Modeling the Distribution of Mountain Permafrost in the Central Andes

MODELING THE DISTRIBUTION OF MOUNTAIN PERMAFROST

IN THE CENTRAL ANDES, SAN JUAN, ARGENTINA

Erika A.P. Schreiber

A thesis submitted to the Faculty of the University of Delaware in partial fulﬁllment of the requirements for the degree of Master of Science in Geography

Summer 2015

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ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 MODELING THE DISTRIBUTION OF MOUNTAIN PERMAFROST

IN THE CENTRAL ANDES, SAN JUAN, ARGENTINA

Erika A.P. Schreiber

Approved: Michael A. O’Neal, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee

Approved: Tracy L. DeLiberty, Ph.D. Chair of the Department of Geography

Approved: Mohsen Badiey, Ph.D. Acting Dean of the College of Earth, Ocean, and Environment

Approved: James G. Richards, Ph.D. Vice Provost for Graduate and Professional Education ACKNOWLEDGMENTS

I am immensely grateful for all of the support I have received throughout my two years at the University of Delaware. This thesis would not have been possible without a large number of people. First and foremost I would like to thank my advisor, Dr. Michael O’Neal, for the numerous research opportunities he has provided, as well as his invaluable guidance and support in developing and implementing this project. I would also like to thank Dr. Brian Hanson for his assistance throughout this process and for all he has taught me as a professor and mentor. I am grateful also for the feedback I received from Dr. Daniel Leathers and Dr. Andres Meglioli in completing this thesis. Furthermore I would like to extend my gratitude to Dr. Tracy DeLiberty, Dr. Dana Veron, and the rest of the Geography Department faculty for creating such a supportive environment in which to attain my degree. The data utilized in this study would have been unattainable if not for the efforts and expertise of Dr. Andres Meglioli. I am enormously thankful to him for giving me the opportunity to conduct research in such a remote environment and for his assistance throughout the ﬁeld campaigns. I am grateful, too, for the ﬁeld assistance of Daniel Hubacz and Renato Kane, as well as Renato’s unending advice and support. I would also like to acknowledge Tessa Montini for her commiserations as we completed our degrees together, along with the rest of the Geography graduate student community for their continuing friendship and en- couragement. Additionally, I want to express my deep gratitude to Jimmy Moore and all other friends I have found here in Delaware. I could not have asked for a better network of wonderful people and I am extremely thankful to have had the opportunity to meet and learn from so many individuals in and out of my department. Finally, I wish to recognize my parents and siblings for their constant support and inspiration; without them I would not have made it so far on this academic journey.

iii TABLE OF CONTENTS

LIST OF TABLES ...... vi LIST OF FIGURES ...... vii ABSTRACT ...... x

Chapter

1 INTRODUCTION ...... 1

2 STUDY AREA ...... 3

3 METHODS ...... 5

3.1 Ground Surface Temperature Measurements ...... 5 3.2 Temperature Data Reduction ...... 5 3.3 Model Radiation and Geographic Data for Sensor Sites ...... 6 3.4 Statistical Analyses of all Field, Radiation, and Geographic Data ..... 7

4 RESULTS ...... 9

4.1 Ground Surface Temperature Measurements ...... 9 4.2 Model Radiation and Geographic Data for Sensor Sites ...... 9 4.3 Spatial Modeling of Temperature Values ...... 10 4.4 Permafrost Distribution and Comparison with Observations ...... 11

5 DISCUSSION ...... 13

5.1 Accuracy of Models ...... 13

5.1.1 ASTER vs. SRTM ...... 14 5.1.2 MAGT vs. BTS ...... 15

5.2 Evaluating Permafrost as a Water Resource ...... 17 5.3 Future ...... 18

iv FIGURES ...... 19

TABLES ...... 52

REFERENCES ...... 59

Appendix

TEMPERATURE SENSOR RECORDS ...... 64

v LIST OF TABLES

1 Summary of changes to elevation values at sensor sites due to probe location adjustments into neighboring DEM cells ...... 52

2 Values of root mean square error and mean absolute error used to evaluate insolation model success in capturing the integrations of direct radiation values (W/m2) ...... 53

3 Summary of parameters used for the local radiation model ...... 54

4 Coefﬁcients and related statistics of multiple linear regression equations derived from MAGT values ...... 55

5 Coefﬁcients and related statistics of multiple linear regression equations derived from BTS values ...... 56

6 Percentage land area of modeled permafrost based on both DEMs and methods of calculation at each study site ...... 57

7 Lowest elevations reached by modeled permafrost extent ...... 58

vi LIST OF FIGURES

1 Locations of El Altar and Los Azules study sites in the high Andes of Argentina ...... 19

2 Marshy "vegas" located in a valley at one of the research ﬁeld sites ... 20

3 Examples of cryogenic landforms found at the ﬁeld sites ...... 21

4 Temperature sensor distribution across the El Altar study site ...... 22

5 Temperature sensor distribution across the Los Azules study site .... 23

6 Elevation and aspect distribution of temperature sensors across the El Altar and Los Azules ﬁeld sites ...... 24

7 Distribution of temperature sensors across El Altar and their usability for temperature modeling ...... 25

8 Distribution of temperature sensors across Los Azules and their usability for temperature modeling ...... 26

9 Periods of individual temperature sensor data collection plotted against each probe’s local elevation ...... 27

10 Comparisons of modeled and recorded average daily incoming solar radiation values at the three weather stations ...... 28

11 MAGT values (◦C) at El Altar site based on the multiple linear regression equation developed with the ASTER DEM ...... 29

12 MAGT values (◦C) at El Altar site based on the multiple linear regression equation developed with the SRTM DEM ...... 30

13 MAGT values (◦C) at Los Azules site based on the multiple linear regression equation developed with the ASTER DEM ...... 31

vii 14 MAGT values (◦C) at Los Azules site based on the multiple linear regression equation developed with the SRTM DEM ...... 32

15 BTS values (◦C) at El Altar site based on the multiple linear regression equation developed with the ASTER DEM ...... 33

16 BTS values (◦C) at El Altar site based on the multiple linear regression equation developed with the SRTM DEM ...... 34

17 BTS values (◦C) at Los Azules site based on the multiple linear regression equation developed with the ASTER DEM ...... 35

18 BTS values (◦C) at Los Azules site based on the multiple linear regression equation developed with the SRTM DEM ...... 36

19 Absolute differences in calculated temperature (◦C) at El Altar site based on the multiple linear regression equations developed using ASTER and SRTM information for BTS values ...... 37

20 Differences in elevation (m) between the ASTER and SRTM digital elevation models at the El Altar site ...... 38

21 Differences in elevation (m) between the ASTER and SRTM digital elevation models at the Los Azules site ...... 39

22 Distribution of permafrost across the landscape at El Altar based on MAGT thresholds using the ASTER DEM ...... 40

23 Distribution of permafrost across the landscape at El Altar based on MAGT thresholds using the SRTM DEM ...... 41

24 Distribution of permafrost across the landscape at Los Azules based on MAGT thresholds using the ASTER DEM ...... 42

25 Distribution of permafrost across the landscape at Los Azules based on MAGT thresholds using the SRTM DEM ...... 43

26 Distribution of permafrost across the landscape at El Altar based on BTS thresholds using the ASTER DEM ...... 44

27 Distribution of permafrost across the landscape at El Altar based on BTS thresholds using the SRTM DEM ...... 45

viii 28 Distribution of permafrost across the landscape at Los Azules based on BTS thresholds using the ASTER DEM ...... 46

29 Distribution of permafrost across the landscape at Los Azules based on BTS thresholds using the SRTM DEM ...... 47

30 Comparison of known El Altar ice locations with predicted permafrost extent based on MAGT ...... 48

31 Comparison of known El Altar ice locations with predicted permafrost extent based on BTS ...... 49

32 Example of how permafrost distribution differs between models based on ASTER and SRTM DEMs ...... 50

33 Extent of BTS permafrost zones beyond MAGT permafrost zones at El Altar site ...... 51

ix ABSTRACT

In the Dry Andes of San Juan, Argentina, interpreting the presence and climatic sig- nificance of many modern cryogenic landforms is an important step in understanding the potential impacts of mining operations. We investigate the distribution of permafrost at two Andes study sites (31◦ 05’ S and 31◦ 29’ S), where permafrost is protected by law as a periglacial feature and is of particular concern as a source of water for local peoples. Discontinuous permafrost zones are predicted based on thresholds of mean annual ground temperature (MAGT) and bottom temperature of winter snow (BTS). We employ a multiple linear regression model to determine these temperatures across the landscape using a dataset of topographic, geographic, and solar radiation information from 69 ground temperature sensor locations. Our results are tested against field knowledge and reveal that the classic BTS method is less accurate than MAGT in predicting the permafrost condition; however, veri- fying ice extent proves difficult in this landscape. Ultimately, the permafrost maps provided herein are a first step in better understanding water resources in this region.

x Chapter 1

INTRODUCTION

Ground ice in permafrost terrains of the Central Andes has received recent attention as a probable contributor to the summer meltwater essential to urban and agrarian commu- nities in downslope lands (Schrott, 1996; Favier et al., 2009; Gascoin et al., 2011; Arenson and Jakob, 2010). Winter precipitation in this region, and therefore available snowpack, ﬂuc- tuates considerably year-to-year, and discrepancies between precipitation records and river discharges demonstrate the importance of ground ice in supplementing these water sources (Brenning, 2010; León and Pedrozo, 2015; Corripio et al., 2007). This discrepancy is particularly evident in very dry years, when precipitation can be less than 10% of average levels, while river discharges remain at or above 50% of their historical averages (León and Pedrozo, 2015; Masiokas et al., 2010). Ground ice contribution to the water budget is expected to be largest in warm and dry years (Brenning, 2010), and will become increasingly important if future temperatures rise without an associated increase in winter precipitation (Corripio et al., 2007). Within the high mountains where most of the regional ground ice resides, areas with signiﬁcant concentrations of precious metals have become economically attractive to mining development in recent years. The extraction of these materials involves surface excavations that potentially disturb both surface and buried ice. This concurrence of mineral and water resources requires careful consideration to balance local economic and environmental interests. However, the extent and spatial distribution of buried ice in the Andes terrain remains largely unknown. Only limited prior research exists on this topic in the region due to the remoteness of the area and the lack of infrastructure to support long-term research on a regional scale. Access to these areas depends on many factors, including availability of equipment and weather conditions, which are prohibitive much of the year.

1 Past research in this region has focused speciﬁcally on rock glaciers, which are generally the most evident ice landforms (e.g., Schrott, 1996; Trombotto et al., 1997; Croce and Milana, 2002; Brenning, 2005; Lecomte et al., 2008; Azócar and Brenning, 2010), or on the general geomorphology of the area (Schrott, 1991; Alonso and Trombotto Liaudat, 2013), with relatively few studies investigating the thermal characteristics across the landscape (e.g., Bodin et al., 2010; Apaloo et al., 2012). In general, throughout mountainous terrains such as these the ground thermal regime, and therefore the distribution of buried ice, can be highly variable, dependent on factors such as insolation, elevation, and snow cover (Gruber and Hoelzle, 2001; Lewkowicz and Ednie, 2004; Zhang, 2005; Julián and Chueca, 2007; Bodin et al., 2010; G˛adek and Leszkiewicz, 2010; Apaloo et al., 2012; Ruiz and Trombotto Liaudat, 2012). Likewise, terrains of the Dry Andes, a mid-latitude mountain setting, display great variability in topography and climatic conditions over short distances. To address the need for a greater understanding of the scope of permafrost terrain in this little-studied region, this study will assess the extent of permafrost in two proposed mine locations, Los Azules and El Altar, in the high Dry Andes within the San Juan province of Argentina. To accurately determine the ground thermal regime in this complex terrain, we installed and monitored an extensive array of near-surface ground temperature sensors over a four-year period. Both mean annual ground temperature (MAGT) and bottom temperature of winter snow (BTS) were analyzed through multiple linear regression, with elevation and direct insolation as explanatory variables. Temperature values produced from multiple linear regression are applied to digital elevation models (DEMs) to generate maps predicting likely permafrost zones across the entire study terrain. A systematic evaluation of these maps is beyond the scope of this thesis; however, we present a preliminary evaluation of their usefulness by comparing their predictions with locations of ground ice discovered through other efforts (i.e., road construction, test pits, bore holes, and limited mining and exploration activities). The results of this analysis will prove useful as the region seeks to determine the effects of proposed mining projects on the local water budget.

2 Chapter 2

STUDY AREA

The two study locations used in this study are El Altar and Los Azules, located at 31◦ 29’ South, 70◦ 29’ West, and at 31◦ 05’ South, 70◦ 14’ West, respectively, within the Cordillera Frontal of San Juan, Argentina (Figure 1). El Altar is located approximately 50 kilometers southwest of Los Azules, and together they have a collective area of about 80 km2, ranging in elevation from 3000 to over 4200 meters. Mean annual air temperature (MAAT) is 2.4◦C at 3375 m a.s.l., and recent estimates place the mean zero degree isotherm altitude (ZIA) at this latitude of the Andes around 4150 m (Carrasco et al., 2005). Recent winters have been particularly dry, with very little snow cover remaining into the summer ﬁeld season (Andres Meglioli, personal communication, January 26, 2015). The study areas are within the Dry Andes mountain range, which is a subsection of the Andes Mountains between approximately 18 and 35◦S, extending along the western edge of South America. This Dry Andes region contains the highest peaks in the Andes, with mean summit heights in excess of 4000 m a.s.l., including Argentina’s Aconcagua (6,962 m), the tallest mountain outside of Asia (Garreaud, 2009; Sulzer and Kostka, 2006). This region is semiarid due to a combination of atmospheric subsidence at these latitudes and orographic barriers to moisture transfer (Strecker et al., 2007). Summers are dry and sunny, with most of the limited precipitation falling in the austral winter (Corripio et al., 2007). This is in contrast with wetter conditions to the south, where high amounts of summer precipitation and overall increased moisture levels prevail. Annual moisture input to the region is highly variable, with close relations to the state of the El Niño-Southern Oscillation (Masiokas et al., 2006). Because of high local elevations and the seasonal pattern, the limited precipitation falls almost exclusively as snowfall (Lliboutry, 1998). Overall, these climatic conditions result in a lack of signiﬁcant vegetation, with only scrub and hardy grasses in the foothills.

3 Within the mountains, some marshy areas with low-lying groundcover, known as vegas, subsist on meltwaters (Figure 2). Active glaciers in this region have equilibrium-line altitudes (ELA) at approximately 4500 m, allowing only the highest peaks to sustain permanent snowfields and ice glaciers, presuming sufficient precipitation (Lliboutry, 1998; Rabassa and Clapperton, 1990). The ELA during the Pleistocene lowered as much as 1250 m in this region, allowing glaciers to occupy many more peaks and extend much further into the valleys (Espizua, 2004). Past glaciations left large numbers of resultant cryogenic landforms, now often relict (Trombotto, 2002). Still, active rock glaciers are fairly abundant, particularly above 3500 m (Brenning, 2005). Within the two study locations, potentially active rock glaciers were found, along with a number of other landforms, including solifluction lobes, protalus ramparts, patterned ground (stripes and nets), and frost-shattered peaks (Figure 3). Of all of these features, some are active while others likely relict; many palimpsest forms are clearly complicated mixtures of active and relict landforms. Overall, the Cordillera Frontal is composed of an early Paleozoic basement with over- lying Permo-Triassic to Tertiary sedimentary and volcaniclastic rock (Rabassa and Clapper- ton, 1990; Heredia et al., 2012). Valley sides within the study area are generally composed of talus slopes sourced from hydrothermally altered andesite and rhyolite. Sediments range from fine sand to coarse gravel and angular boulders. Road access over these geologically rugged terrains is extremely limited, with roads developed and maintained by mining companies, whose interest in maintaining them parallels their current economic interest in a given year. Said companies also maintain weather stations in the area, for which support and reli- ability also vary with the ephemeral interests of the mining companies.

4 Chapter 3

METHODS

3.1 Ground Surface Temperature Measurements Between April 2011 and February 2014, a total of 69 temperature data loggers were installed, with 29 and 40 sensors at the Los Azules and El Altar study locations, respectively (Figures 4 and 5). The study began with 20 sensors in 2011, with annual additions as resources permitted. Two different sensor types were used throughout the study; Ther- moworks TW-USB-1-RCG loggers with an accuracy of 0.4◦C (±0.2◦C) and OnSet’s HOBO pendant loggers with an accuracy of 0.53◦C (±0.14◦C). Both sensor types were programmed to record at 30-minute intervals. Their spatial distribution at each study location reﬂects efforts to maintain a minimum distance (>500 m) between sensors, a stratiﬁed, 500 m elevation range of sensor groups, and inclusion of at least one sensor on slopes facing each cardinal and ordinal direction (see Figure 6). Each temperature sensor was buried to a depth between 10 and 20 cm. Field observations of the site and sediment characteristics were noted for each location (i.e., slope, aspect, grain size, color, shape, sorting, and moisture content). Open framework and coarse materials were avoided, when possible, to limit direct air contact with the sensors. However, in many settings, the surface materials were coarse sand and pebbles. The GPS coordinates of all sensors were recorded, and colored stakes were driven into the substrate adjacent to each sensor to ensure successful annual retrieval for data collection and battery replacement.

3.2 Temperature Data Reduction Optimally, each of the 69 sensors recorded 17,520 measurements each year, for as many as four years, which must be reduced for practical analysis. We calculated the daily

5 average, minimum, and maximum temperatures; monthly average temperatures (if the sensor recorded for at least 24 days in that month); annual average temperatures; and, when appropriate, the bottom temperature of winter snow cover (BTS) as proposed by Haeberli (1973). Because of the limitations imposed by the remoteness of the study locations, no reliable method exists to directly observe the extent snowcover or snow depth during the winter months. Three nearby weather stations – two at El Altar and one at Los Azules – allowed for a gross comparison of near-surface air temperature, but not snow depth or extent. Instead, we use our temporally high-resolution temperature data to note that snow-covered temperature sensors record daily maxima and minima that are fairly close in value, while exposed sensors record much larger diurnal temperature fluctuations, as do the exposed weather stations. We thus restrict our analysis of BTS measurements to sensors that report minimal daily air temperature fluctuations during August, the end of austral winter. Daily range of ground temperature measurements is sufficient for predicting snow cover at individual sites (Rödder and Kneisel, 2012; Teubner et al., 2015). We calculated BTS values based on Zhang’s (2005) threshold of daily amplitude less than 1◦C when snow-covered. Sensors are evaluated under this criterion, and an average August temperature is calculated for eligible data.

3.3 Model Radiation and Geographic Data for Sensor Sites Using geographic information software, the elevation, slope, and aspect at each sensor location were extracted from two 30-meter digital elevation models (DEMs), the ASTER (Advanced Spaceborne Thermal Emission and Reﬂection Radiometer) and SRTM (Shuttle Radar Topography Mission). Potential incoming shortwave radiation (PSWR) was calculated for each study location using the solar radiation model of Fu and Rich (1999), which has been successfully implemented previously in similar studies (e.g., Julián and Chueca, 2007; Bonnaventure and Lewkowicz, 2008). This model takes into account atmospheric attenuation, site latitude and elevation, slope, aspect, sun angle based on daily and seasonal motion, and topography shadows, basing its calculations on an inputted DEM and adjustable parameters such as atmospheric transmissivity, and diffusion proportion (Huang and Fu, 2009). Descriptions of

6 the development of this model and options for implementation, as well as the equations utilized, are detailed in Fu and Rich (1999). The solar insolation model yields total, direct, and diffuse insolation values for each cell of a DEM, and these variables were calculated using both ASTER and SRTM data. The results of the two models are compared with each other and tuned with observed radiation measurements from the three local weather stations. The best model was determined through its success in representing yearly, summer, 30-day, and 10-day integrations of radiation received, with evaluations made via root mean square error and mean absolute error.

3.4 Statistical Analyses of all Field, Radiation, and Geographic Data Statistical-empirical models are a widely used indirect method to determine permafrost distribution, and are often based on topoclimatic inﬂuences (Riseborough et al., 2008). These models can be informed through various approaches, including geophysical drilling, borehole measurement, geophysical sounding, photogrammetry, laser altimetry, GPS surveying, and air or ground temperature records, which we use here (Riseborough et al., 2008; Haeberli et al., 2010). Because ground temperatures in the study locations are known to be dependent on elevation, topography, and insolation (e.g., Bodin et al., 2010; Ruiz and Trombotto Liaudat, 2012; Apaloo et al., 2012), multiple linear regression (MLR) was used to model the relationship between temperature and explanatory variables: elevation, easting, northing, aspect (both north and east components), slope, and solar radiation values (total, direct, and diffuse insolation) (e.g., Gruber and Hoelzle, 2001; Brenning, 2005). We observed a strong multicollinearity between total and direct radiation variables and removed the total duration variable from analysis, due to lower correlation with annual average temperature. Also, because the insolation variables take into account geographic location, slope, and aspect, these variables (easting, northing, aspect, and slope) became redundant in the MLR analysis, a result noted by Brenning (2005) using a similar modeling approach. Thus, the MLR equation takes the following form:

MAGT = a · Z + b · PSWR + c (1)

7 where MAGT is mean annual ground temperature (◦C); Z is land surface elevation (m); PSWR is potential shortwave radiation (W/m2), the direct insolation variable from the radiation model; and a, b, and c represent coefficients of the MLR equation. Using the above equation, an estimated MAGT can be derived for each elevation value in our digital elevation models at both study locations (i.e., one observation per each 30 m by 30 m grid cell in either the ASTER or SRTM DEMs). This process was repeated to derive MLR equations and plot values across the landscape for the BTS method. These models were compared and evaluated against known locations of ice identified during mining explorations, road development, and our own field observations. Maps of estimated permafrost distribution were produced for each site based on previously established (literature) thresholds of MAGT and BTS to determine permafrost likelihood.

8 Chapter 4

RESULTS

4.1 Ground Surface Temperature Measurements Of the 69 sensors emplaced, 7 were lost or failed to collect sufficient data for annual temperature calculations. Final MAGT results analysis therefore relies on 62 sensors, 35 at El Altar and 27 at Los Azules. Thirty-six sensors across the two sites were found to have at least one season with sufficient snow cover to determine BTS values, with diurnal temperature fluctuations less than 1◦C in August (Zhang, 2005). Three sensors were usable for BTS analysis but not for MAGT, having collected winter data but failing before the end of a full year (Figures 7 and 8). Durations of probe records are plotted in Figure 9; on average, probes recorded over two years of data. See Appendix for detailed probe records.

4.2 Model Radiation and Geographic Data for Sensor Sites Values of slope and aspect extracted from the DEMs were found to be considerably different from those noted in the field for a number of sensors. In particular, the aspects of sensors located near ridgelines were difficult to estimate because the aspect changes more rapidly than the coarse resolution of these DEMs can capture. Because the GPS unit used in the field had an accuracy of about 15 meters, half the distance of our DEM cell sizes, data attributed to sensor locations were often taken from neighboring DEM cells that better repre- sented observed field conditions. These adjustments did not drastically change any elevation values (Table 1); however, aspect has a very large influence on modeled radiation values. This became apparent with an initial model run at the site of the Altar Vista weather station, which predicted much lower levels of radiation than expected without a slight location adjustment. This station is on a south-facing slope right below a ridge, but GPS coordinates

9 placed it in a north-facing DEM cell, adjacent to the cells with more accurate aspect values. Modifications of these individual points are expected to have meaningfully increased the accuracy of the final MLR models. The insolation model was tuned to provide the best representation of the available radiation data from the three weather stations (Figure 10). The best model produced values of RMSE and MAE between 9.35 and 28.51 when representing annual, summer, 30-day, and 10-day integrations (Table 2). Changes to all model parameters were tested, but the best- fit model only required adjustment to transmissivity; all others remained at recommended values for the conditions of the region (Fu and Rich, 2000) (Table 3).

4.3 Spatial Modeling of Temperature Values Results of the multiple linear regression analysis indicate that elevation and solar radiation are valid predictors of ground temperature. Two sensors were found to have high Cook’s D values, indicating that they were outliers, and therefore were removed from use in the regression modeling. Also, the diffuse insolation variable was found to not be a significant indicator of temperature and was excluded. The final models included only the DEM- derived annually averaged direct insolation and elevation in the equation. Separate equations were developed for the ASTER and the SRTM DEM information. The final regression analysis included data from 60 sensors, for which the measured annual ground temperatures were regressed against the predictor variables (Table 4). Durbin-Watson values indicate no autocorrelation issues. The studentized residuals are normally distributed for both equations, indicating that errors do not have any particular pattern. The regression equations were used to calculate temperature values across both field sites (Figures 11-14). Depending on the DEM and site, predicted values of MAGT range from -4.0±0.9◦C at the peaks to 9.6±0.5◦C in the valleys. Warmer temperatures consistently reach higher elevations on north-facing slopes, as expected from the insolation component of the equation. Regression analysis of BTS was conducted in the same way as MAGT, predicting temperature values based on the elevations and insolation values modeled from the ASTER

10 and SRTM DEMs at each point. Due to high Cook’s D values, 3 sensors were removed from evaluation as outliers, and the ﬁnal regression was run again using the remaining 33 data points. Durbin-Watson values indicate that neither positive nor negative autocorrelation is present in the data. The ﬁnal regression equations generated (Table 5) were similarly used to model BTS values across the gridded landscape, displayed in Figures 15-18. Depend- ing on the DEM and site, predicted values of BTS range from -11.0±1.0◦C at the peaks to 3.7±0.6◦C in the valleys. Warmer temperatures once again consistently reach higher elevations on north-facing slopes. Notable differences in both the MAGT and BTS models, particularly along ridgelines, were found in predicted ground temperatures across the two different DEMs (e.g., Figure 19). Though across most of the landscape the models are often within 0.25◦C, in some locations they differ up to 4.1◦C for MAGT and 5.0◦C for BTS. Generally, there are larger discrepancies between the BTS values, which may be due to the fewer number of data points used in its regression analysis. The temperature differences stem from a combination of value differences for both predictor variables (Figures 20 and 21).

4.4 Permafrost Distribution and Comparison with Observations The distribution of permafrost was predicted by applying threshold values to the models of MAGT and BTS. Permafrost is likely to be present in zones where MAGT is below 0◦C, or BTS values are below -3◦C, and possibly present with MAGT up to 2.5◦C and BTS values up to -2◦C(Sazonova and Romanovsky, 2003; Osterkamp and Romanovsky, 1999; Haeberli, 1973). Permafrost estimations based on BTS measurements had much greater areal extents across the landscape than estimates based on MAGT, where locations of likely permafrost were limited to south-facing slopes at high elevations (Figures 22-29). Average "likely" permafrost extent with the BTS method is 31% of the land area, and 4% with the MAGT method (Table 6). Comparisons of modeled permafrost maps of the two locations indicate that estimations of permafrost extent consistently reach elevations up to 200 m lower at the El Altar site. This is likely due to its higher latitude and resultant lower radiation levels. Under ideal

11 conditions (south-facing slope, shaded) permafrost may reach down to 3600 m at El Altar, but is expected to only reach 3750 m at Los Azules based on the MAGT method. Likewise, permafrost extent determined by the BTS method reaches 3250 and 3400 m at El Altar and Los Azules, respectively (Table 7). Although there are a number of cryogenic landforms throughout the sites, generally evident of permafrost presence, during the January 2015 field season, researchers dug ten 1-meter pits, five each at El Altar and Los Azules, including in areas of cryogenic landforms, with no evidence of ice found. Overall, there has been very little field evidence of an active layer. Road excavations at high elevations have uncovered some areas of ice, and these and other observations are compared with model predictions in Figures 30 and 31. Fourteen sites known to have ice are within the expected permafrost zone; however, a number of sites where no ice has been observed are also predicted as containing ice.

12 Chapter 5

DISCUSSION

This study seeks to provide insights into the potential of permafrost as a water resource at two sites in the Dry Andes. Because water storage and release from rock glaciers and mountain permafrost in this environment is not controlled by precipitation, but rather the surface energy budget (Azócar and Brenning, 2010), further insight into the seasonal and annual energy balance is essential to understanding the dynamics of this region’s meltwater resources. Evaluating the current extent of mountain permafrost is important in the context of human impacts to this landscape (e.g., mine development) and the effects of regional and global climate change (Hoelzle et al., 2001).

5.1 Accuracy of Models The models developed in this study have been specifically tuned to predict temperature conditions at our research sites. However, as empirical models, these results are re- stricted to predicting permafrost distribution locally within the study region. The form of the models, in terms of what was found to be important and what could be excluded, will inform the fitting of similar models elsewhere, but the coefficients obtained here could not be used. To develop a model that instead determines permafrost characteristics based on the actual energy exchange processes would require more information regarding snow distribution as well as turbulent fluxes, ground surface characteristics (e.g., sediment type, albedo, emissivity), and further meteorological variables such as wind speed and direction (Hoelzle et al., 2001). The significant variation in sediment characteristics across our sites may be of particular importance; for example, as Apaloo et al. (2012) found that openwork boulders/coarse block material surfaces can be cooler by up to 0.8◦C. We preferentially chose sensor locations with

13 finer sediment, and so our model may not be accurate for areas of our sites with coarser materials. Additionally, Noetzli et al. (2007) show that the geometry of landforms can have a significant effect on the depth and distribution of permafrost, suggesting that local irregular- ities (ridges, peaks, spurs) impact ground temperature conditions, making two-dimensional representations of these features insufficient for assessing permafrost conditions.

5.1.1 ASTER vs. SRTM Elevation products developed from differing source data show the importance of understanding nuanced results. Based on our regression statistics alone (Tables 4 and 5), it is difficult to determine which set of temperature models, derived from the ASTER or SRTM data, most accurately represents the temperature conditions across the landscape. There is no consistent pattern in which one DEM results in a prediction of larger permafrost extent than the other (Table 6). Rather, the distribution of permafrost predicted in the two models displays slightly shifted spatial extents (Figure 32). The two elevation models (hereafter referred to as ASTER and SRTM) do display some large differences in calculated temperature, in some cases over 2◦C. In general, ridgelines and valley floors are more likely to display larger temperature differences. South- and west-facing slopes also display greater differences than north- and east-facing slopes. When comparing the DEMs directly (Figures 20 and 21), it is clear that there is a displacement of the land surface with a particularly strong east-west component. This may be due to incorrect georeferencing and rectification, or due to the different methods by which the data were collected. The ASTER DEM was developed using interferometry of visible- near infrared stereo-pair images, collected by NASA’s Terra spacecraft starting in 2000. The SRTM data were collected through the use of interferometric radar data collected by the C-band Spaceborne Imaging Radar instrument on the Space Shuttle Endeavor in February 2000. The 30-meter version of the SRTM DEM has only been released for this region re- cently and is still unavailable for some parts of the world, so literature on its quality is limited. However, the 90 m SRTM DEM counterpart has been found to be highly correlated with the ASTER DEM data and acceptably accurate (Nikolakopoulos et al., 2006). SRTM

14 has an advantage over ASTER in that its radiation penetrates clouds and up to a few meters of vegetation. SRTM data is reported to have known issues penetrating heavy vegetation and accurately capturing coastlines due to irregular reflection and absorption of the signal by water (Chirico, 2004) – neither issue is applicable in the Dry Andes. The vertical accuracy at the 95% confidence level for the ASTER and SRTM DEMs was found to be 17.01 and 7.86 meters, respectively, by Tachikawa et al. (2011). This sufficiently explains the majority of the difference between the two models, and we can assume SRTM does a better job of representing this landscape.

5.1.2 MAGT vs. BTS Because of the remote locations, limited access, and harsh winter conditions of the study area, our field methods provide an effective way of predicting permafrost in this region. The rocky substrates and coarse-grained surface materials make it difficult to perform an on- the-ground assessment of the validity of our MAGT model. However, ten 1-meter deep pits dug for a related study in January 2015 showed no evidence of ice. Fourteen known ice locations mapped by the mine developers fall within the MAGT "possible" permafrost zone, though a number of sites also in this zone with similar excavations have not uncovered any ice (Figures 30 and 31). We do note that the arid conditions may require explorations deeper than 1 meter to fully identify permafrost locations, and we cannot rely on shallow excavations to disprove its presence. Haeberli et al. (1993) indicates that ice-rich continuous permafrost can extend below ELAs (estimated as 4500 m in this region) under extremely dry conditions. A different study conducted at the Laguna Negra catchment, a site 250 km south of our study area, found widespread permafrost above 4000 m in elevation, with discontinuous permafrost in locations with favorable conditions (such as longer lasting snow cover, topographic shading) down to 3200 m in elevation (Bodin et al., 2010). This areal and elevation extent of permafrost is consistent with our findings. Considering the southern location of this alternate site relative to our study area, it would be expected that these ranges reach lower elevations than the permafrost at our sites (detailed in Table 7).

15 The BTS method produces a much larger predicted permafrost area than the average annual data (e.g., Figure 33). However, road excavations and mining exploration wells throughout these sites indicate that this extent is much too broad. As found by Bonnaven- ture and Lewkowicz (2008), the rule-of-thumb thresholds developed by Haeberli cannot be directly applied to different climatic conditions, such as dissimilar temperature regimes and precipitation characteristics – particularly lower levels of snowfall. The BTS method was developed in the Alps, where consistently thick winter snow cover is much more reliable. As was found in this study, many sites do not see the thick snow necessary to insulate ground temperatures from significant daily fluctuations. The limited aerial imagery available for these areas confirms the sporadic nature of snow cover, likely with very few areas with greater than one-meter depth, as road cuts remain visible even in early September; conse- quently, extrapolating BTS conditions across the landscape is problematic. For a more accurate permafrost model based on BTS values, different thresholds for differentiating likely and possible permafrost zones could be utilized; however, extensive validation against field observations would be necessary. We recognize that snow cover variability is likely one of the more difficult and poorly understood issues in these study areas. Haeberli et al. (1993) found that smaller-scale distribution patterns of permafrost are strongly influenced by snow cover characteristics as well as mean annual air temperature and direct solar radiation. Thick snow has a very strong influence on the ground thermal regime, preventing energy transfer into and out of the ground. Significant snow cover in the early winter months can prevent cooling of the ground (Hoelzle et al., 2001); it acts as an insulator from very cold atmospheric temperatures in the winter and from warm air in the summer, when snow lasts into these months. Apaloo et al. (2012) found a cooling effect of up to 0.6◦C per month on mean ground surface temperature due to longer lasting snow cover, though earlier onset snow was not found to be statistically significant in their analysis. Our analysis could not integrate snow cover directly, and the insolation model used does not take into account surface albedo (Huang and Fu, 2009), though use of BTS values may act as a sort of proxy for these conditions.

16 5.2 Evaluating Permafrost as a Water Resource Spring and summer river discharges are highly correlated with winter snowpack sourced from Pacific ocean moisture (Masiokas et al., 2010), although supplementary sources such as glaciers and ground ice are necessary to compensate during drier years (Brenning, 2010; León and Pedrozo, 2015). Still, little research has yet been done on the factors defini- tively controlling the makeup and sources of water in this region (Corripio et al., 2007). Through the end of this century, global climate models indicate a 35% decrease in winter precipitation, and so warming temperatures will likely result in a depletion of any areas of stored ice as they begin to melt and are not replenished (Corripio et al., 2007; Vicuña et al., 2010). Admittedly, the system is likely to respond quite slowly due to the complex heat exchange processes in a coarse-grained active layer and slowing of ice melt due to latent heat effects (Hanson and Hoelzle, 2004). Additionally, an increase in future El Niño events may offset some of the deficit, as these do generally result in higher levels of winter precipitation, but over time this loss will result in further water scarcity (Leiva, 1999; Corripio et al., 2007). Local socioeconomic development is highly dependent on the San Juan River and its tributaries, which both of our study locations feed into. Verification of ice content throughout our predicted permafrost extent has proven problematic, particularly due to the depth of surface debris and dry conditions; direct observation of the frozen ground via excavation at these sites is hindered by physical and logistical difficulties in a terrain where the permafrost layer may lie many meters below the surface, and where few (if any) roads exist for the transport of excavation equipment. Both sites display classic field indications of permafrost environments such as patterned ground, solifluction, rock glaciers, and creeping slopes (Fig- ure 3). While these all suggest the presence of ground ice, it is unclear whether these features result from the presence of current, active permafrost, or if they are relict landforms from Pleistocene and older Holocene environments. A more thorough geomorphic analysis would be required to make definitive conclusions (Zurawek˙ , 2003). Known ice locations do tend to fall in our predicted permafrost zones, though often at great depth (up to 8 meters below the ground surface). It is difficult to draw conclusions from the locations that have been excavated without any ice found, as perhaps there may be

17 ice still deeper. Without knowledge of the depth to bedrock, even with good estimations of permafrost extent, it is very difﬁcult to draw any conclusions about the volume of ice present, and therefore its potential contribution to local waterways.

5.3 Future The continued study of surface temperature data, combined with our results, will allow for an improved understanding of the permafrost conditions across these sites to be developed. Future research at both study locations will rely on new data from the installed sensor network. In January 2015, sets of sensors were placed in nested elevation zones throughout the study areas at depths of 10, 25, 50 and 100 cm. The shallowest sensors can be compared to the rest of the temperature sensor array across the sites. However, data from those at depth provide insights into the surface thermal regime and heat ﬂow into the subsurface that cannot be obtained from the surface sensor array. In addition to the nested sets, humidity sensors were placed 10 cm above the surface at each of these sites to develop a better understanding of moisture availability.

Figure 2: Marshy "vegas" located in a valley at one of the research ﬁeld sites.

Figure 3: Examples of cryogenic landforms found at the ﬁeld sites (a) sorted stripes (b) rock glacier (c) sorted nets (d) soliﬂuction.

21 Figure 4: Temperature sensor distribution across the El Altar study site. Imagery courtesy of Esri.

22 Figure 5: Temperature sensor distribution across the Los Azules study site. Two sensors to the south are omitted for display purposes. Imagery courtesy of Esri.

70°32'W 70°30'W 70°28'W

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31°30'S 31°30'S

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70°32'W 70°30'W 70°28'W MAGT !< Both Kilometers BTS D Failed 012340.5

Figure 7: Distribution of temperature sensors across El Altar and their usability for temperature modeling. Black/white circles indicate sensor locations that were useable for both MAGT and BTS calculations. White circles indicate sensors only usable for MAGT, black indicate locations where sensors were only usable for BTS. X’s indicate locations of failed sensors.

25 70°16'W 70°14'W 70°12'W

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70°16'W 70°14'W 70°12'W MAGT !< Both Kilometers BTS D Failed 012340.5

Figure 8: Distribution of temperature sensors across Los Azules and their usability for temperature modeling. Black/white circles indicate sensor locations that were useable for both MAGT and BTS calculations. White circles indicate sensors only usable for MAGT, black indicate locations where sensors were only usable for BTS. X’s indicate locations of failed sensors.

Figure 11: MAGT values (◦C) at El Altar site based on the multiple linear regression equation developed with the ASTER DEM.

Figure 12: MAGT values (◦C) at El Altar site based on the multiple linear regression equation developed with the SRTM DEM.

Figure 13: MAGT values (◦C) at Los Azules site based on the multiple linear regression equation developed with the ASTER DEM.

Figure 14: MAGT values (◦C) at Los Azules site based on the multiple linear regression equation developed with the SRTM DEM.

Figure 15: BTS values (◦C) at El Altar site based on the multiple linear regression equation developed with the ASTER DEM.

Figure 16: BTS values (◦C) at El Altar site based on the multiple linear regression equation developed with the SRTM DEM.

Figure 17: BTS values (◦C) at Los Azules site based on the multiple linear regression equation developed with the ASTER DEM.

Figure 18: BTS values (◦C) at Los Azules site based on the multiple linear regression equation developed with the SRTM DEM.

Figure 19: Absolute differences in calculated temperature (◦C) at El Altar site based on the multiple linear regression equations developed using ASTER and SRTM information for BTS values.

Figure 20: Differences in elevation (m) between the ASTER and SRTM digital elevation models at the El Altar site. These disparities propagate into calculations of slope, aspect, and solar radiation.

Figure 21: Differences in elevation (m) between the ASTER and SRTM digital elevation models at the Los Azules site. These disparities propagate into calculations of slope, aspect, and solar radiation.

39 70°32'W 70°30'W 70°28'W

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31°32'S 31°32'S $

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Figure 22: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at El Altar. Based on MAGT thresholds of 0 and 2.5◦C calculated using ASTER DEM information.

40 70°32'W 70°30'W 70°28'W

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Figure 23: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at El Altar. Based on MAGT thresholds of 0 and 2.5◦C calculated using SRTM DEM information.

41 70°16'W 70°14'W 70°12'W

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Figure 24: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at Los Azules. Based on MAGT thresholds of 0 and 2.5◦C calculated using ASTER DEM information.

42 70°16'W 70°14'W 70°12'W

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Figure 25: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at Los Azules. Based on MAGT thresholds of 0 and 2.5◦C calculated using SRTM DEM information.

43 70°32'W 70°30'W 70°28'W

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Figure 26: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at El Altar. Based on BTS thresholds of -3 and -2◦C calculated using ASTER DEM information.

44 70°32'W 70°30'W 70°28'W

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Figure 27: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at El Altar. Based on BTS thresholds of -3 and -2◦C calculated using SRTM DEM information.

45 70°16'W 70°14'W 70°12'W

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Figure 28: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at Los Azules. Based on BTS thresholds of -3 and -2◦C calculated using ASTER DEM information.

46 70°16'W 70°14'W 70°12'W

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Figure 29: Distribution of "likely" (dark blue) and "possible" (light blue) permafrost across the landscape at Los Azules. Based on BTS thresholds of -3 and -2◦C calculated using SRTM DEM information.

47 70°32'W 70°30'W 70°28'W

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70°32'W 70°30'W 70°28'W Confirmed Ice No Ice - Pits Kilometers 012340.5 No Ice

Figure 30: Comparison of known El Altar ice locations (white) with predicted permafrost extent based on MAGT. Sites in which researchers dug one meter pits without ﬁnding ice are indicated in purple, sites in which mining employees reported no ice found are in black. Little validation information is available for Los Azules at this time, and so only El Altar is shown. Data displayed is derived from the SRTM DEM.

48 70°32'W 70°30'W 70°28'W

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31°30'S 31°30'S

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70°32'W 70°30'W 70°28'W Confirmed Ice No Ice - Pits Kilometers 012340.5 No Ice

Figure 31: Comparison of known El Altar ice locations (white) with predicted permafrost extent based on BTS. Sites in which researchers dug one meter pits without ﬁnding ice are indicated in purple, sites in which mining employees reported no ice found are in black. Little validation information is available for Los Azules at this time, and so only El Altar is shown. Data displayed is derived from the SRTM DEM.

49 70°32'W 70°30'W 70°28'W

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Figure 32: Example of how permafrost distribution differs between models based on ASTER and SRTM DEMs. Data shown for MAGT permafrost model at El Altar. Blues indicate where models agree for "likely" (dark) and "possible" (light) permafrost zones. Green ("likely") and yellow ("possible") indicate where the SRTM model predicts more probable permafrost than ASTER. Red ("likely") and pink ("possible") indicate where the ASTER model predicts more probable permafrost than SRTM.

50 70°32'W 70°30'W 70°28'W

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Figure 33: Extent of BTS permafrost zones beyond MAGT permafrost zones at El Altar site. Dark purple shows BTS "likely" extent, and light purple shows "possible" extent. Blues show MAGT permafrost extents. Both models shown were developed with SRTM data.

51 TABLES

Table 1: Summary of changes to elevation values at sensor sites due to probe location adjustments into neighboring DEM cells. The adjustments served to ensure the best representation of aspect at each site.

' # ' &

% $ ' * ( %

%% %&

! +( ') )* '( )*

52 Table 2: Values of root mean square error (RMSE) and mean absolute error (MAE) used to evaluate insolation model success in capturing the integrations of direct radiation values (W/m2).

% ! %! %$ "#% $!

53 Table 3: Summary of parameters used for the local radiation model. Default and recommended values come directly from the model authors (Fu and Rich, 2000).

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) /'////0/25 ! 2/, !

%! 21 # $(2/ (%! 7 !" ! "

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54 Table 4: Coefﬁcients and related statistics of multiple linear regression equations derived from MAGT values using both ASTER and SRTM DEM data.

# !)$ ' ')-.(-( ' ''+'0- 1' '''( # $ "' ''.,0/ ' '''/( 1' '''( )- ++,./0 * (,-,'. 1' '''( ' ')0*''/ ' ''+*)0 1' '''( "' ''/'0, ' '''/', 1' '''( ). ./,)(* * '-.(', 1' '''(

55 Table 5: Coefﬁcients and related statistics of multiple linear regression equations derived from BTS values using both ASTER and SRTM data.

# !)$ ' '*++)+. ' '('*.) ' '')+ # $ "' ''.)-- ' '')'-. ' ''(+ (. )())+- / )*'/)+ ' '+,( ' '*+).)* ' '(''+0 ' ''(0 "' ''/*'- ' '')'./ ' '''+ )( )(0-.* . 0('-/, ' '((/

56 Table 6: Percentage land area of modeled permafrost based on both DEMs and methods of calculation at each study site.

# %! $%$ !% $%% $$&! %% # " " !" " ! $"! $% !%% $& $$ $ % $!& "" ! $ #! " $&

57 Table 7: Lowest elevations reached by modeled permafrost at each site on north-facing slopes and in ideal conditions (shaded south-facing slopes). No "likely" permafrost was found on north-facing slopes based on necessary MAGT conditions, and so this level must be above the highest elevations found in these locations.

!$ !$ 1+*'' */'' +',' *-'' 1+*'' */'' +('' *-'' +',' *+,' *0'' *),' +''' *,'' *0'' *),' 1+,'' *0,' +),' *.,' 1+,'' *0,' +),' *.,' +),' *,,' *0,' *+'' +)'' *-,' *0,' *+''

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63 Appendix

TEMPERATURE SENSOR RECORDS

The following pages display the maximum, minimum, and average daily temperatures recorded by each temperature sensor placed at the field sites. Los Azules records are shown first, followed by El Altar records. These graphs help to elucidate the variable thermal characteristics across the landscape, particularly the sporadic nature of snow cover at these locations; snow cover is clear in the records where winter temperatures display little daily fluctuation. Maps of labeled sensor locations are included before each set of temperature graphs for reference. A secondary map of Los Azules is included, showing the two sensor locations that have been left off most maps for display purposes.

64 70°16'W 70°14'W 70°12'W

TP-32

TP-03

31°4'S LA-06 31°4'S TP-04 LA-02 LA-03 TP-33 LA-07 TP-02 TP-05 TP-40 LA-01 TP-01 TP-06

TP-35 TP-34 TP-07 LA-05 TP-08 TP-36 31°6'S LA-04 31°6'S

TP-09 TP-39 TP-10 TP-37 TP-31

TP-38

31°8'S $ 31°8'S

70°16'W 70°14'W 70°12'W

Kilometers 012340.5

65 70°18'W 70°16'W 70°14'W 70°12'W 70°10'W

31°4'S 31°4'S

31°6'S 31°6'S

31°8'S 31°8'S

31°10'S 31°10'S

31°12'S 31°12'S LA-09

LA-10 $

70°18'W 70°16'W 70°14'W 70°12'W 70°10'W

Kilometers 024681

70°30'W 70°28'W

31°28'S TP-14 31°28'S EA-16 TP-15 TP-29 TP-41 EA-19 EA-18 TP-28 TP-11 EA-17 TP-13 TP-12 TP-43 TP-50 TP-46 TP-19 EA-11 TP-45 TP-26 TP-17 TP-25 EA-12 EA-20 TP-44 EA-15 TP-30 TP-16 TP-47 EA-13 TP-24 TP-22 TP-18 TP-23 TP-21 EA-14 TP-48

31°30'S 31°30'S

31°32'S 31°32'S

TP-49 $

70°30'W 70°28'W

Kilometers 012340.5