same climatological change (Garreaud and Aceituno, 2001; Collins et al, 2010; Jaksic,

2001; Baker and Fritz, 2015). However, warmer temperatures tend to favor more El Niño events, while cool temperatures tend towards La Niña events (Tsonis et al, 2005). This makes understanding ENSO dynamics particularly timely and important to predict future events, as well as to understand how global climate change could impact ENSO. As impacts of ENSO are highly variable it is important to quantify the environmental change due to ENSO.

The Altiplano essentially only receives rainfall from December to February in a given year (Garreaud and Aceituno, 2001). Thus, ice and snow are important sources of freshwater during dry seasons. Satellite remote sensing, facilitates rapid measurements of ice and snow form space, allowing us to quantify changes in precipitation over time. Ice cores have shown that the Quelccaya Ice Cap can receive 30 percent less snow during El

Niño years (Birkos, 2009). While the Landsat archive only extends back 43 years, measuring snow flux over that time period can provide significant insight into ENSO, as several strong El Niños and La Niñas have occurred during that time span.

Appendix E. Diatoms on the ice Coropuna

Diatoms are aquatic organisms that produce a glass casing, are very small, and can easily be blown by wind. Thus, diatoms inhabit virtually every body of water on earth. We would also expect that diatoms could be preserved in ice, given nearby water sources where diatoms can grow. Scanning electron microscope examination of ice from the Quelccaya ice core suggests that diatoms are preserved in glacial ice (Fritz et al,

2015). This allows researchers to answer questions about diatom dispersal, climate, and

37 wind patterns (Fritz et al, 2015). Diatoms also have been found in the ice cap at Coropuna

(Fritz, personal communications). However, it is unclear if the source of diatoms is local or regional.

Methods of diatom collection

Diatom samples were collected from bodies of standing water around Coropuna using a whirl pack bag (Figure 19). These samples are currently have been sent to

University of Nebraska where they are being examined for diatom species. This will allow us to better understand microbial dispersal mechanisms and sourcing. This research is ongoing.

Appendix F. Methods of glacier classification via remote sensing

Introduction to glacial remote sensing

The Global Land Ice Monitoring from Space (GLIMS) project asks that regional coordinators set the threshold limit for specific geographic areas based on their experience (Raup et al, 2007). Obviously no threshold value for any band, or combinations of bands, is universally accepted across all study areas. Previous workers have used different threshold values for images of the same site (Silverio and Jaquet,

2012; Veettil et al, 2015). This is not only clumsy given large data sets but also leads to inconsistency and low reproducibility of results.

Several techniques are commonly used to classify ice in satellite imagery including band math, spectral thresholds, digitization, and supervised/unsupervised classifications. Each of these techniques has limitations and drawbacks. Hand digitization not only takes the most time, but also can lead to inconsistency among digitizers,

38 especially when using low-resolution imagery, and has the lowest reproducibility. High- resolution imagery (meter scale) can be effectively used for digitization, although use of these images typically has a higher cost of time and money because they take more time to digitize than low resolution images. Band math and threshold calculations can be effective, especially when an algorithm is developed that can call and analyze images without human intervention. The difficult decision the user must make is where to set thresholds in the classification. These thresholds will vary for each location (although numbers should be similar) and will vary across satellites. Thresholds must be set for each type of band math to determine what will be classified as ice and snow. The number that the user chooses will significantly affect the results from the algorithm, thus it is imperative that care is taken when selecting threshold values for classification.

Clouds can be difficult to distinguish from fresh snow. Thus, the most effective way to distinguish clouds is by a visual textural assessment. It is very difficult if not impossible to do with this with spectral values alone (Dozier, 1989). By visually assessing each Landsat scene, only those that are cloud free can be selected, but also those that do not have shadows from a low sun angle. This allows for the most accurate satellite images to be used for analysis.

Digitization of ice outlines

On screen digitization is time consuming and biased based on user visual interpretation. However, digitization is frequently regarded as the most accurate way to delineate glacial extent (Albert, 2002). Human users are better at seeing textures than computers (i.e. distinguishing clouds from ice), but human users are not as good at recognizing changes in spectral values from RGB or other renderings. Thus, human

39 digitizers can effectively classify glacial area; but, the results provide low reproducibility and are highly time consuming. A computer, with human input (i.e. giving it classification values), is more effective at finding ice areas based on spectral values than humans. This not only provides a rigorous and reproducible method for other researchers, but will classify ice extent based on individual pixels. Human digitizers have the tendency to split pixels in two portions when delineating extent. However, this is misleading because a pixel on the satellite image is the average value for that area on the ground at a point in time. Thus, there is not possible to distinguish what percentage of a pixel is made up of mixed land uses.

High-resolution satellite images (<3 m) provide a precise dataset to measure glacier areas from digitization. These images are typically expensive to obtain and may not provide high temporal resolution. However, available Landsat scenes now provide a free and temporally extensive (1972 to present) way to monitor global environmental change, even though they have low resolution (30+ m). While high resolution images may provide precise means of measurement, Landsat scenes are not a good choice for digitization due to their low spatial resolution. Early Landsat satellites provide little information for spectral classifications because they lack infrared bands. Thus, early

Landsat images must be hand digitized for area classification even though they have poor spatial resolution (60 m). This leads to high levels of uncertainty in 1970s and 1980s scenes.

The Landsat Multi Spectral Scanner (MSS) satellites are not equipped with infrared sensors, as these satellites where in orbit from 1972 to 1992 when the technology was in its infancy. Thus, MSS images must be digitized by hand because multiple

40 materials in the image are highly reflective in the visible color range, requiring the user to make texture-based decisions. For example, materials interpreted to be hydrothermally altered sediments, to the east of Coropuna, are highly reflective in RGB values, and have the same appearance as snow and ice. This can cause errors in area calculations when using automated methods to find snow and ice; thus, user interpretation of textures and location is critical.

Normalized Difference Snow Index

Previous workers have found the NDSI to be one of the few reliable methods for glacial ice classification (Hall et al, 1995; Albert, 2002), even though glacial mapping methods continue to improve, especially for debris covered glaciers (Smith et al, 2015).

When infrared bands are available, such as Landsat 4, 5, 6, and 8, the NDSI is an effective method because it relies on the temperature and reflectivity of the material to accurately identify snow and ice. The NDSI is calculated by using bands 2 and 5

(equation 1).

For Landsat thematic mapper (TM; Landsats 4 and 5) and enhanced thematic mapper (ETM; Landsat 7), band 2 records light in the wavelength from 0.52 to 0.60 microns. On the electromagnetic spectrum this is visible lights we see as green. For

Landsat TM and ETM band 5 is 1.55-1.75 microns, or high wavelength near-infrared light, which is not detectable by human eyes. By setting a site specific threshold value in conjunction with the NDSI, snow classification can be conducted with higher precision.

Band 4 and Band 5 classification

The band 4 to band 5 ratio has been successfully utilized by several authors to calculate glacierized area (Albert, 2002; Paul, 2000; Paul, 2002; Bayr, Hall and

41

Kovakick, 1994). Paul (2000) found that the band 4/band 5 was the most accurate glacial classification system. For Landsat TM and ETM, band 4 records light in the wavelength from 0.76 to 0.90 microns. On the electromagnetic spectrum this is low wavelength near- infrared light. By setting a site specific threshold value in conjunction with the band 4 to band 5 ratio, snow classification can be acquired with high precision. In utilizing the near-infrared bands, this method relies on discerning ice from other materials based on temperature.

Methods of classification

Remote sensors have developed hundreds of methods to classify terrain. Some methods, like digitization by hand, take hours while others, such as unsupervised classifications, can be automated to only take seconds to minutes. The four broad categories of classification are: 1) Spectral values and thresholds, 2) unsupervised classifications, 3) supervised classifications, and 4) fuzzy classification.

The NDSI and band 4 to band 5 ratio are two examples of spectral value and threshold methods. These methods utilize pixel specific values or ratios to classify materials based on any number of bands into any specific number of categories. These methods have been effectively used for glacial classification as they allow for high levels of customization and specificity. These methods are highly reproducible as long as values and band math formulas are shared.

Unsupervised classification typically requires software like ENVI or other remote sensing programs. These methods of classification, such as ISODATA, utilize statistics and clustering to separate the data into different groups based on spectral values (Albert,

2002). The algorithm groups like pixels together and creates classes. While this can be

42 effective, it may not work when there is large variance in pixel values of one class, such as shadows, which decrease reflectance on only some pixels. Because this classification method is not a ratio, it can have difficulties grouping these classes when pixel values vary non-systematically instead of uniformly or systematically. Unsupervised classification techniques can be highly sensitive to changes in inputs set by the user, such as the number of iterations.

Supervised classification techniques also require software such as ENVI. An example of a supervised classification technique is the spectral angle mapper and maximum likelihood. In a supervised classification algorithm the user predefines areas that are known to be a certain material such as snow, ice, lava, vegetation, water, or bedrock. Upon receiving this input, the algorithm is able to find materials that have similar pixel values. In this way, the user can define the number, type, and specificity of groups. However, with the high amount of flexibility comes the inability to readily reproduce results. These techniques will vary widely based on the defined training sets given by the user.

In fuzzy classification, such as linear spectral unmixing, pixels are considered to have multiple types of land cover. This is a good assumption for some pixels and satellites, as some spatial resolutions are still greater than 30 meters. Pixels are classified based on estimated values of various land uses using fuzzy logic. For glacial classification, which is binary (e.g. ice, not ice) this obviously creates problems. When attempting to classify ice versus not ice, most pixels in the scene will not have a mixture of land cover. Fuzzy classification techniques have been shown to be the least accurate with an error of ~10 to 30 percent (Albert, 2002).

43

References Cited

Adams, R.M., Chen, C.C., McCarl, B.A., and Weiher, R.F., 1999, The economic consequences of ENSO events for agriculture: CLIMATE RESEARCH, v. 13, p. 165- 172.

Albert, T., Klein, A., Kincaid, J., Huggel, C., Racoviteanu, A., Arnaud, Y., Silverio, W., and Ceballos, J., 2014, Remote sensing of rapidly deminishing tropical glaciers in the northern Andes, in Kargel, J.S., Leonard, G.J., Bishop, M.P., Kääb, A. and Raup, B.H., eds., Heidelberg; New York; London, Springer, p. 609.

Albert, T.H., 2002, Evaluation of remote sensing techniques for ice-area classification applied to the tropical Quelccaya ice cap, Peru: Polar Geography [1995], v. 26, p. 210- 226.

Ames, A., Evangelista, P., Valverde, A., and Zúñiga, J., 1988, Inventario de Glaciares del Perú, in Inventario de Glaciares del Perú: Huaraz, Peru, Consejo Nacional de Ciencia y Tecnologia, p. 1.

Bahr, D.B., Pfeffer, W.T., and Kaser, G., 2015, A review of volume-area scaling of glaciers: Reviews of Geophysics, v. 53, p. 95-140, doi: 10.1002/2014RG000470.

Baker, P.A., and Fritz, S.C., 2015, Invited review: Nature and causes of Quaternary climate variation of tropical South America: Quaternary Science Reviews, v. 124, p. 31- 47, doi: 10.1016/j.quascirev.2015.06.011.

Barba, D., Robin, C., Samaniego, P., and Eissen, J., 2008, Holocene recurrent explosive activity at Chimborazo Volcano (Ecuador): Journal of Volcanology and Geothermal Research, v. 176, p. 27-35, doi: 10.1016/j.jvolgeores.2008.05.004.

Barnes, R., Williams, M., Parman, J., Hill, K., and Caine, N., 2014, Thawing glacial and permafrost features contribute to nitrogen export from Green Lakes Valley, Colorado Front Range, USA: Biogeochemistry, v. 117, p. 413-430, doi: 10.1007/s10533-013-9886- 5.

Barnett, T.P., Adam, J.C., and Lettenmaier, D.P., 2005, Potential impacts of a warming climate on water availability in snow-dominated regions: Nature [London], v. 438, p. 303-309, doi: 10.1038/nature04141.

Baron, J.S., Schmidt, T.M., and Hartman, M.D., 2009, Climate-induced changes in high elevation stream nitrate dynamics: Global Change Biology, .

Bayr, K.J., Hall, D.K., and Kovakick, W.M., 1994, Observations on Glaciers in the Eastern Austrian Alps using Satellite Data: International Journal of Remote Sensing, v. 15, p. 1733-1742.

44

Bingham, H., 1922, Inca land.

Birkos, E.H., 2009, Controls on stable oxygen isotope concentrations in Coropuna and Quelccaya Peruvian ice cores over the last 200 years [Ph.D. thesis]: United States, .

Bradley, R.S., Vuille, M., Diaz, H.F., and Vergara, W., 2006, Threats to Water Supplies in the Tropical Andes: Science, p. 1755.

Broad, K., Pfaff, A., and Glantz, M.H., 2002, Effective and equitable dissemination of seasonal-to-interannual climate forecasts: Policy implications from the Peruvian fishery during El Nino 1997-98: Climatic Change, v. 54, p. 415-438.

Bromley, G.R.M., Hall, B.L., Schaefer, J.M., Winckler, G., Todd, C.E., and Rademaker, K.M., 2011, Glacier fluctuations in the southern Peruvian Andes during the late-glacial period, constrained with cosmogenic (super 3) He: JQS.Journal of Quaternary Science, v. 26, p. 37-43, doi: 10.1002/jqs.1424.

Bromley, G.R.M., Schaefer, J.M., Winckler, G., Hall, B.L., Todd, C.E., and Rademaker, K.M., 2009, Relative timing of last glacial maximum and late-glacial events in the central tropical Andes: Quaternary Science Reviews, v. 28, p. 2514-2526, doi: 10.1016/j.quascirev.2009.05.012.

Buytaert, W., Cuesta-Camacho, F., and Tobón, C., 2011, Potential impacts of climate change on the environmental services of humid tropical alpine regions: Global Ecology & Biogeography, v. 20, p. 19-33, doi: 10.1111/j.1466-8238.2010.00585.x.

Carey, M., French, A., and O'Brien, E., 2012, Unintended effects of technology on climate change adaptation : an historical analysis of water conflicts below Andean Glaciers (English): J.Hist.Geogr., v. 38, p. 181-191.

Casassa, G., Rivera, A., Haeberli, W., Jones, G., Kaser, G., Ribstein, P., Rivera, A., and Schneider, C., 2007, Current status of Andean glaciers: Global and Planetary Change, v. 59, p. 1-9.

Casimiro, W., and Espinoza, J., 2014, Impactos de El Niño y La Niña en las lluvias del Perú (1965-2007): Revista Brasileira De Meteorologia, v. 29, p. 171.

Clavero R., J.E., Sparks, S.J., Polanco, E., and Pringle, M.S., 2004, Evolution of Parinacota Volcano, Central Andes, northern Chile: Revista Geologica De Chile, v. 31, p. 317-347.

Collins, M., An, S.I., Cai, W.J., Ganachaud, A., Guilyardi, E., Jin, F.F., Jochum, M., Lengaigne, M., Power, S., Timmermann, A., Vecchi, G., and Wittenberg, A., 2010, The impact of global warming on the tropical Pacific ocean and El Nino: NATURE GEOSCIENCE, v. 3, p. 391-397.

45 de Silva, S.L., and Francis, P.W., 1990, Potentially active volcanoes of Peru; observations using Landsat thematic mapper and space shuttle imagery: Bulletin of Volcanology, v. 52, p. 286-301.

Delgado-Granados, H., Miranda, P.J., Núñez, G.C., Alzate, B.P., Mothes, P., Roa, H.M., Cáceres-Correa, B.E., and Ramos, J.C., 2015, Chapter 17. Hazards at Ice-Clad Volcanoes: phenomena, processes, and examples from Mexioc, Columbia, Ecuador, and Chile. in Haeberli, W. and Whiteman, C., eds., Snow and Ice-related Hazards, Risks and Disasters: Amsterdam, Elsevier, p. 607.

Deutsch, C.A., Tewksbury, J.J., Huey, R.B., Sheldon, K.S., Ghalambor, C.K., Haak, D.C., and Martin, P.R., 2008, Impacts of climate warming on terrestrial ectotherms across latitude: Proceedings of the National Academy of Sciences of the United States of America, v. 105, p. 6668-6672, doi: 10.1073/pnas.0709472105.

Dozier, J., 1989, Spectral signature of alpine snow cover from the Landsat Thematic Mapper: Remote Sensing of Environment (USA), .

Duran Alarcon, C., Gevaert, C.M., Mattar, C., Jimenez Munoz, J.C., Pasapera Gonzales, J.J., Sobrino, J.A., Silvia Vidal, Y., Fashe Raymundo, O., Chavez Espiritu, T.W., and Santillan Portilla, N., 2015, Recent trends on glacier area retreat over the group of nevados Caullaraju-Pastoruri (Cordillera Blanca, Peru) using Landsat imagery: Journal of South American Earth Sciences, v. 59, p. 19-26, doi: 10.1016/j.jsames.2015.01.006.

Forget, M.E., Thouret, J.C., Kuentz, A., and Fontugne, M., 2008, Héritages glaciaires, périglaciaires et évolution récente : le cas du Nevado Coropuna (Andes centrales, sud du Pérou) (French): Géomorphologie (Paris), p. 113-132.

Fritz, S.C., Brinson, B.E., Billups, W.E., and Thompson, L.G., 2015, Diatoms at >5000 meters in the Quelccaya Summit Dome Glacier, Peru: Arctic, Antarctic & Alpine Research, v. 47, p. 369-374, doi: 10.1657/AAAR0014-075.

Garreaud, R.D., and Aceituno, P., 2001, Interannual Rainfall Variability over the South American Altiplano: Journal of Climate, v. 14, p. 2779.

Gilbert, A., Wagnon, P., Vincent, C., Ginot, P., and Funk, M., 2010, Atmospheric warming at a high-elevation tropical site revealed by englacial temperatures at Illimani, Bolivia (6340 m above sea level, 16 degrees S, 67 degrees W): JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, v. 115, .

Global Volcanism Program, 2013, Volcanoes of the World: Smithsonian Institute, .

Hall, D.K., Riggs, G.A., and Salomonson, V.V., 1995, Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data: Remote Sensing of Environment, .

46

Hardy, D.R., and Vuille, M., 1998, Annual and Daily Meteorological Cycles at High Altitude on a Tropical Mountain: Bulletin of the American Meteorological Society, v. 79, p. 1899.

Instituto Nacional de Estadistica e Informatico, 2007, Censos Nacionales 2007 XI Poblaciíon y Vi de Vivienda: (http://censos.inei.gob.pe/cpv2007/tabulados/#.

IPCC, 2014, Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. : .

Jaksic, F.M., 2001, Ecological Effects of El Niño in Terrestrial Ecosystems of Western South America: Ecography, p. 241.

Jomelli, V., Favier, V., Rabatel, A., Brunstein, D., Hoffmann, G., and Francou, B., 2009, Fluctuations of glaciers in the tropical Andes over the last millennium and palaeoclimatic implications; a review: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 281, p. 269-282, doi: 10.1016/j.palaeo.2008.10.033.

Kargel, J.S., 2014, Scientific and public perceptions about the importance of fluctuations in glaciers and ice sheets, in Kargel, J.S., ed., Global land ice measurements from space: Berlin, Springer-Verlag, p. xi-xxvii.

Kaser, G., 1999, A review of the modern fluctuations of tropical glaciers: Global and Planetary Change, v. 22, p. 93-103.

Kuentz, A., Ledru, M., and Thouret, J., 2012, Environmental changes in the highlands of the western Andean Cordillera, southern Peru, during the Holocene: Holocene, v. 22, p. 1215-1226, doi: 10.1177/0959683611409772.

Kulkarni, A.V., Bahuguna, I.M., Rathore, B.P., Singh, S.K., Randhawa, S.S., Sood, R.K., and Dhar, S., 2007, Glacial retreat in Himalaya using Indian Remote Sensing satellite data: Current Science (00113891), v. 92, p. 69-74.

Lasage, R., Muis, S., Sardella, C., van Drunen, M., Verburg, P.H., and Aerts, J., 2015, A Stepwise, Participatory Approach to Design and Implement Community Based Adaptation to Drought in the Peruvian Andes: SUSTAINABILITY, v. 7, p. 1742-1773.

Magrin, G., Marengo, J., Boulanger, J., Buckeridge, M., Castellanos, E., Poveda, G., Scarano, F., and Vicuña, S., 2014, Central and South America. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel,

47

A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1499-1566.

Major, J.J., and Newhall, C.G., 1989, Snow and ice perturbation during historical volcanic eruptions and the formation of lahars and floods; a global review: Bulletin of Volcanology, v. 52, p. 1-27.

Ministerio del Ambiente del Perú, 2010, El Perú y el Cambio Climático Segunda Comunicación Nacional del Perú: a la Convención Marco de las Naciones Unidas sobre Cambio Climático 2010: Impresiones & Ediciones Aguilar S.A.C., .

Mothes, P.A., Hall, M.L., and Janda, R.J., 1998, The enormous Chillos Valley Lahar; an ash-flow-generated debris flow from Cotopaxi Volcano, Ecuador: Bulletin of Volcanology, v. 59, p. 233-244.

NASA, 2015, El Niño: (http://science.nasa.gov/earth-science/oceanography/ocean-earth- system/el-nino/.

National Aeronautics and Space Administration, 1998, Landsat 7 Science Data Users Handbook: .

NOAA, 2015, Southern Oscillation Index (SOI): (https://www.ncdc.noaa.gov/teleconnections/enso/indicators/soi/.

Nunez-Juarez, S., and Valenzuela-Ortiz, G., 2001, Mapa Preliminar de Amenaza Volcanica Potencial del Nevado Coropuna; Estudios de Riesgos Volcanicos en el Sur del Peru: Boletin - Instituto De Geologia Y Mineria, Serie C: Geodinamica E Ingenieria Geologica, v. 25, p. 103-103.

Paul, F., 2002, Changes in glacier area in Tyrol, Austria, between 1969 and 1992 derived from Landsat 5 thematic mapper and Austrian Glacier Inventory data: International Journal of Remote Sensing, v. 23, p. 787-799, doi: 10.1080/01431160110070708.

Paul, F., 2000, Evaluation of different methods for glacier mapping using Landsat TM: EARSeL eProceedings, v. 1, p. 239-245.

Paul, F., Kaeaeb, A., Maisch, M., Kellenberger, T., and Haeberli, W., 2004, Rapid disintegration of Alpine glaciers observed with satellite data: Geophysical Research Letters, v. 31, , doi: 10.1029/2004GL020816.

Peduzzi, P., Herold, C., and Silverio, W., 2010, Assessing high altitude glacier thickness, volume and area changes using field, GIS and remote sensing techniques: the case of Nevado Coropuna (Peru): The Cryosphere, , doi: 10.5194/tc-4-313-2010.

Perez, C., Nicklin, C., Dangles, O., Vanek, S., Sherwood, S.G., Halloy, S., Garrett, K.A., and Forbes, G.A., 2010, Climate Change in the High Andes:implications and adaptation

48 strategies for small-scale farmers: The International Journal of Environmental, Cultural, Economic and Social Sustainability, .

Pierson, T.C., Janda, R.J., Thouret, J., and Borrero, C.A., 1990, Perturbation and melting of snow and ice by the 13 November 1985 eruption of Nevado del Ruiz, Colombia, and consequent mobilization, flow and deposition of lahars: Journal of Volcanology and Geothermal Research, v. 41, p. 17-66.

Rabatel, A., Francou, B., Soruco, A., Gomez, J., Caceres, B., Ceballos, J.L., Basantes, R., Vuille, M., Sicart, J.E., Huggel, C., Scheel, M., Lejeune, Y., Arnaud, Y., Collet, M., Condom, T., Consoli, G., Favier, V., Jomelli, V., Galarraga, R., Ginot, P., Maisincho, L., Mendoza, J., Menegoz, M., Ramirez, E., Ribstein, P., Suarez, W., Villacis, M., and Wagnon, P., 2013, Current state of glaciers in the tropical Andes; a multi-century perspective on glacier evolution and climate change: Cryosphere [Online], v. 7, p. 81- 102.

Racoviteanu, A.E., Arnaud, Y., Williams, M.W., and Ordonez, J., 2008, Decadal changes in glacier parameters in Cordillera Blanca, Peru, derived from remote sensing: Journal of Glaciology, v. 54, p. 499-510, doi: 10.3189/002214308785836922.

Racoviteanu, A.E., Manley, W.F., Arnaud, Y., and Williams, M.W., 2007, Evaluating digital elevation models for glaciologic applications; an example from Nevado Coropuna, Peruvian Andes: Global and Planetary Change, v. 59, p. 110-125, doi: 10.1016/j.gloplacha.2006.11.036.

Raup, B., Kaab, A., Kargel, J.S., Bishop, M.P., Hamilton, G.S., Lee, E., Paul, F., Rau, F., Soltesz, D., Khalsa, S.J.S., Beedle, M., and Helm, C., 2007, Remote sensing and GIS technology in the Global Land Ice Measurements from Space (GLIMS) Project: Computers & Geosciences, v. 33, p. 104-125, doi: 10.1016/j.cageo.2006.05.015.

Rivera, A., Benham, T., Casassa, G., Bamber, J.L., and Dowdeswell, J.A., 2007, Ice elevation and areal changes of glaciers from the Northern Patagonia Icefield, Chile: Global and Planetary Change, v. 59, p. 126-137, doi: 10.1016/j.gloplacha.2006.11.037.

Rivera, M., Thouret, J., Marino, J., Berolatti, R., and Fuentes, J., 2010, Characteristics and management of the 2006-2008 volcanic crisis at the Ubinas Volcano (Peru): Journal of Volcanology and Geothermal Research, v. 198, p. 19-34, doi: 10.1016/j.jvolgeores.2010.07.020.

Salzmann, N., Huggel, C., Rohrer, M., Silverio, W., Mark, B.G., Burns, P., and Portocarrero, C., 2013, Glacier changes and climate trends derived from multiple sources in the data scarce Cordillera Vilcanota region, southern Peruvian Andes: Cryosphere [Online], v. 7, p. 103-118.

Salzmann, N., Huggel, C., Calanca, P., Diaz, A., Jonas, T., Jurt, C., Konzelmann, T., Lagos, P., Rohrer, M., Silverio, W., and Zappa, M., 2009, Integrated assessment and

49 adaptation to climate change impacts in the Peruvian Andes: Advances in Geosciences, v. 22, p. 35-39.

Samaniego, P., Monzier, M., Robin, C., and Hall, M.L., 1998, Late Holocene eruptive activity at Nevado Cayambe Volcano, Ecuador: Bulletin of Volcanology, v. 59, p. 451- 459.

Saros, J.E., Rose, K.C., Clow, D.W., Stephens, V.C., Nurse, A.B., Arnett, H.A., Stone, J.R., Williamson, C.E., and Wolfe, A.P., 2010, Melting Alpine Glaciers Enrich High- Elevation Lakes with Reactive Nitrogen: Environmental Science & Technology, v. 44, p. 4891-4896.

Schmidt, P., Lund, B., Hieronymus, C., Maclennan, J., Arnadottir, T., and Pagli, C., 2013, Effects of present-day deglaciation in Iceland on mantle melt production rates: JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, v. 118, p. 3366-3379.

Siebe, C., Abrams, M., Macias, J.L., and Obenholzner, J., 1996, Repeated volcanic disasters in Prehispanic time at Popocatepetl, central Mexico; past key to the future? Geology [Boulder], v. 24, p. 399-402, doi: 10.1130/0091- 7613(1996)024<0399:RVDIPT>2.3.CO;2.

Sigmundsson, F., Pinel, V., Lund, B., Albino, F., Pagli, C., Geirsson, H., and Sturkell, E., 2010, Climate effects on volcanism: influence on magmatic systems of loading and unloading from ice mass variations, with examples from Iceland: Philosophical Transactions: Mathematical, Physical and Engineering Sciences, p. 2519.

Silverio, W., and Jaquet, J., 2012, Multi-temporal and multi-source cartography of the glacial cover of Nevado Coropuna (Arequipa, Peru) between 1955 and 2003: International Journal of Remote Sensing, v. 33, p. 5876-5888.

Silverio, W., and Jaquet, J., 2005, Glacial cover mapping (1987-1996) of the Cordillera Blanca (Peru) using satellite imagery: Remote Sensing of Environment, v. 95, p. 342- 350, doi: 10.1016/j.rse.2004.12.012.

Slemmons, K., and Saros, J.E., 2012, Implications of nitrogen-rich glacial meltwater for phytoplankton diversity and productivity in alpine lakes: Limnology and Oceanography, v. 57, p. 1651-1663.

Slemmons, K., Saros, J.E., and Simon, K., 2013, The influence of glacial meltwater on alpine aquatic ecosystems: a review: ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, v. 15, p. 1794-1806.

Smith, T., Bookhagen, B., and Cannon, F., 2015, Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia: The Cryosphere, v. 9, p. 1747, doi: 10.5194/tc-9-1747-2015.

50

Steinmueller, K., 2001, Modern hot springs in the southern volcanic cordillera of Peru and their relationship to Neogene epithermal precious-metal deposits: Journal of South American Earth Sciences, v. 14, p. 377-385.

Stewart, C., Johnston, D.M., Leonard, G.S., Horwell, C.J., Thordarson, T., and Cronin, S.J., 2006, Contamination of water supplies by volcanic ashfall; a literature review and simple impact modelling: Journal of Volcanology and Geothermal Research, v. 158, p. 296-306, doi: 10.1016/j.jvolgeores.2006.07.002.

Thouret, J., Davila, J., and Eissen, J., 1999, Largest explosive eruption in historical times in the Andes at Huaynaputina Volcano, A.D. 1600, southern Peru: Geology [Boulder], v. 27, p. 435-438, doi: 10.1130/0091-7613(1999)027<0435:LEEIHT>2.3.CO;2.

Tsonis, A.A., Elsner, J.B., Hunt, A.G., and Jagger, T.H., 2005, Unfolding the relation between global temperature and ENSO: Geophysical Research Letters, v. 32, .

Úbeda, J., 2013, Glacial Chronologies from NE Sector of Nevado Coropuna (Peru): Geomorphological and Paleoclimate Implications: Boletín De La Asociación De Geógrafos Españoles, v. 62, p. 473.

Úbeda, J., 2011, El Impacto del Cambio Climático en los Glaciares del Complejo Volcánico Nevado Coropuna, (Cordillera Occidental de los Andes Centrales) [Ph.D. thesis]: Madrid, Spain, Universidad Complutense de Madrid, .

Úbeda, J., Palacios, D., and Vázquez-Selém, L., 2012, Glacial and volcanic evolution on Nevado Coropuna (Tropical Andes) based on cosmogenic 36Cl surface exposure dating: Geophysical Research Abstracts, v. 14, .

United States Energy Information Administration, 2012, 2012, Total Electricity Net Generation: (http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=2&pid=2&aid=12.

USGS, 2013, SLC-off Products: Background: (http://landsat.usgs.gov/products_slcoffbackground.php.

Veettil, B., Bremer, U., Florêncio de Souza, S., Maier, E., and Simones, J., 2015, Variations in annual snowline and area of an ice-covered stratovolcano in the Cordillera Ampato, Peru, using remote sensing data (1986-2014): Geocarto International, , doi: 10.1080/10106049.2015.1059902.

Vergara, W., Deeb, A.M., Valencia, A.M., Bradley, R.S., Francou, B., Zarzar, A., Gruenwaldt, A., and Haeussling, S.M., 2007, Economic impacts of rapid glacier retreat in the Andes: EOS, Transactions, American Geophysical Union, v. 88, p. 261.

51

Vuille, M., Francou, B., Wagnon, P., Juen, I., Kaser, G., Mark, B.G., and Bradley, R.S., 2008, Climate change and tropical Andean glaciers; past, present and future: Earth- Science Reviews, v. 89, p. 79-96, doi: 10.1016/j.earscirev.2008.04.002.

Wolter, K., and Timlin, M., 2015, Multivariate ENSO Index (MEI): (http://www.esrl.noaa.gov/psd/enso/mei/#data.

Worni, R., Huggel, C., Stoffel, M., and Pulgarin, B., 2012, Challenges of modeling current very large lahars at Nevado del Huila Volcano, Colombia: Bulletin of Volcanology, v. 74, p. 309-324, doi: 10.1007/s00445-011-0522-8.

52

Tables Table 3. Landsat Scene ID and ice area NDSI Resolution Date Digitized Landsat Scene ID area (m) LM20030711980347AAA03 12/12/1980 58.0 - 60 LT40040711987339XXX03 12/5/1987 - 52.9 30 LT50040711988334CUB00 11/29/1988 - 52.2 30 LT50040711991278CUB00 10/05/91 - 51.9 30 LT50040711992281CUB00 10/07/92 - 50.3 30 LT50040711993363CUB00 12/02/96 - 51.4 30 LT50040711995273CUB00 09/30/95 - 50.1 30 LT50040711996308CUB00 11/04/96 - 49.0 30 LT50040711998345CUB00 12/11/1998 - 48.0 30 LT50040712002004COA00 01/04/02 - 49.2 30 LT50040712003311CUB00 11/07/03 - 48.6 30 LT50040712004298CUB00 10/28/04 - 48.7 30 LE70040712005340COA00 12/6/2005 - 47.5 30 LE70040712006343COA00 12/9/2006 - 47.5 30 LE70040712007346COA00 12/12/2007 - 46.4 30 LT50040712008325CUB00 11/20/2008 - 45.2 30 LT50040712010298CUB00 10/25/2010 - 43.7 30 LE70040712011309ASN00 11/05/11 - 44.9 30 LE70040712013330CUB00 11/26/2013 - 44.8 30 LE70040712014317CUB00 11/13/2014 - 44.1 30

53

Table 4. Nitrogen Samples from Coropuna ID Date Northing Easting Elevation N concentration Sample test date Collected m m m mg/l 15PERWKN1 6/10/2015 747001 8280438 5216 0.00 6/10/2015 15PERWKN2 6/15/2015 749822 8259031 3844 0.00 6/17/2015 15PERWKN3 6/15/2015 765250 8260619 2190 0.64 6/18/2015 15PERWKN4 6/16/2015 756590 8291492 4571 0.00 6/18/2015 15PERWKN5 6/16/2015 753158 8292424 4487 0.00 6/17/2015 15PERWKN6 6/18/2015 741377 8270659 4446 0.00 6/21/2015

54

Figures

Figure 1. Location maps showing tropical glaciers in Central Peru. Nevado Coropuna ice cap (15.56°S, 72.62°N; 6,425 m) is the largest hosted by a volcano in the tropics, and one of the ten biggest ice masses in the tropics. (Elevation data from SRTM through the

USGS-EROS, NASA, and NGA. Country outline form DeLorme Publishing Company,

Inc.)

55

Figure 2. Little Ice Age moraine on the western flank of Coropuna looking to the east.

The peak of Coropuna is on the right of this photograph.

56

Figure 3. ENSO and snow area since 1987. The NDSI was used to calculate the maximum snow area for 258 Landsat 4, 5, and 7 scenes. Snow minimums interpreted as exposure of glacial ice are shown in darker blue. The MEI is taken from NOAA (2015).

57

Figure 4. Model builder in ArcGIS to process Landsat scenes.

58

Figure 5. The carapace on the western flank of Coropuna. A)The 3-D model constructed using Photoscan. B) Photograph taken as an input to the Photoscan model.

59

Figure 6. Comparison of ice-cover estimate as a function of month. (A) Silverio and

Jaquet, 2012 found that Coropuna had a glacierized area of 96.4 ± 15 km2 on August 1,

1985. Using the same Landsat Scene we were able to replicate the digitized outline and found the area to be 96.3 km2. (B) Using a December 5, 1987 Landsat Scene we found that Coropuna had a glacierized area of 52.9 km2. (C) Composite image is shown with

December 1987 dashed outline and August 1985 solid outline, this represents a difference of 45% change in area.

60

Figure 7. Landsat Satellite images showing ice-surface area change at Coropuna over the

34 year period. A. Image from Landsat 2 taken December 12, 1980 with estimated area of

58.0 km2. B. Image from Landsat 7 taken November 13, 2014 with estimated area of 44.1 km2. C. Schematic map highlighting total net change in ice aerial extent (24%) from 1980 to 2014.

61

Figure 8. NDSI error assessment. 1) NDSI with a 5% variation in NDSI values for all

Landsat 4, 5, and 7 scenes used (orange and yellow points). The band 4 to band 5 ratio is calculated for each image as a comparison technique. Finally, I show the maximum possible error to be the area of the pixels around the perimeter of each Landsat scene, shown as whiskers.

62

Figure 9. Ice areas using two classification schemes, band 4/band 5 and NDSI for all

Landsat 4, 5, and 7 scenes used. These methods show consistency in area measurements.

63

A

B

Figure 10. Comparison of Hiram Bingham photograph to modern. A) Hiram Bingham photograph from 1911. B) Photograph retaken at same spot during June, 2015 field season. The red areas represent places of visible change in comparison with modern day photographs.

64

A

B

Figure 11. Comparison of Hiram Bingham photograph to modern. A) Hiram Bingham photograph from 1911. B) Photograph retaken at same spot during June, 2015 field season. The red areas represent places of visible change in comparison with modern day photographs.

65

Figure 12. Satellite image with approximate location where Hiram Bingham took the

October 1911 photographs and where images were retaken during the June 2015 field season.

66

Figure 13. ENSO and maximum snow area. A) All maximum snow data points with corresponding annual average MEI values. B) Maximum snow area coded for time of year. No clear time of year pattern is present in the maximum snow area. C) Only those points that are “strong” ENSO signals are plotted as snow area vs. MEI. The mean maximum snow area when the MEI is less than 0.5 (95.3 km2) is significantly higher than that for mean snow area when MEI is greater than 0.5 (74.7 km2) (t-Test, P = 0.0486).

67

Figure 14. Glacierized area of Nevado Coropuna, Peru. Silverio and Jaquet (2012) and

Peduzzi et. al. (2010) independently found that Coropuna lost 1.4 km2yr-1 glacierized area since 1955. We found that Coropuna is losing 0.41 km2/yr-1. The error of the this study is contained within the points on the graph.

68

Figure 15. Sabancaya depicted during December 2014 and June 2015 during the current eruption of volcanic gases. In the June 2015 image, Ampato Volcano is depicted to the right. The December image was taken from the town of Chivay to the east of Sabancaya.

The June image was taken from just south of Coropuna to the northwest of Sabancaya.

69

Figure 16. The Cordillera Ampato is make up of Coropuna, Sabancaya/Ampato, Sara

Sara, Solimana, and Firura volcanoes.

70

Figure 17. Nitrogen sample locations are shown with upstream watersheds. The two most northern sites come out of a lava flow, thus they are thought to be springs.

71

Figure 18. Algae on glacial ice. Photograph taken during June 2015 field season.

72

Figure 19. Diatom sample map with locations.

73