the origin and distribution of trace metals in the rio santa
TRANSCRIPT
The origin and distribution of trace metals in the Rio Santa Watershed, Peru
Elizabeth A. Walsh
Department of Earth and Planetary Sciences
McGill University,
Montreal, Quebec, Canada
April, 2013
A thesis submitted to McGill University in partial fulfillment of the requirements of the degree
of Master of Science
© Elizabeth Walsh 2013
ii
Abstract
The world’s highest density of tropical glaciers is found in the Cordillera Blanca of the Peruvian
Andes. During the dry season, glacial meltwater is a vital fresh water resource in the region as it
supplies up to 40% of river discharge. Climate change is driving rapid glacial retreat, causing
serious concerns about future availability and quality of fresh water supplies. The purpose of
this thesis is to survey water quality in the Rio Santa Watershed (which drains the western side
of the Cordillera Blanca), with a particular focus on potentially toxic trace metals released by
acid mine drainage and acid rock drainage. In July 2011, major ion samples were collected from
23 sites in the Rio Santa and nine of its tributaries. Samples for trace metal analysis were
collected from 11 Rio Santa sites and eight tributaries. pH, temperature, and dissolved oxygen
were measured in situ at all sites. Rio Santa discharge was measured directly at 15 locations and
calculated by mass balance analysis for another 12 locations. Water in the Rio Santa Watershed
is characterized by high concentrations of SO4, Ca, and HCO3. These species are derived from
sulfide oxidation (both naturally occurring and enhanced by mining) and carbonate dissolution.
The pH is circumneutral at all sites except for two tributaries, Rio Olleros and Rio Quilcay,
which both had a pH below 5. Fe-oxyhydroxide coatings cover the streambeds of acid tributaries
and some sites along the Rio Santa.
As, Cd, Pb, Cu, Mo, Ni, and U concentrations tend to fluctuate above the detection level but
below 10 ug/L at most sites in the watershed. Locally elevated concentrations occurred in the
acidified Rio Olleros (70 ug/L of Ni) and Rio Quilcay (82 ug/L, 27 ug/L, 34 ug/L and 14 ug/L of
Pb, Cd, Ni, and U, respectively.) Elevated concentrations also occurred in circumneutral
tributaries Rio Tabla (43 ug/L of Ni) and Rio Llullan (720 ug/L of Mo and 180 ug/L of U.) In
the Rio Santa, elevated trace metal concentrations occurred downstream of Ticapampa mine
tailings pile (30 ug/L of As) and the city of Huaraz (73 ug/L, 58 ug/L, 53 ug/L, 40 ug/L, and 23
ug/L of Pb, Cd, U, Mo, and Ni, respectively.) Bulk loads of these indicator trace metals were
near zero at the headwaters of the Rio Santa, and tended to increase steadily along the river’s
length in proportion to increasing discharge, with large spikes in loads at Ticapampa and Huaraz.
The dramatic decline in loads immediately after these sites is indicative of the non-conservative
behavior of dissolved trace elements in the Eh-pH conditions of the Rio Santa. As dry season
discharge continues to decline due to glacier recession, current contamination problems may be
exacerbated and lower flows will inhibit the capacity of the watershed to buffer against acidic
tributary and mining effluent inputs.
iii
Resume
La plus grande densité de glaciers tropicaux se trouve dans la Cordillera Blanca des Andes
péruviennes. Durant la saison sèche, l’eau issue de la fonte glaciaire est une ressource vitale, car
celle-ci alimente jusqu’à 40% du débit des rivières. Les changements climatiques entraînant un
retrait rapide des glaciers, l’approvisionnement de la population en eau potable de qualité devient
une préoccupation sérieuse. L’objectif de ce mémoire est de faire le relevé de la qualité de l’eau
dans le bassin hydrologique du Rio Santa, qui draine la portion occidentale de la Cordillera
Blanca, en mettant l’emphase sur les éléments-traces métalliques potentiellement toxiques
libérés par le drainage minier acide et par le drainage géologique naturel. En juillet 2011, des
échantillons d’ions majeurs ont été récoltés dans 23 sites du Rio Santa et de neuf de ses affluents.
Des échantillons d’éléments-traces métalliques ont quant à eux été récoltés dans 11 sites du Rio
Santa et de 8 de ses affluents. Le pH, la température et l’oxygène dissout ont été mesurés sur
place à tous les sites. Le débit du Rio Santa a été mesuré directement à 15 endroits et calculé par
bilan massique pour 12 autres endroits. L’eau du bassin hydrologique du Rio Santa est
caractérisée par une forte teneur en SO4, Ca et HCO3, dû à l’oxydation, naturelle et accélérée par
les mines, des minéraux sulfureux et de la dissolution du carbonate. Le pH est
approximativement neutre pour tous les sites sauf pour les affluents Rio Olleros et Rio Quilcay,
qui ont tous deux un pH inférieur à 5. Un revêtement de Fe-oxyhydroxide recouvre le lit de ces
affluents acides et quelques autres sites le long du Rio Santa.
Les concentrations de As, Cd, Pb, Cu, Mo, Ni et U tendent à fluctuer au-dessus des niveaux de
détection mais restent sous 10 ug/L pour la majorité des sites dans les bassins hydrologique. Des
concentrations locales élevées apparaissent dans les affluents acides du Rio Olleros (70 ug/L de
Ni) et du Rio Quilcay (82 ug/L, 27 ug/L, 34 ug/L and 14 ug/L de Pb, Cd, Ni et U,
respectivement.) Des concentrations élevées ont aussi été trouvées dans les affluents dont le pH
est approximativement neutre, soit le Rio Tabla (43 ug/L de Ni) et le Rio Llullan (720 ug/L de
Mo et 180 ug/L de U.) Dans le Rio Santa, des concentrations élevées d’éléments-traces
métalliques ont été détectées en aval des résidus de la mine Ticapampa (30 ug/L de As) et de la
ville de Huaraz (73 ug/L, 58 ug/L, 53 ug/L, 40 ug/L et 23 ug/L de Pb, Cd, U, Mo et Ni,
respectivement.) Le débit massique de ces éléments-traces métalliques de référence était près de
zéro à la source du Rio Santa et tendait à augmenter graduellement au long de la rivière
proportionnellement à l’augmentation du débit d’eau avec des grands pics de débits massiques
d’éléments-traces métalliques à Ticapampa et Huaraz. La diminution dramatique de ses débits
massiques après ces sites indique le comportement non-conservatif d’éléments-traces dissous
dans les conditions de pH du Rio Santa. Alors que le débit d’eau de la saison sèche continue de
décliner à cause du recul des glaciers, les problèmes de contamination actuels seront
potentiellement exacerbés et des flux réduit empêcheront le Rio Santa et ses affluents d’agir
comme tampon des affluents acides et des admissions d’eau résiduaires issue des mines.
iv
Acknowledgements
Thank you to Dr. Jeffrey McKenzie for his dedication as a research supervisor. I have
greatly appreciated his insight, availability, helpfulness, and good humor throughout the thesis
process. Under his supervision I have learned so much and had a very positive grad school
experience. Thank you to my former office mate, Dr. Michel Baraer, for assistance in planning
and conducting field work, and for hours spent discussing my research and teaching me about
hydrology.
Dr. Sarah Fortner of Wittenberg University provided valuable assistance while I was
getting started with trace metal research and continued to share her knowledge and datasets ever
since, for which I am very grateful. Thank you to Bryan Mark of the Ohio State University for
his role in project planning, field work, and data analysis. Thank you to Keith Hodson and Alex
Eddie for much-needed ArcGIS assistance and sharing resources from their own research groups.
Thank you also to Ollie Wigmore, Jeff Lafreniere, and Adam French for their help with field
sampling.
The other students in my research group have made it an excellent work environment and
helped in many small ways over the years. Thanks to Danny Chavez, Rob Carver, and Laura
Maharaj. Thank you to Anne Kosowski, Kristy Thornton, and Angela Di Ninno for keeping me
on track all this time, answering my never-ending stream of questions, and making EPS such a
great department. Similar thanks go to Brigitte Dionne for huge amounts of technical assistance.
v
Preface
The following thesis presents original research by the author at the Department of Earth
and Planetary Sciences, McGill University during the 2011-2013 academic years. It is submitted
in a traditional thesis format, and is ultimately intended to form a manuscript to be submitted to a
peer-reviewed journal.
This research was supervised by Dr. Jeffrey McKenzie from McGill University. Field
work took place in the Rio Santa Watershed, Peru, in July 2011. Sample collection was done by
a team of researchers, including the author, that were led by Dr. McKenzie and Dr. Bryan Mark
from Ohio State University. Samples were analyzed at Ohio State University. Data was
incorporated with pre-existing data sets collected principally by Dr. McKenzie and Dr. Mark
since 1998, with additional data supplied by Dr. Sarah Fortner of the Wittenberg University.
Analysis and interpretation of the data were done by the author at McGill University.
vi
Table of Contents
Abstract ............................................................................................................................. ii
Resume ............................................................................................................................. iii
Acknowledgements .......................................................................................................... iv
Preface............................................................................................................................... v
Table of Contents ............................................................................................................. vi
Table of Figures ............................................................................................................. viii
Table of Tables ................................................................................................................. x
1. Introduction ................................................................................................................... 1
2. Study Area .................................................................................................................... 6
3. Methods....................................................................................................................... 11
. 3.1. Preparation of trace metal sample materials………………………………11
3.2. Field sampling .............................................................................................. 12
3.3. Laboratory work........................................................................................... 14
3.4. Other sources of data ................................................................................... 15
3.5. Discharge calculations ................................................................................. 15
3.6. Denudation calculations ............................................................................... 17
4. Results ......................................................................................................................... 18
4.1. Discharge ..................................................................................................... 21
4.2. Major ion geochemistry ............................................................................... 23
4.3. Trace metal geochemistry ............................................................................ 31
4.4. GIS Analysis ................................................................................................ 35
5. Discussion .................................................................................................................. 35
5.1. Discharge relationships ................................................................................ 35
5.2. Major ion chemistry ..................................................................................... 38
5.2.1. Sulfate ........................................................................................... 38
5.2.2. Alkalinity ...................................................................................... 42
vii
5.2.3. Comparison with other rivers........................................................ 44
5.3. Trace element chemistry .............................................................................. 45
5.3.1. Dissolved metals in the Rio Santa Watershed .............................. 45
5.3.2. Aqueous trace metal relationships ................................................ 48
5.3.3. Temporal variations in trace metals .............................................. 53
5.3.4. Implications for water quality ....................................................... 57
5.5. Temporal variations in water chemistry ...................................................... 56
5.5.1. Temporal variations ...................................................................... 56
5.5.2. Case study: Chemical weathering in the Querococha Basin ........ 66
5.5.3. Chemical weathering rates in the Cordillera Blanca..................... 70
5.5.4. Implications of changing hydrology on water chemistry ............. 71
6. Conclusions ................................................................................................................. 74
7. References ................................................................................................................... 77
Appendix ......................................................................................................................... 85
viii
Table of Figures
Figure 2.1. The Rio Santa Watershed, Peru ..................................................................... 7
Figure 2.2. The glacier-rich Cordillera Blanca ................................................................ 8
Figure 2.3. The extremely arid Cordillera Negra ............................................................. 8
Figure 2.4. Surficial geology map of the Rio Santa Watershed..................................... 10
Figure 3.1. Collection of a trace metal sample .............................................................. 15
Figure 4.1. July 2011 sampling locations in the Rio Santa Watershed.......................... 20
Figure 4.2. Discharge profile along Rio Santa ............................................................... 23
Figure 4.3. Rio Santa: major dissolved cations and SiO2 .............................................. 25
Figure 4.4. Rio Santa: major dissolved anions .............................................................. 26
Figure 4.5. Rio Santa: nutrients ..................................................................................... 27
Figure 4.6. Tributaries: major ions ................................................................................ 27
Figure 4.7. Rio Santa: trace metals ................................................................................ 28
Figure 4.8. Rio Santa: bulk loads ................................................................................... 30
Figure 4.9. Tributaries: trace metals .............................................................................. 32
Figure 5.1. Tributaries: discharge versus area ............................................................... 37
Figure 5.2. Tributaries: specific discharge versus glaciation ......................................... 37
Figure 5.3. Rio Santa: SO4 versus total cations ............................................................. 40
Figure 5.4. Tributaries: SO4 versus total cations ........................................................... 40
Figure 5.5. Dissolved oxygen versus SO4 ...................................................................... 42
Figure 5.6. Urban garbage in the Rio Santa near Huaraz .............................................. 47
Figure 5.7. Oxidation control of Fe and Mn .................................................................. 49
Figure 5.8. Relationship between trace metals and Mn ................................................. 50
Figure 5.9. Relationship between As and Mn ................................................................ 50
Figure 5.10. Quilcay Out: dissolved metals ................................................................... 52
Figure 5.11. Dissolved metal concentrations in July 2008 and July 2011 ..................... 55
Figure 5.12. Conductivity versus SO4............................................................................ 57
ix
Figure 5.13. pH values at synoptically sampled sites .................................................... 57
Figure 5.14. SO4 at synoptically sampled sites .............................................................. 57
Figure 5.15. Ca at synoptically sampled sites ................................................................ 58
Figure 5.16. Map of the Callejon de Huaylas ................................................................ 58
Figure 5.17. Querococha: annual chemical weathering rates ........................................ 62
Figure 5.18. Querococha: monthly chemical weathering rates ...................................... 62
Figure 5.19. Querococha: specific discharge and precipitation ..................................... 63
Figure 5.20. Subglacial chemical weathering reactions ................................................. 65
Figure 5.21. Proglacial chemical weathering reactions ................................................. 65
Figure 5.22. Deglaciated chemical weathering reactions .............................................. 66
Figure 5.23. Distal chemical weathering reactions ........................................................ 66
Figure 5.24. Querococha denudation rates versus other watersheds ............................. 68
Figure 5.25. Callejon de Huaylas: dry and wet season CDR ......................................... 70
Figure 5.26. Rio Santa Low: dry and wet season concentrations .................................. 70
x
Table of Tables
Table 4.1. July 2011 sampling locations in the Rio Santa Watershed ........................... 18
Table 4.2. Discharge in the Rio Santa Watershed ......................................................... 22
Table 4.3. Hydrochemistry of the Rio Santa .................................................................. 27
Table 4.4. Hydrochemistry of tributaries ....................................................................... 28
Table 4.5. Dissolved trace metal concentration in the Rio Santa Watershed ................ 32
Table 4.6. Physical characteristics of tributaries to the Rio Santa ................................. 34
Table 5.1. Comparison of Rio Santa to other major rivers ............................................ 45
Table 5.2. Filtered and unfiltered concentrations of As ................................................. 53
Table 5.3. pH in Rio Santa Watershed in 2008 and 2011 .............................................. 52
Table 5.4. Trace metal concentrations in Rio Santa and drinking water guidelines ...... 55
Table 5.5. Legend of site numbers for Figures 5.13-5.15 .............................................. 59
1
1. Introduction
The world’s highest density of tropical glaciers is found in the Cordillera Blanca
mountain range of the Peruvian Andes (Bury et al., 2010). The 700 glaciers in the Cordillera
Blanca (Ames et al., 1989) are undergoing rapid retreat driven by climate change-related
temperature increases. In semi-arid tropic and subtropic settings, such as this region, over 80%
of freshwater available for downstream populations originates in mountains. Glacial meltwater
is vital to the fresh water supply as it provides up to 30% of river discharge during the dry season
(Mark and McKenzie, 2007).
Since 1970, there has been a >22% decrease in glaciated area, with glacier termini rising
by average elevations of 113 m (Racoviteau et al., 2005). With ongoing glacial retreat, there is
initially an increase in runoff as their masses decrease (Mark and McKenzie, 2007). At some
point in time, the glacial ice volume decreases past a critical point, known as peak discharge,
after which the annual runoff decreases (Mark et al., 2005). In the Cordillera Blanca, many
glaciers have passed peak discharge and their annual and dry season discharges are in decline.
These declines are predicted to continue irreversibly for many decades until glacial contribution
to the hydrologic system becomes negligible. Dry season discharge is predicted to decline by
30% with the complete loss of glaciers (Baraer et al., 2012).
In addition to declining water quantity in the Rio Santa Watershed, there are ongoing
concerns about water quality due to glacial loss, mining activities, and increasing anthropogenic
inputs. There is evidence that high levels of potentially toxic trace metals are present in the
surface waters. Preliminary sampling along the length of the Quilcayhaunca tributary, which
drains into the Rio Santa, revealed that the river contained elevated levels of Al, Fe, Mn, Co, Ni,
2
and Pb; these elevated levels were attributed to naturally-occurring acid rock drainage (ARD)
(Burns et al., 2011; Fortner et al., 2011). Along the Rio Santa itself, three out of eight water
samples analyzed for dissolved As concentration had values >10 ug/L (Fortner, unpublished),
which exceeds World Health Organization drinking water standards (WHO, 2008).
Beyond the research by Burns et al. (2011) and Fortner et al. (2011), there have not been
other published trace metal studies from the Rio Santa. Hydrologic research in the region has
primarily focused on using stable isotopes and major ion compositions of water to study physical
hydrology in the upper portion of the watershed, which known as the Callejon de Huaylas. Mark
and Seltzer (2003) collected major ion samples in the Querococha Watershed, at the southern
end of the Cordillera Blanca, on a monthly basis in1998-1999. Subsequently, Mark et al. (2005)
used hydrochemical end-member mixing models to estimate that 66% of water leaving the
Callejon de Huaylas was derived from glacierized tributaries of the Cordillera Blanca. Mark and
McKenzie (2007) used synoptically sampled stable isotopes of water to show that specific
discharge from glacierized tributaries in the Cordillera Blanca has been increasing in recent years
due to increasing annual meltwater contribution from glaciers. Baraer et al. (2009) used a
hydrochemical and isotopic mass balance mixing model to determine that during the dry season,
ground water is the largest contributor to outflow from the Querococha Watershed.
Rich deposits of precious metals in the Rio Santa Watershed have given rise to a large
mining industry. There is a long history of small-scale artisanal mining, although today the
industry is dominated by large multinational corporations. In 2008, mining profits comprised
7.3% of Peru’s gross domestic product (USGS, 2008) and activities are accelerating at a rapid
rate (Bebbington and Williams, 2008). The main commodities are Au, Cu, Pb, Sb, and Zn
(Banco Central de Reserva del Peru, 2009).
3
Acid mine drainage (AMD) is one of the most serious environmental problems associated
with metal ore mining. It occurs when large amounts of sulfide-bearing rocks are fractured and
exposed to air and water, which causes the acceleration of naturally occurring sulfide oxidation
processes (Akcil and Koldas, 2006). Any geologic deposit containing sulfide is a potential AMD
source; however, the mining of metals such as Ag, Fe, Cu, Pb, and Zn are noted as being
particularly prone to causing AMD (Salomons, 1995). AMD is particularly associated with older
mines, unregulated artisanal mining, and tailings that have been stored in poorly constructed
piles or released directly into rivers (McMahon et al., 1999). Leachate from tailings piles and
water that is pumped or naturally discharged from underground mine workings are commonly
contaminated by AMD, particularly when prevention techniques have never been employed
(Johnson, 2003). Naturally occurring, rapid weathering of exposed sulfide mineral lithologies
can also induce this process, in which case it is described as ARD (Gray, 1997).
Chemical parameters affecting the rate of acid generation include pH, temperature,
oxygen concentration, and chemical activity of ferric iron (Salomons, 1995). The concentration
of oxygen in groundwater is small compared to the relatively large amount required for sulfide
oxidation. For typical AMD systems, water must be continually reoxidized through contact with
air. Physical aspects of the waste materials also affect rates of AMD generation. Sediment size
plays an important role: coarse-grained sediments promote oxygenation of water but have a
lower surface area for dissolution, while finer grained sediments have a higher surface area for
dissolution yet lower oxygen diffusion rates. The volume of neutralizing minerals in the vicinity
also affects the rate of AMD formation (Salomons, 1995).
AMD waters are commonly associated with high dissolved metal and metalloid
concentrations. Trace elements found in these waters may include As, Ba, Cd, Cu, Mn, Mo, Ni,
4
Pb, Se, and Zn (Sullivan and Yelton, 1988). The high concentrations occur for three main
reasons: (1) many heavy metals and metalloids are often found in sulfide minerals, (2) the acidic
and ferric iron-rich solution derived from pyrite dissolution readily degrades aluminosilicates
and other minerals, thereby releasing their metals into solution, and (3) many metals are highly
soluble in acidic waters (Johnson, 2003).
The overall impact of AMD in streams is largely controlled by the buffering capacity of
stream water and the quantity of water available for dilution (Olias et al., 2004). The solubilities
of trace metals decrease with increasing pH. At a pH above 4, Fe-oxyhydroxides (“ochre”)
precipitate from solution, forming the rust-covered streambed coatings that are characteristic of
AMD/ARD environments. Other dissolved metals may co-precipitate with the ochre or be
adsorbed to surfaces of clay minerals, carbonates, quartz, feldspars, and/or particulate organic
carbon. These surfaces may already have an ochreous coating, in which case the metals will
adsorb to the coating. The adsorption of metals increases from near zero to near 100% over a pH
range of 1-2 units; thus, a relatively small pH shift may have a significant effect on dissolved
metal content (Salomons, 1995).
Research has demonstrated that chemical erosion rates are linked to physical erosion
rates (e.g. Anderson, 2005, Lyons et al., 2005.) In areas where physical, and therefore chemical,
weathering of sulfide minerals is accelerated by glacial abrasion or mining, there is potential for
initiation of AMD/ARD processes. Fortner et al. (2011) linked high trace metal concentrations
in the Rio Quilcay to rapid chemical weathering of the sulfide-rich Jurassic Chicama Formation.
In addition to the abundant Chicama Formation, there are extensive secondary sulfide deposits
throughout the Cordillera Blanca. Thus, there is high potential for AMD and ARD initiation
throughout the watershed.
5
Seasonal trends in trace metals have been noted in other AMD-affected watersheds with
pronounced wet and dry seasons, including the Odiel River in Spain (Olias et al., 2004) and the
Montalbian mine drainage basin in Australia (Harris et al., 2003). In general, dissolved trace
metal levels gradually increase during the dry season due to evaporative concentration. At the
start of the wet season, flushing of concentrated waters and dissolution of ochreous precipitates
and other soluble salts causes sudden, large increases in concentrations. As wet season river
discharge increases, dissolved metals concentrations are gradually diluted to their lowest annual
levels. Nordstrum (2009) described an amplification of discharge-related trends in trace metal
concentrations in alpine watersheds. In the Cordillera Blanca, glacial loss is increasing the
contrast between wet and dry season river flows, which may similarly amplify current seasonal
trends in weathering of sulfide minerals.
To date, the limited water quality research on the Rio Santa Watershed has focused on the
Callejon de Huaylas. The objective of this study is to characterize dry season water quality and
quantity across the entire Rio Santa Watershed. Trace metal samples will be collected
throughout the watershed to establish preliminary baseline concentrations and to identify
potential sources of contamination, if any. This is an important first step for ongoing hydrologic
monitoring efforts, which are vital in this region given the abundance of mining activities and
potential for AMD and ARD. Trace metal concentrations will be assessed in the contexts of
basic hydrochemical parameters and spatial relations to potential sources. Current dynamics will
be examined in relation to potential changes derived from climate change, glacial recession, and
chemical weathering rates.
6
2. Study Area
The Cordillera Blanca extends 120 km along the South American Continental Divide in
Peru. The river originates at an elevation of 4000 m.a.s.l. at Lake Conococha and flows north
between the Cordillera Blanca to the east and the Cordillera Negra to the west for over 300 km
before turning west and flowing to the Pacific Ocean (Figure 2.1). The total watershed area
drained by the Rio Santa is over 12 000 km2 (Mark and McKenzie, 2007). Glacierized
watersheds on the western side of the Cordillera Blanca drain into the 4900 km2 upper Rio Santa
Watershed. The upper watershed is referred to as the Callejon de Huaylas and delimited to the
north by the La Balsa hydroelectric dam (1320 m.a.s.l.) (Figure 2.2) (Mark and McKenzie,
2007). Below the Callejon de Huaylas, the river flows in a westward direction through the
narrow gorge of the Canon del Pato and emerges onto a flat coastal ridge before flowing into the
ocean.
Temperatures in the Cordillera Blanca are characterized by large diurnal variations but
small average daily annual variations of approximately 0.8° C. Annual oscillations of the
intertropical convergence zone cause distinct wet/dry seasonality. Seventy to eighty percent of
annual precipitation falls between October and April, the austral summer in Peru (Kaser et al.,
2003). During this period, maximum glacial accumulation occurs above the glacier’s
equilibrium line altitude (ELA), and the relatively warmer temperatures and higher humidity
cause maximum ablation below the ELA (Kaser and Ostmaston, 2002). Ablation continues year
round, thus buffering stream melt throughout the year. The rain shadow effect of the Cordillera
Blanca causes the Cordillera Negra to remain extremely arid throughout the year (Figure 2.3)
(Kaser et al., 2003).
7
Figure 2.1. The Rio Santa Watershed, Peru. Modified from USGS (2006).
8
Figure 2.2. The glacier-rich Cordillera Blanca.
Figure 2.3. The extremely arid Cordillera Negra.
9
The geology of high altitude peaks of the Cordillera Blanca is dominated by the Jurassic
Chicama Formation, which is composed of pyrite-rich shales interbedded with quartzite and
argillite (Figure 2.4). The predominant exposures at mid-altitudes are the granodiorites and
tonalities of the Cordillera Blanca Batholith and coeval ignimbrite deposits (Petford and
Atherton, 1996). There are minor deposits of Cretaceous marine sediments exposed in the
northernmost portion of the watershed. The Cordillera Negra is dominated by the Tertiary
Calipuy formation to the south, which is composed of calc-alkalic lava flows, tuffs, and
pyroclastic breccias (Cobbing et al., 1981); to the north there is increasing exposure of the
Cretaceous Goyllarisquizga Formation, which is composed of limestone, sandstone, and siltstone
(Araneda, 2003). At low altitudes between the two mountain ranges, the valley has been infilled
by Quaternary glaciofluvial, glacial, and fluvial sediments. To the northwest, where the Rio
Santa bends in a westerly direction towards the Pacific Ocean, there is exposure of Paleogene-
Cretaceous granodiorites and tonalities. There has been extensive metamorphism and secondary
metal ore deposition throughout the Cordilleras Blanca and Negra. The rich metal deposits occur
along north-west trending fault lines and are known collectively as the Miocene Metallogenic
Belt (e.g. Cobbing et al., 1981; Taylor et al., 2007).
The Callejon de Huaylas is the most densely populated portion of the watershed, with
approximately 267 000 people scattered across small rural settlements and larger urban centers.
Most towns are located along the Rio Santa, including the provincial capital of Huaraz, which
has a population of 120 000 people (INEI, 2007). Nearly half the population of the region
survives on subsistence agriculture, although large-scale, irrigation-intensive activities are
increasing within the Callejon de Huaylas and along the coastal shelf (INEI, 2007; Painter,
2007). Along the final 75 km of the Rio Santa are a series of water diversions that remove
10
significant quantities for coastal irrigation projects. The La Balsa hydroelectric dam at the
entrance to the Canon del Pato provides 10% of Peru’s hydroelectric capacity (MEM, 2008).
Figure 2.4. Surficial geology map of the Rio Santa Watershed. Modified from Cobbing and
Sanchez (1996) and USGS (1996).
11
3. Methods
3.1. Preparation of trace metal sample materials
Trace metal sampling materials underwent rigorous laboratory preparation prior to the
field expedition. New 30 mL high density polyethylene (HDPE) sampling bottles, HDPE
rubber-free syringes, and lengths of HDPE tubing were prepared in accordance with United
States Environmental Protection Agency (U.S. E.P.A) standard operating procedures (U.S. EPA,
2000). New elbow-length polyethylene gloves were worn at all times throughout the preparation
of sampling materials. The materials were immersed in a series of week-long acid baths in large
HDPE basins. The first week was in 10% trace metal grade HCl, the second week in 10% trace
metal grade HNO3, and the third week in 1% ultrapure trace metal grade HNO3. 18-MΩ
distilled-deionized ultrapure (DI) water was used to dilute the concentrated acids and to rinse
basins and materials between baths. After the series of baths were completed, the sample bottles
were completely filled with 2% ultrapure trace metal grade HNO3 and sealed.
HDPE syringe filters were cleaned using methods modified from Shiller (2003). Forty
millimeters of DI water were pressed through the filters, followed by 40 mL of 2% ultrapure
trace metal grade HNO3, followed by an additional 40 mL of DI water. The cleaned filters were
attached to the acid-cleaned tubing and dried with a vacuum pump.
Individual sampling kits were prepared for each site. A syringe and syringe filter were
placed in a polyethylene glove. A sample bottle was placed in a second glove. Both gloves were
tied off and double bagged inside new HDPE ziplock bags.
12
3.2. Field sampling
In the field, trace metal samples were collected upriver from other sampling activities,
bridges, and other obvious potential airborne contamination sources. Samples were collected
according to the “clean hands, dirty hands” standard operating procedure set out by the U.S.
E.P.A. (2004). In this two-person method, one member is designated as “dirty hands” and the
other as “clean hands”. “Dirty hands” (DH) wears elbow-length polyethylene gloves and is
responsible for opening and closing the outer bag of the sampling kit. “Clean hands” (CH)
wears shoulder-length polyethylene gloves and is responsible for opening and closing the inner
bag of the sampling kit and collecting the trace metal sample.
To collect a sample, DH opens the outer bag. CH opens the inner bag, removes the
syringe, filter, and sample bottle, and then closes the inner bag. DH closes the outer bag. CH
rinses the syringe with river water three times, then fills it and attaches the syringe filter. Five
mL of water is pressed through the filter and discarded, 15 mL of water is used to rinse the
sample bottle, and 30 mL is used to fill the sample bottle completely. CH caps the bottle. DH
opens the outer bag, CH opens the inner bag and places the sample inside; the inner and outer
bags are sealed and the package is stored in a cooler or refrigerator.
New HDPE containers were used to collect filtered samples for analysis of major ions,
alkalinity, nutrients, and stable isotopes of water. Discharge, temperature, pH, conductivity, and
dissolved oxygen were measured in situ with an YSI 556 multiprobe meter.
13
Figure 3.1. Collection of a trace metal sample.
An Acoustic Doppler Current Profiler (ADCP) apparatus attached to a small boat
measured discharge. The ADCP contains sonar, which creates a vertical profile of current
velocities. When pulled slowly across a river, it creates a cross sectional profile of area and
current velocity, allowing for a very accurate discharge measurement. A minimum of three
discharge measurements were made at each river site and the average discharge value was
calculated.
Nine major tributaries were sampled near their outlet points to the Rio Santa. Twenty
three water samples were collected in the Rio Santa, often within a few kilometers upstream and
downstream of sampled tributaries. Water samples were also collected at the outlet of Lake
14
Conococha. Fewer samples were collected along the final 100 km of river due to the difficulty
of accessing the river in the Canon del Pato. Trace metal samples were obtained at 11 sites along
the Rio Santa, at the outlets of eight tributaries, and near the mid-length point of the Rio Quilcay.
Field blanks, samples in which DI water is collected using the same method as for trace metal
sampling, were obtained to ensure cleanliness of sampling materials and methods.
3.3. Laboratory work
All chemical analyses were done at The Ohio State University. Major cations were
measured using an Optima 3000 DV Inductively Coupled Plasma-Optical Emission
Spectrometer (ICP-OES), using five calibration standards that bracketed the range of
concentrations within the samples. Major anions were measured using a Dionex DX-120 Ion
Chromatographer. Concentrations of PO4, NO3, NH3, and Si (reported as SiO2) were measured
with a Skalar San++ Automated Wet Chemistry Analyzer. Precision and accuracy were within
5% for all analyses.
Trace elements were measured at the Trace Element Research Laboratory using a
ThermoFinnigan Element2 Inductively Coupled Plasma Sector Field Mass Spectrometer (ICP-
MS). The samples were acidified in the lab with 2% v/v double distilled NO3. The detection
limit for each element was determined using a blank and a 1 ppb calibration standard. Accuracy
was within 10% for all analyses.
Values of δ18O and δ2H were measured were measured at Byrd Polar Research Center
using a Finnigan MAT Delta Plus Mass Spectrometer coupled to a HDO water equilibrator.
15
Stable isotopes were reported using the δ-notation relative to the Vienna-Standard Mean Ocean
Water standard, with an accuracy of ±0.1 ‰ for δ18O and ±1 ‰ for δ2H.
3.4. Other sources of data
Water analyses in this study are compared with data from previous studies from the
Cordillera Blanca. Cations and anion concentrations of samples collected between 2005 and
2010 were measured at The Ohio State University with ion chromatography or at McGill
University with atomic adsorption (e.g. Baraer et al., 2012). Analysis and methods of monthly
discharge and water chemistry from the Querococha Watershed from 1998-1999 are presented in
Mark and Seltzer (2003) and analysis of samples from 2004 in Mark and McKenzie (2005). For
these samples, cations were analyzed at Syracuse University with a direct current plasma
spectrometer and anions were measured using a Dionex ion chromatography system. Major ions
and trace metal samples from Quilcayhaunca and the Rio Santa, 2008, are part of a data set that
was partially published in Fortner et al. (2011). Water analyses were performed at The Ohio
State University. Anion concentrations were measured with an ion chromatography system,
major cation concentrations were measured with ICP-OES, and trace metal concentrations were
measured with ICP-MS.
3.5. Discharge calculations
Mass balance equations were used to determine discharge (Qcalc) for some locations
where direct measurement was not possible. At a site where a tributary enters the Rio Santa (a
16
“triple point”), conservative tracer concentrations at sites upstream of, downstream of, and
within the tributary were required, along with a discharge measurement from one of these three
points (Qmeas). On the subbasin scale, it is assumed that the mass flux of conservative tracers at
the downriver site is the sum of the fluxes from upriver and the tributary (Equation 1):
Massu + Masst = Massd (1)
The subscripts u, t, and d denote upstream, tributary, and downstream, respectively.
To evaluate if a tracer is conservative, we used the following rules based on Baraer et al.
(2009): (1) the tracer concentration at the downstream site must be intermediate between the
upstream and tributary concentrations, (2) the tracer concentration at the downstream sample and
in one other sample must be above the limits of analysis detection, and (3) there must be > 20%
difference between minimum and maximum concentrations.
The mass balance models are derived from Equation (1):
QuCu + QtCt = QdCd (2)
Where Qu, Qt, and Qd are the upstream, tributary, and downstream discharges respectively, and
Cu, Ct, and Cd are the concentrations of a given tracer for the upstream, tributary and downstream
locations respectively. Because Qd = Qu + Qt, Equation (2) can be further simplified:
QuCu + QtCt = QuCd + QtCd (3)
Equation (3) can be solved for Qu (Equation 4) or Qt (Equation 5). A similar manipulation of
Equation (2) yields the solution for Qd (Equation 6):
Qu = Qt(Cd – Ct)/(Cu – Cd) (4)
17
Qt = Qu(Cd – Cd)/(Cd – Ct) (5)
Qd = Qt(Ct – Cu)/(Cd-Cu) (6)
Major ions, nutrients, and the stable isotopes of water were examined for conservative
behavior at each triple point. Tracers deemed conservative were Li, Mg, Ca, SO4, F, and δ18O.
Seven triple point locations had two or more conservative tracer species which enabled the
calculation of discharge. The Qcalc values were averaged for each triple point to determine a final
calculated discharge.
3.6. Denudation calculations
The rate of chemical weathering reactions, normalized to watershed area, can be
determined with the cation denudation rate (CDR), which describes how rapidly cations are
weathered from a given surface area per unit time (Anderson, 2007). ArcGIS is used to obtain
the area of a given watershed. The CDR is then calculated in three steps:
1. Concentration of ion x discharge = bulk load of ion
2. ∑ bulk loads of major cations = cation flux
3. Cation flux / area of watershed = CDR
The silicate denudation rate is also a useful indicator in weathering reactions. The
silicate denudation rate is calculated in a similar manner to the CDR, but for just silicate (i.e. the
second step is omitted.)
18
4. Results
Twenty-eight samples were collected throughout the Rio Santa Watershed (Figure 4.1;
Table 4.1). The tributaries drain basins with a range of lithologies, glaciation, discharge, and
variable proximity to mining activity and hot springs. The hydrochemical characteristics of
surface waters vary widely throughout the watershed. The Rio Santa evolves along its length
from a Ca-HCO3-rich stream trickling from Lake Conococha to a Ca-SO4-rich river flowing into
the Pacific Ocean. There is significant variability in discharge and major ion composition of
tributaries that drain into the Rio Santa.
Table 4.1. Dry season (July 2011) sampling locations in the Rio Santa Watershed.
Sample Latitude Longitude Location notes Known mining
activity
Notes
S1 -8.96759 -78.6223 North of Chimbote
S2 -8.66136 -78.2886 Chavimochic
Bocatoma
S3 RSD -8.65722 -78.2456 Beside main road
S3 Tabla -8.64801 -78.2332 Rio Tabla Yes Hot springs
S3 RSU -8.65788 -78.2336 Puente over Rio
Santa
S4 -8.6641 -78.0462 Puente over Rio
Santa
S7 RSD -8.80976 -77.8792 Huallanca, above
power plant
S7 Quitarscara -8.80189 -77.8517 Rio Quitarscara
S7 RSD -8.80976 -77.8792 La Balsa Bocatoma
S9 RSD -8.9919 -77.82168 Puente
Choquechaca
S9 RSU -9.0556 -77.8126 Cuidad Caraz
S9 Llullan -9.04478 -77.8164 Rio Llullan Barrick
Au-Ag-Zn-Cu-Pb
Drains Lake Paron
S10 Mancos -9.19085 -77.7102 Rio Mancos California mine;
tailings
Hot springs
Continued on page 19.
19
Table 4.1 (Continued.) Dry season (July 2011) sampling locations in the Rio Santa Watershed.
S11 Marcara -9.32475 -77.604 Rio Marcara Tailings in upper
watershed
Hot springs
S11 RSU -9.33162 -77.6031 Cuidad Marcara
S12 RSD -9.50507 -77.5365 Cuidad Huaraz at
Miraflores
S12 Quilcay -9.52371 -77.5256 Rio Quilcay
S12 RSU -9.55964 -77.5401 Cuidad Huaraz Significant mining
and tailings
S12.5 RSD -9.66885 -77.4807 Cuidad Olleros Significant ochreous
precipitates
S12.5 Olleros -9.67205 -77.4789 Rio Negro Silvania Ag-Pb mine;
tailings
Hot springs;
Ochreous precipitates
S12.5 RSU -9.67205 -77.4806 Cuidad Olleros
S13 RSD -9.73509 -77.4484 Cuidad Ticapampa Ochreous precipitates
S13 Yanayacu -9.77532 -77.4177 Rio Yanayacu Tailings Querococha Lake;
Ochreous precipitates
S13 RSU -9.77688 -77.4395 Cuidad Catac
S14 RSD -9.8219 -77.4209 Cuidad Pachacoto Ochreous precipitates
S14 Pachacoto -9.85253 -77.4024 Beside road to
Pastoruri
Tailings in upper
watershed
Ochreous precipitates
S14 RSU -9.84826 -77.4088 Cuidad Pachacoto
S15 RSD -10.0311 -77.3257 Puente over Rio
Santa
S15 Shiki -10.0325 -77.3241 Rio Shiki
S15 RSU -10.034 -77.3249 Beside main road
S16 RSD -10.114 -77.2845 Lake Conococha
S16 Tucco -10.1162 -77.2818 Rio Tucco Yes
S16 Conococha -10.1192 -77.2835 Lake Conococha
20
Figure 4.1. Sampling locations in the Rio Santa Watershed, July 2011.
21
4.1. Discharge
Figure 4.2. Discharge profile along the length of the Rio Santa. The dashed vertical line
represents the location of the Chavimochic water diversion project.
Along the Rio Santa, the minimum discharge was 0.1 m3/s at the river’s headwaters at
Lake Conococha (Table 4.2; Figure 4.2). Discharge increased steadily along the length of the
river to maximum values of 45 m3/s and 38 m
3/s at sites S3 RSD and S2, respectively, located 60
km and 65 km downstream of La Balsa. At site S1, where the Rio Santa flows into the Pacific
Ocean, discharge decreased to 5 m3/s. The estimated error for ADCP measurements is 5-10%
(Personal Communication, O. Wigmore, The Ohio State University, USA, March 26, 2013) and
15-20% for calculated discharge.
0
10
20
30
40
50
60
0 50 100 150 200 250 300 350
Dis
char
ge (
m3 /s
)
Distance from Lake Conococha (km)
Rio Santa: discharge
Calculated
Measured
22
Table 4.2. Measured and calculated discharge at select sampling sites along the Rio Santa and its
tributaries.
Rio Santa Discharge (m3/s)
Tributaries Discharge (m3/s)
S1 5.0
S3 Tabla 12.0*
S2 37.8
S7 Quitarscara 1.9
S3 RSD 45.3*
S9 Llullan 3.8*
S3 RSU 33.4
S10 Mancos 0.7*
S4 30.2
S11 Marcara n/a
S7 RSD n/a
S12 Quilcay 1.3
S7 RSU n/a
S12.5 Olleros 0.8*
S9 RSD 19.4
S13 Yanayacu 0.8*
S9 RSU 10.7*
S14 Pachacoto 0.4*
S10 RSU 13.1
S15 Shiki 0.3*
S10 RSD 13.8
S16 Tucco n/a
S11 RSU 9.4
S12 RSD 10.6*
S12 RSU 9.3
S12.5 RSD 6.7
S12.5 RSU 5.9*
S13 RSD 4.2
S13 RSU 3.0*
S14 RSD 2.5
S14 RSU 2.0
S15 RSD 1.9
S15 RSU 1.1*
S16 RSD n/a
S16 Conococha 0.1
*Calculated values using Equations (4) to (6).
23
4.2. Major ion geochemistry
Major ion samples were analyzed in samples collected at 23 sites along the Rio Santa and
at the outlets of nine major tributaries. Trace elements were analyzed in samples from 11 Rio
Santa sites samples and eight tributary sites. An additional trace element sample was collected
along the Rio Quilcay, approximately 12 km from the headwaters of the tributary, in order to
compare its trace metal concentrations with a previous sample collected in 2008 by Fortner et al.
(2011).
Figure 4.3. Profiles of major dissolved cations and SiO2 (mg/L) with distance along the length of
the Rio Santa, measured in kilometers from its origin at Lake Conococha.
Calcium is the main dissolved cation in the Rio Santa. Its lowest concentration, 12 mg/L,
is at Lake Conococha. It generally increases along the length of the river to a maximum of 64
mg/L at the outlet to the Pacific Ocean (Figure 4.3; Table. 4.2). The next highest species are Na,
SiO2, Mg, K, and Li, in descending order. They all increase in concentration for the first 100 km
0.01
0.1
1
10
100
0 50 100 150 200 250 300 350
Co
nce
ntr
atio
n (
mg/
L)
Distance from Lake Conococha (km)
Rio Santa: dissolved major cations and SiO2
Li
Na
K
Mg
Ca
SiO2
24
of the river, and then remain at a relatively constant concentration along the length of the Rio
Santa.
Figure 4.4. Profiles of major dissolved anions (mg/L) along the length of the Rio Santa,
measured in kilometers from its origin at Lake Conococha.
SO4 and HCO3 are the main anions in the Rio Santa; they have an inverse relationship
with each other (Figure 4.4; Table 4.3). SO4 increases northwards from below detection at Lake
Conococha to a maximum value of 200 mg/L at the mouth of the Rio Santa. There is more
fluctuation in HCO3 concentrations, which vary between 20 - 100 mg/L, but generally decreases
along the length of the river. Concentrations of F and Cl are low in the Rio Santa, varying
between near 0 – 0.5 mg/L and 5 - 10 mg/L respectively. Water along the Rio Santa is slightly
basic at all sites, with pH ranging from 7.5 – 8.3 (Table 4.3).
0.1
1
10
100
1000
0 50 100 150 200 250 300 350
Co
nce
ntr
atio
n (
mg/
L)
Distance from Lake Concococha (km)
Rio Santa: dissolved major anions
F
SO4
Cl
HCO3
25
Figure 4.5. Dissolved nutrient concentrations (ug/L) along the Rio Santa, measured in
kilometers from its origin at Lake Conococha. The black vertical line denotes the location of the
Marcara tributary (at 100 km) and the red vertical line denotes the location of Huallanca (at 195
km.)
Nutrient concentrations fluctuate widely along the length of the Rio Santa (Figure 4.5;
Table 4.3). There is a significant increase in NO3, to its maximum concentration of 1100 ug/L,
at site S11 RSU just downstream of Huaraz and close to the town of Marcara. Concentrations
remain greater than 500 ug/L along the remaining length of the Rio Santa. NH3 concentrations
remain between 10 ug/L and 25 ug/L along most of the Rio Santa, except for elevated
concentrations at Lake Conococha (90 ug/L) and near Huallanca at site S7 RSD (40 ug/L). PO4
concentrations fluctuate slightly, yet consistently remain below 10 ug/L.
0
200
400
600
800
1000
1200
1400
0
20
40
60
80
100
120
-50 0 50 100 150 200 250 300 350
NO
3 (u
g/L)
NH
3 an
d P
O4
(ug/
L)
Distance from Lake Conococha (km)
Rio Santa: nutrient concentrations
NH3
PO4
NO3
26
Table 4.3. Dissolved oxygen, pH, major dissolved ion, and nutrient concentrations in the Rio
Santa.
Sample dO2 (%) pH Li Na K Mg Ca F Cl SO4 HCO3 SiO2 NH3 NO3 PO4
S1 n/a 8 0.12 23.22 3.13 10.14 64.23 0.47 8.74 203.02 42.6 12.45 0.00 0.54 0.02
S2 98.5 8 0.15 20.21 2.97 11.61 52.30 0.48 7.46 190.00 22.8 12.34 0.00 0.67 0.02
S3 RSD 99.8 8 0.16 21.06 2.98 12.37 52.69 0.47 7.74 190.18 29.6 12.16 0.00 0.66 0.02
S3 RSU 99.5 8 0.14 19.95 2.94 8.73 46.98 0.47 7.60 156.74 51 12.69 0.00 0.77 0.02
S4 97.0 7.9 0.14 19.47 2.86 9.21 44.93 0.48 7.16 151.29 47 11.84 0.00 0.62 0.02
S7 RSD 95.5 7.8 0.15 18.15 3.02 4.79 40.93 0.38 6.77 84.27 60 12.33 0.00 0.29 0.07
S7 RSU 88.9 8.2 0.18 20.96 3.51 5.26 43.14 0.34 8.54 83.52 69 13.86 0.00 0.29 0.19
S9 RSU 79.9 8 0.20 22.13 3.91 6.08 48.30 0.26 10.24 92.46 63 13.71 0.01 0.79 0.05
S9 RSD n/a n/a 0.20 21.21 3.81 5.88 47.68 0.31 9.74 90.83 69 13.87 0.01 0.69 0.03
S10 RSD n/a n/a 0.23 22.43 3.87 5.61 40.87 0.24 10.9 76.21 111.2 12.04 0.00 0.38 0.02
S10 RSU 72.3 7.7 0.16 17.35 3.27 4.85 42.72 0.25 8.20 80.13 61 12.53 0.00 0.41 0.12
S11 RSU n/a n/a 0.19 19.50 3.72 4.22 34.04 0.21 10.09 68.38 60 12.05 0.00 1.06 0.02
S12 RSD 68.2 8.3 0.14 15.77 2.98 3.07 21.51 0.18 7.31 55.96 47.6 10.35 0.01 0.28 0.02
S12 RSU 66.6 7.9 0.18 16.42 2.91 2.98 22.60 0.14 8.72 53.00 54.6 9.29 0.00 0.16 0.01
S12.5 RSD n/a n/a 0.21 18.10 2.87 3.44 23.64 0.16 9.25 60.85 53.7 11.34 0.00 0.15 0.02
S12.5 RSU 70.1 7.6 0.20 17.49 2.75 2.82 24.96 0.11 9.03 37.91 50 11.24 0.10 0.10 0.05
S13 RSD n/a n/a 0.12 10.85 2.00 2.50 21.30 0.11 5.18 26.64 27 10.23 0.01 0.05 0.01
S13 RSU 70.9 7.5 0.10 8.95 1.89 2.80 27.13 0.11 3.67 31.95 83.2 9.08 0.00 0.00 0.01
S14 RSD 66 7.8 0.07 7.77 1.64 2.71 26.57 0.12 3.19 30.27 80.0 8.66 0.00 0.00 0.01
S14 RSU n/a n/a 0.06 8.12 1.52 2.35 28.10 0.1 2.97 22.11 95.4 9.86 0.00 0.05 0.01
S15 RSD 66.6 7.9 0.05 6.43 1.30 1.84 28.90 0.1 2.40 20.93 92.6 8.30 0.00 0.00 0.01
S15 RSU 68.6 7.5 0.05 6.58 1.30 2.31 29.97 0.12 3.82 31.49 53 8.12 0.00 0.00 0.02
S16 RSD 66.7 8.1 0.01 2.06 0.62 2.10 26.81 0.1 1.16 21.91 54 4.82 0.00 0.00 0.01
Conococha 66.2 7 0.02 11.39 1.58 1.97 12.56 0.3 2.58 3.47 45 0.36 0.00 0.00 0.09
Concentrations in mg/L. dO2 and pH were measured in situ. n/a denotes sites where dO2 and pH
were not measured due to instrument failure.
27
Figure 4.6. Concentrations of select major constituents (mg/L) in tributaries to the Rio Santa; pH
is in parenthesis after site names.
SO4 is the main anion in seven of the tributaries and HCO3 is the principal species in four
(Figure 4.6; Table 4.4). SO4 concentrations range from 3 - 305 mg/L. HCO3 concentrations
range from near 0 - 130 mg/L. In general, HCO3 dominates tributaries in the southern portion of
the Rio Santa Watershed, while SO4 is more dominant in northern tributaries. Calcium is the
main cation in all tributaries, with concentrations between 2 mg/L and 90 mg/L. K, Mg, and
SiO2 generally remain below 25 mg/L.
There is a significant range of pH in the sampled tributaries (Figure 4.6, Table 4.4). The
most acidic are S12.5 Olleros (pH=3.0) and S12 Quilcay (pH=4.7); the most basic are S16 Tucco
(pH=9.2) and S10 Mancos (pH=8.2). The pH in other tributaries is between 7 and 8.
0
50
100
150
200
250
300
350
Co
nce
ntr
atio
n (
mg/
L)
Tributary
Tributaries: major dissolved species
K
Mg
Ca
SO4
28
Table 4.4. Dissolved oxygen, pH, major dissolved ion, and nutrient concentration in tributaries
to the Rio Santa.
Sample dO (%) pH Li Na K Mg Ca F Cl SO4 HCO3 SiO2 NH3 NO3 PO4
S3 Tabla 98.4 8 0.19 22.03 2.73 21.65 68.17 0.48 7.31 309.11 55.0 10.99 0.00 0.31 0.01
S7 Quitarscara 89.9 7.4 0.02 4.09 0.75 2.69 31.93 0.35 1.30 91.50 12.0 9.12 0.00 0.05 0.10
S9 Llullan 76.9 7.3 0.02 4.94 0.84 1.65 10.64 0.63 1.43 14.66 31.0 9.68 0.00 0.00 0.01
S10 Mancos 75 8.2 0.02 13.02 2.66 13.38 86.61 0.30 2.33 188.11 127.0 23.08 0.05 2.25 0.03
S11 Marcara 72.9 7.4 0.14 10.50 2.36 2.69 16.41 0.42 4.90 46.54 21.0 11.06 0.00 0.09 0.02
S12 Quilcay 71.8 4.7 0.02 2.89 1.07 3.04 14.14 0.34 1.23 71.64 2.0 11.13 0.00 0.23 0.06
S12.5 Olleros 70.2 3 0.21 15.99 2.89 8.22 18.92 0.35 8.79 196.44 0.0 13.03 0.00 0.12 0.09
S13 Yanayacu 67.4 7 0.01 2.73 0.80 1.56 6.40 0.07 1.11 5.99 28.3 8.30 0.00 0.00 0.01
S14 Pachacoto 66.4 7.7 0.13 6.28 1.87 4.59 23.16 0.15 3.88 63.10 30.0 4.47 0.00 0.08 0.01
S15 Shiki 71.1 7.6 0.03 5.57 1.10 1.68 21.84 0.10 1.61 17.33 64.0 11.40 0.00 0.00 0.01
S16 Tucco 45.2 9.2 0.01 1.14 0.52 2.09 28.53 0.09 1.03 23.53 75.0 5.20 0.00 0.00 0.01
Concentration data is given in mg/L. HCO3 was calculated by charge balance using the same
method as Mark and Seltzer (2003).
4.3. Trace metal geochemistry
Figure 4.7. Profiles of select dissolved trace metals (ug/L) along the length of the Rio Santa,
measured in kilometers from its origin at Lake Conococha. Concentrations are accurate within
10%.
Between the headwaters of the Rio Santa and Huaraz, the concentrations of dissolved
indicator trace metals are vary significantly; between Huaraz and the outlet to the Pacific Ocean
concentrations are consistently low (Figure 4.7; Table 4.5). At Lake Conococha, As and Fe are
Ticapampa
Huaraz
29
elevated to 12 ug/L and 202 ug/L, respectively, which is the latter’s highest concentration in the
river. All metals have relatively low concentrations at site S14 RSD, 53 km from the lake. 1.2
km downstream of Ticapampa, at site S13 RSD, As is present at its maximum concentration of
30 ug/L, and Fe is elevated to 94 ug/L. Maximum concentrations of Pb, Cd, U, Mo, and Ni
occur at site S12 RSU in Huaraz, with concentrations of 73 ug/L, 58 ug/L, 53 ug/L, 40 ug/L, and
23 ug/L, respectively. Beyond Huaraz, concentrations of all trace elements fluctuate between
detection and 12 ug/L.
The bulk loads of individual trace elements fluctuate along the length of the Rio Santa
(Figure 4.8). At Lake Conococha, As, Cd, Cu, Mo, Ni, Pb, and U loads are near zero and Fe is 2
kg/day. At Ticapampa (site S13 RSD), 56 km downriver, there are significant increases in As, to
10 kg/day, and Fe, to 33 kg/day. At Huaraz, the load of dissolved As is near zero, and loads of
Pb, Fe, Cd, U, and Ni peak at 60 kg/day, 55 kg/day, 43 kg/day, 42 kg/day, and 19 kg/day,
respectively. Near the northern outskirts of Huaraz, the loads of Pb, Cd, and U decrease to near
zero, Ni decreases to 10 kg/day, Fe decreases to 5 kg/day, and As increases to 5 kg/day. Along
the next 170 km of the river, Ni, Cu, Mo, U, and Cd proportionally increase to maximum
amounts of 30 kg/day, 20 kg/day, 12 kg/day, 10 kg/day, and 2 kg/day, respectively. The As load
remains low, between 2 kg/day and 7 kg/day, Fe loads remain below 2 kg/day, and Pb loads
remain below detection limits.
Between S2 and S1, the bulk loads of all species decrease sharply. This coincides with
the drop in discharge in the vicinity of the Chavimochic irrigation dam. This causes the Rio
Santa to discharge only 0-5 kg/day of each trace metal into the Pacific Ocean.
30
Figure 4.8. Bulk loads of select dissolved trace metals (kg/day) at sampled sites along the Rio
Santa, measured in km from its origin at Lake Conococha.
Huaraz
Chavimochic
Ticapampa
31
The dissolved trace metal concentration profiles vary widely between tributaries (Figure
4.9; Table 4.5). There is high inter-tributary variation in terms of which species have low
concentrations (from 1 – 10 ug/L.) Concentrations greater than 10 ug/L include: Ni in S12.5
Olleros (70 ug/L) and S3 Tabla (43 ug/L), Mo and U in S9 Llullan (720 ug/L and 180 ug/L,
respectively), and Mo in S13 Yanayacu (53 ug/L.) Quilcay Out, located 12 km from the
tributaries outlet to the Rio Santa, has elevated concentrations of Cd (27 ug/L), Ni (34 ug/L), Pb
(82 ug/L), and U (14 ug/L.)
Figure 4.9. Concentrations of select dissolved metals (ug/L) in tributaries to the Rio Santa.
Concentrations are accurate within 10%. All tributaries were sampled near their outlet to the Rio
Santa, with the exception of Quilcay Out (sampled 12 km from outlet.)
32
Table 4.5. Dissolved trace metal concentration in the Rio Santa and its tributaries.
All tributary samples were collected near stream outlet, with the exception of Quilcay Out. All
values are in ug /L. Non detection of results is indicated by a less than sign (<) followed by the
detection limit.
4.4. GIS analysis
The tributary watersheds range in area from 51.0 – 3195.4 km2, with an average area of
464.3 km2
and a median area of 206.1 km2
(Table 4.6). Glaciation within the tributary
watersheds is variable. The Marcara watershed is the most glaciated, with 72.3 km2
of glacial
coverage, equivalent to 26.4% of watershed area, while the Tabla watershed is the least glaciated
with 1.3 km2 of glacial coverage, equivalent to less than 0.05% of the watershed area. The
average glaciated area within the tributaries was 24.3 km2 or 13% of the respective watershed
area. Spatial data was derived from Shuttle Radar Topography Mission digital elevation maps
(USGS, 2006), and digitized glacial coverage data was provided by the Autoridad Nacional del
Agua (ANA, 2010).
33
The lithologies of the subwatersheds are varied (Table 4.6). The main surficial
lithologies are the Chicama Formation, which comprises 0.0 - 724.9 km2
(0-40%) of the
watersheds, Quaternary surficial sediments, which comprise 0.0 - 156.4 km2
(0-63.1%), and the
Cordillera Blanca batholith, which comprises 0.0 – 354.2 km2 (0.0 – 48.7%.) Various other
volcanic and metasedimentary sequences make up the rest of the surficial geology.
34
Table 4.6. Physical characteristics of tributaries to the Rio Santa.
Blank spaces indicate zero.
S3 Tabla
S7 Quitarasca
S10 Mancos
S11 Marcara
S9 Llullan
S14 Pachacoto
S15 Shiki
S16 Rio Tucco
S12 Quilcay
S12.5 Olleros
S13 Yanayacu
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
km
2
%
Are
a
31
95
.4
3
83.6
67
.1
2
73.6
14
3.7
20
6.1
51
.0
9
2.4
24
5.3
17
8.6
27
0.1
Gla
cia
l A
rea
1
.3
0.0
3
1.0
8
.1
14
.6
21
.8
72
.3
26
.4
33
.1
23
.0
21
.8
10
.6
6.5
1
2.7
4
.7
5.1
4
7.3
1
9.3
1
8.8
1
0.5
1
5.9
5
.9
Geo
log
y
G
laci
ofl
uvi
al
sed
imen
ts
37
.0
9.6
3
6.7
5
4.7
1
24.1
4
5.4
5
7.8
4
0.2
7
3.1
3
5.5
3
2.2
6
3.1
4
4.7
4
8.4
1
19.4
4
8.7
8
6.0
4
8.1
1
56.4
5
7.9
Co
rdil
lera
Bla
nca
Ba
tho
lith
3
54.2
1
1.1
1
36.0
3
5.5
9
.0
13
.4
86
.4
31
.6
55
.5
38
.7
11
0.5
4
5.0
3
1.2
1
7.5
8
8.7
3
2.9
Ass
oci
ate
d i
gn
imb
rite
s
11
.7
17
.5
4.7
1
.7
27
.5
19
.2
Ca
lip
uy
Fo
rma
tio
n
12
10
.8
37
.9
66
.2
32
.1
18
.8
36
.9
31
.5
34
.1
0.0
5
.2
2.9
Gra
no
dio
rite
/to
na
lite
2
3.1
0
.7
Ma
rin
e se
dim
ents
2
05.9
6
.4
0.0
1.8
1
.9
Go
ylla
risq
uiz
ga
Fo
rma
tio
n
67
6.6
2
1.2
5
7.1
1
4.9
9
.7
14
.4
31
.5
11
.5
2.8
2
.0
17
.3
8.4
14
.4
15
.5
Ch
ica
ma
Fo
rma
tio
n
72
4.9
2
2.7
1
53.5
4
0.0
26
.8
9.8
49
.6
24
.1
15
.3
6.2
5
6.2
3
1.5
2
4.9
9
.2
35
5. Discussion
5.1. Discharge relationships
In the Rio Santa Watershed, precipitation is minimal during the dry season from May to
September (Kaser et al., 2003). During this time, surface runoff is supplied by groundwater and
glacial meltwater (Baraer et al., 2009). Mark et al. (2005a) used tracers to estimate that glacial
meltwater provides 40% of total dry season discharge in the Rio Santa. In this study, flow within
the tributaries ranges from 0.3 m3/s to 12 m
3/s (Table 4.2). The 11 surveyed tributaries
contribute a net volume of 22 m3/s to the Rio Santa.
Mark and McKenzie (2007) used isotopic methods to calculate that glacier-fed tributaries
supply 66% of the total discharge in the Rio Santa at the outlet of the Callejon de Huaylas (near
site S9 RSD.) In this study, discharge in the Rio Santa at site S9 RSD was 19.4 m3/s. The net
discharge of sampled tributaries upriver of this point was 8.1 m3/s, or 42% of the total discharge.
This indicates that tributary sampling captured almost half of the inflow to the Rio Santa.
Considering that numerous smaller and inaccessible tributaries were tributaries were unable to be
sampled, this suite of tributary samples still provides a good characterization of the
hydrochemical system.
A primary control on total discharge of tributaries was their watershed area (Figure 5.1).
The Tabla watershed has significantly greater surface area and discharge than other tributaries;
the Shiki is the smallest watershed and has the lowest discharge. Net discharge is not related to
specific discharge in the sample watersheds (where specific discharge is the discharge divided by
the watershed area.) The Tabla watershed has the lowest specific discharge, the Llullan
watershed has the highest, and the Shiki watershed is intermediate between the two. High levels
36
of specific discharge are loosely correlated with higher proportions of glacierized area (Figure
5.2). Variations in aquifer permeability and storage capacity, and glacial melt rates likely affect
the total and specific discharge rates from individual watersheds.
Baraer et al. (2012) modelled potential future discharge declines in a number of
tributaries also examined in this study, and determined that the watersheds with the highest % of
glacial cover will experience greater discharge declines. Specifically, discharge from the Llullan
watershed might decline by up to 60%, while there will only be small discharge declines in less
glaciated watersheds such as the Shiki and Querococha (a subwatershed that feeds Rio
Yanayacu.) All of the tributaries in this study have some degree of glaciation and are vulnerable
to declining discharge with ongoing glacial recession. The greatest discharge declines may occur
in Rios Marcara, Llullan, and Mancos, which have the highest proportions of glacierized area.
Rivers such as the Tabla, with effectively no significant glacier coverage, are at this point
immune to the effects of ongoing glacier recession.
Within the greater Rio Santa Watershed, the highest density of glaciers is in the Callejon
de Huaylas, which is where the majority of the region’s population resides. Declining glacial
meltwater contributions will affect this portion of the watershed most, intensifying the water-
resource related difficulties currently experienced by inhabitants.
There was a large decrease in discharge along the Rio Santa, from 35 m3/s – 40 m
3/s to
5.0 m3/s, between sites S1 and S2 (Figure 4.2). The primary causes of this are the Chavimochic
and Chanacis Projects, which divert significant quantities of water from the lower Rio Santa for
irrigation along Peru’s arid Pacific Coastline. Water can also be lost to other minor irrigation
activities and evaporation. The primarily groundwater-fed Rio Tabla contributed 12 m3/s of
37
discharge to the Rio Santa upstream of site S2. As glacial meltwater inputs to the Rio Santa
decline, the inputs from the Tabla subwatershed will become increasingly important for the
maintenance of coastal irrigation activities.
Figure 5.1. Plot of subwatershed area (km2) versus discharge (m
3/s) in tributaries to the Rio
Santa. Labels are shortened by excluding formal name of tributaries.
Figure 5.2. Plot of glaciation (% of total area) versus specific discharge (m/yr) for tributary
watersheds. Values in Table 4.2 were extrapolated to calculate specific discharge; these values
are underestimations as the increases in wet season discharge are not included. Labels are
shortened by excluding formal name of tributaries.
S10 S12 S12.5 S13 S14 S15
S3 Tabla
S7 Quitarscara
S9 Llullan
0
2
4
6
8
10
12
14
16
10 100 1000 10000
Dis
char
ge (
m3/s
)
Watershed Area (km2)
Tributaries: discharge vs. area
S10 Mancos S12 S12.5
S13
S14 S15 Shiki
S3 Tabla S7
S9 Llullan
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20 25 30
Spe
cifi
c D
isch
arge
(m
/yr)
% Glaciation
Specific discharge vs. glaciation
38
5.2. Major ion chemistry
The major ion chemistry of surface waters in the Rio Santa is primarily a result of
chemical weathering reactions. The extent of weathering may be affected by the type of
mineralization, exposure time, quality of water-rock interactions, and rock textures. Weathering
processes are also affected by mining activities and glaciation (Faure, 1991).
5.2.1. Sulfate
One of the most notable aspects of Rio Santa water is the high SO4 content at many
localities throughout the watershed. It was >50 mg/L in six of the sampled tributaries and at all
sites beyond the first 70 km of the Rio Santa. The average concentration in our samples was
77.6 mg/L for the Rio Santa, 93.5 mg/L for the tributaries, and 82.5 mg/L for the watershed as a
whole. This is significantly greater than the world average of 11.5 mg/L for river waters (Berner
and Berner, 1987), and consistent with previous measurements (e.g. Mark et al., 2005; Fortner et
al., 2011). High levels of dissolved SO4 contribute significantly to the overall high TDS within
the watershed.
There is a strong relationship between SO4 and total cations (Figures 5.3 and 5.4),
suggesting that sulfate oxidation is an important mechanism of chemical weathering in the
watershed. If sulfide oxidation were the only mechanism, milliequivalent per liter plots of SO4
versus total cations would fall on a 1:1 line with an intercept of 0. This is not the case, and in
particular, the intercept is not zero but 1.7 meq/L, indicating that there are additional sources of
cations and other processes contributing ions to surface waters.
There are numerous geologic sources of sulfide in the Rio Santa Watershed. This
includes the pyrite-rich Chicama Formation and medium-high sulfate epithermal metal deposits
39
within the Miocene Metallogenic Belt that is present across a range of lithologies in the
Cordillera Blanca, including mineralization at the sites of the large-scale Pierina and California
metal ore mines. Mining of the ore deposits exposes large quantities of finely ground sulfide
minerals to air and water, which can enhance their oxidation rate. Effluent from a mine
discharging into the Rio Yanayacu, sampled in 2007, had a SO4 concentration of 115.2 mg/L
(Appendix A.3).
SO4 is a primary species resulting from subglacial weathering reactions, even in
lithologies where only trace amounts of sulfides are present (see Section 5.5.2.) In addition to
near-surface chemical weathering reactions, SO4 may be contributed to surface waters via inputs
from geothermal springs. The TDS of thermal waters are generally higher than average surface
waters due to enhanced solubility and dissolution rates of minerals at high temperatures (Hem,
1985). The seismically active Cordillera Blanca is host to numerous hot springs. In the Rio
Santa Watershed, they are present in the subwatersheds of the Rios Tabla, Mancos, Marcara, and
Negro. In the Rio Negro (site S12.5 Olleros), the springs are located near the outlet to the Rio
Santa, while in other subwatersheds they are located further from the Rio Santa at higher
elevations. SO4 concentrations in the Olleros hot spring in 2004 and 2006 were 92.6 mg/L and
40.8 mg/L, respectively; there were 136.0 mg/L of SO4 in the Chancos hot spring (in the Rio
Marcara subwatershed) in 2006 (Appendix A.3.)
40
Figure 5.3. Plot of SO4 versus total cations in the Rio Santa. All values in meq/L.
Figure 5.4. Plot of SO4 versus total cations in the tributaries. All values in meq/L.
S2
S1
S4
S3 RSD
S3 RSU
S7 RSU
S11 RSU
S9 RSD
S14 RSD
S16 Conococha
S12.5 RSU
y = 0.8432x + 1.5871 R² = 0.8794
0
1
2
3
4
5
6
0 1 2 3 4 5 6
Cat
ion
s (m
eq
/L)
SO4 (meq/L)
Rio Santa: cations vs. SO4
S3 Tabla S10 Rio Mancos
S12.5 Olleros
y = 0.8438x + 0.7168 R² = 0.7713
0
2
4
6
8
0 2 4 6 8
Cat
ion
s (m
eq
/L)
SO4 (meq/L)
Tributaries: cations vs. SO4
41
The oxidation reactions of pyrite, FeS2, are well known and involve a complex series of
reactions that were described in detail by Banks et al. (1997), Akcil and Koldas (2005), and
others. The first step is oxidation of pyrite into dissolved ferrous iron, sulfate, and hydrogen:
2FeS2 (s) + 7O2 (g) + 2H2O (l) 2Fe2+
(aq) + 4SO42-
(aq) + 4H+ (aq) (7)
The acidity of the water is thus increased. In sufficiently oxidizing environments the ferrous iron
may be further oxidized to ferric iron:
2Fe2+
(aq) + O2 (g) + 2H+ (aq) 2Fe
3+ (aq) + H2O (l) (8)
If the pH is below 2.3, ferric iron will further oxidize the pyrite. At pH > 2.3, the majority of the
ferric iron will precipitate as iron hydroxides:
Fe3+
(aq) + 3H2O (l) Fe(OH)3 (s) + 3H+ (aq) (9)
The overall reaction for the oxidation of pyrite is:
FeS2(s) + 15
/4O2(g) + 7/2H2O(l) → 2SO4
2-(aq) + Fe(OH)3(s) + 4H
+(aq) (10)
Because pyrite is an acid insoluble mineral, this reaction will proceed regardless of pH. The
reaction rates are controlled by access to oxidizing agents and the presence of iron oxidizing
bacteria (Baker and Banfield, 2003).
Within the Rio Santa Watershed, the surface waters are well oxidized and Equations (7)
and (8) will proceed unhindered. Most surface waters have circumneutral pH values, indicating
that a source of alkalinity is neutralizing inputs of H+, thus buffering pH. At circumneutral pH
values, dissolved Fe is expected to precipitate, as in Equation (9). The presence of ochreous
deposits (Table 4.1) at some sites was indicative of this occurrence. The net result of Equations
42
(7) to (9) is dissolved SO4, Fe-oxyhydroxide precipitates, and increased acidity (buffered by
alkalinity.)
Figure 5.5. Plot of SO4 (mg/L) versus dO2 (% saturation) in the Rio Santa Watershed.
Increasing oxygenation of water causes enhanced SO4 mobility (Hem, 1985). In the Rio
Santa Watershed, SO4 concentrations tend to increase with increasing oxygenation (Figure 5.5).
The SO4 concentrations at Tabla, Olleros, and Mancos are notable for being significantly higher
than other sites with similar dissolved oxygen levels. This might be caused by SO4-rich inputs
from geothermal springs within these watersheds.
5.2.2. Alkalinity
The alkalinity of natural surface waters with 5.6 < pH < 9.5 can be assigned entirely to
HCO3 without serious error, in the absence of petroleum or natural gas associations or excessive
DOC (Hem, 1985). Bicarbonate is the second most abundant ion within the Rio Santa
S2
S3 Rio Santa Up
S7 Rio Santa Down S14 RS Down S16 Conococha
S12 RS Down
S3 Tabla
S7 Quitarasca
S10 Rio Mancos
S11 Marcara
S9 Llullan
S12.5 Olleros
0
50
100
150
200
250
300
350
50 55 60 65 70 75 80 85 90 95 100
SO4
(mg/
L)
dO2(% saturation)
SO4 vs. dissolved oxygen
43
Watershed. It may be derived through dissociation of atmospheric CO2 (Equation 11) and/or
dissolution of carbonate minerals (Equation 12):
CO2 + H2O ↔ HCO3- + H
+ (11)
CaCO3 ↔ Ca2+
+ CO32-
(+ H2O) ↔ HCO3- + OH
- (12)
HCO3 derived from carbonate dissolution is effective at neutralizing acidity from the oxidation of
pyrite, resulting in the precipitation of Fe-oxyhydroxides and maintenance of the aqueous system
at a circumneutral pH.
Below a pH of ~5.6, the majority of HCO3 in water is converted to H2CO3. This accounts
for the zero concentration calculated for Rio Olleros (pH=3) and near–zero concentration for Rio
Quilcay (pH=4.7). At these sites, any neutralization must be done by slower-acting silicates and
clays (Salomons, 1995). In the Rio Olleros, thick coatings of Fe-oxyhydroxides prevent the
dissolution of substrate minerals. Ochreous precipitates also coat the upper reaches of the Rio
Quilcay, but were not present at the downstream sampling site.
Carbonate buffering is a vital process for maintaining water quality in the Rio Santa
Watershed, where sulfide weathering adds high amounts of acidity to the surface waters. There
is carbonate mineralization in portions of the Chicama Formation and Goyllarisquizga Group,
which are present throughout the watershed (Wilson et al., 1967). Additionally, subglacial
weathering processes may contribute significant amounts of HCO3 from lithologies that contain
only trace amounts of carbonates (see Section 5.5.1.) Carbonate buffering is likely aided by
dilution of acidic tributary waters upon their mixing with the Rio Santa, particularly during the
wet season when flows are highest.
44
Concentrations of HCO3 are generally highest in the southern portions of the Rio Santa
Watershed, along the first 50 km of the river and in the tributaries along this stretch. In this
region, the Rio Santa and the lower tributaries flow through long stretches of low-gradient
Quaternary glaciofluvial sediments. The elevated concentrations may be due to greater
dissolution of carbonate minerals caused by increased water-rock residence time, and/or lower
rates of sulfide oxidation and associated acidity inputs. Northwards, HCO3 concentrations
generally decrease while SO4 concentrations generally increase.
5.2.3. Comparison with other rivers
In comparison to the mean composition of unpolluted natural waters, the Rio Santa has
similar concentrations of SiO2 and HCO3, notably higher levels of SO4 and Ca, and somewhat
elevated levels of other major ions, including Cl, F, Na, K, and Mg. The SO4 concentration and
pH of the Rio Santa are similar to levels documented in circumneutral waters draining from
pyrite-rich underground coal mines such as the Vryheld Mine in South Africa (Cravotta, 2008)
and the Cameron Mine in Pennsylvania (Azzle, 2002) (Table 5.1). The effluent from these
mines is unique from most mine drainage sites as alkalinity inputs are buffering against AMD-
acidification (Nordstrom, 2011a), in processes similar to those occurring in the Rio Santa
Watershed.
45
Table 5.1. Comparison of Rio Santa water with mean natural river water and that of the Vryheld
and Cameron mines.
Rio Santa
(Site S1)
Mean natural
river water
(Berner and Berner, 1987)
Vryheld Coal Mine
water
(Azzle, 2002)
Cameron Coal Mine
water
(Cravotta, 2008)
SiO2 12.4 8.71 2.52 20.0
SO4 203.0 5.3–16.8 376 510
Ca 64.2 13.4–23.8 71.2 53
HCO3 42.1 48.8 311 0.0
Cl 8.7 5.92 196 5
F 0.5 0.3 0.34 <0.1
Na 23.2 5.52 235 6.7
K 3.1 1.72 26.4 2.7
Mg 10.1 3.4–5.95 74.6 61
pH 8.0 n/a 9.03 4.0
5.3 Trace element chemistry
5.3.1. Dissolved metals in the Rio Santa Watershed
The large ore mining industry in the Cordillera Blanca and the Cordillera Negra are
indicative of the abundant metal-rich mineralizations in the Rio Santa Watershed. The
weathering of lithologies with high concentrations of metallic elements is likely give rise to
higher than average background concentrations of minor and trace elements in surface waters. It
is necessary to determine baseline levels of elements of concern before attempting to assess any
potential contamination issues. In addition to abundance in source rocks, baseline concentrations
of metals are determined by the weatherability of the rocks, pH, redox conditions, and sorption
or precipitation reactions (Nordstrom, 2011b). Anomalously high concentrations may be derived
from mining waste, industrial air-borne particles and aqueous effluents, and/or landfill and
sewage treatment runoff (Faure, 1991).
46
In the Rio Santa Watershed, the majority of indicator elements were present at most sites
at concentrations above detection limits, yet below 10 ug/L (Table 4.4). These are interpreted
primarily as naturally occurring concentrations arising from the chemical weathering of the
Jurassic Chicama Formation and deposits within the Miocene Metallogenic Belt. Most of the
indicator species cannot persist at high dissolved concentrations in the circumneutral waters
found at most sites within the watershed (Driscoll et al., 2004). The dramatic fluctuations in
bulk loads of dissolved trace metals in Figure 4.8 highlight the inability of metals to persist in
their dissolved forms in the pH conditions of the Rio Santa. The samples with anomalously high
dissolved concentrations were likely collected near to the source of contamination.
The spike in the dissolved As load at site S13 RSD is likely caused by the large tailings
pile at Ticapampa, located ~1 km upstream of the sampling site (Figure 4.8). The tailings have
been deposited along a bank of the Rio Santa and are actively eroded by the river. Previous
water sampling by Fortner (unpublished) directly adjacent to the tailings pile yielded maximum
dissolved As concentrations of 190.2 ug/L. Ochreous precipitates occur downstream of
Ticapampa. The strong tendency of As to adsorb to Fe-precipitates likely causes the rapid
decline in its downstream concentrations.
The locally high levels of Pb, Cd, Ni, and U at site S12 RSD, in Huaraz (population
120,000), are at least partly related to poorly enforced environmental standards within the city.
High volumes of garbage and industrial waste enter into the Rio Santa at this point (Figure 5.6).
The high levels of NO3 downstream of the city (Figure 4.5) are likely due to sewage inputs,
which highlight the lack of regulation regarding urban inputs into the river.
47
Figure 5.6. Urban garbage in the Rio Santa in the Miraflores district of Huaraz.
Although baseline levels of trace metals in the watershed may be attributed to weathering
of the metal-rich lithologies of the Cordillera Blanca, the sources of elevated levels of trace
metals are less apparent. As only one sample was collected from each tributary, sampling
resolution is too poor to pinpoint specific points of contamination within subwatersheds. The
subwatersheds are also highly variable in terms of geology, major ion compositions, size,
discharge, glaciation, mining activities, and population (Table 4.6).
In acidified tributaries, the metals may have originated far from the sampling site. In the
Rio Quilcay, the high concentrations of trace elements may have been derived from ARD from
the Chicama Formation at high elevations (as in Fortner et al., 2011). The Rio Olleros receives
metal-rich inputs from hot springs near its outlet to the Rio Santa. In tributaries with
circumneutral pH values and high trace element concentrations, such as Llullan, the source of
contamination was likely located close to the sampling site. In the case of Llullan, this is near
48
the mouth of the tributary, where the largest population lives. Intercomparison of multiple years
of data provides greater insight into potential contamination sources in circumneutral tributaries
(see Section 5.3.3.)
5.3.2. Aqueous trace metal relationships
Manganese is commonly present in Fe-bearing minerals and often co-occurs in natural
waters. In oxidizing conditions, both elements tend to precipitate out of solution, while in less-
oxidizing conditions, any Fe- and Mn- oxyhydroxides precipitates tend to dissolve. Fe is less
mobile than Mn and tends to precipitate more quickly and dissolve more slowly (Hem, 1985). In
Figure 5.7, the water at all sites (except Olleros; pH = 3.0) was circumneutral, but had varying
levels of dissolved oxygen (66 – 100% saturated.) There is an inverse relationship between %
oxygen saturation and dissolved concentrations of Fe and Mn. The samples with less oxidized
waters (< 75 %) tend to have higher levels of these elements and to come from sites where
ochreous precipitates were often observed. The oxidative dissolution of Fe- and Mn-
oxyhydroxides may give rise to the higher Fe and Mn concentrations. Conversely, the lower
concentrations at well-oxidized sites may be due to the precipitation of oxyhydroxides; as highly
oxygenated waters are often the most turbulent, the precipitates are more likely to remain
suspended in solution rather than to coat streambed surfaces.
49
Figure 5.7. Relationship between dissolved oxygen (% saturation) and concentrations of
dissolved Fe and Mn (ug/L). Fe at Olleros (8000 ug/L) is not plotted. Sites which had ochreous
precipitates plotted within in the blue shaded area.
Due to its higher aqueous mobility, Mn persists at a greater distance from the source than
Fe and occurs at higher concentrations in solution. This makes it a useful element for studying
the dynamics of dissolved trace metals within the watershed, especially at sites where direct
measurements of dissolved oxygen are not available (Hem, 1985).
Similar to Mn, other trace metals tend to be less mobile in oxidizing conditions. There is
a good correlation between increasing Mn (a proxy for decreasing oxygenation) and the
increasing concentrations of Al, Cd, Ni, Pb, and Zn (Figure 5.8). Conversely, As tends to
decrease at sites where Mn concentrations are higher (Figure 5.9). In more oxidizing conditions,
the charged arsenate ion predominates, and tends to strongly adsorb to Fe-oxyhydroxides and
other surfaces. In less oxidizing conditions, the more mobile, neutrally charged arsenite ion
predominates (Hem, 1985).
50
Figure 5.8. Dissolved Mn concentration (ug/L) versus concentrations of Al (mg/L) and Cd, Ni,
Pb, and Zn (all in ug/L).
Figure 5.9. Concentrations of dissolved Mn (ug/L) versus dissolved As (ug/L)
0
1000
2000
3000
4000
5000
0
50
100
150
200
250
300
0 200 400 600 800 1000 1200
Al (u
g/L)
Cd
, Ni,
Pb
, Zn
(u
g/L)
Mn (ug/L)
Relationship between trace metals and Mn
Zn
Pb
Ni
Cd
Al
0
5
10
15
20
25
30
35
0 200 400 600 800 1000 1200
As
(ug/
L)
Mn (ug/L)
Relationship between As and Mn
51
Table 5.2. Filtered and unfiltered concentrations of As (ug/L) in the Rio Santa Watershed, July
2008.
Site Filtered Unfiltered pH
Conococha 4.7 3.1 8.96
Jangas 3.5 21.2 7.62
Rio Santa @ Huaraz 20.3 29.4 8.06
Rio Santa 2 <d/l 53.7 8.62
Rio Santa Ticapampa 18.1 66.1 7.6
Rio Santa Ucashaca 4.2 9.2 8.52
Ticapampa 190.2 533.2 6.62
Yan <d/l 4.5 n/a
Catac Bridge 3.1 12.0 8.22
Unpublished data supplied by S. Fortner, Wittenberg University, USA. Analysis is same as in
Fortner et al. (2011). Non-detection is indicated by <d/l.
Filtered and unfiltered samples were collected in July 2008 and analyzed for As using the
methods presented in Fortner et al. (2011). Arsenic concentrations were greater in unfiltered
samples than in filtered (Table 5.2), indicating that the majority of As is adsorbed to particulate
phases or has co-precipitated with other species. Given the pH-Eh conditions of the Rio Santa
Watershed, it is likely that dissolved trace element concentrations presented in this study are
significant underestimates of the true amounts of these species in the aqueous environment.
However, as long as the Rio Santa maintains its strong buffering capacity and circumneutral pH
conditions, these species will continue to remain in the particulate phase and not be of concern to
human health.
5.3.3. Temporal variations in trace metals
In July 2008 and 2011, trace metal samples were collected at site Quilcay Out [site 24 in
Fortner et al. (2011)] (Figure 5.10). The levels of dissolved Co, Cu, Mn, Ni, Sr, and Zn were
similar in both samples. In 2011, there was a notable decrease in Fe and an increase in Pb.
52
Fortner et al. (2011) determined that significant amounts of Pb are sorbed to Fe in the Rio
Quilcay; therefore, a decrease in Fe may have caused more Pb to remain dissolved in solution.
Figure 5.10. Dissolved metal concentrations (ug/L) at site Quilcay Out in July 2008 and July
2011.
Table 5.3. pH at sites in Rio Santa Watershed in 2008 and 2011 (for Figure 5.11).
Sample S9 Llullan S13
Yanayacu
S12.5
Olleros
S11
Marcara Conococha S9 RSD
2008 6.7 8.8 3.43 6.85 9 7.9
2011 7.3 7 3 7.4 7 n/a
Comparison of trace metal concentrations in 2011 to an unpublished dataset compiled in
2008 (Appendix A.1) shows that, similar to Quilcay Out, there is consistency at other sites in the
Rio Santa Watershed (Figure 5.11). Most elements that were present at low levels in the 2008
samples also occurred in low levels in the 2011 samples. They are indicative of background
metal concentrations, which may arise from natural rock weathering, enhanced weathering in
tailings piles, and/or regular anthropogenic inputs. This inter-annual comparison shows that,
1.0
10.0
100.0
1000.0
10000.0
Co Cu Fe Mn Ni Pb Sr Zn
Co
nce
ntr
atio
n (
ug/
L)
Dissolved Metal
Quilcay Out: dissolved metals
July 2008
July 2011
53
despite strong spatial variations within a watershed, there may be temporal consistency in
background trace element concentrations
Figure 5.11. Dissolved metal concentrations (ug/L) at various sites in July 2008 and July 2011.
54
. Discharge data for 2008 at these specific sites is unavailable, as this study performed
the first complete survey of discharge within the watershed. However, in general there is strong
interannual consistency in dry season discharge in the Rio Santa (see Section 5.5.1; Appendix
A.5). Additional years of discharge and trace metal measurements are required to assess
interannual trends with greater confidence.
As discussed in Section 5.3.1, large anomalies may be indicative of point sources of
contamination. The most probable example of this is Mo in the Rios Llullan and Yanayacu. In
Llullan, the concentration changed from 11.5 ug/L to 720 ug/L between 2008 and 2011. At
Yanayacu, the concentration changed from 4.2 ug/L to 55 ug/L in the same time span. Mo is a
common by-product of metallic ore mining, particularly Cu-sulfides (Hem, 1985). It tends to
remain in solution in oxidizing conditions across a wide range of pH values, although at
circumneutral pH values it may react with Pb to form a relatively insoluble precipitate mineral
(Faure, 1998). In both sample years, Pb concentrations and Eh-pH conditions remained
relatively similar, suggesting that the change was due to a net increase in Mo within the
tributaries. The increases in its concentration may be related to up-river mining activities.
Large-scale mining of Cu-bearing sulfides occurs at the Paron Gold mine in the Llullan
watershed; mining activities are also ongoing in the Yanayacu watershed and there are large
tailings piles near the sampling site. Additional undocumented small-scale mining activities may
also be occurring near the sampling sites.
5.3.4. Implications for water quality
Drinking water standards for maximum dissolved concentrations set by WHO (2008) and
Health Canada (2010) are exceeded at three sites along the Rio Santa and in the Llullan and
Quilcay tributaries (Table 5.4). Trace metals are most bioavailable in their dissolved forms.
55
They tend to bioaccumulate and are potentially toxic to human health at high concentrations
(Driscoll et al., 1994). The potential toxicity of the water is of concern because populations
reside along the river at all sites except for Quilcay Out and may use the water for irrigation,
livestock, and/or domestic consumption (INEI, 2007).
Table 5.4. Select dissolved trace element concentrations at sites in Rio Santa Watershed.
Sample As Cd Mo Pb U
WHO Guideline
(Health Canada Guideline)
10
(10)
3
(5)
70
(none)
10
(10)
15
(20)
S12 RSU 0.2 56.7 40 74 53
S13 RSD 30 0.2 1.4 0.1 0.2
S16 Conococha 11.8 <0.01 1 0.1 0.1
S9 Llullan 2 5.5 720 3.8 180
Quilcay Out 0.02 27.3 0.3 82 14
Grey shaded boxes represent values that exceed guidelines set by WHO (2008) and Health
Canada (2010).
Long term ingestion of low levels of As may cause chronic illnesses and skin problems.
At higher doses it may be fatal (ATSDR, 2007a). Cadmium tends to decrease bone density and
to bioaccumulate and cause deterioration in the kidneys (ATSDR, 2012). Similarly, kidney
damage can also result from ingesting aqueous U (ATSDR, 2013). Lead is toxic for the nervous
system. Low level, long term exposure causes reduced mental alertness, while high levels cause
severe brain damage and death (ATSDR, 2007b). Molybdenum is regulated by WHO (2008) but
not Health Canada (2010). Its health effects are still debated although there is evidence that it
causes infertility in cattle and is harmful to humans at high concentrations (Eisler, 1989).
In addition to potentially toxic metals, the acidity and ochreous precipitates in the Rio
Quilcay, Rio Negra, and some sites within the Rio Santa are likely to put significant stress on
56
lotic systems. The ochreous coatings eliminate habitats and significantly reduce biodiversity
within the stream system (Gray, 1997). Studying the diversity and abundance of organisms is an
important additional step for ongoing water quality monitoring efforts in the Cordillera Blanca.
5.5. Temporal variations in water chemistry
5.5.1. Temporal variations
There are large spatial variations in hydrochemistry throughout the Rio Santa Watershed.
They are caused by differences in lithology, glaciation, discharge, and anthropogenic activities.
Temporally, there is a dichotomy between dry and wet season hydrochemical conditions.
However, there is evidence of site-specific consistency on diurnal and interannual time scales.
Near the headwaters of the Rio Quilcay, Burns et al. (2011) took hourly samples over a 24
hour period to study the diurnal variability of water chemistry. Relatively minor diurnal
variations in pH (from 3.3 – 3.7) and specific conductance (from 349 – 466 µS/cm) were
observed. A plot of conductivity versus SO4 for the 2011 sampling data shows a strong linear
relationship between the two parameters (Figure 5.12) (Appendix A.2). Based on this
relationship, the diurnal conductance variations are equivalent to ~25 mg/L variations in daily
SO4 concentrations. Variations are attributed to changes in glacial melt volumes caused by daily
temperature and radiation cycles. Burns et al. (2011) predicted that the buffering effect of
groundwater would decrease diurnal fluctuations with greater distance from glacial headwaters.
57
Figure 5.12. Relationship between SO4 (mg/L) and conductivity (µS/cm) in Rio Santa
Watershed, July 2011. Based on data from Burns et al. (2011)
Figure 5.13. pH values at synoptically sampled sites in July 2005-2007.
y = 2.884x + 81.034
0
200
400
600
800
1000
1200
0 50 100 150 200 250 300 350
Co
nd
uct
ivit
y (u
s/cm
)
SO4 (mg/L)
Conductivity vs. SO4
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10 12 14 16
pH
Site
Interannual pH Variations
2005
2006
2007
58
Figure 5.14. SO4 concentrations at synoptically sampled sites in July 2004-2007. 5% error bars
are shown.
Figure 5.15. Ca concentrations at synoptically sampled sites in July 2004-2007. 5% error bars
are shown.
0
20
40
60
80
100
120
0 2 4 6 8 10 12 14 16
SO4 (
mg/
L)
Site
Interannual SO4 Variations
2004
2005
2006
2007
0
5
10
15
20
25
30
35
40
45
50
0 2 4 6 8 10 12 14 16
Ca
(mg/
L)
Site
Interannual Ca Variations
2004
2005
2006
2007
59
Table 5.5. Legend of site numbers for Figures 5.13-5.15.
Number Site
1 Buin
2 Colcas
3 S16 Conococha
4 Jangas
5 Llan Lakes Out
6 S9 Llullan
7 S11 Marcara
8 S14 Pachacoto
9 Q1
10 Q3
11 Quilcay Out
12 Ranrahirca
13 Rio Santa 1
14 Rio Santa 2
15 Rio Santa Low
16 S13 Yanayacu
At the interannual time scale, there is surprisingly good year-to-year consistency. Figure
5.13 displays the pH at synoptically sampled sites in the summers of 2005 through 2007
(Appendix A.3), including many sites that were examined in this study. Site-specific pH values
remain within ~1 unit over time. At the majority of sites, SO4 concentrations remain within 10
mg/L from year to year with maximum variations of ~30 mg/L, which are comparable to diurnal
SO4 variations noted by Burns et al. (2011) (Figure 5.14). Similarly, Ca concentrations tended to
vary within ~15 mg/L over the time span, with maximum variations of ~20 mg/L (Figure 5.15).
All the above samples were collected in July of each year, during the height of the dry
season. The similar year-to-year concentrations indicate that baseline water conditions within
the Rio Santa Watershed are consistent from year to year, with no major anomalies in SO4, Ca,
or pH. This suggests that inter-year comparisons of water chemistry in this study are valid and
aberrations may be considered as significant.
60
5.5.2. Case study: Chemical weathering in the Querococha Basin
A detailed examination of weathering processes in the Querococha Basin yields greater
insight into potential weathering dynamics of the Cordillera Blanca as a whole. The Querococha
Watershed has two main subbasins of comparable size and similar lithologies. They are
dominated at higher elevations by granitic-plutonic rocks and the Chicama Formation, and
Quaternary glaciofluvial sediments along the valley bottoms. The northern basin, Q2, contains
the Yanamarey glacier and its proglacial zone; the southern basin, Q1, has been completely
deglaciated (Figure 5.16). Monthly discharge and major ion samples were collected at the outlet
streams of Q1 and Q2, the outlet stream of the Yanamarey glacier (YAN), and the distal outlet of
the Querococha Basin (Q3) on a monthly basis from May 1998 – April 1999 by Mark and
Seltzer (2003) (Appendix A.4). This data was reanalyzed to calculate monthly and annual
denudation rates of SiO2, total cations (i.e. CDR), and individual ions in the subglacial,
proglacial, deglaciated, and distal zones. In order to examine processes occurring in each
specific zone, calculations for the Q2 proglacial zone exclude discharge and ion load inputs from
YAN; similarly, calculations for the distal Q3 zone exclude inputs from Q1 and Q2.
61
Figure 5.16. Map of the Callejon de Huaylas. Inset is a map of the Querococha Basin. YAN =
subglacial, Q1 = deglaciated, Q2 = proglacial, Q3 = distal valley. From Mark and Seltzer
(2003).
The annual chemical weathering rate in the subglacial zone (YAN) is nearly twice that of
the proglacial zone (Q2) and over five times greater than in the deglaciated valley (Q1) (Figure
5.17). Chemical weathering rates fluctuate throughout the year (Figure 5.18). Subglacial
weathering peaks in October, at the start of the wet season. Proglacial weathering has a small
peak in October, although its maximum is near the end of the wet season in March, when
proglacial weathering exceeds that of the subglacial area. Comparison of Figures 5.18 and 5.19
shows that specific discharge rates are the primary control on chemical weathering rates.
Discharge and precipitation are strongly correlated in deglaciated and distal areas. Subglacial
discharge is decoupled from precipitation, reaching its annual peak at the start of the dry season,
62
four months before peak runoff. Proglacial discharge is intermediate between the two, with peak
CDR corresponding to maximum glacial melt and to maximum precipitation (Figure 5.21).
Figure 5.17. Annual chemical weathering rates in the Querococha Watershed, April 1998 – May
1999. As some monthly data is missing, these results are underestimates. YAN = subglacial, Q1
= deglaciated, Q2 = proglacial, Q3 = distal valley.
Figure 5.18. Monthly CDR in the Querococha watershed, April 1998 – May 1999. Data is
missing for February. YAN = subglacial, Q1 = deglaciated, Q2 = proglacial, Q3 = distal.
YAN Q1 Q2 Q3
0
10
20
30
40
50
60
Subbasin
CD
R (
t/km
2 /yr
)
Querococha: chemical weathering rates
0
1
2
3
4
5
6
7
8
May Jun Jul Aug Sept Oct Nov Dec Feb Jan Mar Apr
CD
R (
t/km
2 /m
on
th)
Month
Monthly chemical weathering variations
YAN
Q1
Q2
Q3
63
Figure 5.19. Specific discharge and precipitation in the Querococha watershed, April 1998 –
May 1999. Discharge data is missing for February. YAN = subglacial, Q1 = deglaciated, Q2 =
proglacial, Q3 = distal valley.
Chemical fluxes in glacial meltwater do not reflect the composition of the catchment
lithologies but rather reflect the type of chemical weathering reactions that are occurring.
Tranter (2003) describes the series of subglacial chemical reactions that occur. The first is the
hydrolysis of carbonate and silicate minerals when very dilute meltwater meet fresh glacial flour.
Calcium carbonate is the most common carbonate mineral:
CaCO3 (s) + H2O (l) → Ca2+
(aq) + HCO3-(aq) + OH
- (aq) (13)
The dilute water also favors the exchange a single dissolved divalent cation for two monovalent
cation ions from mineral surfaces. Hence, some Ca2+
and Mg2+
released from Equation (13) will
exchange for Na+ and K
+. Sulfide oxidation is the dominant reaction in subglacial environments.
It primarily occurs in subglacial areas where fresh rock is first in contact with water. Microbial
mediation enhances the reaction rate by several orders of magnitude. The reaction proceeds in a
series of steps, with an overall equation of:
FeS2 (s) + 15O2 (g) + 14H2O (l) → 4Fe(OH)3 (s) + 8SO42-
(aq) + 16H+ (aq) (14)
64
The reaction lowers the pH of the water, which enhances carbonate dissolution. The dissolution
of carbonates and oxidation of sulfides by the dilute waters is so effective that Ca and SO4 tend
to be the dominant exported ions, despite usually occurring only in trace amounts in the
substrate. Although silica is a dominant mineral in most basins and its dissolution reactions
dominate non-glaciated fluvial basins, it is weathered very slowly in subglacial environments
due to low water temperatures (Tranter, 2003):
Silicate mineral(s) → clay mineral(s) + cations(aq) + Si (aq) (15)
Weathering dynamics in the proglacial zone are not as well studied as in the subglacial
zone. The proglacial zone is a region of potentially high geochemical activity due to the
presence of abundant comminuted sediments that are continually being reworked by runoff
(Tranter, 2003) although vegetation and soil development are likely to increasingly affect
weathering dynamics with distance from the glacial margin (Anderson et al., 2000).
One of the first studies of proglacial weathering dynamics was by Anderson et al. (2000)
at the Bench Glacier, Alaska. In the immediate proglacial area, carbonate dissolution and sulfide
oxidation were the principal chemical reactions, with rates as high as three times greater than
those occurring subglacially. Further down the valley, where sediments were older, these
reactions declined as sulfide and carbonate minerals became exhausted. Silicate dissolution
increased down-valley due to warmer water temperatures. The overall chemical weathering rate
for the proglacial valley was lower than in the subglacial area (Anderson et al., 2000). Similar
results were found by Wadham et al. (2000) at the Finsterwalderbreen Glacier in Svalbard.
65
Figure 5.20. YAN (subglacial) major chemical weathering products, April 1998 – May 1999.
February data and December SO4 data are missing.
Figure 5.21. Q2 (proglacial) major chemical weathering products, April 1998 – May 1999.
February data and SO4 data for December and January are missing.
May Jun Jul Aug Sept Oct Nov Dec Feb Jan Mar Apr
0
50
100
150
200
De
nu
dat
ion
Rat
e (
t/km
2 /m
on
th)
Subglacial: chemical weathering reactions
Ca
Si
SO4
May Jun Jul Aug Sept Oct Nov Dec Feb Jan Mar Apr
0
10
20
30
40
50
60
70
De
nu
dat
ion
Rat
e (
t/km
2 /m
on
th) Proglacial: chemical weathering reactions
Ca
Si
SO4
66
Figure 5.22. Q1 (deglaciated) major chemical weathering products, April 1998 – May 1999.
August and February data and December, January, and April SO4 data are missing.
Figure 5.23. Q3 (distal) major chemical weathering products, April 1998 – May 1999. February
data and December SO4 data are missing.
The waters draining YAN are acidic (pH <5), therefore HCO3 resulting from carbonate
hydrolysis will be converted into CO2. Herein, all Ca is attributed to carbonate weathering for
evaluation purposes.
May Jun Jul Aug Sept Oct Nov Dec Feb Jan Mar Apr
0
1
2
3
4
5
6
7
De
nu
dat
ion
Rat
e (
t/km
2 /m
on
th) Deglaciated: chemical weathering reactions
Ca
Si
SO4
May Jun Jul Aug Sept Oct Nov Dec Feb Jan Mar Apr
0
0.5
1
1.5
2
2.5
3
3.5
De
nu
dat
ion
Rat
e (
t/km
2/m
on
th)
Distal: chemical weathering reactions
Ca
Si
SO4
67
Sulfide oxidation is the most dominant chemical reaction in the subglacial zone.
Carbonate dissolution is of secondary importance, and silicate dissolution is the least significant
reaction (Figure 5.20). These reactions are similarly occurring in the proglacial zone, but at
lower rates. The most marked decline is in the rate of sulfide weathering (Figure 5.21).
Weathering rates are even lower in the deglaciated terrain. In this zone, carbonate dissolution
tends to dominate over sulfide oxidation. Silicate dissolution assumes an increasingly important
role in weathering reactions due to the decline of carbonate and sulfide weathering (Figure 5.22).
In the distal zone, weathering reactions occur in similar proportions to the deglaciated terrain, yet
at lower rates (Figure 5.23)
Numerous studies have suggested that high physical weathering rates can generate high
chemical weathering rates (e.g. Lyons et al., 2005; Anderson et al., 2002). The shift in
weathering dynamics between subglacial, proglacial, and deglaciated terrains is likely related to
declines in physical weathering with ongoing glacial recession. In the subglacial zone, the
constant abrasion of the overlying glacier maintains a continual supply of fresh, comminuted
debris in the subglacial zone. These are mixed with older sediments in the proglacial zone, with
the supply of fresh sediments dwindling with increasing distance from the glacier terminus. The
influx of glacially-derived fresh sediments has ceased entirely in the deglaciated zone. Sulfide
oxidation and carbonate dissolution occur rapidly in glacial areas, causing SO4, Ca, and HCO3
exports to decline as mineral supplies are exhausted as physical weathering declines. Silica
dissolution is inefficient in the glacial area due to low water temperature; its decline with
deglaciation is less pronounced.
In comparison with other glacierized watersheds, the cation denudation rate below the
Yanamarey glacier is nearly the highest globally observed (see Figure 5.24), which is likely
68
attributable to the sulfide-rich bedrock. Subglacial sulfide mineral oxidation is so effective that
it is a dominant weathering process even in lithologies where these minerals are present in even
trace amounts. The abundant supply of sulfide minerals below the Yanamarey glacier allows for
higher than average rates of sulfide oxidation and contributes to the high overall chemical
weathering in this basin. Most glacial meltwaters have a pH between 7 and 10 (Tranter, 2003);
the high amounts of sulfide weathering cause Yanamarey meltwater to have a pH of < 5.
Carbonate dissolution rates are enhanced in acidic conditions, resulting in a high flux of Ca and a
corresponding cation denudation rate that is higher than average.
Figure 5.24. Denudation rates of dissolved cations and dissolved silica versus annual specific
discharge for a variety of different catchments, including the different zones of the Querococha
basin. Modified from Anderson (2007).
YAN
Q1
Q2
Q3
69
Weathering rates in the proglacial zone are also higher than most glaciated basins (Figure
5.24), due to the abundant sulfide mineralization of comminuted debris that are deposited in this
zone by meltwater, which allows the weathering processes described above to continue at a high
rate. The deglaciated zone plots near the low end of glacial cation fluxes, within other
deglaciated terranes.
In terms of silicate flux, YAN plots among other glaciated basins with similar specific
discharge (Figure 5.24). Because low temperatures slow silicate weathering rates, glaciated
basins tend to have lower silicate flux than other watersheds with similar specific discharge. Q1,
Q2, and Q3 plot at the low end of the group consisting of deglaciated and non-glaciated basins,
which have higher water temperatures and therefore greater SiO2 weathering rates. The
relationship between specific discharge and silicate weathering rates is apparent in the non-
glaciated portions of the Querococha basin. Their low values in relation to other basins in the
world may be related to the strongly seasonal discharge, particularly in the Q1 basin, when dry
season weathering is limited.
70
5.5.3. Chemical weathering rates in the Cordillera Blanca
Figure 5.25. Dry and wet season CDRs for the Callejon de Huaylas, calculated for July 2005,
July 2006, July 2007, and January 2009. (Based on data in Appendix A.5.)
Figure 5.26. Dry and wet season concentrations at Rio Santa Low, the outlet of the Callejon de
Huaylas, measured in July 2005, July 2006, July 2007, and January 2009 (Data in Appendix
A.5.)
71
Cation denudation rates within the Callejon de Huaylas (samples named Rio Santa Low,
near the La Balsa hydroelectric dam) are very consistent during the dry season from year-to-year,
ranging from 1.06 - 1.11 t/km2/month between 2005 and 2007. The wet season CDR in January
2009 was 3.7 t//km2/month, more than three times greater than the dry season rate (Figure 5.25).
Discharge in the dry season ranged from 30.9 - 34.2 m3/s (Appendix A.5). By contrast,
discharge during the wet season in 2009 was 230 m3/s (Appendix A.6). Despite the significantly
greater cation flux, the Rio Santa is more dilute in the wet season than in the dry season (Figure
5.26). From 2004 to 2008, wet season discharge was lower than in 2009, with an average flow
of 123.9 m3/s. Thus, the 2009 CDR may be an over estimate of typical winter weathering rates,
and average wet season flow may be slightly less dilute than indicated.
5.5.4. Implications of changing hydrology on water chemistry
Glaciers play a key role in the current hydrologic regime and water compositions in the
Rio Santa Watershed. Weathering rates are higher in basins with glaciers than in those in which
glaciers have disappeared because glaciers maintain stream flow year round and provide a
continual supply of fresh, finely ground sediments. The efficiency of sulfide and carbonate
weathering in glacierized basins contributes to the high levels of SO4, HCO3, and Ca throughout
the watershed.
The Querococha case study demonstrates that specific discharge is the strongest control
on chemical weathering rates. With ongoing glacier recession, dry season discharge will
decrease and there will be a corresponding increase seasonal variability, with more of the annual
discharge occurring in the wet season. There will be a temporal shift in chemical weathering
72
from year-round, as occurs in the YAN-Q2 basin, towards a predominantly wet season
weathering regime, as occurs in the Q1 basin.
With ongoing glacial retreat, subglacial weathering reactions will initially increase due to
higher annual melting rates, followed by a gradual decline after glaciers have passed their peak
annual discharge. Proglacial zone weathering rates are controlled by fresh sediment inputs and
meltwater from the subglacial zone, therefore a similar evolution of weathering will occur in this
zone. Weathering profiles in glaciated basins will gradually become more similar to those seen
in Q1: overall weathering rates will decline and the importance of sulfide and carbonate
weathering in relation to silicate weathering will decrease. Wet season weathering rates are
significantly higher than dry season rates, although river water tends to be more dilute at this
time. This seasonal pattern will be amplified with time.
The changing hydrological dynamics will affect trace metals in different ways, depending
on their source. Persistent low levels of dissolved metals throughout the watershed suggest that a
significant amount of these species is derived through the natural weathering of sulfide minerals.
These baseline concentrations may initially increase as dry season glacial meltwater enhances
weathering fluxes in glaciated basins. After peak annual discharge, there will be a gradual
decline in naturally weathered trace metals.
As dry season discharge declines, the watershed will become less effective at buffering
against direct contamination inputs from industrial waste, mining effluents, or mine tailings
deposited directly into the rivers. There is likely to be an exacerbation of current pollution issues
in the Rio Santa at Ticapampa and Huaraz, and new problem areas may develop.
73
During the dry season, evaporative concentration may enhance the formation of ochreous
precipitates. Dissolved trace metal levels may decline if they are coprecipitated or adsorbed to
the ochreous coatings, or, they may remain in solution and become increasingly concentrated by
evaporation (e.g. Nordstrum, 2009). A complex combination of both outcomes is likely, due to
the differing aqueous behavior of individual metals (e.g. Olias et al., 2004). At the start of the
wet season, there will be greater potential for sharp increases in trace metal concentrations as
precipitates dissolve and mine workings and isolated surface water bodies are flushed. As the
wet season progresses, levels of dissolved trace metals will likely decline due to the significant
dilution capacity of the Rio Santa at peak flows (e.g. Younger and Blachere, 2004).
The net effect of these different processes will likely lead to decreasing discharge,
increasing ochre precipitation, and acidification of water in the Rio Santa Watershed during the
dry season, particularly in the upper Rio Santa and its tributaries where the impact of glacier
recession will be most conspicuous. Potentially toxic trace metal concentrations may increase
throughout the summer and/or undergo a pronounced spike early in the wet season as metals are
flushed through the hydrologic system. Dilution of trace metals will likely reduce contamination
issues as the wet season progresses.
74
6. Conclusions
The oxidation of sulfide minerals and dissolution of carbonates exerts a strong control on
water composition in the Rio Santa Watershed, where waters are characterized by high SO4, Ca,
and HCO3. The pH at most sites is maintained at circumneutral values by the watershed’s
capacity to buffer against acidity inputs. Acidity derived from the weathering of sulfide minerals
is diluted and neutralized by alkalinity derived from carbonate dissolution. At some sites, acidity
inputs have overwhelmed the neutralization capacity of the river and the positive feedback cycle
of AMD/ARD has been initiated. The Rios Olleros and Quilcay are examples of tributaries in
which this process is occurring. They are characterized by high SO4, low pH, and high dissolved
metal concentrations. The streambeds are coated by ochreous precipitates that prevent efficient
carbonate dissolution and maintain the low pH within these tributaries.
There is a risk that AMD/ARD processes will commence in other areas of the watershed
if sulfide weathering rates increase beyond a critical threshold, after which carbonate dissolution
no longer provides sufficient neutralization (e.g. Banks et al., 1997; Salomons, 1995). Sulfide
weathering may increase due to the acceleration of mining activities and production of tailings.
Additionally, as annual glacial discharge initially increases due to greater rates of melting, rates
of subglacial production of finely ground fresh sediments will accelerate, with an associated
increase in subglacial and proglacial sulfide oxidation rates.
Glacial retreat is changing the hydrologic regime in ways that may affect the acid-base
balance within the watershed. As dry season discharge declines, the buffering capacity of the
Rio Santa will decrease. There will be less river water available for dilution of acidic effluents
released directly from mines or downstream of in-river tailings piles.
75
Trace metals are derived from natural and mining-accelerated weathering of various
lithologies throughout the watershed. They tend to be present in dissolved forms at
concentrations between detection limit and 10 ug/L at most sites. There are significant increases
in concentrations and bulk loads of dissolved trace metals in the Rio Santa at Ticapampa and
Huaraz. The sharp drops in downstream bulk loads indicate that metals do not persist in their
dissolved forms at high concentrations in the circumneutral waters of the Rio Santa. It is likely
that dissolved trace metal concentrations are lower than particulate trace metal concentrations
throughout most of the watershed. As sulfide weathering is accelerated, potential acidification of
the watershed may have serious environmental consequences as particulate-phase metals
redissolve into their toxic aqueous form.
To assess climate change or mining-related changes to trace metals within the watershed,
a strong baseline record of current concentrations is required. Sampling should be repeated over
a multi-year period and multiple times per year. Future sampling efforts should include non-
filtered trace metal samples, in order to quantify the amount of particulate metals within the
watershed. Greater spatial resolution is required to better define potential sources of current
contamination issues.
The Cordillera Blanca, with its multitude of tropical glaciers, has been a focus of research
on the impacts of climate change on water resources (e.g. Vergara et al., 2007). In particular,
this research has focused on physical hydrology and the quantity of water available for domestic,
agricultural, hydroelectric, and mining uses. The research presented here shows that quantity is
only part of the story - although not polluted along its entire length, there are numerous point
sources of contamination, both natural and anthropogenic. While these ‘hot spots’ are generally
treated naturally by the hydrologic system (through precipitation and dilution), they are cause for
76
great concern. Water that is polluted beyond a critical threshold is effectively removed from use,
further stressing fresh water resources. As discussed in this thesis, with receding glaciers and
decreasing melt runoff, potentially toxic trace metals will likely put further stress put on water
quality. Combined with increasing population and mining activities, these issues will only
exacerbate in the future. The outstanding question that cannot be easily answered is, "what will
be the extent of water quality degradation and what will be the timing or rate of these changes?"
77
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85
Appendix
Appendix A.1. Trace metal concentrations in Rio Santa Watershed in July 2008.
As Cd Cr Cu Fe Mn Mo Ni Pb V Zn
Buin 1.3 0.0 1.6 0.6 159.7 13.7 2.1 1.1 0.3 0.5 1.3
Conococha 12.2 0.0 0.5 0.3 434.1 53.5 0.8 2.1 0.2 0.2 0.8
Cordillera Negra1 3.3 0.0 0.9 0.3 115.3 21.2 0.1 0.6 0.0 0.1 1.7
Cordillera Negra 0.9 0.0 0.1 0.3 26.7 31.0 0.6 0.2 0.0 0.2 2.8
Negra Low 23.6 0.1 8.9 2.7 13.1 5.5 36.2 0.0 0.4 4.3 6.0
Olleros 0.4 0.2 0.8 2.8 4,328.2 499.3 0.0 57.8 0.6 0.0 110.2
Olleros Hot Spring 8,593.2 0.0 3.8 66.0 3,319.0 266.3 0.4 14.7 0.2 1.2 40.8
Rio Santa @ Ticapampa 14.4 0.3 2.5 4.0 169.9 73.3 1.8 47.5 1.1 2.3 75.8
Ticapampa 346.1 1.2 39.9 9.9 246.9 331.7 0.7 1.6 0.9 0.1 192.8
Ishinca B 0.5 0.1 0.0 0.4 53.8 8.8 1.8 0.3 0.3 0.1 3.0
Jangas 4.0 0.2 3.5 1.8 31.9 191.6 1.4 5.0 0.2 0.1 62.4
Llullan 2.1 0.1 0.5 0.7 72.8 20.6 11.5 0.4 0.2 0.1 6.9
Marcara 1.8 0.3 1.1 0.5 75.5 179.2 1.1 5.3 0.2 0.2 33.5
Mi Casa 0.2 0.1 0.3 0.5 109.8 3.5 3.9 1.7 0.0 0.1 196.8
Nev Pastoruri 0.4 0.6 0.5 2.4 943.6 983.9 0.0 37.7 1.5 0.0 80.2
Pariacs 0.5 0.1 0.0 0.2 51.3 36.6 1.9 1.3 0.0 0.1 5.5
Puente Choquechaca 5.1 0.1 4.2 1.2 15.4 46.4 3.1 2.0 0.2 0.3 14.5
Recuey 0.4 3.0 0.2 31.8 46.8 582.5 0.0 2.1 5.3 0.0 676.3
Rio Colloca 12.0 0.0 0.1 0.2 4.0 1.4 1.9
0.0 0.3 0.7
Rio Santa Huaraz 20.6 0.2 3.3 1.3 123.3 152.4 1.8 3.8 0.2 0.1 24.0
Rio Tucco 1.7 0.0
0.9 14.3 4.1 0.6 0.2 0.0 0.0 3.5
RioSanta1 4.9 0.0 0.6 1.1 25.1 15.6 0.6 0.3 0.0 0.1 2.3
RioSanta2 2.6 0.3 2.3 1.2 26.9 160.1 0.9 7.8 0.1 0.0 32.7
Ranrahirca 0.8 0.2 0.3 2.6 101.4 56.6 4.7 0.6 0.4 0.1 31.0
RS Ucashaca 5.5 0.0 0.5 0.7 15.6 10.7 0.7 0.3 0.0 0.1 1.1
Santa Bridge Catac 4.0 0.1 0.9 0.7 21.5 61.2 0.6 2.0 0.2 0.1 7.9
Santa Low 5.4 0.1 3.8 1.2 15.9 45.0 3.2 1.8 0.2 0.3 10.4
Yanayacu 2.6 0.0 0.1 0.2 40.0 4.1 4.2 0.4 0.0 0.2 1.3
Unpublished data supplied by S. Fortner, Wittenberg University, USA. Analysis methods were
the same as in Fortner et al. (2011).
86
Appendix A.2. Concentration of SO4 (meq/L) and conductivity (µS/cm) in the Quilcayhaunca
basin in July, 2009.
Site
SO4
(meq/L)
Conductivity
(µS/cm)
Cuchillacocha Out 3.15 478
Lower Lake (Tupla) Out 3.08 495
Jatun 2.70 390
Cuchi Con 2.23 304
Tulpa Low 2.04 281
V2 Ab Pacsa 2.01 258
Tulp Ab Conf 2.01 259
Cay High 1.44 226
Cay Ab Conf 2.85 391
Quil Bel Conf 2.18 296
Casa de Agua 2.14 293
Park Entrance 1.85 228
Quilcay 1.48 241
Jatun Upper Conf 1.48 176
Jatun Mid 1.09 133
Cay L1 2.04 421
Cay Red 11.69 314
Cay L2 1.71 242
Cay L3 5.19 130
South Waterfall 1.20 179
North Waterfall 0.19 53
J Spring 0.34 88
Cay Spring 0.13 26
Quil Spring 0.67 116
Data from Burns et al. (2009).
87
Appendix A.3. Synoptically sampled pH, Ca, and SO4 in the Rio Santa Watershed for July 2004
– 2007.
Name pH Ca SO4
2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007
Buin 7.5 8.5 8.9 21.5 12.70 30.60 29.69 29.64 21.04 44.40 25.51
Colcas
8.1 8.1 19.3
19.00 13.73 29.46 0.00 30.81 28.49
Conococha 9.1 8.2 8.6 19.6 19.60 15.10 19.03 4.63 3.47 4.37 2.43
Jangas 7.2 7.9 8.0 27.9 20.90 22.90 39.42 61.74 53.19 61.55 60.81
Llan Out 7.1 7.3 7.3 6.0 5.27 5.15 9.10 8.33 8.24 8.52 7.57
Llullan
7.8 7.7 7.6 7.81 6.57 12.12 10.00 9.52 8.53 10.55
Marcara 6.9 7.9 7.8 15.5 11.40 12.20 18.26 46.02 37.25 42.21 37.72
Pachacoto 7.2 8.2 7.8 22.1 19.30 24.60 40.91 65.36 50.14 67.10 58.16
Q1 7.6 8.3 8.1 9.5 10.60 9.51 14.40 7.89 6.83 6.40 5.41
Q3 7.3 8.4 8.0 7.4 6.71 7.02 6.10 14.34 13.09 13.15 11.12
Quilcay 4.7 4.4 5.0 18.1 11.80 13.60 10.39 73.27 69.35 84.36 92.50
Ranrahirca
8.0 7.9 18.0 9.40 11.00 5.24 36.31 17.45 22.45 21.45
Rio Santa 1 8.6 7.0
28.7 30.50 29.00
19.10 18.43 16.92 0.00
Rio Santa 2 8.1 7.5 22.9 24.40 35.80 23.75 63.24 67.98 68.05 62.17
Rio Santa Low
8.4 42.0
39.15 71.16 0.00 0.00 77.37
Yanayacu 7.2 8.0 7.7 5.7 5.74 5.55 5.60 6.00 5.54 5.81 6.04
Methods and partial dataset are published in Mark et al. (2007). Concentration data is given in
mg/L.
88
Appendix A.4. Monthly concentration (mg/L) and discharge (m3/s) in the Querococha Basin
from April 1998-April 1999. Month Ca Si Mg K Na Cl SO4 HCO3 Discharge
YA
N
Apr 9.29 3.23 1.11 0.45 1.41 0.02 37.85 0.00 0.60
May 8.36 3.05 1.10 0.39 1.34 0.06 36.97 0.00 0.24
Jun 8.73 3.41 1.14 0.44 1.87 0.06 38.52 0.00 0.10
Jul 9.79 3.16 1.26 0.47 1.30 0.00 41.15 0.00 0.11
Aug 9.21 3.07 1.19 0.42 0.87 0.05 39.80 0.00 0.08
Sep 8.93 3.26 0.95 0.35 0.96 0.04 33.72 0.00 0.10
Oct 10.10 2.99 1.17 0.49 1.57 0.06 39.18 0.00 0.31
Nov 9.97 2.85 1.31 0.51 3.95 0.05 43.81 0.00 0.20
Dec 10.10 2.77 1.20 0.41 0.53 0.12 0.00 0.00 0.14
Jan 12.70 2.79 1.58 0.54 0.66 0.08 52.99 0.00 0.11
Feb
Mar 14.50 2.90 1.63 0.60 0.89 0.05 53.67 0.00 0.07
Apr 14.50 3.19 1.59 0.50 0.94 0.05 53.62 0.00 0.03
Q1
Apr 5.83 2.68 0.39 0.35 5.02 0.19 4.87 27.10 0.60
May 6.85 3.16 0.60 0.40 4.77 0.24 5.40 30.08 0.37
Jun 8.92 3.21 0.72 0.49 6.14 0.17 7.56 38.15 0.14
Jul 9.54 3.49 0.81 0.52 7.02 0.20 9.86 39.89 0.09
Aug
Sep 9.40 3.21 1.00 0.64 2.46 0.15 11.00 27.25 0.07
Oct 7.60 3.07 1.02 0.47 7.91 0.33 18.47 26.64 0.20
Nov 6.98 2.94 0.62 0.46 1.45 0.19 6.08 21.17 0.22
Dec 5.41 2.39 0.50 0.32 1.00 0.17 0.00 22.17 0.21
Jan 4.38 2.54 0.34 0.38 0.95 0.12 0.00 18.16 1.31
Feb
Mar 4.39 2.64 0.34 0.40 0.95 0.08 3.11 14.47 1.33
Apr 5.30 2.61 0.39 0.35 0.98 0.09 0.00 21.22 0.68
Continued on page 89.
89
Appendix A.4. (Continued.) Monthly concentration (mg/L) and discharge (m3/s) in the
Querococha Basin from April 1998-April 1999.
Month Ca Si Mg K Na Cl SO4 HCO3 Discharge
Q2
Apr 6.00 2.90 0.68 0.35 1.15 0.06 18.28 2.09 2.59
May 7.10 3.12 0.99 0.39 1.13 0.07 23.54 0.36 1.12
Jun 8.81 3.10 1.02 0.48 1.43 0.10 26.26 3.21 0.42
Jul 8.39 3.42 1.13 0.52 1.63 0.09 29.76 -1.38 0.26
Aug 9.19 2.95 1.45 0.51 1.66 0.08 36.97 -6.33 0.34
Sep 10.20 2.49 1.35 0.39 1.30 0.08 34.41 -1.67 0.43
Oct 8.86 2.61 1.09 0.45 1.14 0.14 31.11 -3.32 1.24
Nov 8.30 2.99 1.04 0.42 1.11 0.09 29.71 -3.66 0.71
Dec 7.64 2.76 0.90 0.35 0.95 0.15 0.00 30.76 1.27
Jan 5.59 3.20 0.62 0.36 1.04 0.14 0.00 23.51 2.10
Feb
Mar 5.71 3.40 0.60 0.37 1.04 0.23 14.44 4.84 2.17
Apr 6.50 3.49 0.67 0.38 1.14 0.08 0.00 26.93 1.19
Q3
Apr 6.52 3.23 0.61 0.42 1.32 0.11 12.62 10.89 3.34
May 6.84 3.05 0.63 0.44 1.19 0.09 13.40 10.74 1.40
Jun 7.37 3.41 0.71 0.44 1.41 0.10 14.24 12.35 0.60
Jul 7.30 3.16 0.71 0.45 1.35 0.10 14.35 11.80 0.27
Aug 7.18 3.07 0.73 0.44 5.42 0.18 15.57 20.67 0.34
Sep 7.65 3.26 0.72 0.44 1.49 0.09 14.07 13.82 0.33
Oct 7.98 2.99 0.80 0.55 1.60 0.37 16.82 11.68 0.90
Nov 7.22 2.85 0.73 0.50 1.24 0.20 16.00 9.24 1.24
Dec 7.08 2.77 0.72 0.45 1.17 0.11 0.00 28.99 1.23
Jan 6.48 2.79 0.67 0.44 1.17 0.10 0.00 26.84 4.23
Feb
Mar 6.16 2.90 0.61 0.45 1.19 0.13 12.92 9.16 4.96
Apr 5.59 3.19 0.55 0.44 1.36 0.09 0.00 24.10 2.17
Data published, in part, in Mark and Seltzer (2003). Blank spaces indicate data is n/a.
90
Appendix A.5. Concentration (mg/L) and discharge (m3/s) at Rio Santa Low, the outlet of the
Callejon de Huaylas.
Date Ca Na Mg K SO4 Si Discharge
July 2005 33.60 14.00 5.97 3.07 66.61 3.55 34.15
July 2006 28.10 20.30 5.58 3.78 74.33 4.62 32.48
July 2007 39.15 10.04 7.29 3.86 77.40 3.97 30.89
July 2008 28.58
January 2009 19.48 2.67 4.52 1.47 32.18 3.40 230.47
Discharge data collected by Duke Energy. Methods and partial concentration data are published
in Mark et al. (2007) and Baraer et al. (2012). Discharge for January is the average of January
and February volumes; discharge for July is the average of June and July volumes. Blank spaces
indicate data is n/a.
Appendix A.6. Wet season discharge data at Rio Santa Low, the outlet of the Callejon de
Huaylas.
Year Discharge
2005 101.65
2006 101.19
2007 123.76
2008 169.03
2009 230.47
Values presented are the average of January and February volumes. Data collected by Duke
Energy.