Journal of Hydrology 609 (2022) 127687
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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Research papers
Effects of land use on the hydrologic regime, vegetation, and hydraulic
conductivity of peatlands in the central Peruvian Andes
Eduardo Oyague a, b, c, *, David J. Cooper a, b, *, Eusebio Ingol b
a
Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO 80523, USA
Programa de Maestría en Recursos Hídricos, Universidad Nacional Agraria la Molina, Apartado Postal 12-056 – La Molina, Lima, Peru
c
División de Limnología, CORBIDI, Lima, Peru
b
A R T I C L E I N F O
A B S T R A C T
This manuscript was handled by Sally Elizabeth
Thompson, Editor-in-Chief
The vegetation, geographic distribution, and pastoralist uses of peatlands in the central Andes have been
extensively studied over the past 20 years. However, little information exists to characterize the hydrologic
processes that support these groundwater-dependent ecosystems or the hydrologic alterations occurring due to
human activities or climate changes. This lack of information significantly limits our understanding of how the
central Andean Puna, one of the world’s most threatened ecoregions, may be altered by increasing temperature
and water extraction for human use. In addition, these peatlands provide critical pasture for native and domestic
livestock and carbon storage, but overgrazing is an important modifier of their ecological trajectories.
We analyzed three groundwater-fed fen peatlands with differing hydrologic regimes, annual precipitation,
land use, and vegetation composition. Water table depth was a key factor significantly contributing to differences
in soil hydraulic conductivity (p < 0.05) and vegetation composition (p < 0.05). Saturated hydraulic conduc­
tivity (K) varied from 0.57 m day− 1 to 0.03 m day− 1 for the horizontal component (Kh) and 0.07 to 0.002 m
day− 1 for the vertical component (Kv). The principal differences in K were in the seasonally unsaturated upper
soil layers at 0–75 cm depth. The annual deepening in the water table and Distichia muscoides dominance drive
the variability in K for these layers. Those drivers were themselves correlated but can individually modify K,
increasing the decomposition rate and porosity (WT deepening) and altering the initial peat structure
(D. muscoides dominance). Three vegetation communities were identified, one in sites with the deepest water
tables, the lowest hydraulic conductivities, and dominance of Werneria pygmaea and Plantago rigida. The second
community, dominated by bunch grasses in the genus Calamagrostis, occurred in areas with the most variable
water table and medium hydraulic conductivity. The third community occurred in the most hydrologically stable
areas, with the shallowest water table, highest hydraulic conductivities, and was dominated by Distichia mus­
coides. The study peatlands appear to have originated as the third community – groundwater-supported fens
dominated by Distichia muscoides cushion and pool communities. However, modern hydrologic changes caused
by human land uses and climate variability have caused a divergence in the vegetation, and in the more
disturbed sites created higher decomposition rates in the shallow peat layers, and differences in soil structure.
Keywords:
Central Andes
Fens
Groundwater
Hydraulic conductivity
Peat structure
Vegetation
1. Introduction
Research on South America’s Andean wetlands has traditionally
focused on the vegetation (Squeo et al., 2006) and its importance for
livestock grazing and agriculture (Sawyer, 2008). More recently, inter­
est in conservation and ecosystem services has stimulated the develop­
ment of maps (Chimner et al., 2019) to quantify carbon stocks because
many Andean wetlands are peat accumulating (Planas-Clarke et al.,
2020). Research in a few areas has attempted to document a hydrologic
connection between glaciers, streams, groundwater, and wetlands
(Baraer et al., 2012; Vuille et al., 2018). However, there has been little
detailed research on the hydrological drivers shaping these wetlands
and maintaining their ecological condition (Cooper et al., 2019, 2010).
Understanding the hydrologic processes that support them and the
factors that can disturb them is critical for managing and restoring these
vital ecosystems (Millar et al., 2018; Price et al., 2005). Research is
needed to evaluate the role of land uses on hydrologic processes such as
water table depth and dynamics, soil hydraulic conductivity, rainfall
* Corresponding authors at: Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO 80523, USA
E-mail addresses: Eduardo.Oyague@colostate.edu (E. Oyague), David.Cooper@colostate.edu (D.J. Cooper).
https://doi.org/10.1016/j.jhydrol.2022.127687
Received 24 December 2021; Received in revised form 23 February 2022; Accepted 1 March 2022
Available online 5 March 2022
0022-1694/© 2022 Elsevier B.V. All rights reserved.
E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
water table dynamics, and vegetation composition and its production.
The Puna region extending along the Andes from central Peru to
northern Chile and Argentina is arid, with a long dry season and sparse
grassland or desert vegetation in the uplands. Peatlands, regionally
termed ’bofedales’ (Cooper et al., 2010; Squeo et al., 2006), are con­
spicuous landscape elements. Bofedales provide many critical environ­
mental services, including pasture for livestock, natural biodiversity,
and carbon storage (Benavides et al., 2013). Like other peatland types,
bofedales form organic soils when net primary production (NPP) ex­
ceeds ecosystem respiration (ER), and net ecosystem productivity (NEP)
is positive on a long-term basis (Rydin and Jeglum, 2015). Future
increased air temperature and evapotranspiration are predicted to affect
peatland carbon dynamics more in tropical than high latitude regions
due to the longer warm season (Gallego-Sala et al., 2018). The rainy
season could also be shortened, and precipitation events may be more
intense with higher runoff and reduced soil infiltration and groundwater
recharge (Westra et al., 2014).
Bofedales in the Puna region are groundwater-fed fens (Cooper et al.,
2019, 2010) with vegetation dominated by cushion-forming plant spe­
cies in the genera Distichia and Oxychloe (family Juncaceae) (Ruthsatz,
2012). The nearly 12 months-long growing season, rapidly growing
plants, and groundwater stability supports some of the highest peat
accumulation rates known for any mountain region in the late-Holocene
(Hribljan et al., 2015). Alteration of the vegetation composition or its
production, or groundwater recharge, storage, flow, and water table
depth can reduce NPP and increase ER, resulting in an annual net loss of
organic matter (Millar et al., 2017). In addition, intensive year-round
livestock grazing can reduce, and in many instances kill, the dominant
cushion plants that are replaced by species more resistant to grazing
(Cooper et al., 2015). These changes can modify the peat soil’s physical
characteristics, influencing its structure and functioning as natural sys­
tems with the capacity to capture and store carbon and provide pasture
(Cochi Machaca et al., 2018; Schimelpfenig et al., 2014).
The upper layers of peat soils are composed of partially decomposed
plant matter, live roots, and rhizomes and typically have high porosity
and hydraulic conductivity (K) (Baird et al., 2004). The number and type
of pores controlling soil K depend on plant inputs and organic matter
accumulation and decomposition rates (McCarter et al., 2020). A deeper
water table created by ditching or climate changes can dry upper soil
horizons increasing decomposition rates and reducing the percentage of
large pores resulting in altered groundwater flow patterns (Rezanezhad
et al., 2016). The high Andes have a long history of human land use and
management, particularly in peatlands used by pastoralist communities
(Domic et al., 2018). Heavy livestock grazing can modify the vegetation,
break up peatland soils and compress the peat (McCarter et al., 2020).
This increases soil density reducing porosity and K affecting hydrolog­
ical functioning (Price et al., 2003).
In this paper, we identify linkages between land uses, vegetation
composition, hydrologic regime, and soil hydraulic properties in three
peatlands with different land-use histories in the central Peruvian
Andes. We focus on the mechanism of land use driven changes in bofedal
vegetation and water table changes and the alteration of peat soil hy­
draulic conductivity. We hypothesized that the study peatlands had
similar vegetation, hydrologic regime, landforms, and soil before the
initiation of intensive livestock grazing and land management practices
since the colonization period (Domic et al., 2018). We tested this hy­
pothesis by addressing the following questions: (1) do the study sites
have similar hydraulic conductivities in deep soils indicating similar
organic matter inputs and peat formation processes in the past? (2) Is the
current vegetation composition correlated with current land uses and
water table dynamics? (3) Do shallow soils in the three study sites have
distinct soil K related to their current and recent differences in land use,
vegetation, and hydrological regime?
2. Methods
This research was conducted in three peatlands in central Peru’s Nor
Yauyos – Cochas Landscape Reserve (Fig. 1). The peatlands Huachi­
pampa and Piticocha are on the western side of the Central Cordillera in
the upper Cañete River basin in Lima Region. This area drains to the
Pacific Ocean and exhibits a well-defined rainy season from December to
April when ~ 85% of the total annual precipitation occurs, with an
interannual mean of 720 mm/year (Rau et al., 2017). Moyobamba is in
the upper Cochas River basin on the eastern side of the range, with a
slightly wetter climate and drains to the Atlantic (Amazon Basin). Wet
season precipitation accounts for 65 to 75% of the annual rainfall of
approximately 950 mm (SENAMHI, 2016). The bedrock of the Central
Cordillera is of Mesozoic and Eocene age and is intensely deformed and
covered by volcanic and sedimentary rocks of post-Eocene age. The base
of the stratigraphic column is typically Neoproterozoic rocks, including
shale and granite (Quispesivina and Navarro, 2003). These rocks are
overlain by sedimentary shale, sandstone, conglomerate, and volcanic
horizons of Mississippian and Permian ages, mostly from continental
environments. The instability of the earth’s crust caused a marine in­
vasion in the Mesozoic, allowing the accumulation of calcareous sedi­
ments
during
the
Triassic-Jurassic,
that
influence
local
hydrogeochemical processes (Megard et al., 1996). Water in the study
area is mainly neutral in reaction (pH 6.5 – 7.2), with Ca(HCO3)2 and Mg
(HCO3)2 as the dominant hydro-chemical signatures (Galindo and
Raymundo, 2018).
The study peatlands occur in valley bottoms at > 4000 m asl, sur­
rounded by hillslope glacial moraines that store and transmit ground­
water. The principal peat-forming species is Distichia muscoides Nees &
Meyen that grows in large clonal cushions. Plant shoots in the cushions
are tightly packed, blocking the horizontal flow of water, forming pools
between cushions, and creating complex landforms with pool and
cushion microtopography (Squeo et al., 2006). The three study peat­
lands have different sizes, locations, use characteristics, and disturbance
levels (Table 1). Huachipampa covers 24.6 ha, is located at 4617 m asl,
has natural topography unaltered by people, and supports moderate
intensity grazing by alpaca (Vicugna pacos) and sheep (Ovis aries). Pit­
icocha, located 3.5 km south of Huachipampa at 4394 m asl, is an un­
modified peatland with 18.7 ha bordering a downslope lake and is
intensively grazed by sheep, alpaca, and cattle (Bos primigenius taurus).
Moyobamba is located 15.2 km east of Huachipampa on the eastern
slopes of the Central Cordillera at 4662 m asl covering 12.1 ha and was
drained in the early ’50 s using ditches to facilitate intensive grazing by
sheep, alpaca, and cattle (Cooper et al., 2019).
2.1. Hydrological monitoring
Hydrological monitoring stations were installed in a grid across the
study peatlands based on their sizes and average slope. Each station
included one groundwater monitoring well for measure water table
depth, and a nest of three piezometers for estimating hydraulic head at
the installation depth. All the study peatlands were previously surveyed
using a total station (Leica FlexLine TS03). Monitoring wells were con­
structed using 5.08 cm inside diameter PVC pipe, slotted every 5 cm.
Wells were installed 1.5 to 2.0 m deep to match the maximum antici­
pated water table depth. After installation, each well was developed by
bailing. The piezometers were unslotted 1.27 cm inside diameter PVC
pipe opened only at the bottom. They were completed at three depths
around the monitoring well using the direct push technique: a steel rod
was located inside the pipe, and both were pushed to the desired depth,
then the rod was removed, and the piezometer purged using a pump
(Cardenas and Zlotnik, 2003). Seventy-eight hydrological stations were
installed between November 2016 and May 2017, with 29 in Huachi­
pampa distanced in average 75 m, 16 in Piticocha (average distance 75
m), and 33 in Moyobamba (average distance 50 m). Monitoring wells
were used to quantify water table depth, while piezometers were used to
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E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
Fig. 1. Location of the study areas in Nor Yauyos – Cochas Landscape Reserve, central Peru. (A) Study sites on the western and eastern slopes of the Central
Cordillera. Locations of hydrologic monitoring stations, pressure transducers, and meteorological stations in (B) Huachipampa, (C) Piticocha, and (D) Moyobamba.
One monitoring well on each site, Huachipampa (HUA-12), Piticocha
(PIT-11), and Moyobamba (MOY-18), was instrumented with a pressure
transducer (HOBO U20L, HOBO Computer Corporation) and the records
corrected using barometric loggers (Baro Diver DI500, Van Essen In­
struments). In addition, a tipping bucket rain gauge was installed
(HOBO RG3) 460 m east of Huachipampa. All automatic loggers and a
climate station in Huachipampa were installed in June 2018 and
recorded data hourly. Further information to characterize the climate in
the study area was from three additional climate stations: (1) Pau­
carcocha Dam, located 5.5 km southeastern to the Piticocha peatland
with data from 2006 to 2020; (2) Tanta, located 7.3 km south to the
Piticocha peatland and with data from 1967 to 2020; and (3) Qarwa­
qucha 4.1 km northeast to Moyobamba peatland.
Table 1
Physical characteristics and average livestock abundance and density per year
for the study peatlands.
Site
Huachipampa
Piticocha
Moyobamba
Drainage
Area (ha)
Altitude (m asl)
Livestock (total ind./site)
Livestock density (ind./ha)
Pacific
24.6
4617
225
9
Pacific
18.7
4394
240
13
Atlantic
12.1
4662
265
22
measure the relative vertical hydraulic gradient (vHG) through the ratio
Δh/ΔL (where Δh is the change in water level between piezometers and
ΔL is the difference in elevation of the piezometer tips) (Baxter et al.,
2003). The vHG helped to identify aquifer discharge (peatland water
sources, positive vHG) or recharge areas (peatland sinks, negative vHG)
(Fetter, 2001; Rydin and Jeglum, 2015). A 7.5 m steel rod was used to
estimate peat thickness at each monitoring well by pushing until a
change in density indicated clay, sand, gravel, or rock and the basal
mineral soil position (Householder et al., 2012; Hribljan et al., 2015).
2.2. Saturated hydraulic conductivity field tests
Modified bail tests were performed to measure saturated hydraulic
conductivity (K) at different depths in 33 of the 78 hydrological stations.
PVC pipes with a diameter of 3.18 cm were installed to measure
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E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
horizontal conductivity (Kh). Four openings, 10 cm long and covering
approximately 60% of the pipe circumference in all orientations, were
cut into the pipe walls 5 cm above the bottom. A polymer cone generated
with a 3D printer was used to seal the pipe bottom opening (Holden and
Burt, 2003). An identical PVC pipe but opened only at the bottom was
used to measure a mixed K value dominated by the vertical component
(Kv). The openings for both piezometers were covered with a geotextile
mesh 250 µm pore aperture.
Piezometers were installed in pairs to measure Kh and Kv, with three
to six pairs at different depths at each hydrological station depending on
WT depth and peat thickness. The shallowest pipe at each station was
installed at 0.25 m below ground level (bgl), with K assessed at this
depth for stations with a water table within 0.15 m bgl during the rainy
season. Depending on peat thickness, modified bail tests were also
performed on piezometers installed at 0.50, 0.75, 1.00, 1.25, and 1.50 m
bgl. Piezometers were purged before each bail test until the same re­
covery response was obtained for three consecutive withdrawals (Car­
denas and Zlotnik, 2003). Following the last purge, a pressure
transducer (Micro Diver, Van Essen Instruments) and a steel rod of
known volume were installed in the pipe. After 24 h, the steel rod was
quickly removed to produce an immediate water drop that started the
test. Tests were repeated two or three times for each piezometer. All K
tests were conducted during the wet season from March – April 2017 and
December 2017 – March 2018. The hydraulic conductivity tests were
performed when it had not rained during the previous 48 h to avoid
errors due to changes in water availability (Hanschke and Baird, 2001).
We also discarded all the test results that had a recovery period longer
than 24 h and those with recoveries that differ in more than 5% to the
original WT (supplementary material 1).
Additionally, ten soil samples, each 2500 cm2 (50 × 50 cm on each
side) and 65 cm deep, were extracted from different hydrological sta­
tions to perform vertical and horizontal falling head Darcy’s column Ksat
tests (Fetter, 2001). Those samples were extracted from four stations in
Huachipampa, four in Moyobamba, and two in Piticocha during April
2017. After extraction, each sample was isolated using wax and trans­
ported to the Soil Hydrology Laboratory, Agricultural Engineering
Department, La Molina National University in Lima.
response to individual precipitation events independent of seasonal or
temporal trend changes. It is obtained by subtracting seasonality and
trend from the measured water table values (Eq. (1)).
WT random = WT observed − (WTseasonality + WT trend )
(1)
The random component was used to evaluate whether the WT
responded to rain falling directly on the peatland during the dry season
or to groundwater discharge. For that comparison we used a crosscorrelation analysis that compared the WT random component at
HUA-12, PIT-11, and MOY-18 with precipitation measured at Huachi­
pampa, Paucarcocha Dam, and Qarwaqucha (Heliotis and DeWitt,
1987).
Vertical hydraulic conductivity (Kv) was calculated using the Kirk­
ham soil permeability method (Reeve and Kirkham, 1951) and applying
the Hvorslev equation transformation (Eq. (2)) that is recommended for
shallow water table depths (D) (15.2 ≤ D ≤ 152 cm, Schwartz and
Zhang, 2003):.
( )
2πrc
y1
Kv =
(2)
ln
11(t1 − t2 )
y2
Where rc is the inner radius of the piezometer, y1 and y2 are the heads
at times t1 and t2, and the constant value 11 derivates from the shape
factor applied for this method.
For horizontal hydraulic conductivity (Kh) the Bouwer & Rice (Eq.
(3)) method was applied (Bouwer, 1989; Zlotnik, 1994):.
Kh =
rc2 ln(Re /rwe ) 1 y0
ln
2L
t yt
(3)
Where L is the piezometer slotted length, rwe the anisotropycorrected diameter of the piezometer, y0 the head level at the begin­
ning of the test, and yt at time t. Because our experiments were con­
ducted using partially penetrating wells, ln(Re/rwe) was estimated using
Eq. (4):
[
]− 1
1.1
A + Bln[(H − D)/rwe ]
ln(Re /rwe ) =
(4)
+
ln(D/rwe )
L/rwe
Where H is the saturated aquifer thickness, and A and B are dimen­
sionless coefficients approximated with polynomial functions. To
interpret slug tests in anisotropic aquifers we used the correction term
for well diameter (Eq. (5)) (Zlotnik, 1994):
√̅̅̅̅̅̅̅̅̅̅̅̅̅
rwe = rw Kv /Kh
(5)
2.3. Vegetation sampling
Vegetation composition was sampled in August 2018 using five plots,
each 1 m2, around each hydrological station. Percent canopy cover was
visually estimated for each vascular plant species present, as well as five
categories: moss, shallow pools, deep pools, dead cushions, and bare
mineral soil (Chytrý and Otýpková, 2003). Plant species nomenclature
follows the Angiosperm Phylogeny Group version IV (APG IV, Chase
et al., 2016).
Where rw is the piezometer diameter, and Kv/Kh is the anisotropy
ratio. Because the relation between vertical and horizontal K was un­
known, the Bower and Rice equation was solved using an iterative
approach, beginning with a literature-based ratio (Schlotzhauer and
Price, 1999). Then both hydraulic conductivities (Kh and Kv) at each
depth and site were compared using one-way and two-way ANOVA and
post hoc Tukey Honestly Significant Difference (HSD) tests (Borcard
et al., 2018).
Indicator Value Analysis (IndVal) (De Cáceres et al., 2012) was used
to identify indicator species for each plant community previously
defined, applying a k-means cluster analysis. A canonical correspon­
dence analysis (CCA) (Ter Braak, 1986) was performed to explore the
relationships between the plant communities and measured environ­
mental variables, including the deepest and shallowest WT, WT range,
average vHG, and peat layer thickness. We evaluated the significance of
the CCA based on the total variability explained by the first two axes
(eigenvalues) and using a Monte Carlo permutation test to assess the
consistency of the ordination (Borcard et al., 2018). The influence of
each environmental variable on the ordination was evaluated based on
the intra-set correlation between environmental variables and the CCA
axes. The segregation obtained using this method was compared with
the k-means partition to explain differences in vegetation composition
related to the environmental variables. Linear Mixed Models were used
2.4. Data analyses
Hydrological data were analyzed to identify WT seasonality and
variability between peatlands. The water level measurements for all
monitoring wells were used to create an interpolated WT map. The
horizontal hydraulic gradient was used to describe the principal water
flow patterns. The flow pattern combined with the vHG measurements
were used to identify groundwater recharge and discharge zones in each
peatland.
After correcting for atmospheric pressure, the logger-measured WT
data for HUA-12, PIT-11, and MOY-18 were converted to daily time
series and decomposed in their seasonal and random components
(Madsen, 2008; Mudelsee, 2014). Finally, the data from these stations
were compared with precipitation and manual WT measurements in all
hydrological stations to identify seasonal patterns.
A time-series analysis was applied to decompose the daily WT series
into its temporal components: seasonality, trend, and random. The WT
random component allowed us to identify water level changes in
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Journal of Hydrology 609 (2022) 127687
to identify the hydrological and vegetation variables most correlated
with peat hydraulic conductivity at 0.25, 0.50, and 0.75 m bgl. These
comparisons interpreted groundwater flow patterns (governed by K),
vegetation composition differences, and water availability changes.
All statistical analyses and figures were developed using R 4.1.0 (R
Core Team, 2021) and Q GIS 3.18.3 (Q GIS Development Team, 2020).
Huachipampa. The deepest WT occurred in the dry season, May through
November, with average depths of 0.46, 0.51, and 0.39 m bgl for the
three sites. Logger-based WT readings at HUA-12, PIT-11, and MOY-18
had similar patterns, with the WT rising during the rainy season and
deepening during the dry season. At Moyobamba, where an artificial
drainage network occurs, WT variance was most significant and dry
season WT was deeper, even though mean annual precipitation was
highest.
Contour maps of the WT and vertical hydraulic gradients (Fig. 3)
indicated that ground water entered each peatland in its highest eleva­
tion area. The WT contours generally followed the ground surface slope,
with minor variations influenced by topographic rises that increased
water levels and the presence of streams or ditches that functioned as
hydrologic sinks. Maps of the vertical hydraulic gradient (vHG)
3. Results
3.1. Water table and flow patterns
A strong seasonal pattern of WT depth occurred in all three study
sites (Fig. 2). The shallowest WT occurred during the wet season aver­
aging 0.09, 0.29, and 0.28 m bgl at Moyobamba, Piticocha, and
Fig. 2. (Top panel) Monthly total precipitation (bars). (Bottom three panels) 95% Confidence Interval (colored area) water table in 78 hand-measured monitoring
wells in the three study peatlands and daily water table depth in MOY-18, PIT-11, and HUA-12 (solid lines).
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Journal of Hydrology 609 (2022) 127687
Fig. 3. Maps of interpolated water table surface (1 m contour lines) and vertical hydraulic gradients: (A) Huachipampa wet season, (B) Huachipampa dry season, (C)
Piticocha wet season, (D) Piticocha dry season, (E) Moyobamba wet season, and (F) Moyobamba dry season. Arrows illustrate groundwater (white) and streamflow
(blue) direction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
identified areas of perennial groundwater discharge with distinct sea­
sonality. During the wet season, the vHG was predominantly positive
indicating upward flow due to discharge from surrounding aquifers or
neutral (Fig. 3A, 3C, and 3E). These patterns reversed during the dry
season (Fig. 3B, 3D, and 3F), indicating that in most cases the flow
trended downward, recharging the underlying aquifer or, more
commonly, discharging to nearby streams. In all sites and seasons, vHG
was positive near the upper peatland margins where perennial aquifer
discharge occurred.
The analysis of WT data in HUA-12, PIT-11, and MOY-18 shown ach
precipitation event during the dry season caused a slow and gradual WT
rise lagged by more than 24 h. These lag-times had statistically signifi­
cant cross-correlations for periods of one to four days after each pre­
cipitation event, indicating a response related to transit times in the
surrounding aquifer system (Table 2).
3.2. Hydraulic conductivity
The highest Kh and Kv occurred at 0.25 to 0.75 m soil depth in all
sites, while the lowest and least variable Kh and Kv occurred at 1.00 to
1.50 m depth (Fig. 4). The geometric mean of Kh at 0.25 to 1.25 m bgl
ranged from 0.57 m day− 1 to 0.03 m day− 1. The most significant and
abrupt difference in Kh occurred between 0.25 and 0.75 m bgl,
decreasing from 0.57 to 0.08 m day− 1. Meanwhile, from 1.00 to 1.50 m
bgl Kh was lower but more homogenous and varied from 0.05 to 0.03 m
day− 1. Differences between the shallow and deeper soil layers were
statistically significant for Kh (ANOVA, p < 0.05). Paired comparisons
(Tukey HSD) indicated that the differences were in soil layers from 0.25
to 0.75 m bgl (p < 0.05), while at 1.00 m bgl and deeper no significant
differences occurred (Table 3).
The Kv geometric mean ranged from 0.07 m day− 1 at 0.25 m to 0.002
m day− 1 at 1.00 m bgl. Similar to Kh, the highest variability of Kv
occurred from 0.25 to 0.75 m bgl, while peat at 1 m and deeper was
relatively homogenous. The difference in Kv between 0.25 and 0.75 m
bgl ranged from 0.07 m day− 1 to 0.009 m day− 1, while in the three
deepest peat layers, Kv varied from 0.008 to 0.002 m day− 1. Also for Kv,
differences between strata were significant (p < 0.05) and the paired
comparisons demonstrated that these differences are caused by the two
shallowest layers (0.25 and 0.50 m bgl). The anisotropy ratio (Kv/Kh)
varied from 0.057 to 0.119 with an average of 0.081, but no significant
differences occurred between strata (ANOVA, p greater than 0.05)
(Table 3).
Laboratory Ksat values (constant head Darcy’s experiments) were
slightly lower than the field measurements. However, laboratory and
field tests had the same Kv/Kh ratio (~0.10) and a similar relationship
between the 0.25 and 0.50 m bgl layers, indicating that the field
Table 2
Cross-correlation values for water table rise in three instrumented wells, MOY18, PIT-11, and HUA-12 as response to rain events during the 2018 and 2019
dry seasons.
Time-lag
MOY-18
PIT-11
HUA-12
0 days
1 day
2 days
3 days
4 days
5 days
0.157*
0.175*
0.184*
0.203*
0.126
0.093
0.053
0.123
0.216*
0.186*
0.154*
0.102
0.048
0.198*
0.245*
0.172*
0.145
0.075
6
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Journal of Hydrology 609 (2022) 127687
Fig. 4. Horizontal (Kh) and vertical (Kv) hydraulic conductivities by depth for the three study sites. Points at each depth represent geometric means, and horizontal
lines represent ± 1 standard deviation.
Table 3
One-way Analyses of Variance (ANOVA) for horizontal (Kh) and vertical (Kv) hydraulic conductivities and anisotropy ratio (Kv/Kh). p-values obtained by post-hoc
paired Tukey HSD test comparing Kh and Kv between different strata, and comparison of mean Kh and Kv values obtained and 0.25 and 0.50 m bgl using field and
laboratory methods. In bold types, all the p-values with significant results (α = 0.05).
ANOVA
Kh
Depth
Residuals
Df
5
133
Tukey HSD for Kh by depth (p-values)
-1.25
-1.00
-0.75
-0.50
-0.25
Tukey HSD for Kv by depth (p-values)
-1.25
-1.00
-0.75
-0.50
-0.25
Kv
Anisotropy
Pr (>F)
<2.0E-16
Df
5
87
Pr(>F)
5.90E-10
Df
5
87
Pr (>F)
0.299
-1.50
0.34
0.45
0.03
<2.0E-16
<2.0E-16
-1.25
0.99
2.70E-06
<2.0E-16
<2.0E-16
-1.00
2.20E-06
<2.0E-16
<2.0E-16
-0.75
<2.0E-16
<2.0E-16
-0.50
0.01
-1.50
0.99
0.43
0.95
0.01
2.00E-04
-1.25
0.13
0.99
0.01
0.002
-1.00
0.03
1.00E-07
<2.0E-16
-0.75
0.01
0.01
-0.05
0.89
Anisotropy
0.12
0.16
0.50 m bgl
Kh
4.27E-06
4.01E-06
Kv
4.75E-07
3.12E-07
Anisotropy
0.11
0.08
Field and Laboratory (Darcy’s experiment) Kh and Kv
0.25 m bgl
Kh
Kv
Field
6.58E-06
8.15E-07
Laboratory
3.75E-06
5.88E-07
measurements accurately represented depth variability and the ratio
between the vertical and horizontal components of K.
communities characterized by 17 species were identified using k-means
cluster analysis (supplementary material 2). Community C1 occurred in
heavily grazed parts of Piticocha and Moyobamba, with Werneria pyg­
maea, Plantago rigida, and Lachemilla pinnata having the highest canopy
cover and Distichia muscoides having<20% cover. The WT in C1 was
more than 0.30 m bgl during the wet season and 0.60 m during the dry
3.3. Vegetation
Fifty vascular plant species were found in the study plots, and three
7
E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
season. Community C2 was dominated by the grasses Calamagrostis
spicigera and Calamagrostis chrysantha and occurred in the lower parts of
Huachipampa and Moyobamba. The WT in C2 had the highest vari­
ability, ranging from 0.04 m in the wet season to 0.68 m bgl in the dry
season. Community C3 occurred in upper Huachipampa, Piticocha, and
Moyobamba in areas with light grazing pressure and was dominated by
Distichia muscoides. The WT was within 0.50 m bgl all year.
Twenty community indicators were identified using the IndVal
procedure. C1 had the most indicators with nine species, including
Lachemilla pinnata, L. diplophyla, Werneria pygmaea, Plantago tubulosa,
and P. rigida. C2 had seven indicator species, including Calamagrostis
spicigera, C. chrysantha, Festuca rigida, and Carex humahuacaensis, occu­
pying seasonally flooded pools. C3 had only two indicator species, Dis­
tichia muscoides and Oritrophium limnophilum, both typical Andean
peatland species (Benavides et al., 2013; Cooper et al., 2019, 2010).
The constrained eigenvalue for CCA axis 1 is 83.3, explaining 61.2%
of the total variance in the vegetation data set (Fig. 5). CCA axis 2 had an
eigenvalue of 41.8, explaining 30.7% of the variance. Both CCA1 and
CCA2 were statistically significant (Monte Carlo permutation tests, p <
0.05) (Table 4). The intra-set correlations indicate that CCA axis 1
represents a complex gradient of vHG (r = 0.75), peat thickness (r =
0.66), maximum WT depth (r = 0.49) and WT range (r = 0.31). CCA axis
2 is positively correlated with the shallowest WT (r = 0.96), wide WT
range (r = 0.52) and negatively correlated with peat thickness (r =
-0.31).
C1 plots are on the right side of the ordination space correlated with
the deep dry season WT and moderate annual WT range. The centroid
for Werneria pygmaea (Wepy) occurs within these plots indicating its
dominance in this community. C2 sites were plotted near the top of the
ordination space, positively correlated with the shallowest WT in the
rainy season and the highest WT range. Three indicator species for this
group, Festuca rigida (Feri), Calamagrostis spicigera (Casp), and Calama­
grostis chrysantha (Cach), have their centroids located inside the cluster.
C3 plots are on the left of the ordination space associated with the
thickest peat, positive or neutral vHG, a relatively shallow WT during
the dry season, and small annual WT range. Distichia muscoides (Dimu) is
the dominant species in C3, and its centroid was located within this
group.
Table 4
CCA eigenvalues, Monte Carlo permutational tests for axis and environmental
variables, and correlation values between environmental variables and canoni­
cal axes one and two (CCA1 and CCA2). Bold numbers indicate statistical
significance.
Eigenvalues
CCA1
CCA2
Constrained
83.26
41.83
Unconstrained
293.14
85.45
Sum of constrained eigenvalues (number)
Sum of unconstrained eigenvalues (number)
Intra-set correlations
deepest water WT
variability range of WT
average vHG
shallowest WT
porous media (peat) thick
Monte Carlo test for axis
CCA1
CCA2
CCA3
CCA4
CCA5
CCA3
CCA4
6.30
68.84
136.17
594.53
CCA1
0.49
0.31
¡0.75
− 0.27
¡0.66
Pr(>F)
0.01
0.05
0.96
0.99
0.96
2.78
55.60
(5)
(20)
CCA2
− 0.12
0.52
0.02
0.96
¡0.31
3.4. Effects of hydrology and vegetation composition on Kh and Kv
Significant differences in Kh by soil layer were found within and
between communities (two-way ANOVA, p < 0.001), and the interac­
tion of soil depth and community was also significant (p < 0.01)
(Table 5). Paired community comparisons indicated that C3 had higher
Kh and was significantly different from C1 (Tukey HSD tests, p < 0.05)
while C2 was intermediate and highly variable. Differences between
communities were driven by higher and more variable Kh at 0.25 to 0.75
m bgl than in strata deeper than 1.00 m bgl, while Kh was similar for all
layers.
Statistically significant differences in Kv occurred between commu­
nities and peat layers (two-way ANOVA, p < 0.001). The paired analysis
identified soil layers at 0.25 to 0.75 m bgl in C3 as the principal dif­
ference between communities (Tukey HSD). Kv was higher in C3 than C1
and C2 at 0.25 to 0.75 m bgl, but at 1.00 m bgl and deeper was not
significantly different.
We found significant effects of hydrological variables and the percent
Fig. 5. CCA ordination for all sampling plots (in
gray) and centroids of indicator species (red) along
axes 1 and 2. The vectors for environmental variables
and polygons for communities were also shown. In­
dicator species: Dimu = Distichia muscoides, Orli =
Oritrophium limnophilum, Feri = Festuca rigida, Casp
= Calamagrostis spicigera, Cach = Calamagrostis chri­
santha, Ladi = Lachemilla diplophylla, Lapi = Lache­
milla pinnata, Pltu = Plantago tubulosa, Plri = Plantago
rigida, and Wepy = Werneria pygmaea. Vegetation
plots: ‘H’ plots = Huachipampa, ‘P’ plots = Piticocha,
and ‘M’ plots = Moyobamba. Environmental factors:
average vHG = average vertical hydraulic gradient in
the dry season, shallowest WT = shallower measured
water table (wet season), deepest WT = deeper
measured water table (dry season), peat thick = total
thickness of the peat layer, WT range = range of
variability of the water table. (For interpretation of
the references to colour in this figure legend, the
reader is referred to the web version of this article.)
8
E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
Skrzypek et al., 2011). The peatland hydraulic conductivity was highest
in the upper soil layers and lower in deeper layers. However, land use
and vegetation cover changes modified these patterns (McCarter et al.,
2020). In Nor Yauyos Cochas, significant land-use changes have
occurred since colonial times, including the introduction of nonnative
sheep and cattle (Domic et al., 2018), progressive intensification of
grazing as the human population increased (Struelens et al., 2017), and
ditching in the 20th century (DeWind, 1975; Sarmiento et al., 2000).
This has resulted in significant changes in vegetation composition and
structure of the peat bodies, particularly at Moyabamba and Piticocha.
The three study sites maintained similar hydraulic conductivities in the
deeper soils, but significant differences now occur in the upper soil
layers where modern land uses have modified the vegetation and soil
(Table 7).
Table 5
Two-way ANOVA, paired Tukey HSD tests, and Linear Mixed Models for hori­
zontal and vertical conductivity (Kh and Kv) values by depth and community.
Bold numbers indicate statistical significance.
Two-way ANOVA for Kh by community and depth
Community
Depth
community:depth
Residuals
Df
2
5
10
143
Sum Sq
5.4
165.0
8.7
50.8
Mean Sq
2.7
33.0
0.9
0.4
Two-way ANOVA for Kv by community and depth
Df
Sum Sq
Mean Sq
Community
2
69.2
34.6
Depth
5
151.8
30.4
community:depth
10
21.0
2.1
Residuals
92
119.3
1.3
Tukey HSD for Kh by community
C1
C2
C2
0.10
–
C3
0.01
0.09
F value
7.52
92.84
2.46
Pr(>F)
0.01
<2.0e-16
0.01
F value
26.69
23.41
1.62
Pr(>F)
7.2e-10
4.2e-15
0.11
4.2. Hydrology
Tukey HSD for Kv by community
C1
C2
C2
0.73
–
C3
6.0e-07
9.0e-07
Many mountain peatlands worldwide are groundwater-fed fens,
particularly in arid or semiarid regions (Bao et al., 2010; Millar et al.,
2018; O’Neill et al., 2020; Wolf and Cooper, 2015). Our study peatlands
developed in mountain valley bottoms with underlying glacial till, al­
luvium, and lacustrine sediments (Megard et al., 1996; Quispesivina and
Navarro, 2003), and surrounding hillslopes covered by glacial moraines,
talus, alluvium, and colluvium. Seasonal precipitation directly recharges
the peatland, as well as hillslope aquifers that produce perennial
groundwater discharge into each peatland supporting high-water tables
(Elmes and Price, 2019; Somers and McKenzie, 2020). Glacier meltwater
is often suggested to be the principal water source for Andean peatlands
(Dangles et al., 2017; Polk et al., 2017), but the absence of glaciers in the
study watersheds indicates the water source for peatlands is precipita­
tion and precipitation recharged groundwater, not glaciers.
A two-to-three day time lag of the WT response to dry-season pre­
cipitation events created a smooth water table rise at Huachipampa that
is indicative of slowly increasing groundwater discharge as its water
source (Ahmad et al., 2021; Heliotis and DeWitt, 1987). Most peatlands
in the central Andes have seasonally variable groundwater discharge
from surrounding aquifers that maintain a shallow WT during the wet
season and deepens during the dry season, yet still supports peat accu­
mulation (Cooper et al., 2019; Millar et al., 2018). Reduced annual
precipitation could affect aquifer storage, producing insufficient
groundwater discharge in the dry season that could degrade peatland
integrity (Elmes and Price, 2019).
canopy cover of Distichia muscoides on peat Kh and Kv at 0.25 to 0.75 m
bgl (p < 0.05, Linear Mixed Models). Percent cover of D. muscoides (p =
0.023) and annual ΔWT (p = 0.027) were the most influential variables
explaining Kh at 0.25 m bgl. Percent cover of D. muscoides (p = 0.049)
and the deepest measured WT were significantly correlated (p = 0.013)
with Kh at 0.50, but at 0.75 m bgl only the deepest WT had a statistically
significant effect (p = 0.046) (Table 6). For the vertical component of
hydraulic conductivity, Distichia muscoides canopy cover (p = 0.047) had
a statistically significant correlation with Kv at 0.25 m bgl and the
deepest WT at 0.50 m (p = 0.038).
4. Discussion
4.1. Origin and modern trajectories of central Andes peatlands
The similar deep soil K values, ground surface cushion and pool
microtopography, and dominance of Distichia muscoides in areas with
stable water availability, suggest that the three sites had a common
origin, similar to other peatlands in the Peruvian Puna (Skrzypek et al.,
2011). We recently collected peat cores 3–10 m long from Huachi­
pampa, Piticocha, and seven other peatlands in the Cañete Basin head­
waters, and preliminary analyses indicate that the peat is relatively
homogeneous and composed almost entirely of Distichia muscoides re­
mains (unpublished data, 2021). The three study sites formed in areas
where perennial groundwater discharge supported cushion plant com­
munities and peat accumulation filled glacially carved valley bottoms
(Benavides et al., 2013; Cooper et al., 2019, 2010; Hribljan et al., 2015;
4.3. Water table and vegetation composition
The vegetation composition of peatlands is strongly related to gra­
dients of water table depth, source water chemistry, and other
Table 7
Summary of hydrological characteristics and livestock load (animals/ha) for the
study peatlands.
Table 6
Resulting p-values for Linear Mixed Models used to analyze the dependence of
Kh and Kv on Distichia muscoides canopy (%) and hydrological indicators. Bold
numbers indicate statistical significance.
Fixed effects for Kh
D. muscoides (%)
Deepest WT
Shallowest WT
Average vHG
WT Range
0.25 m
0.02
0.12
0.26
0.48
0.03
0.5 m
0.05
0.01
0.84
0.79
0.42
0.75 m
0.77
0.05
0.71
0.27
0.85
Fixed effects for Kv
D. muscoides (%)
Deepest WT
Shallowest WT
Average vHG
WT Range
0.25 m
0.05
0.09
0.47
0.34
0.13
0.5 m
0.24
0.04
0.52
0.42
0.73
0.75 m
0.32
0.18
0.45
0.32
0.56
Peatland
Huchipampa
Piticocha
Moyobamba
Dominant community
Average D. muscoides canopy
cover (%)
Geometric mean Kh at 0.25 m bgl
C3
68
C1
41
C1 + C2
47
0.87 m day−
1
0.25 m day−
1
Geometric mean Kh at 1.00 m bgl
0.03 m day−
1
0.04 m day−
1
Average WT during dry season
0.39 m bgl
0.63 m
day− 1
0.03 m
day− 1
0.51 m bgl
Average livestock load (individuals/ha) per unit area
Native camelids: llama and
5
8
alpaca
Sheep
4
2
Cattle
0
2
9
0.46 m bgl
6
10
5
E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
environmental processes (Cooper et al., 2010; Domic et al., 2021; Harris
and Baird, 2019; Lemly and Cooper, 2011). Recent changes in climate
and human activities also affect the vegetation (Dieleman et al., 2015;
Kokkonen et al., 2019; Schimelpfenig et al., 2014). The vegetation
composition of our study sites is correlated with hydrological regime
and comparable to peatland vegetation differences found in other
mountain (Glina et al., 2019; Millar et al., 2018) and non-mountain
regions (Breeuwer et al., 2009; Menberu et al., 2016).
Our three study peatlands had distinctly different water table depths
and vegetation composition even though their seasonal precipitation
regime, landscape positions, peat thickness and likely age, are similar.
The small watersheds limit total water delivery, conditions where
drainage ditches can significantly impact water table depth. At Moyo­
bamba, ditches have increased WT variability and maximum depth. The
duration and intensity of water table decline, particularly during the
long dry season, is one plausible cause for explaining differences in
vegetation composition. In areas with shallow and stable water tables,
Distichia muscoides, a key species of Andean alpine peatlands, is domi­
nant with a high capacity to accumulate peat (Benavides et al., 2013;
Chimner et al., 2019; Cooper et al., 2015). In contrast, where seasonally
deeper water tables occur, the vegetation is dominated by species
adapted to drier soil conditions, including Plantago rigida, Plantago
tubulosa, Werneria pygmaea, Calamagrostis spicigera, and Calamagrostis
chrysantha (Salvador et al., 2014). These species have little capacity to
form peat because they produce little below-ground biomass (Medrano
et al., 2012). Additionally, they are adapted to drier conditions with
higher decomposition rates (Rydin and Jeglum, 2015).
5. Conclusion
Groundwater inflow from adjacent hillslope aquifers was the prin­
cipal water source for the three study peatlands. Its variability was re­
flected in different water-table dynamics, hydraulic conductivities, and
vertical hydraulic gradients between sites. Nevertheless, similar K of the
deeper peat layers, combined with a common Distichia muscoides created cushion and pool microtopography, suggests a common origin
for the three sites even though Distichia is dominant today only in areas
with the most stable shallow groundwater levels. We suggest that these
peatlands originated as groundwater-fed fens dominated by D. muscoides
that formed most of the peat and created the landforms that exist today.
However, due to human intervention the hydrological patterns, vege­
tation composition, and soils of the three peatlands are now distinct.
Understanding the hydrologic dynamics of central Andes peatlands is
particularly important given the essential ecosystem services they pro­
vide and their potential sensitivity to climate changes. Reductions in
water availability and increased thermal regimes have been occurring in
the central Andes since the Little Ice Age (Rabatel et al., 2005), and are
predicted to continue in the future (Huerta & Lavado-Casimiro, 2021;
IPCC, 2021). The high Andes have historically been occupied by humans
engaged in pastoralist activities (Domic et al., 2018; Yager et al., 2019),
but ongoing and future changes in peatlands could limit their use for
livestock pastures, which would significantly reduce the economic op­
portunities for indigenous communities. Research on groundwater
availability can inform helpful interventions and restoration activities to
mitigate past land use impacts and help Andean residents adapt to
ongoing climate changes.
4.4. Implications for management under future climates in the central
Andes
CRediT authorship contribution statement
The study peatlands are supported by hillslope aquifers, and direct
precipitation during the rainy season, and are highly susceptible to
climate change that reduces available water due to their small size,
location in headwater catchments, and lack of connection to larger
regional aquifers that could provide more stable water sources (Kløve
et al., 2014). This is a concern in the Central Andes because the long dry
season creates a regional water deficit that cannot be fully compensated
by wet season precipitation in all years (Correa et al., 2016; Giráldez
et al., 2020; IPCC, 2021, 2014). Hydrological alterations associated with
climate changes could trigger future vegetation and peat structure
changes. Combined with intensive human interventions such as
drainage and overgrazing, these alterations can exacerbate peatland
degradation (Breeuwer et al., 2009; Cooper et al., 2019; Menberu et al.,
2016). These processes could combine to reduce or eliminate the ca­
pacity of some peatlands to provide ecosystem services such as carbon
capture and sustainable pastures for livestock. This new ecosystem state
lacking dominance by cushion plants and with high rates of carbon loss
may be difficult to restore (Ahmad et al., 2021; Chimner et al., 2017;
Harris and Baird, 2019; Holden, 2005; Schimelpfenig et al., 2014). More
detailed knowledge of peatland hydrologic processes could help identify
early stages of degradation that are essential to propose effective in­
terventions to manage or restore these valuable ecosystems.
This research is the initiation of a long-term analysis of bofedale
hydrologic regimes and ecosystem processes in the Peruvian Puna. A
longer data record through additional wet and dry periods should pro­
duce new and informative conclusions. Additional rain gauges through
the complex mountain region would also help provide a better record of
precipitation in watersheds supporting bofedales. Peat aging, and iso­
topic and macrofossil analyses of peat cores will also help clarify the
development of these peatlands. Additional and larger diameter
piezometer networks, infiltration analyses, and laboratory tests will also
aid in further quantifying vHG, Kv, Kh, porosity, specific yield, and other
important hydrologic and hydraulic indicators.
Eduardo Oyague: Conceptualization, Methodology, Formal anal­
ysis, Writing – original draft. David J. Cooper: Conceptualization,
Methodology, Supervision, Writing – review & editing. Eusebio Ingol:
Validation.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
Nor Yauyos – Cochas Landscape Reserve Patronage (PRPNYC) sup­
ported this research through the “Nor Yauyos – Cochas wetlands hy­
drology” project (Agreement PRPNYC-CORBIDI/2016-2020). The
authors wish to thank Pedro Lerner, Carmela Landeo, Niskar Peña,
Ángela Baldoceda, and Danilo Ávila for their help with funding, field­
work, data recording, and logistics. The data analysis and preliminary
versions of this manuscript were financially supported by a Peruvian
National Science and Technology Council fellowship (CONCYTEC Con­
tract 057-2019 to EO). Professor Rosa Miglio (La Molina University)
provided helpful comments on an early draft of this document. We thank
Dr. Jonathan Price and one anonymous reviewer for helpful comments
that significantly improved this manuscript. The Peruvian National
Parks Service (SERNANP) provided us with the research authorization
002-2018-SERNANP-JEF/RPNYC.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jhydrol.2022.127687.
10
E. Oyague et al.
Journal of Hydrology 609 (2022) 127687
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