water
Article
Water Storage–Discharge Relationship with Water Quality
Parameters of Carhuacocha and Vichecocha Lagoons in the
Peruvian Puna Highlands
Samuel Pizarro 1,* , Maria Custodio 2 , Richard Solórzano-Acosta 1 , Duglas Contreras 1 and
Patricia Verástegui-Martínez 1
1 Dirección de Supervisión y Monitoreo en las Estaciones Experimentales Agrarias, Instituto Nacional de
Innovación Agraria (INIA), Carretera Saños Grande−Hualahoyo Km 8 Santa Ana, Huancayo,
Junin 12002, Peru; investigacion_labsaf@inia.gob.pe (R.S.-A.); contreraspino17@gmail.com (D.C.);
patymarve@gmail.com (P.V.-M.)
2 Facultad de Medicina Humana, Centro de Investigación en Medicina de Altura y Medio Ambiente,
Universidad Nacional del Centro del Peru Av. Mariscal Castilla N◦ 3909, Huancayo, Junin 12002, Peru;
mcustodio@uncp.edu.pe
* Correspondence: sam20048130@gmail.com
Abstract: Most Andean lakes and lagoons are used as reservoirs to manage hydropower generation
and cropland irrigation, which, in turn, alters river flow patterns through processes of storage
and discharge. The Carhuacocha and Vichecocha lagoons, fed by glaciers, are important aquatic
ecosystems regulated by dams. These dams increase the flow of the Mantaro River during the
dry season, supporting both energy production and irrigation for croplands. Water quality in the
Carhuacocha and Vichecocha lagoons was assessed between storage and discharge events by using the
Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) and multivariate
statistical methods. The quality of both lagoons is excellent during the storage period; however, it
decreases when they are discharged during the dry season. The most sensitive parameters are pH,
dissolved oxygen (DO), and biochemical oxygen demand (BOD). This paper details the changes in
water quality in the Carhuacocha and Vichecocha lagoons during storage and discharge events.
Citation: Pizarro, S.; Custodio, M.;
Solórzano-Acosta, R.; Contreras, D.;
Verástegui-Martínez, P. Water Keywords: Andean lagoons; discharge; water quality; physical−chemical parameters
Storage–Discharge Relationship with
Water Quality Parameters of
Carhuacocha and Vichecocha Lagoons
in the Peruvian Puna Highlands. 1. Introduction
Water 2024, 16, 2505. https://doi.org/ The growth in population, coupled with economic expansion and urban industrial
10.3390/w16172505 development, has led to an increased demand for water in both urban areas and agricultural
Academic Editor: Christos S. Akratos lands. In Peru, nearly nine million residents of Lima depend on highland water bodies for
nearly half of their drinking water [1], while highland populations use intermontane valleys
Received: 9 July 2024 for agricultural activities [2]. Similarly, hydropower developments depend on highland
Revised: 8 August 2024 lakes and water transfer, which modify river flow patterns. In order to overcome these
Accepted: 13 August 2024 issues, most parts of the Andean lakes and lagoons are dammed to provide an essential
Published: 4 September 2024 freshwater resource, modifying in the process the characteristics of the ecosystems’ hotspots
for biological species through a complex interaction between geographical reconfiguration
and, therefore, changing the sociopolitical scene [3]. Climate change has led to alterations
Copyright: © 2024 by the authors. in rainfall patterns and higher temperatures in highland regions. This has led to heavy
Licensee MDPI, Basel, Switzerland. rainfall events, and current trends indicate that these changes are likely to persist, resulting
This article is an open access article in increased runoff [4] and glacier retreat [5]. Consequently, flood runoff is a potential
distributed under the terms and pollution source for Andean lagoons.
conditions of the Creative Commons Water quality is a critical environmental concern globally that must be assessed across
Attribution (CC BY) license (https:// various contexts to safeguard public health and safety. It should also be integrated into
creativecommons.org/licenses/by/ policies to prevent specific hazards through an adaptable and participatory approach [6].
4.0/). The diminishing of water quality restricts the potential uses of freshwater sources and
Water 2024, 16, 2505. https://doi.org/10.3390/w16172505 https://www.mdpi.com/journal/water
Water 2024, 16, 2505 2 of 13
adversely impacts ecosystems related to them by leading to conditions such as hypoxia (low
oxygen levels), excessive algal growth (algal blooms), reduced species diversity, and the
accumulation of heavy metals and toxins [7]. Lagoons acquire basin-specific characteristics
which make them unique. Even if they are in the same area, they can respond differently to
similar stimuli.
The Peruvian Environmental Quality Standard (EQS) for water [8] regulates 104 pa-
rameters and categorizes water use into four distinct groups: public and recreational use;
cultivation, extraction, and other coastal and inland fishfarming activities; irrigation of
crops and livestock watering; and the preservation of aquatic ecosystems. Regarding
each water body, it is necessary to measure certain parameters, according to the National
Classification of the Superficial Continental Water Bodies [9]. Lagoons are in category
four. For this study, the physical, chemical, and microbiological parameters specified in
the methodology were evaluated. These parameters influence habitats and the diversity of
biological communities, and their impacts are managed through the analysis of various
environmental indicators According to Panhwar et al. [10], pH and electrical conductivity
(EC) are some of the most important parameters for biological processes.
The Carhuacocha and Vichecocha lagoons are aquatic ecosystems sustained by glaciers
and the runoff from other small glacier-fed ponds. They hold significant economic value
as they regulate dams designed to enhance the flow of the Mantaro River during the dry
season, thereby supporting cropland irrigation and electricity generation at the Santiago
Antúnez de Mayolo hydroelectric power plant [11]. These areas are considered as habitats
for an array of bird species. Their administration, which is in charge of the Nor yauyos
Cochas Landscape reserve, has the objective of preserving biodiversity while regulating
human activities around the lagoons.
Due to the unpredictable nature of floods, the complexity of hydrodynamic processes,
and harsh environmental conditions, conducting field experiments is very challenging.
Consequently, there is still a lack of field measurements and simulation studies on the
effects of flood discharge for canyon-shaped drinking water reservoirs. In addition, there is
limited research on the water quality of regulated lagoons in Peru. To fill this knowledge
gap, we conducted this research to assess the impact of storage–discharge events on
the physical−chemical properties of water in the Carhuacocha and Vichecocha lagoons.
Water quality data were collected in situ during storage and discharge events of these two
lagoons, while assessment was conducted using the Canadian Council of Ministers of the
Environment Water Quality Index (CCME WQI) and multivariate statistical methods.
2. Materials and Methods
2.1. Study Area
The Carhuacocha and Vichecocha lagoons are situated at the head of the Pachacayo
river sub-basin, within the Mantaro basin, at elevations of 4420 and 4480 m.a.s.l., respec-
tively. They are surrounded by moraine and puna grasslands. These lagoons are in the
territory of the Tupac Amaru livestock system, affiliated with the Canchayllo community
and managed under the Nor Yauyos Cochas Landscape Reserve (Figure 1). The climate in
this area is cold, with an average annual temperature of 3.4 ◦C and annual precipitation
reaching 814 mm. Rainfall typically occurs from January to March, with dry periods from
June to August. However, rainfall increases between August and September, becoming
more significant from October until reaching maximum values in February [12].
2.2. Water Extension
We used Planet−NICFI imagery surface reflectance four-band imagery (blue, green,
red, and near-infrared) in composite (NIR–GREEN−RED), from January 2023 to February
2024, and analyzed this in the GEE platform [13]. In order to estimate the extension for the
two lagoons, we used the Normalized Difference Water Index (NDWI), calculated as the
difference between green and near-infrared reflectance (Band 2–Band 4) divided by the sum
of green and near-infrared reflectance (Band 2 + Band 4/5) [14]. This index yields values
Water 2024, 16, 2505 3 of 13
ranging from −1 to 1, where values close to 1 indicate open-water pixels, which are more
distinguishable from non-water pixels. These products combined with band composition
Water 2024, 16, x FOR PEER REVIEWN IR−GREEN−RED help to set the boundaries for both lagoons by photointerpretatio3 nofi n16 
 
the same platform. After that, we contrasted the area with the dry and rainfall season of
two weather stations close to the lagoons (Figure 2).
 
Figure 1. Locations where water samples were taken from Carhuacocha and Vichecocha lagoons.
Figure 1. Locations where water samples were taken from Carhuacocha and Vichecocha lagoons. 
2.3. Sampling and Analytical Procedure
2.2. EWigahtetr sEaxmtepnlsiinogn stations were set up around each lagoon, with four samples taken
per staWtieo nusdeudr iPnlgantwet−oNeIvCeFnIt sim(satogreargye saunrfdacdei srcehflaercgtaen).cIen fotoutra-lb,a1n2d8 wimaategrersya m(bplulees, wgreereen, 
corelldec, taendd( 6n4easra-minpfrleasrepde)r ilna gcomonp)obseittwe (eNenIRJ–uGlyRaEnEdND−eRcEeDm)b, efrro2m02 J3a.nSuaamryp l2in0g23w toas Fceabrrruieadry 
ou20t2d4u, raindg athniaslypzeerido dthbise icna uthse ,GfrEoEm pAlaptfroilrmto [J1u3ly]., Irna ionrfdaellrd toec ersetaimseastde rtahset iecxatlelynsainodn, fforro mthe 
Atuwgou slat goonownasr,d ws,ei tuisnecdre tahse sNuonrtmil iatlirzeeadch Desiffitesrmenacxei mWuamterv aInludeesxi n(NFDebWruIa),r yca[l1c5u]l.aEteledc tarsic tahle 
codniffdeurcetnivciet ybe(EtwCe),enp Hg,retemn panerda tnueraer,-ainfdradreisds orlevfleedctoaxnycge e(nBa(DndO )2–wBearnedm 4e)a dsuivrieddedin bfiye ltdhe 
wsiuthma oHf AgNreNenA a®ndH In9e8a1r2-9inafrnadreHdI 9r1efl46e−ct1a0ncme u(lBtaipnadr a2m +e tBear,nrde s4p/e5c)t i[v1e4l]y. .This index yields 
valuWesa trearnsgaimngp flreosmw −e1re toco 1l,l ewctheedreu vsianluge5s0 c0lomseL tgol 1a sisndboictatltes o, pwehni-cwhawteerr peisxtelrsi,l iwzehdicahn adre 
rimnsoerde wditshtindgisutilslhedabwlea tferor mpr inoornto-wmaitcerro pbioxleolsg.i cTahl easnea lpyrsoisd.uActdsd citoimonbailnlye,dt wwoitehx tbranbdo tctolems -
wpeoresituiosend NfoIRr−cGhRemEEicNal−aRnEaDly hseislpa ntod shete athvey bmouetnadlaterisetsin fgo,r rbeostphe lcatgivoeolny.s Fboyr phheoatvoyinmtereptarle-
antaatliyosnis i,nt htehes asmample spwlaetfroerpmre. sAerftveerd thbayt,a dwdei ncgon1t.r5amstLedo fthceo nacrenat rwaittehd tnhietr dicryac aidndp erraliintfearll 
osfewasaotenr ,offo tlwloow winegattheer psrtaotcieodnus rcelsosoeu ttoli ntheed labgyotohnesA (Fmigeurircea 2n).P ublic Health Association
(APHA) [16]. After collection, the water samples were transported to the laboratory and
stored at 4 ◦C until preparation and analysis. All analyses were conducted in triplicate,
and the findings were reported as the mean value. Detailed procedures for each analyzed
parameter are outlined in Table 1.
 
WWataetrer2 022042,41, 61,62, 5x0 F5OR PEER REVIEW 44 ooff 1136  
 
FFigiguurere2 2. .L Laakkees shhoorreelilnineessd deelilnineeaateteddf rfroommP PlalanneetStSccooppeef rfroommp peerrioioddJ aJannuuaarryy2 2002233––FFeebbrruuaarryy2 2002244f oforr 
CCaarhrhuuaaccoocchhaaa annddV Vicihcheceocochchaal alakkese.s. 
T2a.b3l.e S1a.mDpleitnagil sanodf Athneailnysttircualm Pernotcaetdiounreu sed, methods employed, and quality control measures
implemEeignhtetd sadmurpinlgintgh estaantaiolynssis w. ere set up around each lagoon, with four samples taken per 
station during two events (storage and discharge). In total, 128 water samples were 
Parameter collecUtesde d(6I4n sstarummpelnetsa ptieorn langdooMne)t hboedtws/eSeonlu Jtuiolyns and DecemberA( 2
c0c2u3r.a ScySensitivaitmy)plingR awnagse cTaersrtied 
out during this period because, from April to July, rainfall decreases drastically and, from 
pH August onwards, it increases until it reaches its maximum±± 
0v.0a5lues in Fe0b–r1u4ary [15]. 
Conductivity (EC, µS/cm)
ElectrHicAalNN
®
 conAducHtIi9v8it1y29
1 0–3999
Temperature (T, ◦C)  (EC), pH, temperature, and dissolved o±xy0.g5en (DO) we0r.e0 –m60e.a0sured 
in field with a HANNA ® HI98129 and HI9146−10 multiparameter, respectively. 
Dissolved oxygen (DO, mg/L) WHaAtN
®
erN sAampHleI9s1 w46e−re1 0c,ollected using 500 mL glass bottles±, w0.0h6ich were s0te.0r0il–i4z5e.d00 and 
Chemical oxygen demand rinsedC hwemithic adl iOsxtiyllgeedn Dweamtearn dp,rCiolors etdo RmeflicurxoCboiololorigmiceatrli canalysis. Additionally, two extra 
(COD, mg/L) bottleMs wetheroed used for chemical analysis and heavy metal testi±n0g.,4 respectivel0y–.2 For heavy 
metalS aMnEaWlyWsis−, AthPeH sAam−ApWlesW wAe–rWe EpFrePsaerrtv5e2d20 bDy. 2a3drddi.n2g0 117.5.  mL of concentrated nitric acid 
Biochemical oxygen demand per liBteior choef mwicaatleOr,x yfoglelnowDeinmga ntdhe(B pOrDo)c.e5d−uDraeys BoOuDtliTneesdt  by th±e2 American P0u–b5lic Health (BOD5, mg/L) AssocSiMatEioWnW (A−PAHPHAA) −[1A6W]. WAAft–eWr EcFolPleacrtti5o2n1,0 Bth. 2e4 twh aEtde.r2 0s2a2m. ples were transported to the 
laboraMtourlyti palne-dT usbtoerdeedfe artm 4e°nCta utinontilT epcrhenpiaqruaetfioornM anemd baenras loyfsis. All analyses were conducted 
Thermotolerant coliforms in tripthliecaCtoel,i faonrmd Gthreo ufipn.dEi.ncgolsi Pwroecreed ruerpeoUrsteindg aFslu tohreo gmeneiacn value. Detailed procedures for 
(TC, NMP/100 mL) each aSnuablsytrzaetde. pSiamraumltaenteeor uasreD oetuetrmination of Thermotolerant
±1.8 −−
Coliforms and E. coli. lined in Table 1. 
  
Chloride (Cl−, mg/L) Determination of inorganic anions by ion chromatography
Nitrate (NO −3 , mg/L) Environmental Protection Agency. Methods for Chemicals ±0.02 0.02–0.300
Sulfate (SO 2−4 , mg/L) Analysis (EPA) ±0.2 2–70
Ascorbic Acid Method
Phosphorus (P, mg/L) SMEWW−APHA−AWWA–WEF Part 4500–P B (Item 5) y E, ±0.002
24th Ed. 2023.
 
Water 2024, 16, 2505 5 of 13
Table 1. Cont.
Aluminum (Al, mg/L) ±0.001
Bario (Ba, mg/L) ±0.00008
Boron (B, mg/L) ±0.0003
Calcium (Ca, mg/L) ±0.001
Copper (Cu, mg/L) ±0.0001
Strontium (Sr, mg/L) ±0.00002
Iron (Fe, mg/L) Determination of trace Elements in Water and Waste ±0.001
Lithium (Li, mg/L) Inductively Coupled Plasma–Mass spectrometry. ±0.00003
Magnesium (Mg, mg/L) Method 200.8 Revision 5.4 1994 ±0.0006
Manganese (Mn, mg/L) ±0.00002
Potassium (K, mg/L) ±0.003
Silica (SiO2, mg/L) ±0.001
Silicon (Si, mg/L) ±0.0002
Sodium (Na, mg/L) ±0.0003
Vanadium (V, mg/L) ±0.0001
Note(s): EPA: US Environmental Protection Agency. Methods for Chemicals Analysis; SMEWW: Standard
Methods for the Examination of water and Wastewater; APHA: American Public Health Association.
2.4. Data Analysis
Statistical analysis and data processing were conducted using R Studio software
(Version 3.4.3). The modified Shapiro−Wilks test was employed to assess the normality
of the data. Pearson correlation analysis was utilized to explore the degree of correlation
among the water parameters. Principal component analysis (PCA) was utilized to assess the
suitability of the data for factor analysis. Kaiser−Meyer−Olkin (KMO) and Bartlett’s tests
were conducted. KMO assesses sampling adequacy by indicating the proportion of variance
that might be attributable to underlying factors. A high KMO value (approaching 1)
typically suggests that factor analysis is appropriate, while a value below 0.5 indicates it
may not be useful. Bartlett’s test of sphericity determines whether the correlation matrix
resembles an identity matrix, which would indicate that variables are unrelated [17]. This
analysis is used to identify possible associations with the physicochemical parameters of
both lagoons.
Water Quality Assessment
To evaluate the health of aquatic ecosystems, we employed the Canadian Council of
Ministers of the Environment’s Water Quality Index (CCME WQI) [18]. This index consists
of three factors (F1, F2, and F3), scaled between 0 and 100, respectively. The values of the
three measures of variance from selected objectives for water quality are combined to create
a vector in an imaginary “objective exceedance” space. The length of the vector is then
scaled to range between 0 and 100 and subtracted from 100 to produce an index which is 0
or close to 0 for very poor water quality and close to 100 for excellent water quality. Since
the index is designed to measure water quality, it was considered that the index should
produce higher numbers for better water quality indicators. The mathematical formulation
of CCME WQI is given below. √ F2 2 2− 1 + F2 + F3CCMEWQI = 100  (1)1.732
where:
F1 (Scope) assesses the extent of noncompliance with the water quality standards over
a specified period; F2 (Frequency) indicates the proportion of individual tests that fail to
meet the set objectives (“failed tests”); and F3 (Amplitude) measures the degree to which
the failed test values deviate from their objectives.
Water 2024, 16, 2505 6 of 13
The CCME WQI scores are grouped into five categories of water quality: poor (0–44),
marginal (45–64), fair (65–79), good (80–94), and excellent (95–100). Previously, water
quality index scores were determined for 11 physicochemical parameters.
3. Results
3.1. Spatial Variability of Water Parameters Content
Descriptive statistics for physicochemical parameters from Carhuacocha and Vichecocha
lagoons, encompassing both storage and discharge events, are presented in Table 2. These
statistics are compared with the Environmental Quality Standards (EQS) established by Pe-
ruvian regulations for the conservation of aquatic environments [8]. The pH levels of water
in both lagoons exhibit only minor variations between events, consistently falling within the
acceptable ranges for the preservation of lacustrine and aquatic environments (6.5–9.0) and
recreational use (6.0–9.0). However, at most sites, the pH values exceed the recommended
range for drinking water (6.5–8.5), with particularly higher values observed in Vichecocha
Lagoon. Average temperature values ranged from 12.69 to 13.98 ◦C in Carhuacocha Lagoon
and from 11.41 to 12.06 ◦C in Vichecocha Lagoon. There were no significant differences in
temperature between storage and discharge events in either lagoon.
Dissolved oxygen (DO) concentrations measured in both lagoons during storage
events fall within the range specified by the Peruvian EQS and meet the World Health
Organization’s recommended minimum of ≥5.5 mg/L for the overall health of aquatic
systems [8].
However, during discharge events, this parameter falls below the expected range
and shows a significant difference. The average EC in water samples ranged from 104.38
to 235.75 µS/cm, with the lowest mean EC recorded in Vichecocha, showing a signifi-
cant difference between events. For both lagoons, EC values are below the upper limit
(1000 µS/cm).
The mean BOD5 values recorded for Carhuacocha did not surpass the EQS water stan-
dard (5 mg/L) for the conservation of the aquatic environment. Although, in Vichecocha,
this parameter exceeds the EQS water standard during discharge events, although, statisti-
cally, there is no significant difference between events. The mean values of chemical oxygen
demand (COD) did not exceed the category 3 EQS water standard (40 mg/L) for vegetable
irrigation and animal drinking. The thermotolerant coliform counts at all sampling sites
for both lagoons did not exceed the EQS water standards.
The mean total phosphorus values at all sampling sites in both lagoons are lower than
the Peruvian EQS water standard (0.035 mg/L). The mean chloride values vary between
events for both lagoons. Nitrate values remain consistent between events and are below the
Peruvian EQS water standard (13 mg/L) at all sampling sites in both lagoons. Sulfate levels
decrease during discharge events in the Vichecocha lagoon, while silica content increases
during discharge events in the Carhuacocha lagoon. The concentrations of the remaining
elements in the water samples varied by sector and event, with the order of abundance in
the Carhuacocha lagoon being Ca > Mg > Si > Na > K > Sr > Fe > Ba > Mn > Al > B > V >
Cu > Li.
For Vichecocha, the order of abundance was Ca > Si > Na > Mg > K > Sr > Al > Fe
> Mn > B > Ba > Cu > V > Li. The results reveal significative difference between water
parameters (DO, Cl−, Al, B, Ca, Cu, Sr, Fe, Mg, SiO2, Si, and Na) for Carhuacocha lagoon
and (DO, Cl−, B, Na, EC, SO−2, Li, and V) for Vichecocha between events. Significant
variations in the described parameters were observed with each event for both lagoons.
Water 2024, 16, 2505 7 of 13
Table 2. Event variation in physicochemical parameters and thermotolerant coliforms in Carhuacocha and Vichecocha Lagoons during 2023.
Carhuacocha Vichecocha EQS
Parameter Storage Discharge Storage Discharge Water—Conservation of
Upper Low Upper Low Upper Low Upper Low the Aquatic Environment
pH 8.3500 ± 0.3836 a 8.5225 ± 0.2405 a 8.1250 ± 0.0954 b 8.1900 ± 0.1227 ab 8.6575 ± 0.2061 a 9.0925 ± 0.2965 a 8.7075 ± 0.3917 a 8.6825 ± 0.5789 a 6.5–9.0
Temperature (◦C) 11.8500 ± 2.1825 a 13.5250 ± 2.4446 a 13.9250 ± 1.8998 a 14.0250 ± 0.7500 a 11.6250 ± 1.5218 a 12.5000 ± 0.5354 b 12.3250 ± 0.6850 b 10.5000 ± 0.4967 ab ∆ 3
DO (mg/L) 7.6775 ± 0.1420 a 7.9125 ± 0.3836 a 2.2525 ± 0.1335 b 2.2225 ± 0.4665 b 7.7075 ± 0.2317 a 8.1050 ± 0.3743 a 1.6400 ± 0.3222 b 1.4700 ± 0.2082 b ≥5
EC (µS/cm) 195.2900 ± 78.5408 a 249.0000 ± 3.1623 a 235.0000 ± 36.3043 a 236.5000 ± 8.3865 a 113.7500 ± 3.5940 a 118.0000 ± 10.9545 a 102.0000 ± 9.3452 b 106.7500 ± 1.8930 b 1000
BOD (mg/L) 4.9900 ± 0.0000 a 4.9900 ± 0.0000 a 4.9900 ± 0.0000 a 4.9900 ± 0.0000 a 4.9900 ± 0.0000 a 4.9900 ± 0.0000 b 5.6950 ± 1.1530 a 4.9900 ± 0.0000 a 5
COD (mg/L) 1.9900 ± 0.0000 a 1.9900 ± 0.0000 a 1.9900 ± 0.0000 a 1.9900 ± 0.0000 a 1.9900 ± 0.0000 a 1.9900 ± 0.0000 a 2.2675 ± 0.5550 a 1.9900 ± 0.0000 a 40
TC(NMP/100 mL) 2.4750 ± 1.3500 ab 1.7900 ± 0.0000 ab 2.4675 ± 1.3550 a 2.4675 ± 1.3550 b 1.7900 ± 0.0000 a 1.7900 ± 0.0000 b 8.2750 ± 3.5132 a 2.4675 ± 1.3550 a 1000
P (mg/L) 0.0059 ± 0.0000 a 0.0059 ± 0.0000 a 0.0059 ± 0.0000 ab 0.0059 ± 0.0000 b 0.0059 ± 0.0000 a 0.0059 ± 0.0000 b 0.0220 ± 0.0109 a 0.0079 ± 0.0040 a 0.035
Cl− (mg/L) 0.3232 ± 0.4445 a 0.1045 ± 0.0013 a 0.9900 ± 0.0000 b 0.9900 ± 0.0000 b 0.1085 ± 0.0013 a 0.1125 ± 0.0013 a 0.9900 ± 0.0000 b 0.9900 ± 0.0000 b −−
NO −3 (mg/L) 0.1870 ± 0.1680 a 0.1292 ± 0.1605 ab 0.4175 ± 0.3931 b 0.5600 ± 0.4968 ab 0.1648 ± 0.0773 ab 0.1320 ± 0.1000 a 0.4250 ± 0.3779 b 0.4125 ± 0.3958 b 13
SO 2−4 (mg/L) 44.9525 ± 19.3041 a 57.1100 ± 0.6914 ab 56.4250 ± 13.8454 b 54.8250 ± 0.6602 a 19.0875 ± 0.2617 ab 19.3650 ± 0.2525 a 16.6500 ± 2.1810 c 18.7000 ± 0.2000 bc −−
SiO2 (mg/L) 3.1677 ± 0.6338 a 3.6868 ± 0.0823 a 4.8045 ± 0.8920 b 4.1830 ± 0.1450 b 4.9533 ± 0.3605 a 4.4910 ± 0.2667 b 5.3595 ± 1.1776 ab 4.1242 ± 0.5154 b −−
Al (mg/L) 0.0029 ± 0.0000 a 0.0029 ± 0.0000 a 0.0932 ± 0.0455 b 0.0257 ± 0.0180 b 0.0442 ± 0.0655 a 0.0680 ± 0.0232 b 0.1735 ± 0.2116 b 0.0150 ± 0.0109 ab −−
Ba (mg/L) 0.0089 ± 0.0040 a 0.0132 ± 0.0019 ab 0.0128 ± 0.0039 ab 0.0138 ± 0.0023 b 0.0056 ± 0.0021 a 0.0039 ± 0.0004 ab 0.0058 ± 0.0057 b 0.0019 ± 0.0012 b 0.7
B (mg/L) 0.0009 ± 0.0000 a 0.0009 ± 0.0000 a 0.0174 ± 0.0013 b 0.0168 ± 0.0004 b 0.0091 ± 0.0113 a 0.0009 ± 0.0000 a 0.0126 ± 0.0023 b 0.0144 ± 0.0011 ab −−
Ca (mg/L) 0.0090 ± 0.0000 a 0.0090 ± 0.0000 ab 0.0002 ± 0.0000 bc 0.0002 ± 0.0000 c 0.0090 ± 0.0000 a 0.0090 ± 0.0000 ab 0.0002 ± 0.0000 b 0.0002 ± 0.0000 b −−
Cu (mg/L) 0.0002 ± 0.0000 a 0.0002 ± 0.0000 a 0.0007 ± 0.0002 b 0.0008 ± 0.0001 b 0.0002 ± 0.0000 ab 0.0085 ± 0.0055 a 0.0057 ± 0.0008 a 0.0025 ± 0.0025 b 0.1
Sr (mg/L) 0.1846 ± 0.0821 a 0.2434 ± 0.0044 a 0.2809 ± 0.0646 ab 0.2629 ± 0.0094 b 0.0790 ± 0.0031 a 0.0765 ± 0.0012 b 0.0695 ± 0.0111 b 0.0827 ± 0.0056 ab −−
Fe (mg/L) 0.0360 ± 0.0094 a 0.0437 ± 0.0250 ab 0.0720 ± 0.0488 b 0.0988 ± 0.0153 b 0.0510 ± 0.0442 a 0.0302 ± 0.0075 b 0.1143 ± 0.1330 b 0.0055 ± 0.0006 b 5
Li (mg/L) 0.0001 ± 0.0000 a 0.0001 ± 0.0000 ab 0.0001 ± 0.0000 b 0.0001 ± 0.0000 b 0.0001 ± 0.0000 a 0.0001 ± 0.0000 a 0.0023 ± 0.0027 b 0.0014 ± 0.0003 b −−
Mg (mg/L) 2.3093 ± 0.8920 ab 2.9712 ± 0.0830 a 3.5375 ± 0.3057 bc 3.2715 ± 0.1133 c 0.8914 ± 0.0328 a 0.9264 ± 0.0448 a 0.8728 ± 0.0796 a 0.9498 ± 0.1216 a −−
Mn (mg/L) 0.0059 ± 0.0021 a 0.0090 ± 0.0022 a 0.0086 ± 0.0048 a 0.0057 ± 0.0023 a 0.0251 ± 0.0269 a 0.0127 ± 0.0087 b 0.0091 ± 0.0066 b 0.0014 ± 0.0015 b −−
K (mg/L) 0.7888 ± 0.0763 a 0.7480 ± 0.0157 b 0.8875 ± 0.0561 a 0.7685 ± 0.0471 a 0.1752 ± 0.0439 a 0.1942 ± 0.0688 b 0.1305 ± 0.0515 ab 0.2115 ± 0.0583 ab −−
Si (mg/L) 1.4782 ± 0.2958 a 1.7203 ± 0.0385 a 2.2422 ± 0.4163 b 1.9520 ± 0.0677 b 2.3116 ± 0.1682 a 2.0958 ± 0.1244 b 2.5010 ± 0.5495 ab 1.9246 ± 0.2407 b −−
Na (mg/L) 1.1654 ± 0.3604 a 1.4573 ± 0.0237 a 1.5747 ± 0.3018 ab 1.5274 ± 0.0297 b 1.8290 ± 0.0181 a 1.8021 ± 0.0833 b 1.9106 ± 0.2413 b 2.9350 ± 1.2264 b −−
V (mg/L) 0.0003 ± 0.0000 a 0.0003 ± 0.0000 a 0.0003 ± 0.0000 ab 0.0003 ± 0.0000 b 0.0003 ± 0.0000 a 0.0003 ± 0.0000 b 0.0017 ± 0.0008 a 0.0008 ± 0.0006 a −−
Note(s): means with different letters in the same column for each event level are statistically different (LSD Fisher, α = 0.05). TC: thermotolerant coliforms.
Water 2024, 16, 2505 8 of 13
3.2. Correlation Analysis
Figure 3 shows how the different physical−chemical parameters and metals in the
water samples relate to each other. The analysis identified clusters of parameters that tend
to be present together. One group, including potassium (K), barium (Ba), sulfate (SO 2−4 ),
magnesium (Mg), strontium (Sr), and calcium (Ca), likely comes from the same source and
seems to influence electrical conductivity (EC) and temperature. Another group, including
aluminum (Al), phosphorus (P), vanadium (V), lithium (Li), silicon dioxide (SiO2), silicon
Water 2024, 16, x FOR PEER REVIEW(S i), and iron (Fe), might be linked to higher biological oxygen demand (BOD) and ch1e1m oifc a1l6 
 oxygen demand (COD).
 
Figure 3. Pearson’s correlation coefficients between physical–chemical parameters and metals in the
wFaigteurresa 3m. Ppeleasr.son’s correlation coefficients between physical─chemical parameters and metals in the 
water samples. 
These findings emphasize the importance of monitoring these parameters together
t3o.3u. nWdaetresrt Qanudalwitya ter quality. The analysis also revealed negative correlations between
someTphaer armeseutletsr so. f Ftohre ewxatmerp qleu,aclaitlyc iausmses(Csma)e,nstt rboansteidu mon( Sthr)e, CmCaMgnEe WsiQuI mfor( Malgl )s,asmuplflainteg 
(sSiOte4s2 −in), tbhaer iCuamrh(uBac),oachnad apnodta sVsiicuhmec(oKch) as hloawgoeodnns eagraet isvheocwonrr einla tTiaobnlse w3i. thThseilsiec ornes(uSlit)s,  
siinlidcoicnatdei oexicdeell(eSnitO w2)a, tleitrh qi umali(tLyi )a,tv alnl asditieusm in( VC)a,rphhuoascpohcohrau lsa(gPo)o, ann adnadl uamt siinxu omf e(Aiglh).t Tsihtiess 
siung Vgeicshtsedcoifcfherae nlatgdoiossno ldvuerdinsgo luthte ssotuorcaegse oerveennvt.i rHonomweenvtearl, bdeuhraivnigo rtshfeo rdtihscehsearogpep eovsienngt, 
pwaraatemr etqeuraglritoyu pisn. ICntaerrheustaicnogclhy,at hleagwoaotner dquecarlietayspedar afmroemte redxicseslolelvnetd tox yggoeond(,D wOh) ihlea,d ian 
nVeigcahteivcoecchoa- rlealgaotioon,s iht idpewteirtihorcahtleodr ifdroem(C el−xc)ealnledntb toor ognoo(Bd) a. nTdh efsaeirr. eTshueltsse sfiungdgiensgtst hcoatnCfirl−m 
aan ndoBticceoaubllde cdoemclienfer oinm wdaitfefer rqeunat lsitoyu froclelsoworinhga vtheec donistcrhaastrigneg eevfefenct.t s in the environment.
While some correlations, such as those involving alkali−nonferrous metals, could point to a
Table 3. Water quality of Carhuacocha and Vichecocha lagoons according to the Canadian Council 
of Environment Ministers Water Quality Index (CCME WQI). 
Event Lagoon Sampling Site F1 F2 F3 CCME WQI WQI according to Color 
1 − − − 100.00 Excellent 
2 − − − 100.00 Excellent 
3 − − − 100.00 Excellent 
Storage Carhuacocha 
4 − − − 100.00 Excellent 
5 − − − 100.00 Excellent 
6 − − − 100.00 Excellent 
 
Water 2024, 16, 2505 9 of 13
common source in the transported material, others probably originate from anthropogenic
inputs [19].
3.3. Water Quality
The results of the water quality assessment based on the CCME WQI for all sampling
sites in the Carhuacocha and Vichecocha lagoons are shown in Table 3. These results
indicate excellent water quality at all sites in Carhuacocha lagoon and at six of eight sites in
Vichecocha lagoon during the storage event. However, during the discharge event, water
quality in Carhuacocha lagoon decreased from excellent to good, while, in Vichecocha
lagoon, it deteriorated from excellent to good and fair. These findings confirm a noticeable
decline in water quality following the discharge event.
Table 3. Water quality of Carhuacocha and Vichecocha lagoons according to the Canadian Council of
Environment Ministers Water Quality Index (CCME WQI).
Event Lagoon Sampling Site F1 F2 F3 CCME WQI WQI according to Color
1 − − − 100.00 Excellent
2 − − − 100.00 Excellent
3 − − − 100.00 Excellent
4 − − − 100.00 Excellent
Carhuacocha 5 − − − 100.00 Excellent
6 − − − 100.00 Excellent
7 − − − 100.00 Excellent
Storage 8 − − − 100.00 Excellent
1 − − − 100.00 Excellent
2 − − − 100.00 Excellent
3 − − − 100.00 Excellent
4 − − − 100.00 Excellent
Vichecocha 5 − − − 100.00 Excellent
6 − − − 100.00 Excellent
7 9.09 9.09 0.23 93.00 Good
8 9.09 9.09 0.43 93.00 Good
1 9.09 9.09 17.19 88.00 Good
2 9.09 9.09 15.92 88.00 Good
3 9.09 9.09 16.38 88.00 Good
4 9.09 9.09 17.79 87.00 Good
Carhuacocha 5 9.09 9.09 17.12 88.00 Good
6 9.09 9.09 13.88 89.00 Good
7 9.09 9.09 17.19 88.00 Good
Discharge 8 9.09 9.09 21.29 86.00 Good
1 18.18 18.18 21.24 81.00 Good
2 9.09 9.09 26.67 83.00 Good
3 9.09 9.09 22.34 85.00 Good
4 9.09 9.09 18.29 87.00 Good
Vichecocha 5 9.09 9.09 20.90 86.00 Good
6 9.09 9.09 23.02 85.00 Good
7 9.09 9.09 27.14 83.00 Good
8 18.18 18.18 24.52 79.00 Fair
Note(s): F1: range factor; F2: frequency factor; F3: amplitude factor.
3.4. Analysis of Main Components
Principal component analysis (PCA) was performed to analyze the water quality
data from the Carhuacocha and Vichecocha lagoons. This analysis considered various
parameters and elements measured during storage and discharge events. The suitability of
the data for PCA was confirmed by two tests: the KMO sample adequacy measure (scoring
0.55) and Bartlett’s test of sphericity (with a p-value of 0.01).
Water 2024, 16, 2505 10 of 13
The PCA identified two main components (PC1 and PC2) that explained over 60%
of the overall variation in the data (Figure 4). For Carhuacocha lagoon during discharge
events, PC1 was strongly linked to factors like temperature, electrical conductivity (EC),
Water 2024, 16, x FOR PEER REVIEW
 a
 nd concentrations of barium (Ba), calcium (Ca), strontium (Sr), magnesium (Mg), 1s3u loffa t1e6 
(SO 2−4 ), and potassium (K). In contrast, PC1 for the Vichecocha lagoon showed a stronger
association with factors like pH, sodium (Na), and copper (Cu).
 
FFiigguurree 44.. PPrriinncciippaall ccoommppoonneenntt aannaallyyssiiss ((PPCCAA)) ffoorr 2277 wwaatteerr ppaarraammeetteerrss ffoorr ssttoorraaggee aanndd ddiisscchhaarrggee 
eevveennttss iinn CCaarrhhuuaaccoocchhaa aanndd VViicchheeccoocchhaal laaggoooonnss..  
4.. Discussion 
Thee qquuaaliltiytyo foaf qauqautaictiecc oescyostyesmtesmasro aurnodunthde twhoe rwldoirslde icsl indiencglindiunegt odaued otuo bale dthourebalte: 
nthartuearat:l npartoucreasls pesrolickeesswese alitkhee rwinegatahnedrinagtm aonsdp ahtemriocstprahnesrpico trrtaannsdpohrut manadn hauctmivainti easctliivnikteieds 
tloinpkoepdu tloa tpionpuglraotwiotnh garnodwrtahp aindde croapniodm eiccodneovmelioc pdmeveenlto.pTmhiesndt.e Tclhinise dtehcrleiantee nthsroeuatregnlso obualr 
wglaotberals ewcuartietry [s2e0c]u.rAitycc o[2rd0i]n. gAtcocothrediinngd ivtoid uthael eivnadluivaitdeudapl aervamaluetaetresd,  wpaatrearmquetaelritsy, iwnattheer 
Cqaurahliutyac oinc htahea nCdaVrhicuhaeccoocchhaa laangdo oVnischfoercostcohraa gleagaonodnds isfcohr asrtgoeraevgeen atsndre vdeiasclehdarvgaer iaebvielnittys 
(rTeavbelaele2d). vHaorwiaebvileitry, i n(Tbaobtleh 2la).g Hooonwsethveerc, oinn cbeontthra ltaiognoolenvse tlhseo fcomnocsetnotrfatthioenp alervamelse toefr smdoidst 
noof ttheex cpeaerdamtheeteernsv diriodn nmote nextacleewda tthere qeunavliirtoynsmtaenndtaalr dwsaftoerr Pqueraulivtyia sntalenndtaicrdssy sfoterm Pewruavteiarns  
elestnatbicl issyhsetdemby wMatIeNrsA eMsta(abclirsohneydm byfo Mr tIhNeAMMin (iasctrryonoyfmEn fvoirr othnem Menint iisntrSyp oafn Eisnhv)i[r2o1n]m. Wenatt einr  
pSHparneissuh)lt s[2s1h].o Wwaedtera npHin creresausltins gshtorewneddt aonw ianrcdrseaaslkinagli ntrietyn.dT thoiws aprHdsb aelhkaavliinoirtyw. Tohuilsd pbHe  
rbeelahtaevdiotor pwoonudlds ebdeim reelnattecodn tdoi tipoonnsd, p sheodtiomsyenntth ceotincdaicttioivnisty, ,pahnodtoasgyrinctuhletutirca lacatcitvivitiyti,e asnind 
agricultural activities in the area [22]. It is well documented that DO and temperature as 
a function of depth in the water column are factors that determine aquatic life [23]. 
Custodio et al. [24] evaluated the water quality of Peruvian high Andean lagoons 
with similar characteristics to the mentioned lagoons in this study and found results of 
physicochemical parameters that are consistent with our results. However, the average 
DO concentration during discharge events in both lagoons fell below the minimum 
 
Water 2024, 16, 2505 11 of 13
the area [22]. It is well documented that DO and temperature as a function of depth in the
water column are factors that determine aquatic life [23].
Custodio et al. [24] evaluated the water quality of Peruvian high Andean lagoons
with similar characteristics to the mentioned lagoons in this study and found results of
physicochemical parameters that are consistent with our results. However, the average DO
concentration during discharge events in both lagoons fell below the minimum regulatory
value of ≥5.0 mg/L. This decline may be attributed to the impact of agricultural activities
on these water bodies. Our findings are in line with the observations reported by Arenas-
Sánchez et al. [25]. They found that lower oxygen levels could be explained by several
factors: increased organic matter from a shrinking ecosystem, faster respiration rates at
higher temperatures, and reduced oxygen solubility in the water. Our measurements of
COD and BOD in the Carhuacocha and Vichecocha lagoons varied across locations. This
spatial difference likely stems from fish farm activities in nearby areas. This finding aligns
with research by Xiao et al. [26], who identified a positive correlation between COD and
land-use changes, highlighting the impact of agriculture on water quality. Other studies
point out that changes in land use influence all water quality indicators [27]. Therefore, to
ensure that the COD concentration in the studied lagoons remains within the Environmental
Quality Standards (EQS) for water, it is essential to promote the responsible use of fertilizers
in agricultural areas. Although the average concentrations of most physical–chemical
elements, metals, and heat-tolerant coliforms fell within the acceptable range for protecting
aquatic life (EQS), a significant positive correlation between certain parameters (Figure 3)
indicates a potential common source of environmental pressure. Specifically, COD and
BOD were positively correlated with Al, P, V, Li, SiO2, Si, and Fe. This finding highlights the
importance of monitoring these elements together, as several are linked to human activities
like agriculture. Furthermore, the analysis revealed statistically significant differences in
one third (33.33%) of the water quality parameters evaluated.
The quality of water is impacted by natural processes like erosion and by human
actions such as industrial waste [28]. Rainfall throughout the year can influence water
quality by washing away and diluting pollutants. This can cause seasonal fluctuations in
contaminant levels [29]. Farming practices can significantly increase the amount of phos-
phorus, nitrogen, nitrate, ammonia, and sediment in waterways. Fertilizers and pesticides
commonly used in agriculture can contribute to water pollution and eutrophication, which
harms aquatic ecosystems. In addition, changes in water flow patterns caused by dams
can also affect how pollutants move from land to water [30]. Our study revealed that,
according to the CCME WQI, the water quality of the Carhuacocha and Vichecocha lagoons
was predominantly classified as good or excellent quality at most sampling sites. However,
one site in the Vichecocha lagoon was classified as having medium water quality. Our
findings in the Vichecocha lagoon align with Mohamed et al. [31]. They point out that
external sources of phosphorus, such as those from fish farms like the one in our study, can
significantly influence water quality.
5. Conclusions
This study evaluated the water quality of the Carhuacocha and Vichecocha lagoons
throughout the storage and discharge events using the WQI method. The results showed
excellent water quality in both lagoons during storage. However, the water quality de-
clined when the lagoons discharged water during the dry season. Ammonia, nitrate, and
phosphate were identified as the key factors most directly affecting the WQI when their
influence was analyzed individually.
Our study identified pH, dissolved oxygen (DO), and biological oxygen demand (BOD)
as the most sensitive indicators of water quality in the lagoons. Since limited research
exists for the Mantaro head basin, this fact highlights the need for the managers of the
Carhuacocha and Vichecocha lagoons to implement mitigation measures. These measures
should aim to maintain or improve the water quality to ensure the lagoons remain suitable
for their various uses.
Water 2024, 16, 2505 12 of 13
Author Contributions: S.P. and P.V.-M. designed the methodology; P.V.-M. and D.C. provided and
validated field data; S.P. and P.V.-M. performed the data processing; S.P. and P.V.-M. analyzed the
data; R.S.-A. managed funding acquisition; S.P., P.V.-M. and M.C. wrote the manuscript. All authors
have read and agreed to the published version of the manuscript.
Funding: This research was funded by the INIA project “Mejoramiento de los servicios de inves-
tigación y transferencia tecnológica en el manejo y recuperación de suelos agrícolas degradados
y aguas para riego en la pequeña y mediana agricultura en los departamentos de Lima, Áncash,
San Martín, Cajamarca, Lambayeque, Junín, Ayacucho, Arequipa, Puno y Ucayali”, CUI 2487112.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: Santa Ana’s LABSAF teams for providing infrastructure and equipment for soil
data collection and laboratory analysis.
Conflicts of Interest: The authors declare that they have no known competing financial interest or
personal relationships that could have appeared to influence the work reported in this paper.
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