A Mixed Method Approach to Exploring and Characterizing Ionic Chemistry in the Surface

A mixed method approach to exploring and characterizing ionic chemistry in the surface

waters of the glacierized upper Santa River watershed, Ancash, Peru

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the Graduate School of The Ohio State University

Alex Michelle Eddy

Graduate Program in Geography

The Ohio State University

2012

Master's Examination Committee:

Dr. Bryan Greenwood Mark, Advisor Dr. Darla Munroe Dr. Ola Ahlqvist

Copyrighted by

Alex Michelle Eddy

2012

Abstract

Dramatic glacier loss in the upper Santa River watershed in Ancash, Peru has significant impact on proglacial hydrologic systems, with implications for downstream impact on human water use activities. Glacial resources serve as freshwater reservoirs, mitigating the rain shadow effect that deprives the western slopes of the Andes of regular annual water resources via precipitation. The reduction of glacial resources is coincidental with economic and population growth, and concern for the quality and quantity of water resources drives research that contributes to understanding regional hydrologic systems. This thesis integrates hydrochemical analysis and spatial exploration with the aim of assessing inorganic water quality characteristics and determinant processes within the region. The chemistry of proglacial surface waters is primarily determined by weathering processes in rock-water contact areas, and pristine glacial meltwater inherits the chemical properties of the surficial lithology along a flow path.

Hydrochemical analysis methods identify elemental characteristics that are unique to the study region. Dominant hydrochemical processes include silicate weathering, coupled pyrite oxidation with silicate weathering, and to a lesser extent, carbonate weathering.

Sulfate constituent is unusually high for portions of the study region and is attributed to highly acidified waters immediately downstream from glacial point sources.

Geovisualization and exploratory data analysis extend the results of the hydrochemical analysis by showing temporal change and suggesting connections between lithology, areas of high erosion and weathering rates, rapid deglaciation, and elevated sulfate concentrations.

iii

Dedication

Dedicated to my parents, Karen and Doug, and my sisters, Renée and Heather, for their love and support.

Acknowledgments

I wish to thank my advisor, Bryan Mark, for his continuous encouragement and guidance throughout my Master of Arts program. Without his support and positive attitude this thesis would not have been possible.

I thank Darla Munroe for her valuable review of my thesis and for her support in helping me seek new and exciting opportunities within the field of Geography.

I wish to thank Ola Ahlqvist for his insightful feedback on my thesis and for introducing me to the artistic and analytical possibilities of cartography during my undergraduate career.

I thank Kathleen Welch and Sue Welch for their insights related to water chemistry analysis and interpretation. I am indebted to them for lending their tremendous expertise throughout our hydrochemistry explorations.

I also thank our Peruvian colleagues, as well as the faculty and graduate students in our research group whose collaborative attitude and immense efforts in the field make our research possible.

Finally, I’d like to thank my family and friends for their endless love, support, and sense of humor.

Vita

2005 - 2010 ...... B.S. Geography (2nd Major: Geology), The Ohio State University

2007 - 2010 ...... GIS Intern, Ohio Department of Natural Resources, Columbus, Ohio

2009 - 2010 ...... Student Research Assistant, Byrd Polar Research Center, The Ohio State University

2010 ...... GIS/Natural Resource Specialist, Bureau of Land Management, Washington, D.C.

2011 - 2012 ...... Graduate Research Associate, Department of Geography, The Ohio State University

Publications

Howat, I.M., & A. Eddy. (2011) Mutidecadal retreat of Greenland’s marine-terminating glaciers. Journal of Glaciology, 57 (203), 389-396(8).

Szabo, J.P., M.P. Angle, & A. Eddy. (2011). Pleistocene Glaciation of Ohio, U.S.A. Quaternary Glaciations -Extent and Chronology, Volume 15: A Closer Look. Edited by Ehlers, J., Gibbard, P.L., & Hughes, P.D., 513-520. Amsterdam, The Netherlands: Elsevier Press.

Fields of Study

Major Field: Geography

Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vi

Table of Contents ...... vii

List of Tables ...... x

List of Figures ...... xii

Chapter 1: Introduction ...... 1

1.1 Research Objectives and Outline ...... 2 1.2 Physical Geography of the Santa River Watershed ...... 4 1.3 Water Resources and Human Vulnerability in the Santa River watershed ...... 11 1.4 Justification and Limitations for the Work ...... 14 1.5 Overview of Methodology ...... 15

Chapter 2: Spatial Data and Synthesis ...... 18

2.1 Introduction ...... 18 vii

2.2 Managing and Designing GIS Attribute Data ...... 19 2.3 Managing Accuracy/Precision Issues and Manually Adjusting Point Data ...... 21 2.4 Refining Point Dataset and Establishing Subset Catchments ...... 24 2.5 Existing Ancillary Data: Collection, Conversion, Repairs, and Editing ...... 27 2.6 Spatial Intersection ...... 31 2.7 Vector Editing: Snapping, Tracing, and Attribution of Stream Order ...... 34 2.8 Summary ...... 36

Chapter 3: Hydrochemical Data and Analytical Methods ...... 37

3.1 Introduction ...... 37 3.2 Background and Operational Hypothesis ...... 37 3.3 Water Sample Data Collection and Ion Concentration Measurement ...... 39 3.4 Units Conversions ...... 40 3.5 Charge Balance ...... 41 3.6 Ionic Ratios and Source Rock Deduction ...... 48 3.7 Piper Diagrams ...... 54 3.8 Summary ...... 58

Chapter 4: Exploring Spatial and Temporal Patterns ...... 60

4.1 Introduction ...... 60 4.2 Discussion of Results ...... 61 4.3 Thematic Maps ...... 65 4.3 Parallel Coordinate Plots ...... 71 4.4 Temporal Change ...... 73 4.5 Summary ...... 79

Chapter 5: Discussion and Conclusion ...... 81

5.1 Introduction ...... 81 5.2 Review of the Content ...... 81 5.3 Discussion of Contribution...... 83 viii

5.4 Further Research ...... 84

Appendix A: Glossaries ...... 88

A.1: Geology and Chemistry Glossary ...... 88 A.2: GIS and Geography Glossary ...... 91

Appendix B: Software Packages ...... 93

Appendix C: Reference Maps, Ancillary Spatial Datasets, and Classification Notes ...... 94

Appendix D: Calculation and Analysis Results ...... 111

References ...... 124

List of Tables

Table 1: List of results from accuracy check using ion ratios ...... 51

Table 2: Two parameters that were applied to a hydrochemistry dataset for interpretation of source-rock types, possible conclusions based on value ranges, and resulting samples sites that fell within those value ranges (Hounslow 1995) ...... 53

Table 3: Glacierized percentage of catchment area (source and collection notes in Table 5, Appendix C) ...... 66

Table 4: Summary table of water sample locations, spatially associated catchments, major contributing streams, and stream order. An asterisk (*) indicates an unnamed catchment that is designated as the water sample location name ...... 97

Table 5: Summary of spatial data, data type, source, and collection notes ...... 98

Table 6: Symbols classification codes for landuse coverage (source and collection notes in Table 5) ...... 100

Table 7: Symbols classification for Holdridge Life Zone coverage (source and collection notes in Table 5)...... 102

Table 8: Symbols classification for climate zone coverage (source and collection notes in Table 5) ...... 104

Table 9: Symbols classification for geology coverage (source and collection notes in Table 5) ...... 106 x

Table 10: Classification interpretation for lithology coverage (source and collection notes in Table 5) ...... 108

Table 11: Table that shows the results of an spatial intersection between subset catchments, water sample sites, districts with population, geology coverage, lithology coverage, ecological life zone coverage, climate zone coverage, and land-use coverage ...... 108

Table 12: Average major ion concentrations (meq/L) at all water sample locations ..... 117

Table 13: Percent change for major ion concentrations at all water sample locations (negative values indicate percentage increase and positive values indicate percentage decrease) ...... 118

Table 14: Time range averages (A, B, & C) and percentage change for sulfate/calcium ratios in each sample site ...... 119

List of Figures

Figure 1: Map of Santa River watershed, rivers, glacial coverage, major cities, and Huascarán National Park...... 5

Figure 2: Top: Graph that shows average high and low monthly temperatures for Huaraz, Peru; Bottom: Graph that shows monthly average precipitation and rainfall days for Huaraz, Peru (World Weather Online 2011) ...... 7

Figure 3: Map of Yanayacu catchment or drainage basin, glaciers, lakes, and rivers ...... 9

Figure 4: Conceptual Andean environment, showing rain shadow effect across the Andes and water use for human activities described by elevation intervals ...... 12

Figure 5: Left: Mixed smallholder and communal agricultural/pastoral landscape; Right: Juxtaposition between a small community and Mount Huascarán in Huascarán National Park and Buffer Zone (photos taken by the author) ...... 13

Figure 6: Example datasets from different years, demonstrating a variety of notation and formatting (top left, top center, top right); A portion of the final multi-year dataset with a primary key field and standardized notation (bottom) ...... 21

Figure 7: Figure 7: Map showing original sample site locations incorrectly scattered (top left); Map showing sample site locations that are adjusted (top right); Screenshot of Google Earth reference point with latitude and longitude data (bottom) ...... 23

Figure 8: Map of complete set of water sample locations (left); Map of spatially and temporally limited set of water sample locations used in the present study (right) ...... 25

xii

Figure 9: Map showing original set of polygons that define catchments in the Santa River watershed (left); Map showing subset of polygons that define catchments that are associated with water sample sites (right) ...... 27

Figure 10: Map showing geology coverage results from the KML conversion tool in a free software (left); Map showing geology coverage results from the KML conversion tool in ArcGIS 10 (center); Map showing geology coverage that is corrected and simplified ...... 29

Figure 11: Example of scanned quadrangle geology map that is used to generate data or inform vector editing...... 31

Figure 12: Visual representation of an intersection in mathematical set theory (top) and a spatial intersection in a GIS environment (bottom) ...... 33

Figure 13: Map of the results of a spatial intersection between the Yanayacu catchment and the lithology coverage ...... 34

Figure 14: Map that shows an incorrect stream network in which segments are fragmented and gaps are visible (left); Map that shows valid stream network with gaps removed and stream order attributed to individual segments (right) ...... 36

Figure 15: Series of charts that show several different patterns observed by plotting the cation sum against the anion sum ...... 44

Figure 16: Map showing the locations of the catchments that show Pattern B, Pattern C, Pattern D, and no pattern, with lithology coverage ...... 45

Figure 17: Graph of the charge balance for Yanayacu with sample points grouped by sample site location ...... 47

Figure 18: Map of Yanayacu catchment with sample site locations labeled ...... 48

xiii

Figure 19: Piper diagram showing areas that describe different water types and water sample examples that show different source rock origins (adapted from Hounslow 1995) ...... 55

Figure 20: Piper plots for all water sample sites within the upper Santa River watershed56

Figure 21: Piper plot for water samples with the Yanayacu catchment ...... 58

Figure 22: Map showing the percentage of each lithology type within a circle that is sized proportional to the ratio of sulfate to calcium ...... 69

Figure 23: Map showing the percentage of glacial coverage within a circle that is sized proportional to the ratio of sulfate to calcium ...... 70

Figure 24: Average major ion concentrations and pH for the study region...... 72

Figure 25: Average sulfate/calcium ratio for, percent glacial coverage, stream order, and elevation for all sample sites ...... 73

Figure 26: Sulfate/calcium concentration ratios for each time range for each sample site ...... 76

Figure 27: Percentage change in sulfate/calcium concentration ratios within the study region during the past decade ...... 77

Figure 28: Sulfate/calcium concentration ratios for each time range for each sample site in the Yanayacu catchment ...... 78

Figure 29: Percentage change in sulfate/calcium concentration ratios within the Yanayacu catchment during the past decade ...... 79

Figure 30: Reference map for sample site locations and names ...... 95

xiv

Figure 31: Reference map for catchment boundaries and names ...... 96

Figure 32: Map of landuse coverage for subset catchments (source and collection notes in Table 5; symbol definitions in Table 6) ...... 99

Figure 33: Map of Holdridge Life Zone coverage for subset catchments (source and collection notes in Table 5; symbol definition in Table 7) ...... 101

Figure 34: Map of climate zone coverage for subset catchments (source and collection notes in Table 5; symbol definition in Table 8) ...... 103

Figure 35: Map of geology coverage for subset catchments (source and collection notes in Table 5; symbol definitions in Table 9) ...... 105

Figure 36: Map of lithology coverage for subset catchments (source and collection notes in Table 5; symbols definition in Table 10) ...... 107

Figure 37: Charge balance plots by catchment ...... 112

Figure 38: Charge blance plot of sample points on the Santa River ...... 115

Figure 39: Scatterplot for each ionic relationship, including sodium/potassium, chloride/sodium, calcium/magnesium, calcium/sulfate, fluoride/chloride; an additional scatterplot is included to show fluoride/chloride where fluoride is very high ...... 116

Figure 40: Bar charts for each major ion, reflecting concentration values for each time range for each sample site ...... 121

Chapter 1: Introduction

The Santa River watershed is a culturally and environmentally dynamic system in north-central Peru. The Cordillera Blanca mountain range contains the highest concentration of tropical glaciers in the world - approximately 600, most of which drain westward toward the Pacific Ocean along the Santa River (Mark et al. 2010). Glaciers can seasonally provide between 40 and 60 percent of the region’s freshwater resources and function as a hydrologic buffer that mitigates water shortages during the dry season

(ibid.). The rapid retreat of these climatically sensitive freshwater reservoirs has tremendous implications for the downstream environment (Mark 2003; Silverio 2005;

Casassa 2007; Kaser 2007; Vuille 2008; Baraer 2009).

Tropical glaciers provide hydrologic resources that support a variety of human activities, including hydroelectricity, large- and small-scale crop irrigation, grazing, mining, and tourism (Chevallier 2010). However, the amplification of water demand due to population and economic growth and the coupled retreat of glaciers and decreased flow of the Santa River have increased concern for the sustainability of regional water resources (Higa Eda 2010; Mark et al. 2010; Baraer 2012). Research calls for interdisciplinary approaches addressing the social and environmental implications of glacier hydrologic changes in this data sparse region, and there is a fundamental need for

1 sustained quantitative assessments to accurately predict glacial impacts on the watershed

(Hegglin 2008; Mark 2008; Mark et al. 2010; Baraer 2012). Decreased quantity of water coupled with increased demand further motivates new research objectives that focus on water quality.

Rapid change in the environmental characteristics in the Santa River watershed reinforces the urgency for scientists to better understand the hydrologic and hydrochemical processes in the region’s surface water in order to improve future projections and to inform hydrologic resource management policy. This thesis contributes to this understanding by synthesizing and characterizing ionic chemistry in the upper Santa River watershed using a multi-method approach for the interpretation of point-source water sample data. The water chemistry data are part of a synoptic survey in which water samples were collected in previous years at the same locations along the main trunk of the Santa River and at the pour points of major tributaries. The sample ionic chemistry is analyzed and interpreted using hydrochemical techniques as well as through geographic visualization and exploratory spatial data analysis. The integrative approach of this study is appropriate based on the challenges of natural systems research in data poor regions and is effective in establishing a preliminary exploration that will inform future studies.

1.1 Research Objectives and Outline

As outlined in this chapter, this thesis aims to research hydrochemistry in an environmentally and socially dynamic tropical mountain environment by exploring the

2 spatial and temporal patterns of dissolved ion concentrations in the upper Santa River watershed, Ancash, Peru. The research objectives are to: (1) explore the relationships between multiple dissolved ion concentrations in the surface waters of the Santa River watershed; (2) relate ionic concentrations to potential geologic sources on a watershed scale; (3) quantify changes in dissolved ion concentrations over approximately the past decade; and (4) visualize, explore, and interpret ionic chemistry spatially.

The thesis is divided into five chapters and three appendices, including a glossary and a list of references. A chapter-by-chapter outline follows:

This first chapter introduces the thesis by providing background information on the study region and contextualizing the work through a review of the literature.

Justification for the work is provided, research questions are outlined, and methodology is summarized.

The second chapter describes the procedures through which the spatial data used in the study are synthesized and prepared. Data collection and assimilation are detailed, and the challenges of natural systems research in data sparse regions are briefly discussed. Other topics include creating spatial data, cleaning and editing vector data, and the challenges of synthesizing multiple datasets.

Chapter three describes a body of methodologies used in the preparation and exploration of hydrochemical data. Preparation methods include molar conversion, assessment of dissolved ion charge balance, and accuracy checking using the ratios of specific sets of ions. The hydrochemical data are then analyzed through a series of Piper

3 plots, and source rocks and processes are derived systematically using standard techniques.

The fourth chapter discusses the results of the hydrochemical analysis by interpreting ionic relationships and augmenting key details of the findings. This chapter reviews the concept of geographic visualization and describes a set of exploratory spatial data analyses that expound upon the hydrochemical analysis results. Dissolved ion concentrations and environmental parameters are described through a set of maps and graphs.

Chapter five concludes the main body of the thesis. Findings are summarized for both sets of methods, and emphasis is placed on the most significant findings.

Contribution of the work is discussed, and potential for further research is outlined.

1.2 Physical Geography of the Santa River Watershed

The Santa River watershed is an integrated natural system with complex hydrology, geology, and ecology. The Santa River flows northwest over 300 km, draining a total watershed of 12,200 km2, and is located in the Department of Ancash, approximately 400 km north of the Peruvian capital city, Lima (Figure 1).

Figure 1: Map of Santa River watershed, rivers, glacial coverage, major cities, and Huascarán National Park

The Santa River watershed constitutes the drainage area of two major mountain systems, including the dry Cordillera Negra and the heavily glacierized Cordillera Blanca

(Baraer 2012). Temperature is inversely proportional to altitude and varies little between two distinct precipitation seasons - wet (summer) and dry (winter). The summer wet season extends from September to April - peaking between January and March - and the winter dry season ranges from May to August (Kaser 1999). Rainfall in this region is strongly influenced by orographic uplift, which creates a rain shadow effect – or area of low precipitation - along the western side of the Andean ridge. Prevailing easterly winds travel across the Amazon basin, uplift at the Andean ridge, and condense to form precipitation. The air then descends the western leeward side of the mountain chain, warms, and creates the dry desert climate along the Pacific coast (Aguado & Burt 2007).

Figure 2 shows annual precipitation and temperature details for the City of Huaraz,

Ancash, Peru.

The glaciers found in the Cordillera Blanca provide the largest source of water during the dry winter season, functioning as a hydrologic buffer that allows continuous water flow through the system despite the relative absence of precipitation. The glaciers collectively function as a freshwater “reservoir” or “water tower” for the region (Silverio

2005). Glaciers are sensitive to a variety of climate parameters, including temperature, 7 precipitation, and humidity, and Andean glaciers have been receding since the Little Ice

Age (LIA) with an increased rate of reduction and wasting within the past 50-60 years.

Rapid retreat of tropical glaciers is projected to have significant impact on dry season water resources due to fluctuations in meltwater discharge (Dyurgerov 2003; Mark 2005;

Vuille 2008). The fluctuations can be characterized over time by a series of conceptual hydrologic impact phases, in which meltwater discharge first increases as glaciers lose volume then decreases and eventually reaches a plateau in which glaciers no longer contribute to stream discharge (Jansson et al. 2003). Many glacier-fed catchments in the

Santa River watershed have already surpassed the initial phases and are described by the declining phases of the conceptual hydrograph (Baraer 2012). Figure 3 shows an example of a glacier-fed catchment, or sub-basin, of the Santa River watershed. The Yanayacu catchment is frequently used in this thesis as an example to illustrate concepts and details at a higher resolution; Yanayacu is chosen specifically for this purpose because it has the highest concentration of water sample locations and because it contains several prominent and interconnected features of interest, including glaciers, proglacial lakes, and a stream network.

Figure 3: Map of Yanayacu catchment or drainage basin, glaciers, lakes, and rivers

The Santa River watershed is the product of a variety of tectonic and erosional processes. The Andean mountain chain is located along the subduction zone of the

Nazca oceanic plate and the continental South American plate (Stokes 1968). Some of the highest peaks in Peru, including Mount Huascarán, are located along the active

9 detachment fault of the Cordillera Blanca (Mark 2008). The regional lithology is strongly spatially correlated with altitudinal climatic zones and is influenced by the erosional and depositional activities of the glaciers. The upper slopes of the Cordillera

Blanca are dominated by metamorphic rocks, including quartzite, hornfels and shale, and igneous rocks, including granodiorites, diorite, and granite (Egeler and de Booy 1955;

Wilson 1967; Mark 2005). The trunk valley of the watershed is characterized by sediment deposits, including alluvium and glacial-fluvial deposits. The region demonstrates complex geomorphology, with the presence of glacial features, such as terminal and lateral moraines. The creation of moraine dams along glacial tributaries can lead to the formation of proglacial lakes, which become extremely hazardous to downstream populations due to the risk of moraine dam failure and large-scale mass wasting events (Carey 2010; Egeler and de Booy 1955; Mark 2008). Refer to Figure 35 and Figure 36 in Appendix C for additional detail on geologic formations and lithology coverage for the specific catchments within the study region.

The natural ecology of the Santa River watershed ranges from tropical premontane and montane desert scrub to grasslands, dry forests, and alpine tundra.

Individual tree types vary across the region, and high-altitude forest coverage can include non-endemic species, such as eucalyptus, and local species, such as polylepis (Byers

2000). Polylepis is important endemic forest coverage as it regulates water supplies and supports other endemic species, and yet it is highly threatened due to over-exploitation and poor management practices (Jameson 2007). Huascarán National Park and Buffer

Zone represents one of the major regional conservation efforts aimed at ecological

10 preservation and land management. The park was designated in 1977, loosely following the North American model for national park systems, but remains a limited nexus between growing populations and natural systems (Byers 2000). See Figure 1 for the defined boundary of Huascarán National Park, and see Figure 33 in Appendix C for additional detail on ecological life zone coverage of the specific study region catchments.

1.3 Water Resources and Human Vulnerability in the Santa River Watershed

Concerns for the quantity and quality of water resources in the Santa River watershed are compounded by recent social and political changes. Peru has experienced an upheaval in its political and economic structure in the past decade due to the integration of neoliberal policies and privatization of various sectors. Its transformation into a mineral-based, export-oriented country has a number of social implications, including the increase in mineral claims and the restructuring of Andean livelihoods through shifting access to natural and social resources (Bury 2005; Bury 2010). These changes have also led to environmental issues, and often environmental regulatory frameworks are not fully integrated into neoliberal-driven policy changes (Lovei 2002;

World Bank 2005; Liverman and Vilas 2006). Thus, degradation of water resources and projections for future water shortages become increasingly ominous as the economy develops and population grows and densifies (Higa Eda 2010).

Human vulnerability describes the capacity for individuals and societies to adapt to a range of pressures within a given period of time (Fussel 2007; Higa Eda 2010).

Adaptive capacity in the Andes can be directly linked to availability of water resources,

11 and human vulnerability in the Santa River watershed is therefore markedly high due to the large number of human activities that are supported by glacial hydrologic systems

(Bury 2008). Figure 4 uses a conceptual mountain environment to describe the range of human activities that rely on glaciers and glacial water resources in this region (elevation concept adapted from Chevallier 2010, with additional details from Bury 2008; Carey

2010; Mark 2010).

Figure 4: Conceptual Andean environment, showing rain shadow effect across the Andes and water use for human activities described by elevation intervals

The steep slopes, glaciers, and streams in the Huascarán National Park and Buffer

Zone provide opportunities for mountaineering and other outdoor recreation activities that support the local tourist economy (Chevallier 2010). Glacier systems themselves also can be considered cultural resources, because they provide a key aesthetic for local identity, and their associated features, such as lakes, play a significant role in the beliefs and rituals of local cultures (Carey 2010). Figure 5 shows a photo example of other higher-altitude activities, which include smallholder and communal agriculture, livestock breeding, and grazing that relies on irrigation canals supplied by local water resources.

Finally, water-use for human activities increasingly includes urban potable water consumption, mining, hydroelectric power generation across the region and large-scale irrigated agriculture in the dry desert coastal areas (Bradley 2006; Chevallier 2010; Mark

2010).

There is an urgent need to better understand the hydrologic and hydrochemical processes in the Santa River watershed given the dependency of the region on the water resources. Most previous research explores the impacts of glacial retreat on water quantity, but limited research has assessed recent changes in water quality (Mark 2003;

Mark 2005; Mark 2007). Recent studies have explored elemental concentrations in specific catchments and have described poor water quality in the headwaters of the

Cordillera Blanca. For example, the reduction of glacier volume has exposed sulfide-rich rock surfaces that change water composition and led to degradation of downstream water quality (Fortner 2011). There is therefore a need to better characterize a variety of water quality parameters across the region and assess the potential impact on human activities.

1.4 Justification and Limitations for the Work

This study provides an opportunity to better understand the hydrochemical processes in the Santa River watershed by integrating geospatial data with an available multi-year hydrochemical dataset. Within the context of ongoing climate and environmental change, retreat of glaciers and decrease in water availability raises acute concerns for water quality. However, there is a lack of long term consistent water quality analyses in the area due to absence of monitoring and data collection efforts. This thesis expands upon the small body of previous studies that analyze water quality and water chemistry in the study region by relating hydrochemistry to geologic and spatial parameters that can be explored for patterns and processes.

This thesis provides an exploration that may serve as a starting point for future studies that aim to quantitatively characterize water quality, assess the driving forces behind hydrochemical changes, and evaluate the relationship between water quality and human vulnerability. As mentioned in previous sections, there is an urgent need to understand and model environmental processes in the Santa River watershed, so that scientists may improve projections of human impact and better inform policy and management practices.

Social and logistical challenges account for much of the limitation of the scope of the present study. Information from Peruvian governmental agencies is sparse due to political and economic constraints that impact the existence of environmental data or the availability of such data to researchers (Heggelin 2008). The work is therefore based on a unique water quality dataset gathered over time by a small collaboration of academic researchers. Logistical challenges arise with the difficulty of obtaining sustained or long- term measurements in high-elevation tropical environments, and therefore the available dataset is limited in both spatial coverage and temporal range (Baraer 2012).

1.5 Overview of Methodology

In the thesis a number of procedures are used to assimilate and prepare spatial data for exploration and analysis. Heterogeneous tabular datasets are standardized and corrected to be amenable for conversion to a GIS, which is used for spatial context and data exploration (Worboys & Duckham 2004). Geologic maps are scanned and

15 integrated in a GIS with other spatial data. Other ancillary data are cleaned and synthesized through a number of editing and geoprocessing techniques (ESRI 2010).

Hydrochemical data analysis techniques are performed to assess dataset accuracy.

Accuracy checking can be a crucial step to establishing the integrity of a hydrochemical dataset. Preliminary accuracy checking methods include performing a set of unit conversions and calculating ratios for specific sets of dissolved ions. Charge balances are used to establish the consistency of the dataset and to derive processes influencing chemical characteristics (Hounslow 1995).

The dissolved ionic composition data are analyzed using Piper diagrams. Piper diagrams place ternary diagrams of the major cations and major anions on either side of a diamond-shape plot (Piper 1944). The ternary diagrams show the ratios of three variables on a triangular-shaped plot, and are useful in interpreting the relationships between major constituents in surface waters. The central diamond represents the intersections of cations and anions and can include circles that represent total dissolved solids in parts per million. Piper diagrams are used to interpret hydrologic relationships and to make inferences about source rock lithology and the characteristics of the environment processes influencing the water chemistry (Hounslow 1995; Drever 1988).

Geographic visualization and exploratory data analysis (EDA) are used to discover patterns in data that can inform the interpretation of hydrochemical analysis and potentially help inform future formal hypotheses. These methods emphasize the relationship between human visual perception and graphic representations of patterns within the data (Bailey 1995; Slocum 2005). Thematic maps are generated, map

16 interpretation issues are discussed, and other graphic summaries are employed to explore temporal change. The process is useful for perceiving hidden patterns and processes and is appropriate for the reconnaissance nature of the study.

Chapter 2: Spatial Data and Synthesis

2.1 Introduction

The production, assimilation, editing, and integration of various spatial data are crucial to our research goals: exploring and characterizing the hydrochemistry of the

Santa River watershed. Building a robust and usable dataset is important to research but is a challenge in data-sparse regions, where lack of data availability, quality, and continuity impedes quantitative research methods for hydrologic characterization

(Thieme 2007). While uncertainty is an inherent component of geospatial information, it is imperative to understand sources of error and to rigorously address issues of accuracy, precision, and completeness within the data (Couclelis 2003).

This chapter describes the various spatial data employed and the procedures used to synthesize them. The water samples were collected at point locations over several years by several different researchers (Section 2.2), so that records of the water samples present inconsistent notation, formatting, and locational standards. These heterogeneous tabular datasets are first standardized into a consistent format, cleaned for errors and duplicates, and redesigned for use in a GIS database (Section 2.3). Water sample locations are adjusted, respecting issues of uncertainty, and ultimately a subset of data is extracted for analysis within the present study based on specific delimitations (Section

2.4). Subset catchments are established and ancillary data cleaned and synthesized using editing and geoprocessing techniques (Sections 2.5-2.7) in GIS software (ESRI 2010).

2.2 Managing and Designing GIS Attribute Data

The raw products of water sample collection and analysis are recorded in a set of

Microsoft Excel spreadsheets. Records demonstrate a number of data quality issues because the samples were collected and analyzed across several years by different individual researchers, using facilities in different labs. Multi-source data quality problems exist in two different levels: (1) instance, which refers to data entry errors

(misspellings, typos, duplicates, invalid input) or overlapping, contradicting, and inconsistent records; and (2) schema, which refers to naming conflicts (semantic and syntactic differences) and structural conflicts (differences in organization of the data)

(Rahm 2000). In the study, instance-level error-checking is partially automated by performing basic spell-check and duplicate-check procedures in Excel. However, the unique Spanish and indigenous terms found in these data make complete automation impossible. A large portion of instance matching and integration is performed through an iterative process in consultation with various researchers, accessing field notes, and ground truthing in the field. Syntactic and semantic schema-level naming conflicts are addressed by assigning specific and uniform notation format across common fields, including date, time, latitude, longitude, ion concentration. Semantic schema-level naming conflicts are more challenging, as they require intuitive decisions on how different researchers understand and apply terms to specific concepts, e.g. researchers

19 might use the same name to denote a different concepts (homonyms) or use a different names for the sample concept (synonym) (Hasselbring 2000). One major area of data quality concern is the incorrect application of a notation format on units for ion concentrations; if unit terms are misinterpreted during the standardization process, then error propagates and future analysis produce misleading results, i.e. “garbage in, garbage out” (Rahm 2000).

The cleaned and homogenized Excel spreadsheets are merged into a single spreadsheet in preparation for use in a GIS database. A GIS database has several format requirements for field or column names in an attribute table, e.g. name length cannot exceed thirteen characters and unusual characters or symbols, such as hyphen, pound, or spaces, are not permitted. Field names that do not fulfill these parameters are adjusted, and a dictionary is created containing definitions of the field names. A field is created to serve as a primary key, which identifies a unique record, links the spatial with the attribute information, and can be used to join two attribute tables (Harmon 2003;

Worboys and Duckham 2004). The primary key contains three letter characters that identify the sample site, two characters that identify the year, and one character that indicates whether there is more than one record within a single year (e.g. “ANT07B” indicates that the sample was taken at Anta during 2007, and it was the second sample taken that year). Figure 6 shows examples of three original datasets and a portion of a dataset that is cleaned, standardized, and prepared for use in a GIS.

2.3 Managing Accuracy/Precision Issues and Manually Adjusting Point Data

The cleaned and prepared hydrochemistry dataset is added as a table to an ArcGIS map document and converted to a vector point ESRI shapefile referencing the latitude and longitude coordinates found in the table. Locational accuracy and precision are initially extremely variable across the dataset due to the diverse methods for establishing site coordinates. Many of the coordinates for water sample locations are located in situ using different handheld GPS devices, while other sites are recorded retrospectively by

21 referencing Google Earth or examining georeferenced photos taken near the water sample location. Accuracy refers to the correlation or error between a recorded data location and its actual position on the surface of the earth, while precision refers to specificity or the number of significant figures used to establish a data location (Harmon 2003; Worboys and Duckham 2004). Locational accuracy is checked using a stream dataset that was ground-truthed in 2011 using a GPS. Water sample point accuracy is found to be variable; many points are correctly positioned on a stream segment, while others are positioned away from stream segments (projection problems were ruled out, as the incorrectly-located points are determined to have a non-systematic direction and distance from the stream segments). Precision issues also are present, as some coordinates demonstrate higher resolution, or with more significant figures, than other coordinates.

Points are manually adjusted in ArcGIS based on expert interpretation, using the stream network and Google Earth as locational references. Figure 7 shows an example of this process.

2.4 Refining Point Dataset and Establishing Subset Catchments

A subset dataset is extracted from the water sample points for the purpose of the study. Temporal analysis requires a sample subset that represents repeated measurements at the same location over a given time span. Three time ranges are established to represent a time sequence and to capture the maximum possible number of water sample points: 2004 – 2006 (Time Range A); 2007 - 2008 (Time Range B); and 2009 – 2011

(Time Range C). Sample points that fall outside of the time ranges or are spatially isolated (i.e. only taken a particular location once or twice) are excluded from the study.

The resulting dataset contains approximately 200 points, or 60 – 70 water samples per time range. Figure 8 shows an example of the dataset before and after the application of the above spatial and temporal restrictions.

Figure 8: Map of complete set of water sample locations (left); Map of spatially and temporally limited set of water sample locations used in the present study (right)

Surface water chemistry is strongly influenced by the environmental characteristics of the area over which rivers and streams are flowing. Surface stream flow is collected and drained in a topographic area referred to as a watershed, catchment, or drainage basin (Mays 2011). “Catchment” is used for the purpose of the study to 25 describe small drainage basins formed by ravines, or quebradas, that are situated approximately perpendicular to the Santa River. Catchment boundaries are delimited by drainage flow; catchments are separated by a linear division defined by land whose drainage flow is toward a given stream or whose drainage flow is away from that stream

(ibid.). A vector polygon coverage for catchment areas is used in the study, and contains boundaries that generally follow the topographic peaks around the perimeters of the quebradas and become narrow at the outlet or mouth, where major tributaries discharge into the Santa River. A subset of catchment polygons is extracted based on spatial correlation with the subset water sample point dataset (Figure 9). The resulting project study region lies within an area of 1,847 km2 (184,700 ha) with a bounding box extending from approximately 8˚ 51’ 54” S, 77 58’ 58” W to 10° 14' 19” S, 77° 9' 26” W.

Figure 30 in Appendix C provides a reference map for sample site locations and names,

Figure 31 in Appendix C provides a reference map for subset catchment names, and

Table 4 in Appendix C lists each sample data point and its associated catchment. The water samples taken from the Santa River do not have associated catchments because the sample locations receive water flow from all upstream catchments.

2.5 Existing Ancillary Data: Collection, Conversion, Repairs, and Editing

In contrast to the previously described hydrochemistry data, a robust set of supporting, or ancillary, spatial data existed requiring relatively little cleaning and

27 editing. Ancillary data includes vector files that delimit: (1) hydrologic features, including catchments, glaciers, and stream locations; (2) ecological and climate zones, determined by temperature, precipitation, and vegetation types; (3) human and geopolitical information, including aggregated population, district - or province - boundaries, and simplified land use; and (4) surficial geology or lithology. Additional ancillary data includes a 30 meter ASTER DEM and paper maps that describe lithology coverage. Most ancillary data are provided by collaborating researchers or through a web map service (http://foroaguasanta.org/geoportal/) that offers a browser-based interface for viewing and sharing spatially referenced data that are specific to the Santa River watershed area. Many of the datasets served on the web map service are generated by

Peruvian government agencies, including the Ministry of Energy and Mines, the Ministry of the Environment, and the National Water Authority. Table 5 in Appendix C summarizes all spatial data themes, formats, and sources.

The web map service provides vectorized spatial data through Keyhole Markup

Language (KML or KMZ). KML is widely used to store spatial data because it is relatively flexible and has a web-friendly structure (ESRI 1010). Conversion of KML- format data to the ESRI shapefile format is a prerequisite to synthesis and analysis within a GIS environment. Early versions of ArcGIS do not natively support conversion of

KML files to shapefiles, but free software is available online that perform the function, notably XToolsPro toolbar (see Appendix B). Results from application of the “Import

Data from KML” conversion tool in the XToolsPro toolbar contain topological errors, including overlaps and extraneous nodes that defined arbitrary linear boundaries and

28 created gaps in the data. ArcGIS 10 supports KML or KMZ file conversion through a tool called, “KML to Layer,” which creates a feature class as well as a layer file that contains the sample graphic output as the original KML. Results from the ArcGIS 10

“KML to Layer” conversion tool are significantly cleaner, containing only minor errors in topological connections. Lithology and geology are of particular interest in the study, and a systematic simplification and generalization process is applied to these data using expert interpretation informed by reference literature and data. Refer to Figure 10 for an example of the results from conversion and simplification.

Additional techniques are applied to the datasets to improve technical integrity and aid overall interpretation and understanding. Data that undergo conversion or data that are obtained from unknown/uncertain sources occasionally fail to follow documented specifications of shapefile format, increasing the risk of return errors or system crashes during geoprocessing tasks (ESRI 2010). Valid geometry formats are automatically checked using the “Check Geometry” tool ArcGIS 10, which reports any problems within selected feature classes, and input formats are corrected using the “Repair Geometry” tool, which automatically fixes any geometry problems in the data (ibid.). Finally, content understanding is aided through defining symbol names and interpreting/translating Spanish terms within the attribute tables, and a set of paper geology maps (1:100,000 scale) is scanned and used to inform the attribution of the lithology and geology shapefiles. Figure 11 provides an example of a scanned paper geology map, and Appendix C provides an example of each set of ancillary data and a descriptive table that contains attribute symbol definitions and Spanish language interpretation (Figure 32 – Figure 36; Table 6 – Table 10).

Figure 11: Example of scanned quadrangle geology map that is used to generate data or inform vector editing

2.6 Spatial Intersection

A spatial intersect is applied to obtain the environmental characteristics of each subset catchment within the study region. The environmental characteristics captured in the ancillary data include: population by district, geology, lithology, ecological life zones,

31 climate zones, and land use. An intersection in mathematics refers to a Boolean operation performed on two sets, or collections of elements that returns a set whose members belonged in both original sets (Worboys and Duckham 2004); an intersection within a GIS environment describes the calculation of the geometric or spatial overlap of two or more features (ESRI 2010). Figure 12 is a visual representation of an intersection in mathematical set theory and a spatial intersection in a GIS environment (ibid.). The subset catchments provide the primary constraint on the spatial extent of the features that were overlapped. Therefore, an intersection of seven data layers returns a feature showing the spatial extent of the subset catchment, but containing the attributes of all of the chosen data layers. Figure 13 shows an example of the results of a spatial intersect between a single catchment and lithology coverage, and Table 11 in Appendix C shows a summary of the information derived from the results of the spatial intersection between seven data layers.

Figure 12: Visual representation of an intersection in mathematical set theory (top) and a spatial intersection in a GIS environment (bottom)

Figure 13: Map of the results of a spatial intersection between the Yanayacu catchment and the lithology coverage

2.7 Vector Editing: Snapping, Tracing, and Attribution of Stream Order

A network of stream segments is redrawn using two editing techniques – snapping and tracing – and attributed with stream order. Stream order describes a hierarchical classification system in which each stream segment is categorized based on its position in 34 a branched network. Using the Strahler methods for stream ordering, a first-order stream is a non-branching segment, or a stream that has no tributaries; first-order streams are often headwaters, and therefore demonstrate chemical characteristics most strongly influenced by solute source materials. Second-order streams are assigned at the intersection of two first-order streams; third-order streams are assigned at the intersection of two second-order streams; and so forth (Mays 2012). Highest order streams receive all of the lower order streams in a single catchment, and therefore demonstrate chemical characteristics that are heavily influenced by upstream concentrated flow (Strahler 1957;

ESRI 2010; ibid.).

Correct attribution of stream-order requires the creation of a complete, center- lined stream network that is free of topological errors. Existing stream datasets for the study region contain gaps where lakes are located, parallel lines delineating two banks of wide rivers, fragmented stream segments, inconsistent line direction, and misaligned intersections. The stream network is redrawn in ArcGIS based on existing stream network features and topography using the “snapping” tool to connect features and the

“trace” tool to duplicate features. The segments comprising the final valid stream network are attributed with stream order assigned to the segments (shown in Figure 14).

2.8 Summary

The procedures and methodologies applied in this section address several issues that arise when using spatial information from a diverse set of sources. Such procedures are a prerequisite to many analyses, particularly those that focus on data-poor regions where accurate, precise, and complete spatial data are often difficult to obtain. Correct application of data management, cleaning, and editing procedures is crucial for the correct interpretation of the products found in the next two chapters.

Chapter 3: Hydrochemical Data and Analytical Methods

3.1 Introduction

This chapter introduces the hydrochemistry of the study region through an earth sciences perspective, and presents the methodologies for exploring and analyzing dissolved ionic compositions in surface waters sampled in discrete locations. General considerations for the hydrochemical nature of proglacier waters as presented in the literature are reviewed to support a general operational hypothesis guiding our analysis

(Section 3.2). The acquisition details of the hydrochemical dataset are presented briefly

(Section 3.3) before describing the techniques applied to standardize and assess the dataset accuracy and consistency (Section 3.4.), including performing unit conversion

(Section 3.4) and calculating charge balances (Section 3.5). Ionic ratios (Section 3.6) and Piper plots (Section 3.7) are used to derive source characteristics and to determine the hydrochemical processes, or chemical weathering, affecting ionic chemistry within watersheds.

3.2 Background and Operational Hypothesis

Rivers in glacierized catchments are commonly characterized by high concentrations of dissolved ions, which include positively charged cations - calcium

(Ca2+), sodium (Na+ ), magnesium (Mg2+), and potassium (K+) - and negatively charged anions - fluoride (F-), sulfate (SO42-), nitrate (NO3-), and chloride (CL-).

Cations and anions result from the dissolution of a solute in a solution, in which an individual molecule is broken into positively- and negatively-charged constituents

(Drever 1988; Hounslow 1995; Benn & Evans 2010). Glacial meltwater at its source point, or headwaters location, is considered essentially “pristine,” containing low concentrations of total dissolved solids (TDS). The inorganic chemical composition of downstream water is largely a function of chemical weathering and depends on several parameters, including: (1) existing ionic concentrations from precipitation input; (2) rock- water contact area; and (3) and length of time over which water is in contact with rock surface. Rock-water contact area is sometimes very high in glacierized catchments due to the presence of large reaction surfaces created by changes in glacier position or volume; abundant fresh, fine grained sediments created through glacial erosion have a higher surface area relative to volume, increasing the contact area and the rate at which solids are dissolved in meltwater (Oliva 2003; Benn & Evans 2010; Fortner 2011).

Concentrations of dissolved ions can therefore be used as natural tracers, or indicator species, to identify water sources (Elango & Kannan 2007; Bove 2009). This study focuses on rock-water interactions for source derivation, which is appropriate due to the modest influence of the thin soil coverage and sparse vegetation found in glacierized catchments (Brown 2002; Tranter 2003; Benn & Evans 2010).

Chemical weathering is defined as dissolution or chemical alteration of minerals, e.g. ion exchange processes (Drever 1988). Different rock types exhibit varying

38 resistance to chemical weathering; limestones, basalts, marbles, and calcite-cemented sandstones are relatively easily-weathered and have rapid weathering rates, while shales, gneiss, granites, and quartzites are very resistant to weathering and have slower weathering rates (Maybeck 1987, ibid.; Elango and Kannan 2007). Hydrogen cations

(H+) are an important factor in weathering processes and are used to derive pH in natural waters. A low pH (or high acidity) increases the dissolution of minerals and a higher pH value (or low acidity) decreases the dissolution of minerals (Benn & Evans 2010).

Acidification can occur where surface waters interact with chemically resistant rock, and neutralization can occur through interaction with other types of natural environments, such as wetlands (Drever 1988; Hemond 1988).

The analysis is guided by a general operational hypothesis based on current understanding of the processes and sources that determine inorganic chemistry in proglacial hydrologic systems. We hypothesize that ionic concentrations in the upper

Santa River watershed are primarily determined by weathering processes in rock-water contact areas, and pristine (i.e. low total dissolved solids, TDS) glacial meltwater inherits the chemical properties of the surficial lithology along a flow path. Typically, low pH environments induce chemical weathering and reducing environments – such as wetlands or peat bogs – neutralize waters downstream.

3.3 Water Sample Data Collection and Ion Concentration Measurement

Water samples were taken predominately during the austral winter months of

June, July, and August in 2004 – 2009 and 2011. A small subset of samples was taken

39 during the months of January, February, and March in 2008 and 2009. The stream samples used in the study were taken at 27 stream localities. In order to analyze hydrochemical variability across space, four samples were taken along the Santa River and the remaining samples were taken at first-, second-, and third-order tributaries within different catchments, or sub-basins. Many sample sites were located in situ using a handheld GPS device, while other sample site locations were recorded retrospectively using a variety of other methods (detailed in Sections 2.2 – 2.3). The pH value of each sample was measured in situ using a hand-held probe (both YSI and Thermo Orion®), and samples for major ions were collected in Nalgene bottles, using standard dilution and storage procedures (Mark 2005; Fortner et al. 2011). Across the years, samples were analyzed for calcium (Ca2+), sodium (Na+), magnesium (Mg2+), and potassium (K+) using current plasma spectroscopy (Beckman SpectraSpan-V or an Optima 3000 DV

2- Inductively Coupled Plasma-Optical Emission Spectrometer) and for sulfate (SO4 ) and chloride (CL-) using ion chromatography (Dionex DX500) (ibid.). The analyses were carried out in different laboratories in both McGill University and The Ohio State

University.

3.4 Units Conversions

Units of ion concentrations are converted in preparation for subsequent accuracy checks and analysis. Concentrations of individual ions are expressed in two ways: (1) the amount of the solute, and (2) the amount of water in which the solute has been dissolved

(Drever 1988). Reported results from laboratory analysis of cation and anion concentrations are expressed in terms of the volume of a solution:

parts solute per million parts solution (ppm) = milligrams per liter (mg/L)

Volume units are converted in terms of the compound’s molarity, in millimoles (mmol), where molar mass is defined as the mass of a given chemical element or compound

(ibid.):

(mg/L) / molar mass = mmol/L

3.5 Charge Balance

Charge balance is a useful tool for determining dataset accuracy, calculating unmeasured constituents, and aiding interpretation of water chemistry characteristics and processes. The sum of the cations in milliequivalents per liter (meq/L) should be equal to the sum of the anions in meq/L because a solution must be electrically neutral (Hounslow

1995). Milliequivalent is obtained using the valency, or charge of the ion (number of bonds the ion can form):

meq/L = mmol/L * valency

The sum of the anion concentration and the sum of the cation concentration is calculated using the following:

∑cations = (Na + K + Mg + Ca) meq/L

∑anions = (F + Cl + NO3 + SO4) meq/L

The sum of the cations and the sum of the anions may not be equal in several situations:

(1) the data are inaccurate due to errors created through sampling, recording, or calculation; (2) the calculation excludes minor constituents that may be present; (3) the solution is highly acidic and the calculation does not account for H+ ions; or (4) the analysis does not account for the presence of a large concentration of organic material

(Hounslow 1995).

Charge balance is explored by plotting the cation sum against the anion sum on a scatterplot. Scatterplots are generated with respect to a 1:1 balance line, or equiline, and are categorized into several patterns in consultation with geochemistry researchers at The

Ohio State University. Figure 15 illustrates four patterns found in charge balance scatterplots: Pattern A occurs where the sum of the cations and the sum of the anions are approximately equal, indicating that the dataset is accurate and the analysis has accounted for all ionic constituents; Pattern B occurs where excess cations exist for the entire dataset, potentially indicating that the analysis has not accounted for carbonate constituents – a common observation in datasets where alkalinity is unmeasured - and the sample chemistry is primarily determined by calcium (carbonate) weathering; Pattern

C occurs where excess anions exist for the entire dataset, potentially indicating that the sample has high acidic content and sample chemistry is primarily determined by silicate weathering; and Pattern D occurs when excess anions exist at high TDS and excess cations exist at low TDS indicating high acidic content in one portion of the sample and a failure to account for carbonates in another portion of the sample. Pattern D is of particular interest when excess anions and excess cations correlate with elevation or stream order within a single catchment, describing changes in stream chemistry along a water flow path (Datta & Tyagi 1996; Elango & Kannan 2007).

Figure 15: Series of charts that show several different patterns observed by plotting the cation sum against the anion sum

Plots of the cation sum against the anion sum are generated for a series of catchments.

Figure 16 shows the locations of the catchments that show Pattern B, Pattern C, Pattern

D, and no pattern, with lithology coverage included for context. Figures 37 in Appendix

D contain the cation/anion plots for each catchment. Figure 38 in Appendix D shows the cation/anion plot, exhibiting Pattern B, for the water samples that are located directly on the Santa River and therefore not associated with specific catchments.

Figure 16: Map showing the locations of the catchments that show Pattern B, Pattern C, Pattern D, and no pattern, with lithology coverage

Pattern B is most frequently observed in the study region, Pattern C is observed in only one catchment (Quilcayhuanca), and Pattern D is observed in only one catchment

(Yanayacu). The Yanayacu catchment contains a distribution of points from the headwaters to the pour point at the Santa River and thus provides a case study catchment for exploring the changes in charge balance along a flow path from high to low elevation.

Figure 17 shows a graph of the charge balance for Yanayacu, with sample points grouped by their respective sample site location, and Figure 18 shows a map of Yanayacu catchment with sample site locations labeled and geology included for context.

Comparison between the charge balance plot and the position of sample sites in

Yanayacu reveals a significant pattern: water chemistry from samples taken at the highest elevations, or those nearest the glacial source point, exhibit excess anions while water chemistry from samples taken at lower portions of the catchment exhibit excess cations.

Figure 17: Graph of the charge balance for Yanayacu with sample points grouped by sample site location

Figure 18: Map of Yanayacu catchment with sample site locations labeled

3.6 Ionic Ratios and Source Rock Deduction

Ionic ratios are useful for checking the accuracy of a dataset, revealing unique relationships between water constituents, and affording insight to source rock mineralogy. An ionic ratio is obtained by dividing the ion concentration (mmol/L) of one

48 constituent by the ion concentration of another. Several ratios are commonly observed, as most natural freshwater is in contact with sandstone, limestone, dolomite, glacial till, or granitic rocks types. Source-rock deduction is informed by several of these ratios, particularly when expected relationships are not observed in the dataset:

Sodium >> Potassium: The concentration of sodium ions is generally greater than the concentration of potassium ions. Potassium and sodium are equally common in rocks that result from the weathering of feldspars and micas, but potassium is readily removed from water by plants and clays.

Sodium >= Chloride: The primary source of chloride is halite, or salt (NaCl), but sodium has several sources, including silicate weathering, e.g. plagioclase dissolution, and ion exchange. Halite dissolution is occurring where sodium concentration is equal to chloride concentration, and reverse softening is occurring when sodium concentration is less than chloride concentration.

Calcium >= Magnesium: The most soluble minerals are sedimentary carbonates, such as calcite, dolomite, and gypsum, and so calcium concentration is generally higher than magnesium concentration in most environments, excluding dolomite aquifers (where calcium is equal to magnesium) or ultramafic rock aquifers

(where calcium is less than magnesium).

Calcium >= Sulfate: Concentration of calcium is typically higher than sulfate because the main source of sulfate is CaSO4, which is found in the form of anhydrite, gypsum, or pyrite-source sulfuric acid. Sulfate may be higher in environments containing highly acidified water, and source rock characteristics can be inferred using the ratio

49 between calcium and sulfate: a gypsum-rich source rock is inferred where calcium concentration is equal to sulfate concentration; oxidized pyrite is present where sulfate concentration is greater than calcium concentration; and calcium, dolomite, or silicates are present where calcium concentration is greater than sulfate concentration (Hounslow

1995; Elango & Kannan 2007).

Chloride >> Fluoride: Chloride ion concentration is generally higher than fluoride concentration, except in environments that contain fluoride-rich metamorphic sediments or volcanics, such as high-silica rhyolite (Bove 2009).

Table 1 shows the results of the ion ratio check, and Figure 39 in Appendix D shows a scatterplot for each ionic relationship. Most of the ionic ratios demonstrated common characteristics with very few exceptions, but the ratio between calcium and sulfate and the ratio between chloride and fluoride demonstrated largely inconsistent relationships, i.e. sulfate is sometimes found at higher concentrations than calcium, and fluoride is sometimes found at higher concentrations than chloride. Ranrahirca and

Yanayacu catchments contained most of the water samples with high sulfate to calcium ratios and high fluoride to chloride ratios.

Table 1: List of results from accuracy check using ion ratios

Ratio Result Comments Check Na >> K True A few isolated exceptions attributed to data quality Na >= Cl True A few isolated exceptions attributed to data quality Ca >= Mg True A single exception attributed to data quality Ca >= SO4 False True for approximately two-thirds of the dataset; see Section 3.6 Cl >> F False True except at very low chloride concentrations; see Section 3.6

Methods of source-rock deduction additionally focus on the ways in which ion ratios relate to other ion ratios. Certain ratios may indicate different chemical reactions when they are examined simultaneously with other ratios, e.g. deductions based on calcium/sulfate ratios also depend on the relationship between silica and bicarbonate

– (Hounslow 1995). Bicarbonate (HCO3 ) and silica (SiO2) were calculated as a prerequisite to the application of these methods. The cation and anion sums calculated in the previous section were used to determine carbonate alkalinity, which was then applied to calculate bicarbonate:

alkalinity expressed in terms of CaCO3 = ∑cations - ∑anions

– HCO3 = (CaCO3/equivalent weight of CaCO2)* equivalent weight H

– Concentrations of zero HCO3 were used when actual calculated values were negative.

The reported silicon (Si) concentration value was then used to calculate silica:

SiO2 mg/L = (Si/molar weight of Si) * molar weight of SiO2

Two parameters were applied to the ion concentration dataset, and the results were linked with their respective interpretation. Table 2 lists the two parameters and groups the resulting values with their interpretation and correlative sample sites. The majority of the sample sites exhibited granitic (silicate) weathering, while four sample sites exhibited limestone-dolomite (carbonate) weathering. The results for the majority of sample sites suggest a calcium source other than gypsum, supporting the interpretation of carbonate or silicate weathering. Finally, two sample sites in the upper portion of the Yanayacu catchment and one site in the Quilcayhuanca catchment indicate weathering, or oxidation of pyrite.

Parameter Value Interpretation Sample Site

<0.5 where carbonate weathering: Anta; Conococha; Olleros; (HCO –/SiO ) limestone-dolomite 3 2 Pachacoto > 10 weathering Broggi; Cordillera Negra 1; Cordillera Negra 2; Mg2+ Cordillera Negra Low; 2+ 2+ Jangas; Kinzl; Llan Lakes (Ca + Mg ) <0.5 where silicate weathering: Out; Llulan: Marcara; (HCO –/SiO ) 3 2 granitic weathering Pariac; Q1; Q2; Q3; < 5 Ranrahirca; Rio Santa 2;Yan Other Glacier; Yan Out; Yan Pampa Down; Yanayacu

Pachacoto; Pariac; = 0.5 (+/-.05) gypsum dissolution Q3;Ranrahirca; Rio Santa 2

Anta; Broggi; Conococha; Cordillera Negra 1; calcium source other Cordillera Negra 2; Kinzl; than gypsum 2+ >0.5 Llan Lakes Out; Llulan; Ca (carbonates or Q1; Rio Collota; Rio Santa (Ca2+ + SO 2-) silicates) 4 1; Rio Santa Low; Yanayacu calcium removal <0.5 where Marcara; Q2; Yan Other through ion exchange neutral Glacier or calcite precipitation <0.5 where Quilcay; Yan Out; Yan pyrite oxidation pH <5.5 Pampa

3.7 Piper Diagrams

Piper diagrams are used to interpret hydrologic relationships and make inferences about the characteristics of the environmental processes influencing water chemistry.

Ternary diagrams that show the ratios of the major cations and major anions are placed on either side of a diamond-shape plot, which represents the intersections of cations and anions in a solution (Piper 1944). Piper diagrams inform interpretation in four different areas: (1) water type, designated by four different basic water types; (2) precipitation or solution, indicating the addition or removal of a constituent from a solution; (3) mixing of two waters based on amounts of end members; and (4) ion exchange, or solution and removal of ions (Hounslow 1995). This study focuses predominately on water type, which is derived from specific portions of the Piper diamond in which water samples are located. Figure 19 shows different water types in the diamond portion of the Piper diagram and includes example points of waters from different source rocks. Permanent hardness refers to waters that are dominated by (calcium + magnesium) and (chloride + sulfate) ions - possibly indicating gypsum - while temporary hardness refers to waters that are characterized by high concentrations of calcium, magnesium, and bicarbonate - possibly indicating calcite, dolomite, rhyolite, or basalt sources. Water characterized by alkali carbonates is dominated by (sodium + potassium) and (bicarbonate + carbonate) and water described as saline is primarily composed of (sodium + potassium) and

(chloride + sulfate), indicating shale, sea water, or brine (Hounslow 1995; Drever 1988,

Elango & Kannan 2007).

Figure 19: Piper diagram showing areas that describe different water types and water sample examples that show different source rock origins (adapted from Hounslow 1995)

Ion concentration values are averaged for each sample site (Table 12, Appendix

D), and their relative proportions are plotted on Piper diagrams. Figure 20 shows the results of the sample sites, which are sorted alphabetically and divided across two Piper plots due to symbology limitations in the diagram software program. The majority of the sample sites fall within temporary hardness, demonstrating source rock types of calcite, dolomite, rhyolite, or basalt. Several samples sites, including Quilcay, Q2, Yan Other

Glacier, Yan Out, Yan Pampa, and Yanayacu, fall within permanent hardness, indicating 55 source rock type containing gypsum. It is important to note that the “dominant” ion values are obtained by the sum of two constituents, and that several of the samples showing high (chloride + sulfate) are largely dominated by the sulfate constituent.

Figure 20: Piper plots for all water sample sites within the upper Santa River watershed

Figure 20 Continued.

The Yanayacu catchment is again used as a case study catchment for exploring variability within a smaller system (Figure 21). Several of the sample sites high elevation portion of the Yanayacu catchment demonstrate permanent hardness; these samples represent the streamflow within the catchment that is most proximal to its glacial origin.

The remaining Yanayacu sample sites demonstrate temporary hardness and lower TDS than the samples that are found near the glacial source.

Figure 21: Piper plot for water samples with the Yanayacu catchment

3.8 Summary

This chapter used a suite of techniques for hydrochemical analysis to explore the relationships between dissolved ionic concentrations in surface waters of the Santa River watershed and to connect ionic concentrations to potential geologic sources and processes. The following chapter discusses the findings from the hydrochemical analysis and continues the reconnaissance approach to this study though a series of exploratory spatial data analyses. These analyses aim to quantify the temporal change in ionic water chemistry for the Santa River watershed within approximately the last decade and to 58 visualize and explore the spatial relationships between ionic water chemistry and local environmental characteristics.

Chapter 4: Connecting Hydrochemistry with Spatiotemporal Patterns

4.1 Introduction

Geovisualization, or geographic visualization, and exploratory data analysis

(EDA) emphasize the relationship between human visual perception and graphic representation of patterns in data (Anselin 1993; Bailey 1995; Slocum 2005). These methods are used to make spatial contexts more visible, revealing patterns that can serve as the basis for formal hypotheses and provide an empirical foundation for higher-level analytical tests, such as multivariate regression models (Haining 1998; MacEachren

1999; Messner 1999). Geographic visualization and EDA are used in this study to expound upon the hydrochemical analysis results. The goals are to integrate hydrochemical relationships with several spatial environmental characteristics, including stream order, elevation, lithology coverage, and glacial coverage, and to quantify and visualize temporal changes within the last decade.

This chapter discusses the results of the hydrochemical analysis (Section 4.2) and links the findings to spatial and temporal patterns using geographic visualization and

EDA methods. Thematic maps (Section 4.3) are used to visually compare the geology and glacial coverage of a catchment with a specific ionic relationship identified in the hydrochemical analysis: the ratio between sulfate and calcium. This ratio is explored at

60 length because of the unique nature of this relationship as being variable across the region, with sulfate concentration exceeding calcium concentration in several areas.

Parallel coordinate plots (Section 4.4) expand upon the concepts explored in the thematic maps through juxtaposition of a series of variables, and finally bar graphs are used to explore temporal change in ionic chemistry ratios (Section 4.5).

4.2 Discussion of Hydrochemical Analysis Results

Inorganic water quality characteristics in the upper Santa River watershed are driven by several different processes. The charge balance analysis suggests a dominance of carbonate weathering, or the dissolution of limestone or dolomite. However, the exploration of ionic ratios that include the unmeasured alkalinity override part of the carbonate weathering story by providing evidence for silicate, or granitic, weathering across the watershed. The geologic data coverage shows that both weathering processes are possible; surficial geology in the region includes both limestone and granitic materials, both of which are subject to rapid erosional processes in glacierized catchments. A predominance of silicate weathering, however, is consistent with other studies that explore Andean water chemistry on a much larger scale; silicate weathering is higher in the Andes than the rest of the Amazon basin (Mortatti 2002).

The charge balance analysis and the exploration of ionic ratios also explore relationships that were unique to specific sample locations within the study region. The charge balance for the sample locations within the Yanayacu catchment suggest highly acidic waters and silicate weathering, while the ion ratio check revealed unusually high

61 sulfate concentrations. Sulfate may be higher in environments containing highly acidified water, and a comparison between calcium/sulfate ratios and pH suggests the presence of pyrite oxidation for several sample locations in Yanayacu and one sample location in Quilcayhuanca. These results are consistent with previous studies that described the hydrochemistry of these two catchments as the natural equivalent to acid mine drainage (Mark 2005; Fortner 2011). The local geology contains sulfide-rich lithologies, including pyrite schist, phyllite, and pyrite-bearing quartzite intruded by granodiorites and tonalite (ibid.). The inorganic chemical relationships and the local geology reflect an environment that is driven by coupled sulfide-oxidation and silicate dissolution (SO-SD), which refers to a linked reaction in which sulfide minerals and silica minerals are weathered simultaneously (Benn & Evans 2010). An example of such a reaction involving plagioclase is as follows:

4FeS2 (pyrite) + KAISi3O8 (K feldspar) + 15O2 + 86H2O  16K + 4Al4Si4(OH)8

(kaolinite) + 32H4SiL4 + 4Fe(OH)3 (ferric oxyhydroxides)

The following coupled sulfide-oxidation and carbonate dissolution (SO-CD) is suggested in other portions of the catchment where limestone carbonate weathering is dominant:

4FeS2 (pyrite) + 16CaCO3 (K feldspar) + 15O2 + 86H2O  16Ca2 + 16HCO3 + 8SO4 +

4Fe(OH)3 (ferric oxyhydroxides)

Both appear to be present in the upper Santa River watershed, though it is generally thought that SO-CD is more common than CO-SD in glacierized catchments (Tranter

2002; Benn & Evans 2010).

Glaciers play a significant role in weathering processes in glacierized catchments, as they provide a high energy process through which rock-water contact area and time are increased (Oliva 2003). The charge balance and the piper plot for Yanayacu catchment reveals a relationship between the position of the sample location in the catchment and its inorganic water chemistry. The four sample points receiving the most direct influence from glacial meltwater (Q2, Yan Out, Yan Pampa, Yan other Glacier) are characterized by high acidity, high TDS, and permanent hardness, with two of those samples exhibiting

SO-SD. The remaining three locations (Q1, Q3, and Yanayacu) are characterized by lower acidity, lower TDS, temporary hardness, and silicate weathering. The Q1 sample site is not directly influenced by glacial meltwater chemistry because it is located on a non-glacierized upper tributary. The Q3 and Yanayacu sample sites are located farther downstream from the glacial sources, below a glacial lake and at the pour point at which the catchment drains into the Santa River (respectively). The precise mechanism for dissolution and neutralization of the water chemistry for the downstream sample sites is unknown; the ionic concentrations may be diluted from mixing with other waters or the water may flow through a natural filtration system. The Santa River watershed contains a number of peat bogs, or bofedales, which may function as a neutralizer to acidic waters and as a retention area for other dissolved ions (Kaser 2003; Baraer 2012).

Finally, the Santa River watershed exhibits a unique ionic relationship between fluoride and chloride: fluoride is often higher in concentration than chloride. The water sample dataset contains concentrations in fluoride that vary from 0.01 to 0.73 mg/L, with an isolated observation of 3.13 mg/L at a hot spring (sample site “Chancos,” which is excluded from other portions of this study). The highest concentrations are found in the upper portions of the catchments, indicating an independence from potential anthropogenic sources of fluoride. Non-anthropogenic fluoride ionic concentrations are used as a natural tracer in water to determine source rock characteristics in a similar manner as the major cations and anions. Research demonstrates a correlation with high fluoride concentration and volcanic systems (Gaciri 1993). Inferences of process and source lithology include weathering of high-silica metamorphic and granitic igneous rocks, including minerals such as biotite, hornblende, albite, and pyroxene (Bove 2009;

Keshavarzi 2010; Naseem 2010). High concentrations of fluoride (> 1.5 mg/L) are considered a human health hazard; effects may include skeletal fluorosis and damage to the kidneys, nerves, and muscles (Lahermo 1984; Singh 2011). Concentrations of fluoride have not reached hazardous levels in the upper Santa River watershed, but may become a concern if concentrations increase due to high weathering rates in glacierized catchments.

This section describes unique ionic ratios and makes suggestions for source rock lithology: in particular the high ratio between sulfate and calcium is attributed to silicate weathering of granitic rock types – granite, granodiorite, and diorite - coupled with oxidation of pyrite found in metamorphic schist, phyllite, and quartzite. The following

64 section applies thematic mapping techniques to connect the known lithological coverage and glacial area to this unique ionic relationship and explore the basic patterns found in the local geology.

4.3 Thematic Maps

Maps serve several functions throughout this thesis; previous chapters use reference maps to add context or to identify locations of significant features, such as water sample sites, catchments, or glaciers. The current chapter uses thematic maps to emphasize the patterns of one or more variables across space (Slocum 2005). Thematic maps exist in myriad forms, using both static and interactive mediums and a large range of symbolization techniques. Symbolization can range from a choropleth, which applies a graded hue or color to a feature based on a data value, to a proportional symbol map, which uses a geometric symbol that is proportional in size to the data value (Bailey 1995; ibid.).

Several calculations and procedures are applied as a prerequisite to mapping the spatial patterns of variables. Average ionic ratios and area are calculated for each catchment within the study region. Lithology is clipped to the extent of the catchment boundaries, and a union is performed to create a new feature that contains the lithology coverage and catchment boundaries. The areas of individual lithology types are calculated within each catchment, and percentage lithology type is obtained:

% lithology type = (area of lithology type/area of catchment)*100

A similar process is applied to calculate the percentage of glacial coverage (in 2010) for each catchment, which is summarized in Table 3.

Table 3: Glacierized percentage of catchment area (source and collection notes in Table 5, Appendix C)

Catchment % Glacierized Anta 0 Conococha 0 Cordillera Negra 1 0 Cordillera Negra 2 0 Cordillera Negra Low 0 Llulan 23.5 Marcara 26.5 Negro 10.7 Pachacoto 10.9 Pariac 14.9 Quilcayhuanca 19.1 Ranrahirca 31.0 Shiqui 12.4 Yanayacu 5.8

A thematic map is created that combines proportional symbols and pie charts to show the percentage lithology coverage for each catchment and the average ionic ratios for sulfate and calcium (Figure 22). Several concepts are shown through interpretation of the thematic map. First, sulfate concentrations exceed calcium concentrations for five of the southern glacierized catchments, whose surface lithology is characterized by granodiorite, diorite, and granite, glacial fluvial deposits and a smaller proportion of metamorphics (limestone, shale, marl) and mixed volcanic materials (breccia,

66 agglomerates, etc.). The higher proportions of glacial fluvial deposit visible in these catchments reflect higher rates of glacial erosion (and glacial loss) than the northern glacierized catchments, which is consistent with findings in previous studies (Baraer

2012). High weathering rates are suggested for these catchments, due to the abundance of sulfate and the surficial evidence of glacial erosion within high-silica granitic and metamorphic rock types. This is consistent with the findings of the hydrochemical analysis. Exceptions exist, however, as two of the southern catchments which are dominated by mixed volcanic materials demonstrate an approximate one-to-one ratio, and other catchments that are dominated by granites and glacial fluvial lithology types have low sulfate to calcium ratios. Second, sulfate to calcium ratios are generally high for larger catchments, which is consistent with the idea that concentration is determined by the amount of time or area that a solution has passed over materials. Again, there is an exception to this statement, in this instance with the Cordillera Negra 1 catchment in the southern portion of the watershed.

An additional thematic map is created that combines proportional symbols and pie charts to show the percentage glacial coverage for each catchment and the average ionic ratios for sulfate and calcium (Figure 23). Ranrahirca is the most heavily glaciated, while the other Cordillera Blanca (eastern) catchments are less glacierized toward the south.

Sulfate to chloride ratios are very high for most of the deglacierized catchments and are generally low for the non-glacierized catchments, with the exception of the Cordillera

Negra 1 catchment.

The application of thematic maps has several limitations. Thematic maps in general must be observed with care, as interpretations and perceptions vary between individuals. The conclusions drawn may be largely subjective or influenced by methods of symbolization or expectations by the reader (Slocum 2005). Further, this particular approach loses resolution and variability by using catchments as the primary organizational unit, rather than individual sample locations, e.g. the use of averages eliminated the variability of sulfate to calcium ratios across the Yanayacu catchment.

This is an example of the modifiable areal unit problem (MAUP), in which the aggregation of a dataset into spatial units can produce different results depending on the definition of the spatial boundaries (Rogerson 2007). Section 4.4 explores average ionic concentrations and unique ratios by sample site, employing additional variables.

Figure 22: Map showing the percentage of each lithology type within a circle that is sized proportional to the ratio of sulfate to calcium

Figure 23: Map showing the percentage of glacial coverage within a circle that is sized proportional to the ratio of sulfate to calcium

4.4 Parallel Coordinate Plots

Parallel coordinate plots are used to explore variation in the concentration of major ions and to examine the relationships between unique hydrochemical ratios and environmental characteristics, including lithology, stream order, elevation, and glacial coverage. Parallel coordinate plots are applied to multivariate datasets to visualize how variables relate to one another. The diagram consists of observations drawn across of a series of parallel axes which represent the range of each variable (Anselin 1993).

Elevation for each sample site was estimated in preparation for this exercise using a 30

Meter ASTER DEM, and all variables were combined into a single shapefile for comparison.

Figure 24 shows a plot of the pH and average major ion concentrations across the study region. This figure shows the relationships between different ions, visually reflecting the results of the ion ratio checks in the previous chapter (note that parallel lines are not on the same numeric scale). One notable observation is that the highest values for sulfate correlate with the lowest pH values, reflecting the suspected weathering of sulfate-bearing materials in highly acidified water.

Figure 24: Average major ion concentrations and pH for the study region

Environmental variables are plotted with sulfate to calcium ratios for all sample sites across the study region (Figure 25). Natural inverse correlation is found between stream order and elevation. However, correlation between sulfate/calcium ratios and the three variables is unclear, suggesting that care should be taken when considering environmental variables as covariates in higher level statistical analysis.

Figure 25: Average sulfate/calcium ratio for, percent glacial coverage, stream order, and elevation for all sample sites

4.5 Temporal Change

The last decade has been a time period of rapid environmental change and glacier loss with the upper Santa River watershed (Baraer 2012). The links between glacial recession and variation in water chemistry cannot be rigorously tested within this thesis; however, changes in ionic chemistry are quantified for approximately the same time period as recent glacier loss. Three time ranges are used to represent a time sequence

73 over approximately the past decade: 2004 – 2006 (Time Range A); 2007 – 2008 (Time

Range B); and 2009 – 2011 (Time Range C). The time ranges are chosen to capture the maximum possible number of water sample points, as the data are not sufficiently complete to generate a more continuous time series. Temporal change is approached by comparing ionic concentrations within each time series and through calculation of percentage change for each major measured anion and cation. Percentage change is obtained through the following:

% change = (earliest observation – most recent observation)/earliest observation * 100

Calculations of percentage change for each major cation and anion are found in Table 13,

Appendix D. In certain cases, percentage change is a misleading value, particularly for ionic concentrations that are very low; percentage change calculated from concentrations that are near minimum detection limits - concentrations so low that instrumentation barely recognizes the value - can result in very high or very low results that do not accurately reflect the nature of the system. Actual calculated values are recorded, but final graphic products are adjusted to minimize the bias created by these outlier values.

Bar charts are created for each major ion, reflecting concentration values for each time range for each sample site (Figure 40, Appendix D), and additional bar charts are generated for sulfate/calcium concentration ratios for each time range for each sample site (Figure 26). Sulfate/calcium concentration ratios decrease over time for

74 approximately one third of the sample sites, while concentration ratios increase over time for the remaining two thirds.

The sample site locations are shown on a map with percentage change, highlighting the sample sites with the greatest and least change in sulfate/calcium concentration ratios within the study region during the past decade (Figure 27).

Decreasing ratio values are found in the central area of the study region, at Pariac,

Quilcay, Marcara, and Llullan, and the highest increases in sulfate/calcium ratios are found in several of the southern catchments and in Ranrahirca toward the north. Several of the southernmost catchments in the Cordillera Negra do not contain glaciers, and thus the increase in sulfate concentrations may be a result of a different process than those discussed previously. Mining claims in the Santa River watershed have significantly increased in recent decades due to neoliberal reform and the privatization of the mining sector (Bury 2005). The southern catchments in the Cordillera Negra, including

Cordillera Negra 1, contain several active mining claims (Ministry of Energy and Mines

2010). Thus, the weathering of sulfate-bearing minerals may be linked to the acidification of water through acid mine drainage in proximity to the active mines (World

Bank 2005).

Figure 26: Sulfate/calcium concentration ratios for each time range for each sample site

Figure 27: Percentage change in sulfate/calcium concentration ratios within the study region during the past decade

The Yanayacu catchment contains a distribution of points from the headwaters to the pour point and shows variability in direction and magnitude of change in the ratio between sulfate and calcium concentrations. The Q2, Q3, Yan Out, Yan Pampa, and

Yanayacu sample points show an increase in sulfate/calcium ratio, while the Q1 and Yan

Other Glacier sample sites show a decrease (Figure 28). A map of percent change in sulfate/calcium ratio shows similar patterns observed in previous sections; sulfate is higher for upper tributaries that receive the most direct contribution of glacial meltwaters

(Figure 29). However, Yanayacu also shows an increase in sulfate concentration.

Figure 28: Sulfate/calcium concentration ratios for each time range for each sample site in the Yanayacu catchment

Figure 29: Percentage change in sulfate/calcium concentration ratios within the Yanayacu catchment during the past decade

4.6 Summary

This chapter discusses the results of the hydrochemical analysis, quantifies temporal change in ionic concentrations, and visually explores the spatial relationships between ionic chemistry. The thematic maps reinforced the hydrochemical analysis discussion points, including: (1) erosion and rapid weathering processes in catchments 79 experiencing glacier loss; and (2) low pH and high concentrations of sulfate found in the presence of high-silica granitic and metamorphic surface lithology. Changes in ionic concentrations are variable across space and for each ion species, but appear to be highest in the southern catchments. The following chapter summarizes the findings, discusses the contributions of the work, and provides suggestions for future work with these data.

Chapter 5: Discussion and Conclusion

5.1 Introduction

This chapter concludes the main body of the thesis by briefly reviewing the work detailed in the preceding chapter, discussing the contributions of the completed work, and providing suggestions for future work and extensions of this study.

5.2 Review of the Content

This thesis integrates hydrochemical analysis and spatial exploration with the aim of assessing inorganic water quality characteristics and determinant processes within the upper Santa River watershed, Ancash, Peru. Emphasis is placed on identifying source characteristics for water constituents and discussing the impact of glacier change on downstream water quality. This exploration enhances our understanding of the dominant factors affecting water chemistry, contributing to a body of work whose overarching goal is to model the dynamic processes in the Santa River watershed and improve projections of human impact and vulnerability in a rapidly changing environment.

Background is provided on the physical geography and water resources of the

Santa River watershed, and on human vulnerability within the Santa River watershed.

Regional climate is described, and emphasis is placed ocn the importance of glaciers as a

81 hydrologic buffer, or water reservoir, for the region during the winter dry season. Glacial water resources help the region to grow economically by providing opportunities for tourism, hydroelectric generation, urban potable water, and irrigation for agriculture and grazing. Concern for the quality and quantity of water resources drives research that contributes to understanding regional hydrologic systems.

Data preparation is a prerequisite to the study. Raw hydrochemical data contains inconsistencies that are common when data are obtained through multiple researchers working in a data sparse environment. Data management and integration procedures are applied in preparation for subsequent chapters. Supporting data are included to help inform the study; ancillary data are converted to a new format, corrected for errors, and summarized.

The hydrochemical methods chapter reviews basic concepts of chemical weathering in glacial systems and summarizes expected characteristics for glacial meltwater. Procedures include molar conversion, accuracy checking, comparison of ionic ratios, and creating piper plots. Results suggest silicate and carbonate weathering in the system, and sulfate is identified as an unusual ionic ratio that can be attributed to pyrite weathering. While not an empirical test, this procedure is useful for describing hydrochemical variables at the selected sample sites.

Geovisualization and EDA are used in this thesis in lieu of higher-level analytical tests due to limitations on the scope of the current work, but contribute to a conceptual foundation for more advanced spatial statistical analyses. Maps are used to explore

82 relationships between ionic concentrations, lithology, and glacial coverage, and temporal changes are quantified and explored.

5.3 Discussion of Contribution

The need to examine hydrochemical processes in the upper Santa River watershed is established in the first chapter of this thesis. The retreat of glaciers and the decrease in water availability in this region raises acute concerns for water quality. Glacial resources mitigate rain shadow effect, which deprives the western slopes of the Andes of regular annual water resources via precipitation. The extreme verticality of the region, however, provides an opportunity space for high biodiversity and cultural diversity. Human activities that rely on glacial water resources vary and include tourism, irrigation agriculture, mining, and urban potable water consumption. There is an urgent need to better understand the hydrochemical processes at work in this area, as previous studies have focused more rigorously on water quantity than on water quality.

Constraints to research in data poor regions exist to due to social, logistical, and environmental challenges. Prior studies have examined these water quality data using a piecemeal approach, focusing on a single year of data or on a single catchment within the study region. This study was based on a unique set of water quality data that was spatially and temporally more extensive than those used previously.

The results of the hydrochemical methods procedures identify elemental characteristics that are unique to the study region. Dominant hydrochemical processes are narrowed down to silicate weathering, coupled pyrite oxidation with silicate

83 weathering, and to a lesser extent, carbonate weathering. Sulfate constituent is unusually high for portions of the study region and is attributed to highly acidified waters immediately downstream from a glacial point source. These findings are consistent with those found in previous studies, and lend themselves well to informing future studies that may more explicitly relate local glacial erosion to chemical weathering. Fluoride constituent is also unusually high, but is examined to a lesser extent than sulfate because high fluoride/chloride ratios are only found at very low concentrations. Detection of unusual values in fluoride constituent is helpful, however, as it alerts future studies to the need to examine fluoride rigorously using a more robust dataset.

Thematic maps show large areas of glacial deposits where known deglaciation is occurring at a rapid rate. High weathering rates of silica-rich granitic and metamorphic materials are suggested at these locations based on patterns found in the ionic concentrations in the hydrochemical analysis. These results can serve as a basis for future studies that analyze other indicators of physical and chemical weathering rates - denundation - and quantify chemical and physical denundation for the region.

Hydrochemical temporal change is shown to be higher in southern catchments and extremely variable overall, reflecting the highly dynamic and potentially vulnerable hydrologic regime in this region.

5.4 Further Research

This thesis serves as a preliminary data synthesis and assessment of patterns and processes that contribute to the overall understanding of the hydrochemistry of the upper

Santa River watershed. The exploratory nature of the study exposes many opportunities for future work in a variety of directions that were beyond the scope of this study. Below is a list of suggested future work.

Sample Size: Continue to increase the sample size. A portion of the original water samples was excluded from the study due to lack of geographic references or due to spatial and temporal isolation. The dataset should be rebuilt from scratch to develop a more robust sample dataset.

Study Area: In 2011, sample site locations were collected across a much larger area, from the mouth of the Santa River at the Pacific Coast to Conococha Lake, which represents the southern-most extent of study region. Sampling should continue at these locations to expand the set of repeatedly sampled sites.

Seasonality: Water chemistry is expected to vary significantly between the summer wet season and the winter dry season. Water sample data are collected exclusively during the dry season, and a multi-seasonal dataset would help to more fully characterize the water quality in this region.

Ancillary Data: Additional supplementary data should be used to better inform research questions and explore processes. A soils dataset and a higher resolution vegetation landcover dataset would be useful for exploring variables outside of geology that might be affecting water composition. Also, sample data of mineralogy and rock types should be collected and analyzed to determine source rock composition, and newly exposed rock surface should be quantified to better understand local weathering processes.

Additional Water Quality Indicators: Water quality is determined by a number of parameters. Systematic testing of water quality should include nutrient data, biological data (using sechhi disks or other techniques), trace metals analysis, etc. There is an increasing recognition of the role of microorganisms in glacier meltwater, and so an analysis of the biogeochemistry of surface water in this region would provide better understanding of regional hydrology as part of an ecological system (Anderson 2007;

Benn & Evans 2010). Additionally, indicators of physical and chemical weathering rates

– denundation – should be calculated.

Remote Sensing: Application of remote sensing provides a number of opportunities to better understand the processes in the study region. An analysis that includes land-use/land-cover change and elevation could be used to model hydrologic processes in this region and better understand streamflow characteristics and potential impacts on water quality.

Statistical Analysis: Principle Component Analysis (PCA) is a procedure used on large, multi-variable datasets to determine the subset of variables that account for the most variation. Those variables can then be used in a multivariate regression analysis to model and analyze the relationships between variables (Li 2011). A large body of research employs multivariate statistics to assess surface water quality and variability across time and space (Alberto et. Al 2001; Shreshtha 2007; Tsihrintzis1996; Zhao 2011).

Human Geographical Methods: A multi-disciplinary approach may be useful to determine how water is used in this region and to determine whether perceptions of water quality align with local perceptions. Local knowledge can also help describe water

86 characteristics of the past, where chemical data measurements are unavailable. This approach may take the form of surveys, interviews, or focus groups.

Appendix A: Glossaries

A.1: Geology and Chemistry Glossary

Acidity – the ability for a solution to neutralize a strong base; a solution characterized by a pH less than 7 (Hounslow 1995)

Agglomerate – a later part of the process of aggregation in which mineral grains coheres to organic detritus; agglomeration may be important to the removal of fine clay particles in non-ice settings (Benn and Evans 2010)

Alkalinity – “capacity of a solution to react with a strong acid, usually reported in terms of equivalent amount of calcium carbonate” (Hounslow 1995)

Alluvium (Alluvial Deposit or Alluvial Fill) – unconsolidated, stratified deposited by flowing water in river channels, and primarily consisting of silt and clay, but also may include sand and gravel (Huggett 2007)

ANA/UGRH – Autoridad Nacional de Aguas Unidad de Glacioloía y Recursos Hídricos – National Water Authority Division of Glaciology and Water Resources

Anion – a negatively charged ion (various sources; Hounslow 1995; Huggett 2007) Bedrock – solid rock in place, largely unaffected by weathering and geomorphic processes (Huggett 2007)

Breccia – a bedded conglomeration of angular fragments larger than sand grains within a fine cement matrix (Huggett 2007)

Cation – a positively charged ion (Hounslow 1995; Huggett 2007)

Cation-Anion Balance – the difference between the sum of the anions and the sum of the cations, used to check the accuracy of many water analyses because a solution must be electrically neutral (Hounslow 1995)

Conglomerate – a bedded composite of rounded sedimentary fragments, including pebbles, cobbles, and boulders, found within a fine-grained sandy matrix (Huggett 2007) 88

Dacite – an extrusive (volcanic) igneous rock; consists of quartz, plagioclase, hornblende or augite; extrusive version of granodiorite (Huggett 2007)

Detachment Fault – a planar fracture with a very large displacement area, associated with large-scale tectonics (Stokes 1968)

Diorite – an intrusive (plutonic) igneous rock; consists of amphibole, acid plagioclase, pyroxenes, and occasional small amounts of quartz (Huggett 2007)

Till – unconsolidated, poorly sorted, poorly graded material deposited by glaciers (Benn & Evans 2010)

Geochemistry – the study of the chemical composition of the earth, chemical processes, cycles, reactions that govern the composition of rocks and soils (Sarkar 2007)

Granite – an intrusive (plutonic) igneous rock; consists of quartz, feldspar, and mica (Huggett 2007)

Granodiorite – an intrusive (plutonic) igneous rock; consists of quartz, plagioclase, and potassium feldspars with biotite, hornblende, and occasionally pryroxene (Huggett 2007)

Igneous Rock – rock that results from the cooling and solidification of silicates; extrusive (or volcanic) igneous rocks are formed at the surface of the earth and are characterized by small crystal sizes or fine grains due to rapid cooling; intrusive (or plutonic) igneous rocks are formed deep below the surface of the earth and are characterized by large crystal sizes or coarse grains due to slow cooling; examples include granite, basalt, andesite, and peridotite (Hounslow 1995)

Isotope – forms of an element composed of atoms with the same atomic number, but different mass numbers (Hounslow 1995)

Lava – molten rock from a volcano; sometimes used to describe pumice or tuff (Huggett 2007)

Limestone – sedimentary rock; semi-fine grained and composed largely of calcite (Huggett 2007)

Marl – a soft unconsolidated rock composed of clay, silt, and mud (often aragonite or calcite); clay or silt proportions are quite high, generally between 30 and 70 percent (Huggett 2007)

Metamorphic Rock – rock that results from a solid-state crystallization of pre-existing rocks through changes in temperature, pressure, and stress; foliated metamorphic rocks 89 have a planar structure and can include slate, schist, and gneiss; nonfoliated metamorphic rocks have a granular texture and can include quartzite, marble, amphibolite, and hornfels (Hounslow 1995)

Mineral – an inorganic, naturally occurring, solid substance with a known chemical composition and structure (Huggett 2007)

MINEM – Ministerio de Energia y Minas – Ministry of Energy and Mines of Peru

MINAM – Ministerio de Ambiente – Ministry of the Environment of Peru

Mole – one gram formulate of a given compound given as 6.02x1023 atoms or molecules of that material (Hounslow 1995) pH – potential hydrogen - “negative log of the hydrogen ion concentration in mol/l” (Hounslow 1995)

Piper Diagram – a diagram used in geochemical or hydrochemical analysis in which the cations and anions are plotted in ternary (triangular) diagrams on either side of a diamond in which each analysis is plotted as a circle whose area is proportional to total dissolved solids (Hounslow 1995)

Pyroclastic – solid fragments expelled from an explosive or pyroclastic volcano; examples include ash and pumice (Huggett 2007)

Quartzite – a metamorphic rock; consists of large amounts of quartz (Drever 1988)

Rhyolite – an igneous rock; fine grained material that is chemically similar to granite (Drever 1988)

Sandstone - a sedimentary rock; composed of mostly of sand-sized quartz materials with variable amounts of feldspar, often cemented by calcite or silica (Drever 1988)

Sedimentary Rock – rock that results from a variety of environmental processes, including the consolidation of loose sediment, as a precipitate, or from the accumulation of plant and animal matter; examples of sedimentary rocks include sandstone, shale, limestone, and evaporate (Hounslow 1995)

Shale – sedimentary rock; described by fine-grained laminated sediments composed of silt- and clay-sized particles (Huggett 2007)

Siltstone – sedimentary rock; composed of silt-sized particles, particles larger than clay and smaller than sand (Huggett 2007)

Solubility – the measure of solute (material dissolved in a liquid) at a specific temperature (Hounslow 1995)

TDS – Total Dissolved Solids – total amount of solids remaining when a water sample is completely evaportated, or the sum of all dissolved constituents (Drever 1988)

Weathering – the breakdown of rocks through chemical, mechanical, and biological processes (Huggett 2007)

A.2: GIS and Geography Glossary

Choropleth – a map with data units with an intensity (color or saturation) that is proportional to the value of the represented data units (Slocum 2005)

DEM - Digital Elevation Model – topographic data in the form of a gridded, or raster format (Slocum 2005)

Descriptive statistics – used to explore quantitative characteristics of a sample or population; includes the generation of computation of histograms, mean, mode, median, variance, standard deviation, etc. (Slocum 2005)

Digitize – the process of constructing points, polylines, or polygons in a GIS through freehand, tracing, or other methods (ESRI 2010)

Georeference – the process of obtaining scanned maps, aerial photography, or other raster datasets and defining how the data are situated/located by aligning them with a map coordinate system (ESRI 2010)

Geographic Visualization – the interactive relationship between a human and a computer in which spatial data is perceived through a digital graphic environment (Slocum 2005)

Geoprocessing: the automation of workflows, or GIS tasks, spatial analysis, and modeling the requires multiple steps (ESIR 2010)

GIS – Geographic Information Systems/Science – a concept whose definition varies across texts, but generally refers to computer-based capture, storage, analysis, and display of spatial data (Slocum 2005)

KML – Keynote Markup Language – the primary language employed by Google for exchange, sharing, and viewing geospatial information, particularly through Google products, such as Google Earth and Google Maps (ESRI 2010)

Merge – the fusion of geometric features, along lines or edges of polygons (Slocum 2005)

Metadata – documentation for a dataset that provides detail about the source, construction, projection, definitions, etc.; metadata is generally described as “data about data” (2003)

Point – a discrete vector object with a single latitude, longitude geographical reference (ESRI 2010; Slocum 2005)

Polygon – a vector object that is defined by coordinate pairs in a closed shape (ESRI 2010), used to store areal geographic data

Polyline – a vector object that is defined by geographically referenced paths, or sequence of paths defined by connected segments (ESRI 2010)

PNUD – Programa de las Naciones Unidas para el Desarrollo – United Nations Development Programme, Peru

Raster – an image composed of pixels that contains one piece of information per pixel (Slocum 2005)

Shapefile – an ESRI data format that is used to store location and attribute information of geographic data, consisting of points, lines, and polygons (ESRI 2010)

Snapping – a tool that allows a user to construct or digitize features based on the locations of existing features; allows the constructed point, polyline, or polygon to automatically align with a vertex, edge, end, or point of another map feature (ESRI 2010)

Topology – a branch of mathematics focused on the preservation of properties under deformation or stretching; in a GIS, topology refers to the arrangement of points, polylines, and polygons and defined the ways in which features share geometry and common boundaries (ESRI 2010)

Vector – a digital format that employs points, polylines, and polygons to represent spatial data (Slocum 2005)

Vertex (Vertices) – defined by the node that connects two line segments in polyline or polygon (ESRI 2010)

Appendix B: Software Packages

ArcGIS 10.0 (ARC/INFO, with extensions) by ESRI

GeoDa 1.0 by Arizona State University GeoDa Center for Geospatial Analysis and Computation (http://geoda.asu.edu/software/downloads)

GW_Chart 1.6.1.0 by Winston, R.B. at United States Geological Survey (http://water.usgs.gov/nrp/gwsoftware/GW_Chart/GW_Chart.html)

Office 2010 (Excel, Word) by Microsoft

SPSS 17.0.3 by IBM

XToolsPro 8.1 Extension for ArcGIS by Data East (http://www.xtoolspro.com/)

Appendix C: Reference Maps, Ancillary Spatial Datasets, and Classification Notes

Reference maps and portions of the cleaned and processed ancillary datasets are summarized in this appendix. The datasets are listed with notes on data type, collection, and sources; examples are represented with a map; and classification codes are presented, interpreted, and translated from Spanish to English.

Figure 30: Reference map for sample site locations and names

Figure 31: Reference map for catchment boundaries and names 96

Sample Site Name Catchment or Drainage Basin Stream Order Anta Anta* 2 Broggi Ranrahirca 2 Conococha Santa 1 Cordillera Negra 1 Cordillera Negra 1* 2 Cordillera Negra 2 Cordillera Negra 2* 1 Cordillera Negra Low Cordillera Negra Low* 1 Jangas Santa 5 Kinzl Ranrahirca 1 Llan Lakes Out Ranrahirca 2 Llulan Llulan 3 Marcara Marcara 3 Olleros Negro 3 Pachacoto Pachacoto 3 Pariac Pariac 3 Q1 Yanayacu 1 Q2 Yanayacu 2 Q3 Yanayacu 2 Quilcay Quilcayhuanca 3 Ranrahirca Ranrahirca 3 Rio Collota Shiqui 2 Rio Santa 1 Santa 4 Rio Santa 2 Santa 4 Rio Santa Low Santa 5 Yan Other Glacier Yanayacu 1 Yan Out Yanayacu 1 Yan Pampa Yanayacu 1 Yanayacu Yanayacu 3

Table 5: Summary of spatial data, data type, source, and collection notes

Theme Data Original Description and Collection Notes Type(s) Source Catchment KML Unknown Set of polygons that define catchments Boundaries according to topography, stream drainage, and position left or right of the Santa River; http://foroaguasanta.org/geoportal/ Climate ESRI Unknown Collected by J. Bury from the Chavimochic Zones Shapefile Irrigation Project, Trujillo Peru Elevation DEM Bury, J. 30 meter ASTER DEM created by J. Bury Geology KML Gerencia Set of polygons that define the rock units RRNN- Base characterized by common properties; provided de datos ELI by the regional government of Ancash; Santa http://foroaguasanta.org/geoportal/ Geology ESRI MINEM Set of polygons that define the rock units Shapefile characterized by common properties; collected by J. Bury from the Chavimochic Irrigation Project, Trujillo Peru Geology paper map MINEM Set of 1:100,000 quadrangle geology maps; collected by B.G. Mark in Peru. Scanned by A.Eddy Glaciers ESRI ANA/URGH Set of polylgons that define glacier coverage; shapefile collected by J. Bury Hydrology ESRI Eagle Collected by B.G. Mark Shapefile Mapping Land Use ESRI Unkonwn Collected by J. Bury from the Chavimochic Shapefile Irrigation Project, Trujillo Peru Life Zones KML MINAM Set of polygons that define areas of life based on the Holdridge classification, which is based on the identification of homogenous land areas according to bioclimatic characteristics; http://foroaguasanta.org/geoportal/ Lithology ESRI Unknown Collected by J. Bury from the Chavimochic Shapefile Irrigation Project, Trujillo Peru Population KML PNUD Set of polygons that define district geopolitical by District boundaries containing range values that correspond with the approximate number of people that dwell within each district; http://foroaguasanta.org/geoportal/ Water Tabular Inorganic chemistry data referenced using GPS Chemistry Shapefile coordinates; collected by several researchers Samples (B.G. Mark, J. McKenzie, and M.Baraer) yearly between 2004 and 2011; analyzed at OSU and McGill University; compiled and converted to shapefile by A.Eddy

Figure 32: Map of landuse coverage for subset catchments (source and collection notes in Table 5; symbol definitions in Table 6)

Table 6: Symbols classification codes for landuse coverage (source and collection notes in Table 5)

Symbol Original Description Interpretation A2s(r)-P2se- n/a Unknown Xse Lag Rios y Lagunas Rivers and lakes Nevados Nevados Ice P2sec-Xse Pastoreo de paramo Calidad Grazing on “páramo” Agrologica Media - Proteccion (subalpine tundra); medium Limitacion por suelo erosion y agrological quality with clima limitation in soil erosion and climate. P3sec-Xse n/a Unknown Pob Centros Poblados Population centers Xs n/a Unknown Xse-C3se(r)- Proteccion - Cultivos Protection; permanent crops; A3se(r) Permanentes - Cultivos en crops in low agrological quality Limpio calidad Agrologica Baja with limitations in soil erosion Limitacion por suelo erosion and irrigation requirements. requiere riego Xse-P3se-A3se n/a Unknown Xse-P3se- Proteccion - Pastoreo - Cultivos Protection; grazing; crops in A3se(r) en Limpio requieren riego low agrological quality with Calidad Agrologica Baja limitations in soil, soil erosion, Limitacion por suelo y erosion and irrigation requirements. Xse-P3sec Proteccion - Pastoreo de paramo Protection; grazing on Calidad Agrologica Baja ”páramo”; low agrological Limitacion por suelo erosion y quality with limitation in soil clima erosion and climate.

100

Figure 33: Map of Holdridge Life Zone coverage for subset catchments (source and collection notes in Table 5; symbol definition in Table 7) 101

Table 7: Symbols classification for Holdridge Life Zone coverage (source and collection notes in Table 5)

Symbol Original Description Interpretation bh-MT bosque humedo - montana tropical montane moist forest tropical bmh-MT bosque muy humedo - tropical montane wet forest montana tropical bs-MBT bosque seco – montana tropical low montane dry forest bajo tropical ee-MBT espinosa – montana bajo tropical low montane thorn tropical woodland e-MT estepa - montana tropical tropical montane steppe (grasslands) md-MBT monte desértico - montana tropical low montane desert scrub bajo tropical md-PT monte desértico - tropical premontane desert scrub premontano tropical NT nevados tropical tropical ice ph-SaT paramo humedo – tropical subalpine – moist paramo subalpino tropical pmh-SaT paramo muy humedo – tropical subalpine – very wet subalpino tropical paramo pp-SaT pluvial paramo – subalpino tropical subalpine – pluvial paramo tropical tp-AT tundra pluvial – alpino tropical alpine - pluvial tundra tropical

102

Figure 34: Map of climate zone coverage for subset catchments (source and collection notes in Table 5; symbol definition in Table 8) 103

Table 8: Symbols classification for climate zone coverage (source and collection notes in Table 5)

Symbol Altitude Original Description Interpretation (m) BiC'a' 3500 a Clima Húmedo (B) y Frío wet climate; cold; little 4500 (C'), deficiente de lluvias en rainfall during the winter; invierno(i) y sin cambio very little difference térmico invernal bien definido between winter-summer (a') temperatures CiB3'a' 2000 a Clima Sub Húmedo (C) y sub wet climate; moderately 3500 Semifrio (B3') deficiente de cold; little rainfall during lluvias en invierno (i) y sin the winter; very little cambio termico invernal bien difference between winter- definido (a') summer temperatures EiA'a' 500 a Clima muy seco (E) y cálido very dry climate; warm; 2000 (A') , deficiente de lluvias en little rainfall during the inierno (i) y sin cambio winter; very little difference térmico invernal bien definido between winter-summer (a') temperatures Npg 4500 a Clima tipo Pluvil Gélido chilly pluvial (rainy) climate más

104

Figure 35: Map of geology coverage for subset catchments (source and collection notes in Table 5; symbol definitions in Table 9) 105

Table 9: Symbols classification for geology coverage (source and collection notes in Table 5)

Symbol Original Description Interpretation Js-chic Formacion Chicama Chicama Formation Ki-g Grupo Goyllarisqusga Goullariqusga Group Ki- Formacion Inca, Chulec, Inca, Chulec, Pariahuanca, i/ch/P/P Pariahuanca, Pariatambo Pariatambo Formation Nevados Nevados Ice QP-fg Deposito Fluvioglaciar Glacial Fluvial Deposit QP-g Deposito Glaciar Glacial Deposit Qr-al Deposito Aluvial Reciente Recent Alluvial Deposits Qr-co Deposito Coluvial Colluvium Deposit T-gd Granodiorita Granodiorite T-gd/to Granodiorita, tonalita Granodiorite, tonalite Ti-ca Volcanico Calipuy Volcanic Calipuy T-mz Monzonita Monzonite Ts-yu Formacion Yungay Yungay Formation

106

Figure 36: Map of lithology coverage for subset catchments (source and collection notes in Table 5; symbols definition in Table 10)

107

Table 10: Classification interpretation for lithology coverage (source and collection notes in Table 5)

Lithology Lithology Interpretation areniscas-lutitas-cuarcitas sandstone, shale, quartzite brechas, aglomerados, volcanicos, breccia, agglomerates, volcanics, lavas, lavas, piroclasticos, rioliticos, pyroclastics, rhyolite and dacite calizas-lutitas-margas limestone, shale, marl conglomerados-areniscas-fluviales conglomerate, sandstone, siltstone depositos aluviales y fluviales alluvial and fluvial deposits despositos fluvio-glaciares glacial fluvial deposits depositos glaciares glacial deposits granodiortia-diorita-granitos granodiorite, diorite, granite lutitas-areniscas-cuarcitas shale, sandstone, quartzite nevados ice piroclasticoos, tufoblanco dacitico- pyroclastics, white tuff, dacite, rhyolite igninbrita dacitica y riolitica

Catchment Sample District/ Geology Lithology Life Zone Climate Landuse Site Pop Description Zone Anta* Anta Anta Ki-g breccia, agglomerates, bh-MT BiC'a' P3sec- (2,368) Ki-i/ch/P volcanics, lavas, bs-MBT CiB3'a' Xse Ti-ca pyroclastics, rhyolite e-MT Xse- and dacite; ee-MBT P3se- limestone, shale, marl; pmh-SaT A3se Xse- P3sec Conococha* Conoco Catac Ki-g breccia, agglomerates, bmh-MT BiC'a' Lag cha (4,036) Qr-al volcanics, lavas, pmh-SaT P2sec- Chiquian Ti-ca pyroclastics, rhyolite Xse (4,087) T-mz and dacite; P3sec- glacial fluvial Xse deposits; granodiorite, diorite, granite; Cordillera Cordille Catac Ki-g breccia, agglomerates, bmh-MT BiC'a' P3sec- Negra 1* ra (4,036) Ti-ca volcanics, lavas, pmh-SaT Xse Negra 1 pyroclastics, rhyolite and dacite;

108

Table 11 Continued.

Cordillera Cordill Catac Ti-ca breccia, bmh-MT BiC'a' P2sec- Negra 2* era (4,036) agglomerates, pmh-SaT Xse Negra volcanics, lavas, P3sec- 2 pyroclastics, rhyolite Xse and dacite;

Cordillera Cordille Huallanc Js-chic breccia, agglomerates, e-MT BiC'a' Xse- Negra Low* ra a (955) Qr-co volcanics, lavas, ee-MBT CiB3'a' C3se(r)- Negra Huaylas T-gd/to pyroclastics, rhyolite md-PT A3se(r) Low (1,894) Ti-ca and dacite; ph-SaT Xse- Mato Ts-yu limestone, shale, marl; P3se- (2,109) sandstone, shale, A3se quartzite; Xse- shale, sandstone, P3se- quartzite; A3se(r) Llulan Llulan Caraz Nevados alluvial and fluvial ee-MBT BiC'a' Lag (23,580) QP-fg deposits; e-MT CiB3'a' Nevados QP-g granodiorite, diorite, md-MBT Npg P3sec- Qr-al granite; NT Xse T-gd/to ice; pmh-SaT Pob Ts-yu pyroclastics, white tp-AT Xse- Ts-yu tuff, dacite, rhyolite; P3se- A3se Xse- P3se- Marcara Marcar Marcara Js-chic glacial deposits; bh-MT BiC'a' A2s(r)- a (8,634) Ki-g glacial fluvial bs-MBT CiB3'a' P2se-Xse San Nevados; deposits; e-MT Npg Lag Miguel QP-fg granodiorite, diorite, NT Nevados de Aco Qr-al granite; pmh-SaT Xs (2,552) T-gd/to limestone, shale, marl; tp-AT Xse- Ts-yu shale, sandstone, P3se- quartzite; A3se(r) Xse- P3sec Negro Olleros Olleros Js-chic alluvial and fluvial bh-MT BiC'a' A2s(r)- (2,582) Nevados deposits; NT CiB3'a' P2se-Xse Recuay QP-g breccia, agglomerates, pmh-SaT Npg Lag (5,015) Qr-al volcanics, lavas, pp-SaT Nevados T-gd pyroclastics, rhyolite tp-AT P3sec- and dacite; Xse granodiorite, diorite, Xs granite; Xse- limestone, shale, marl; P3se- sandstone, shale, A3se(r) quartzite; Xse- P3sec Pachacoto Pachac Catac Js-chic breccia, agglomerates, bmh-MT BiC'a' Lag oto (4,036) Ki-g volcanics, lavas, NT Npg Nevados QP-g pyroclastics, rhyolite pmh-SaT P3sec- Ti-ca and dacite; pp-SaT Xse glacial fluvial pp-SaT Xse- deposits; tp-AT P3sec granodiorite, diorite, granite; limestone, shale, marl 109

Table 11 Continued.

Pariac Pariac Huaraz Nevados breccia, bh-MT BiC'a' A2s(r)- (56,186) QP-g agglomerates, bs-MBT CiB3'a' P2se- T-gd/to volcanics, lavas, NT Npg Xse Ti-ca pyroclastics, rhyolite pmh-SaT Lag and dacite; pp-SaT Nevados glacial deposits; tp-AT P3sec- glacial fluvial Xse deposits; Xs granodiorite, diorite, Xse- granite; P3se- A3se(r) Xse- P3sec Quilcayhua Quilcay Huaraz Js-chic breccia, agglomerates, bh-MT BiC'a' A2s(r)- nca (56,186) Nevados volcanics, lavas, bs-MBT CiB3'a' P2se-Xse Independ QP-g pyroclastics, rhyolite NT Npg Lag encia Qr-al and dacite; pmh-SaT Nevados (62,853) T-gd/to glacial deposits; tp-AT P3sec- Ti-ca glacial fluvial Xse deposits; Pob granodiorite, diorite, Xs granite; Xse- P3se- A3se(r) Xse- P3sec Ranrahirca Broggi Ranrahir Ki-g alluvial and fluvial bh-MT BiC'a' Lag Kinzl ca Nevados deposits; ee-MBT CiB3'a' Nevados Llan (2,818) QP-fg glacial fluvial e-MT Npg Nevados Lakes Yungay QP-g deposits; NT Xse- Out (20,075) Qr-al granodiorite, diorite, pmh-SaT P3se- Ranrahi T-gd/to granite; pmh-SaT A3se rca Ts-yu ice; tp-AT Xse- limestone, shale, marl; P3se- pyroclastics, white A3se(r) tuff, dacite, rhyolite; Xse- P3sec Shiqui Rio Catac QP-g breccia, agglomerates, NT BiC'a' Nevados Collota (4,036) Qr-al volcanics, lavas, pmh-SaT Npg P2sec- Ti-ca pyroclastics, rhyolite pp-SaT Xse and dacite; tp-AT P3sec- glacial fluvial Xse deposits; Xs Xse- P3sec Yanayacu Q1;Q2; Catac Js-chic glacial fluvial bh-MT BiC'a' A2s(r)- Q3 (4,036) QP-g deposits; NT CiB3'a' P2se-Xse Yan Ticapam T-gd breccia, agglomerates, pmh-SaT Npg Lag Other pa volcanics, lavas, pp-SaT Nevados Glacier (2,436) pyroclastics, rhyolite P3sec- Yan and dacite; Xse Out limestone, shale, marl; Xs Yan granodiorite, diorite, Xse- Pampa granite P3se-

110

Appendix D: Calculation and Analysis Results

A portion of the calculation and analysis results from hydrochemistry procedures and from exploratory spatial analysis are contained here.

111

Figure 37: Charge balance plots by catchment

112

Figure 37 Continued.

113

Figure 37 Continued.

114

Figure 38: Charge blance plot of sample points on the Santa River

115

116

Table 12: Average major ion concentrations (meq/L) at all water sample locations

Site Ca Mg Na K CO3 HCO3 Cl SO4 Name Anta 2.530532 1.561336 0.632392 0.063906 2.409193 4.892589 0.116108 0.640988 Broggi 0.369827 0.036643 0.045055 0.012575 0.096481 0.195934 0.032383 0.250283 Conococha 0.725867 0.14713 0.447837 0.046063 0.610588 1.239981 0.092103 0.40433 Cordillera 0.238992 0.106216 0.407136 0.066604 0.280428 0.569493 0.221236 0.21662 Negra 1 Cordillera 1.679185 0.49356 0.43498 0.015355 1.324103 2.688988 0.013265 0.39899 Negra 2 Cordillera 2.12236 0.306547 1.26114 0.042087 1.503162 3.052621 0.127987 1.078967 Negro Low Jangas 1.18486 0.403093 0.582577 0.075483 0.624883 1.269012 0.33687 0.85164 Kinzl 0.18287 0.032063 0.030737 0.018337 0.072051 0.14632 0.007462 0.129427 Llan Lakes 0.258973 0.030145 0.046129 0.015555 0.097181 0.197355 0.025364 0.148075 Out Llullan 0.39838 0.064353 0.169031 0.018971 0.249752 0.507197 0.030447 0.179898 Marcara 0.695172 0.25642 0.37954 0.054848 0.128777 0.261521 0.259448 0.885384 Olleros 0.757835 0.748145 0.354663 0.047235 0.318433 0.646673 0.19691 2.012425 Pachocoto 1.018825 0.401649 0.182628 0.03782 0.25499 0.517833 0.152857 1.114542 Pariac 0.508355 0.14495 0.08702 0.017618 0.24975 0.507193 0.010603 0.327785 Q1 0.42254 0.061106 0.061431 0.013354 0.252567 0.512912 0.009077 0.124851 Q2 0.453615 0.09722 0.054309 0.012863 0.039585 0.080389 0.007935 0.55675 Q3 0.315504 0.062584 0.045391 0.014263 0.097293 0.197582 0.011369 0.26176 Quilcay 0.67954 0.40495 0.085005 0.018365 0 0 0.020363 1.616465 Ranrahirca 0.457754 0.103494 0.131484 0.023509 0.180205 0.36596 0.025559 0.374386 Rio Collota 1.048773 0.090847 0.184613 0.023457 0.574473 1.16664 0.030637 0.356273 Rio Santa 1 1.39407 0.221253 0.357365 0.04582 0.933543 1.89584 0.171052 0.28894 Rio Santa 2 1.325787 0.44952 0.666 0.074318 0.429093 0.871403 0.509318 1.275927 Rio Santa 1.86848 0.538273 0.631847 0.072293 0.814613 1.654316 0.43525 1.304853 Low Yan Other 0.639346 0.142269 0.053206 0.012671 0.047117 0.095685 0.008506 0.757963 Glacier Yan Out 0.936707 0.191165 0.032631 0.014836 0 0 0.005709 1.487858 Yan Pampa 0.5548 0.116644 0.040692 0.011096 0.00051 0.001035 0.008693 0.834542 Yanayacu 0.26704 0.085547 0.091047 0.017055 0.170224 0.34569 0.013573 0.196437

117

Table 13: Percent change for major ion concentrations at all water sample locations (negative values indicate percentage increase and positive values indicate percentage decrease)

Site Ca Mg Na K CO3 HCO3 Cl SO4 Name Anta 22.8673 25.2486 6.3431 12.9338 21.6637 21.6637 30.524 20.3064 Broggi 43.0528 -41.6044 32.5785 -1.8403 37.0142 37.0142 -819.787 35.4716 Conococha 34.183 14.3379 17.1428 11.5974 16.9166 16.9166 68.3433 - 1123.87 Cord Negra 1 42.226 29.3752 86.0308 81.257 32.647 32.647 17.304 - 279.686 Cord Negra 2 -95.0528 -114.457 18.4295 -155.802 -2.9111 -2.9111 -40.1346 - 2084.87 Cord Negra 2.7903 -5.0143 -1.3987 -10.2705 2.2948 2.2948 -6.9207 -0.6118 Low Jangas -4.5669 -2.0011 -15.8739 -9.5657 0.8658 0.8658 -11.1504 -7.286 Kinzl 4.5191 -77.9982 24.6303 28.9782 51.6944 51.6944 -735.045 5.7483 Llan Lakes Out 5.4592 -85.6947 12.9983 -8.638 -2.8239 -2.8239 -1211.94 22.1668 Llullan -12.915 -100.54 -50.899 -45.4518 -61.6913 -61.6913 -26.8369 7.7588 Marcara -17.2067 -6.5363 -16.0228 -16.2869 -169.364 -169.364 22.5178 -4.08 Negro 20.811 7.5808 -40.5185 -14.7534 55.5751 55.5751 -0.8876 - 33.7257 Pachacoto 5.2654 30.7825 32.1069 18.4345 -34.9523 -34.9523 60.6854 7.527 Pariac -168.897 -340.239 -135.002 -77.6115 -1152.48 -1152.48 56.6257 45.9314 Q1 -9.375 -116.486 -9.6614 -11.9718 -27.026 -27.026 -122.083 1.6335 Q2 24.7162 0.8192 24.8654 12.2199 8.0322 8.0322 -271.291 7.2138 Q3 16.5995 -26.0172 14.9175 -23.6105 23.7439 23.7439 -163.43 3.8202 Quilcay -9.6292 -11.9936 9.2543 14.4949 0 0 -1.8422 -1.3014 Ranrahirca 36.3417 20.1568 28.7333 8.7674 44.1661 44.1661 -9.585 -7.3467 Rio Collota -19.485 -57.2863 -11.1687 -23.0694 -20.2995 -20.2995 -44.6421 - 19.9561 Rio Santa 1 9.9609 -10.0639 10.3343 12.0848 11.8456 11.8456 -8.7554 - 31.1027 Rio Santa 2 28.6387 2.1536 8.7314 11.5708 3.2032 3.2032 55.2638 7.9913 Rio Santa Low -59.6458 -102.466 -36.3715 -34.4288 -9.9847 -9.9847 -44.302 - 85.8001 Yan Other -76.9374 -135.6 -518.233 -256.032 -1867.97 -1867.97 -5476.59 -39.57 Glacier Yan Out -25.469 -50.2604 10.2875 -1.8603 0 0 -257.151 - 37.9095 Yan Pampa -20.3828 -64.0306 3.4918 -17.0361 100 100 -469.534 - 29.9304 Yanayacu -11.0209 67.5005 -32.5476 -21.0701 -0.4699 -0.4699 70.7447 125.981 9

118

Table 14: Time range averages (A, B, & C) and percentage change for sulfate/calcium ratios in each sample site

Site Name & Measure SO4:Ca Site Name & Measure SO4:Ca Anta (A) 0.26 Kinzl (A) 0.71 Anta (B) 0.20 Kinzl (B) 0.78 Anta (C) 0.27 Kinzl (C) 0.70 Anta (% chg) -3.32 Kinzl (% chg) 1.32 Broggi (A) 0.66 Llan Lakes Out (A) 0.64 Broggi (B) 0.54 Llan Lakes Out (B) 0.54 Broggi (C) 0.75 Llan Lakes Out (C) 0.53 Broggi (% chg) -13.31 Llan Lakes Out (% 0.00 chg) Conococha (A) 0.10 Llullan (A) 0.53 Conococha (B) 0.11 Llullan (B) 0.32 Conococha (C) 1.78 Llullan (C) 0.44 Conococha (% chg) -1759.64 Llullan (% chg) 18.30 Cordillera Negra 1 (A) 0.55 Marcara (A) 1.34 Cordillera Negra 1 (B) 0.28 Marcara (B) 1.24 Cordillera Negra 1 (C) 3.62 Marcara (C) 1.19 Cordillera Negra 1 (% chg) -557.29 Marcara (% chg) 11.20 Cordillera Negra 2 (A) 0.05 Negro (A) 2.04 Cordillera Negra 2 (B) 0.04 Negro (B) 0.00 Cordillera Negra 2 (C) 0.60 Negro (C) 3.44 Cordillera Negra 2 (% chg) -1020.87 Negro (% chg) -68.89 Cordillera Negra Low (A) 0.69 Pachacoto (A) 1.11 Cordillera Negra Low (B) 0.07 Pachacoto (B) 1.14 Cordillera Negra Low (C) 0.71 Pachacoto (C) 1.08 Cordillera Negra Low (% -3.49 Pachacoto (% chg) 2.38 chg) Jangas (A) 1.09 Pariac (A) 1.14 Jangas (B) 1.12 Pariac (B) 0.81 Jangas (C) 0.12 Pariac (C) 0.23 Jangas (% chg) 88.97 Pariac (% chg) 79.89

119

Table 14 Continued.

Site Name & Measure SO4:Ca Site Name & Measure SO4:Ca Q1 (A) 0.30 Rio Santa 2 (A) 0.94 Q1 (B) 0.27 Rio Santa 2 (B) 0.78 Q1 (C) 0.27 Rio Santa 2 (C) 1.22 Q1 (% chg) 10.07 Rio Santa 2 (% chg) -28.94 Q2 (A) 1.13 Rio Santa Low (A) 0.63 Q2 (B) 0.93 Rio Santa Low (B) 0.63 Q2 (C) 1.40 Rio Santa Low (C) 0.73 Q2 (% chg) -23.26 Rio Santa Low (% chg) -16.38 Q3 (A) 0.80 Yan Other Glacier (A) 1.27 Q3 (B) 0.50 Yan Other Glacier (B) 0.96 Q3 (C) 0.92 Yan Other Glacier (C) 1.00 Q3 (% chg) -15.29 Yan Other Glacier 21.12 (% chg) Quilcay (A) 2.53 Yan Out (A) 1.52 Quilcay (B) 2.38 Yan Out (B) 1.54 Quilcay (C) 2.11 Yan Out (C) 1.68 Quilcay (% chg) 16.30 Yan Out (% chg) -9.91 Ranrahirca (A) 0.83 Yan Pampa (A) 1.49 Ranrahirca (B) 0.69 Yan Pampa (B) 0.00 Ranrahirca (C) 1.40 Yan Pampa (C) 1.61 Ranrahirca (% chg) -68.63 Yan Pampa (% chg) -7.93 Rio Collota (A) 0.34 Yanayacu (A) 0.43 Rio Collota (B) 0.34 Yanayacu (B) 0.44 Rio Collota (C) 0.34 Yanayacu (C) 1.08 Rio Collota (% chg) -0.41 Yanayacu (% chg) -154.01 Rio Santa 1 (A) 0.26 Rio Santa 1 (B) 0.24 Rio Santa 1 (C) 0.37 Rio Santa 1 (% chg) -45.58

120

Figure 40: Bar charts for each major ion, reflecting concentration values for each time range for each sample site 121

Figure 40 Continued.

122

Figure 40 Continued.

123

References

Aguado, E. & Burt, J.E. (2007). Understanding Weather and Climate, 4th Ed. Upper Saddle River, NJ: Prentice Hall, Inc.

Alberto, W. D., María del Pilar, D., María Valeria, A., Fabiana, P. S., Celcilia, H. A., & Angeles, B. M. (2001). Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River Basin (Córdoba-argetina). Water Resources, 35(12), 2881-2894.

Anderson, S. P. (2007). Biogeochemistry of glacial landscape systems. Annual Review of Earth and Planetary Sciences, 35, 375-399.

Anselin, L. (1993). Exploratory spatial data analysis and geographic information systems. Paper presented at the DOSES/Eurostat workshop on new tools for spatial analysis, research paper 9329, 18-20. WV: Regional Research Institute.

Bailey, T. C., & Gatrell, A. C. (1995). Interactive spatial data analysis. Harlow Essex, England: Longman Scientific & Technical.

Baraer, M., McKenzie, J. M., Mark, B. G., Bury, J., & Knox, S. (2009). Characterizing contributions of glacier melt and groundwater during the dry season in a poorly gauged catchment of the Cordillera Blanca (Peru). Advances in Geosciences, 22, 41-49.

Baraer, M. , B.G. Mark, J.M. McKenzie, T. Condom, K.I. Huh, R.J. Gomez, & Rathay, S. (2012). Glacier recession and pro-glacial hydrology in the Cordillera Blanca, Peru. Journal of Glaciology, forthcoming.

Benn, D. I., & Evans D.J.A. (2010). Glaciers & glaciation (2 ed.). London, UK: Hodder Education.

Bove, D.J., Walton-Day K., & Kimball, B.A. (2009). The use of fluoride as a natural tracer in water and the relationship to geological features: examples from the

124

Animas River Watershed, San Juan Mountains, Silverton, Colorado. Geochemistry Exploration, Environmental Analysis, 9, 125-138.

Bradley, R. S., Vuille, M., Diaz, H. F., & Vergara, W. (2006). Threats to water supplies in the tropical Andes. Science, 312, 1755-1756.

Brown, G.H. (2002). Glacier meltwater hydrochemistry. Applied Geochemistry, 17¸855- 883.

Bury, J. (2005). Mining mountains: Neoliberalism, land tenure, livelihoods, and the new Peruvian mining industry in Cajamarca. Environment and Planning, 37, 221-239.

Bury, J., Mark, B. G., McKenzie, J. M., French, A., Baraer, M., Kyung In, H., Gomez Lopez, R. J. (2010). Glacier recession and human vulnerabilty in the Yanamarey watershed of the Cordillera Blanca, Peru. Climate Change.

Bury, J., French, A., McKenzie, J., & Mark, B. G. (2008). Adapting to uncertain futures: A report on new glacier recession and livelihood vulnerability research in the Peruvian Andes. Mountain Research and Development, 28(3/4), pp. 332-333.

Byers, A. C. (2000). Contemporary landscape change in the Huascarán National Park and Buffer Zone, Cordillera Blanca, Peru. Mountain Research and Development, 20(1), 52-63.

Carey, M. 2010. In the Shadow of Melting Glaciers: Climate Change and Andean Society. NY: Oxford University Press.

Casassa. (2007). Current status of Andean glaciers. Global and Planetary Change, 59, 1- 9. doi:10.1016/j.gloplacha.2006.11.013

Chevallier, P., Pouyaud, B., Suarez, W., & Condom, T. (2010). Climate change threats to environment in the tropical Andes: Glaciers and water resources. Regional Environmental Change. doi:DOI 10.1007/s10113-010-0177-6

Couclelis, H. (2003). The Certainty of Uncertainty: GIS and the Limits of Geographic Knowledge. Transactions in GIS,7(2),165-175.

Datta, P.S. & Tyagi, S.K. (1996). Major ion chemistry of groundwater in Delhi area: Chemical weathering proceses and groundwater flow regime. Journal of the Geological Society of India, 7(2), 179-188

Drever, J.I. (1988). The Geochemistry of Natural Waters. Englewood Cliffs, NJ: Prentice hall, Inc.

125

Dyurgerov, M. (2003). Mountain and subpolar glaciers show an increase in sensitivity to climate warming and intensification of the water cycle. Journal of Hydrology, 282, 164-176. doi:10.1016/S0022-1694(03)00254-3

Egeler, C.G., & de Booy, T. (1955). Challenges of the Andes: The Conquest of Mount Huantsán. NY: David McKay Company, Inc.

Elango, L. & Kannan, r. Rock-water interaction and its control on chemical composition of groundwater. In Sarkar, D., Datta, R., & Hannigan, R. (eds.). Concepts and applications in environmental geochemistry: Developments in Environmental Science, Vol. 5. Amsterdam; Oxford: Elsevier.

ESRI (2010). Help Library. ArcGIS: Release 10. Redlands, CA: Environmental Systems Research Institute.

Fortner, S. K., Mark, B. G., McKenzie, J. M., Bury, J., Trierweiler, A., Baraer, M., & Munk, L. (2011). Elevated stream trace and minor element concentrations in the foreland of receding tropical glaciers. Applied Geochemistry, 26, 1792-1801. doi:10.1016/j.apgeochem.2011.06.003

Fussel, H. (2007). Vulnerability: A generally applicable conceptual framework for climate change research. Global Environmental change, 17, 155 – 167.

Gaciri, S. J., & Davies, T. C. (1993). The occurrence and geochemistry of fluoride in some natural waters of Kenya. Journal of Hydrology, 143, 395-412.

Haining, R., Wise, S., and Ma, J. (1998). Exploratory Spatial Data Analysis in a Geographic Information System Environment. Journal of Royal Statistical Society, Series D. (The Statistician), 47 (3), pp. 457-469.

Harmon, J.E., Anderson, S.J. (2003). The Design and Implementation of Geographic Information Systems. Hoboken, NJ: John Wiley & Sons, Inc.

Hasselbring, W. (2000). Information Systems Integration. Communications of the ACM, 43 (6), pp. 33-38.

Hegglin, E., & Huggel, C. (2008). An integrated assessment of vulnerability to glacial hazards: A case study in the Cordillera Blanca, Peru. Mountain Research and Development, 28 (3/4), pp. 299-309.

Hemond, H.F. & Benoit J. (1988). Cumulative impacts on water quality functions of wetlands. Environmental Management, 12(5), 639-653

126

Higa Eda, L. E., & Chen, W. (2010). Integrated water resources management in Peru. Procedia Environmental Sciences, 2, 340-348.

Hounslow, A.W. (1995). Water Quality Data: Analysis and Interpretation. NY: CRC Press, Inc.

World Weather Online. (2011). [Graph illustrations the Average High/Low Temperature for Huaraz, Peru and Average Rainfall for Huaraz, Peru]. Huaraz Weather, Peru Weather Averages. Retrieved from < http://www.worldweatheronline.com/Huaraz-weather-averages/Ancash/PE.aspx>

Huggett, R. J. (2007). Fundamentals of geomorphology (2nd ed.). London; NY: Routledge.

Jameson, J. S., & Ramsay, P. M. (2007). Changes in high-altitude polylepis forest cover and quality in the Cordillera de Vilcanota, Peru, 1956-2005. Biological Conservation, 138, 38-46.

Jansson, P., Hock, R., Schneider, T. (2003). The concept of glacier storage: a review. Journal of Hydrology, 282, 116-129.

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

Keshavarzi, B., & Moore, F. (2010). The source of fluoride toxicity in Muteh area, Isfahan, Iran. Environmental Earth Sciences, 61, 777-786.

Li, Y., & Migliaccio, K. W. (2011). Water quality concepts, sampling, and analyses. Boca Raton, FL: CRC Press.

Liverman, D. M., & Vilas, S. (2006). Neoliberalism and the environment in Latin America. Annual Review of Environmental Resources, 31, 327-363. doi:10.1146/annurev.energy.29.102403.140729

Lovei, M., & Gentry, B. S. (2002). The environmental implications of privatization: Lessons for developing countries. Washington, D.C.: World Bank.

MacEachren, A.M., Wachowicz, M., Edsall, R., Haug, D., 1999. Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization and knowledge discovery in database methods. International Journal of Geographical Information Science 13 (4), 311-334.

127

Mark, B. G., & Seltzer, G. O. (2003). Tropical glacier meltwater contribution to stream discharge: A case study in the Cordillera Blanca, Peru. Journal of Glaciology, 49(165).

Mark, B., McKenzie, J. M., & Gomez, J. (2005). Hydrochemical evaluation of changing glacier meltwater contribution to stream discharge: Callejon de Huaylas, Peru. Hydrological Sciences, 50(6).

Mark, B. G., & McKenzie, J. M. (2007). Tracing increasing tropical Andean glacier melt with stable isotopes in water. Environmental Science and Technology.

Mark, B. G. (2008). Tracing tropical Andean glaciers over space and time: Some lessons and transdisciplinary implications. Global and Planetary Change, 114. doi:10.1016/j.gloplacha.2006.07.032

Mark, B. G. (2010). Climate change and tropical Andean glacier recession: Evaluating hydrologic changes and livelihood vulnerability in the Cordillera Blanca, Peru. Annals of the Association of American Geographers, 100(4), 1-12.

Mays, L. W. (2011). Ground and surface water hydrology. Hoboken, NJ: Wiley.

Ministry of Energy and Mines. (2010). Mining Concessions in Peru. Lima: Ministry of Energy and Mines.

Messner, S.F., Anselin, L., Baller, R.D., Hawkins, D.F., Deane, G., Tolnay, S.E. (1999). The spatial patterning of county homicide rates: an application of exploratory spatial data analysis. Journal of Quantitative Criminology,15(4), 423-450.

Meybeck, M. (1987). Global chemical weathering of surficial rocks estimated from river dissolved loads. American Journal of Science, 287, 401-428.

Mortatti, J., & Probst, J. (2003). Silicate rock weathering and atmospheric/soil CO2 uptake in the amazon basin estimated from river water geochemistry seasonal and spatial variations. Chemical Geology, 197, 177-196.

Naseem, S., Rafique, T., Bashir, E., Bhanger, M. I., Laghari, A., & Usmani, T. H. (2010). Lithological influences on occurence of high-fluoride groundwater in Nagar Parkar area,Thar Desert, Pakistan. Chemosphere, 78, 1313-1321.

Oliva, P., Viers, J., & Dupre, B. (2003). Chemical weathering in granitic environments. Chemical Geology, 202, 225-256.

Piper, A.M., (1944) A graphic procedure in the geochemical interpretation of wateranalyses.Trans. Am. Geophys. Union 25, 914-923. 128

Rahm, E. & Do, H.H. (2000). Data Cleaning: Problems and Current Approaches. IEEE Data Engineering Bulletin, 23 (4).

Rogerson, P.A. (2007). Statistical Methods for Geography: A student guide. 2nd Ed. Los Angeles, CA: Sage Publications, Ltd.

Sarkar, D., Datta, R., & Hannigan, R. (2007). Concepts and applications in environmental geochemistry: Developments in Environmental Science, Vol. 5. Amsterdam; Oxford: Elsevier.

Shreshtha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji River Basin, Japan.

Silverio, W., & Jaquet, J. (2005). Glacial cover mapping (1987 - 1997) of the Cordillera Blanca (Peru) using satellite imagery. Remote Sensing of Environment, 95, 342- 350. doi:10.10.1016/j.rse.2004.12.012

Singh, K. C., & Rina, K. (2011). Geochemical modeling of high fluoride concentration in groundwater of Pokhran area of Rajasthan, India. Bulletin, 86, 152-158.

Slocum, T.A., McMaster R.B., Kessler, F.C., & Howard, H.H. (2005). Thematic Cartography and Geographic Visualizatio. 2nd Ed. Upper Saddle River, NJ: Prentice Hall, Inc.

Strahler, A.N. (1957). Quantitative Analysis of Watershed Geomorphology. Transactions, American Geophysical Union, 38 (6), 913 – 920.

Stokes, W.L & Judson, H. (1968). Introduction to Geology: Physical and Historical. Englewood Cliffs, NJ: Prentice Hall.

Thieme, M., Lehner, B., Abell, R., Hamilton, S.K., Kellndorfer, J., Powell, G., & Riveros, J.C. (2007). Freshwater conservation planning in data-poor areas: An example from a remote Amazonian basin (Madre de Dios River, Peru and Bolivia). Biological Conservation, 135, 484-501.

Tranter, M., Sharp, M.J., Lamb, H.R., Brown, G.H., Hubbard, B.P. & Willis, I.C. (2002). Geochemical weathering at the bed of Huat Glacier d’Arolla, Switzerland: a new model. Hydrologic Process, 16, 959 – 993.

Tranter, M. (2003). Geochemical weathering in glacial and proglacial environments. In Drever, J.I. (ed.), Treatise on Geochemistry. Elsevier, vol. 5, 189 – 205.

129

Tsihrintzis, V.A., Hamid, R. & Fuentes, H.R. (1996). Use of Geographic Information Systems (GIS) in Water Resources: A Review. Water Resources Management, 10, 251-277.

Vuille, M., Francou, B., Wagnon, P., Irmgard, J., Kaser, G., Mark, B. G., & Bradley, R. S. (2008). Climate change and tropical Andean glaciers: Past, present, and future. Earth-Science Reviews, 89, 79-96.

Wilson, J., L.Reyes, & Garayar, J. (1967). Geología de los cuardrángnulos de Pallasca, Tayabamba, Corongo, Pomabamba, Carhuaz y Huari. Serv. Geol. Minería Bol. 16, 95.

World Bank. (2005). Republic of Peru, wealth and sustainability: The environmental and social dimensions of the mining sector in Peru. ( No. 38044-PE).Environmentally and Socially Sustainable Development Department, Latin America and the Caribbean Region.

Worboys, M.F. and Duckham, M. (2004). GIS: A Computing Perspective, 2nd Ed. CRC Press.

Zhao, J., Fu, G., Lei, K., & Li, Y. (2011). Multivariate analysis of surface water quality in the Three Gorges area of China and implications for water management. Journal of Environmental Sciences, 23(9), 1460-1471.

130