Spatio-Temporal Variation in Water Quality in a High-Andean Protected Area: A Multivariate Analysis of the Diablo Sacha River, Ecuador

Abstract

High-Andean páramo ecosystems regulate streamflow and water quality through water storage, subsurface flow, and natural hydrogeochemical buffering. However, increasing land-use pressures may generate early water-quality signals that are difficult to distinguish from natural geogenic variability in protected headwater catchments. This study evaluated the spatiotemporal variability of water quality in the Diablo Sacha River, located within the Quinllunga Water Protection Area, Ecuador. Water samples were collected at ten monitoring stations during six bimonthly campaigns from March 2024 to January 2025, generating 60 spatiotemporal observations per parameter. An integrated hydrogeochemical and multivariate framework was applied, combining Piper diagrams, Spearman correlation analysis, independent principal component analyses for hydrogeochemical and anthropogenic variables, and two-way PERMANOVA. Results showed a predominant Ca–Mg–HCO₃ hydrochemical facies, indicating that water chemistry is mainly controlled by natural mineral weathering, water–rock interaction, and longitudinal solute accumulation. The hydrogeochemical PCA explained 52.75% of the variance and identified a mineralization gradient associated with EC, HCO₃⁻, SO₄²⁻, Ca²⁺, Mg²⁺, and hydrological dilution. The anthropogenic PCA explained 61.77% of the variance and revealed secondary signals related to nutrients, organic matter, suspended solids, oils and grease, and microbiological indicators. PERMANOVA confirmed significant spatiotemporal structuring for hydrogeochemical variables and seasonal modulation for anthropogenic indicators. Overall, the Diablo Sacha River functions as a hydrogeochemically buffered high-Andean headwater system, where natural páramo processes maintain water-quality stability, while emerging anthropogenic signals act as early-warning indicators of ecosystem pressure.

1. Introduction

High-Andean ecosystems, particularly páramos, play a strategic role in regulating the hydrological cycle due to their capacity to capture, store, and gradually release water, thereby serving as essential sources of water supply for human populations, agricultural systems, and hydroelectric generation [1,2]. This function is closely linked to their hydrogeological and ecological characteristics, including organic soils with high water retention capacity, specialized vegetation, and wetlands, which regulate flow pathways and promote the persistence of baseflow [3,4]. Beyond water quantity regulation, páramo ecosystems also contribute to the stabilization of water quality through hydrological buffering, subsurface flow regulation, and natural solute attenuation processes [4].

However, these ecosystems are increasingly threatened by land-use change, agricultural and livestock expansion, afforestation with exotic species, and extractive activities [5]. These disturbances can alter hydrological functioning by increasing surface runoff, reducing baseflow, and enhancing the export of nutrients and contaminants, thereby compromising the long-term sustainability of water resources [1,5]. In headwater catchments, these pressures may not immediately produce severe hydrochemical degradation, but they can generate early, spatially localized signals in nutrients, organic matter, suspended solids, and microbiological indicators [6,7].

Throughout the Andean mountain range, páramo ecosystems have shown recurrent degradation patterns mainly associated with land-use intensification, including reductions in native vegetation cover, increased hydrological variability, and the progressive loss of ecological integrity [8,9]. These conditions, together with complex topography and strong orographic influence, generate marked spatial heterogeneity in precipitation, leading to substantial local variability in water availability and hydrological dynamics [4,10]. Therefore, understanding the relationships among precipitation, hydrological pathways, and water quality is essential for interpreting the ecohydrological functioning of páramo ecosystems and for supporting the long-term sustainability and resilience of their water resources under increasing environmental pressures [11]. Recent studies have also emphasized that river water quality dynamics are often shaped by the interaction among hydrochemical facies, lithological conditions, hydrological variability, and anthropogenic inputs. This supports the use of integrated hydrogeochemical and multivariate approaches for assessing river water quality [12,13,14]. In this context, hydrogeochemical stability and water quality are fundamental components of hydrological ecosystem services in páramo ecosystems [9]. Natural processes such as water storage, water–rock interaction, and ecohydrological buffering can attenuate or even mask emerging anthropogenic signals, thereby hindering the early detection of incipient environmental degradation [6,7]. Therefore, identifying early anthropogenic gradients is essential for strengthening preventive conservation and long-term monitoring strategies in protected ecosystems [6,7]. This is particularly relevant in high-Andean protected areas, where natural mineralization processes may dominate the hydrochemical signature, while anthropogenic pressures remain secondary, intermittent, and seasonally modulated.

In Ecuador, several studies have highlighted the critical role of precipitation in regulating páramo hydrology. At the Zhurucay Ecohydrological Observatory (ZEO), located within Cajas National Park, more than 80% of rainfall events correspond to low-intensity precipitation, which contributes substantially to baseflow maintenance and modulates nutrient and sediment concentrations in river systems. Similarly, studies conducted on the slopes of the Antisana volcano have documented marked spatial variability in annual precipitation and atmospheric humidity, confirming that precipitation plays a key role in maintaining the thermal, hydrological, and ecological balance of Ecuadorian páramo ecosystems [8,15]. These findings indicate that páramo water quality cannot be interpreted solely as a response to land-use pressure; rather, it must also be analyzed in relation to rainfall seasonality, subsurface connectivity, residence time, and lithological controls.

Rivers located within páramo ecosystems exhibit marked physicochemical variability along longitudinal gradients and across hydrological seasons as a result of interactions among altitudinal, hydroclimatic, geological, and land-use factors [8,15]. High-Andean headwater rivers generally show low to moderate EC values and well-oxygenated conditions; however, these parameters should be interpreted quantitatively and in relation to lithology, altitude, hydrological seasonality, and land-use pressure. Published studies in Andean systems have reported EC values of approximately 70.5 µS/cm in minimally disturbed rivers of Cajas National Park and 98.4–473.8 µS/cm in an Ecuadorian Andean watershed. Similarly, high-Andean rivers in Peru have shown EC values ranging from 141.17 to 377.7 µS/cm and DO concentrations between 5.58 ± 0.18 and 7.98 ± 0.75 mg/L [16,17]. In the Diablo Sacha River, EC ranged from 101.8 to 359.0 µS/cm, while DO varied from 5.0 to 12.1 mg/L, indicating moderate mineralization and generally favorable oxygenation.

Similarly, recent water-quality studies have used hydrochemical facies, water-quality indicators, spatial analysis, and multivariate statistics to distinguish the relative influence of natural geochemical processes and human pressures on aquatic systems [12,13,14]. Despite these advances, studies that explicitly differentiate natural hydrogeochemical controls from anthropogenic signals under seasonal ecohydrological dynamics remain scarce in protected páramo ecosystems [9]. In this context, no previous study has comprehensively evaluated the spatiotemporal variability of water quality in the Diablo Sacha River, located within the QWPA, through the integrated analysis of physicochemical and hydrochemical parameters using multivariate statistical approaches.

This lack of systematic information limits the understanding of the processes controlling water quality dynamics in this protected area and constrains the development of evidence-based management and conservation strategies. This knowledge gap is particularly relevant because the Diablo Sacha River drains a high-Andean protected catchment where natural lithological weathering, páramo soil regulation, and seasonal hydrological connectivity may interact with localized livestock, agricultural, and rural-settlement pressures.

Based on the available literature, three main lines of evidence support this study: (1) the páramo ecosystems regulate streamflow and water quality through organic soils, vegetation cover, wetlands, and subsurface flow pathways; (2) the hydrochemical variability in high-Andean rivers is strongly influenced by natural processes such as water–rock interaction, mineral weathering, residence time, and longitudinal solute accumulation; (3) the land-use pressures may generate early anthropogenic signals associated with nutrients, organic matter, suspended solids, oils and grease, and microbiological indicators; however, these signals may remain spatially localized and seasonally modulated. Therefore, an integrated approach is required to distinguish natural hydrogeochemical background conditions from emerging anthropogenic pressures in protected headwater systems.

In high-Andean páramo ecosystems, where water chemistry is predominantly controlled by natural hydrogeochemical and hydrological processes, distinguishing natural variability from land-use-related signals remains a key scientific challenge. The Diablo Sacha River, located within the Quinllunga Water Protection Area, represents a protected páramo headwater system where water quality may reflect both natural hydrogeochemical controls and emerging anthropogenic pressures. Based on previous studies, three main lines of evidence support this research: páramo ecosystems regulate streamflow and water quality through organic soils, vegetation, wetlands, and subsurface flow pathways; hydrochemical variability in Andean rivers is strongly influenced by water–rock interaction, mineral weathering, residence time, and hydrological seasonality; and land-use pressures may generate early signals related to nutrients, organic matter, suspended solids, oils and grease, and microbiological indicators [9,18].

Accordingly, this study hypothesizes that water quality in the Diablo Sacha River is primarily structured by natural hydrogeochemical processes, while anthropogenic pressures generate secondary, spatially localized, and seasonally modulated signals. The innovation of this research lies in the explicit separation of natural hydrogeochemical gradients from early anthropogenic signals through the combined use of hydrochemical facies analysis, non-parametric correlation, independent principal component analyses for hydrogeochemical and anthropogenic variables, and spatiotemporal multivariate testing. Therefore, this study evaluates the spatiotemporal variability of water quality in the Diablo Sacha River, with the aim of distinguishing natural background conditions from anthropogenic pressures and supporting early-warning, conservation, and water-security strategies in protected high-Andean ecosystems.

2. Materials and Methods

2.1. Study Area

Figure 1A, shows the Quinllunga Water Protection Area (QWPA), located in San Simón rural parish, Guaranda canton, Bolívar province, in the central Ecuadorian Andes. The protected area extends approximately between 78°55′ W and 78°53′ W longitude and 1°37′ S and 1°39′ S latitude. Within this area, the Diablo Sacha River originates and flows through a high-Andean mountainous landscape characterized by steep slopes, deeply incised valleys, and a pronounced altitudinal gradient. These geomorphological features strongly influence hydrological dynamics, erosion processes, sediment transport, and water physicochemistry. Panels (B) and (C) show the regional and national location of the study area within Ecuador, providing geographical context at different spatial scales.

The Diablo Sacha River drains longitudinally through the protected area, from approximately 4100 m a.s.l. in the upper headwaters to 3352 m a.s.l. at the outlet. This altitudinal gradient defines a clear geomorphological transition from narrow, high-energy headwater channels to relatively wider middle and lower reaches, where slopes decrease and lateral hydrological connectivity progressively increases. This spatial configuration favors longitudinal variability in hydraulic conditions, sediment transport, and solute dynamics. To support this longitudinal interpretation,

Table 1. Cumulative drainage area and spatial characteristics of monitoring stations along the Diablo Sacha River.

Station ID	X Coordinate	Y Coordinate	Elevation (m a.s.l.)	River Reach	Drainage Area (ha)	Perimeter (km)
E10	734,928.21	9,818,820.08	4100	Upper reach	69.99	1.62
E9	734,527.98	9,818,958.98	3920		4.07	5.89
E8	734,121.19	9,819,025.12	3390		79.51	4.04
E7	733,316.52	9,819,038.35	3880	Middle reach	66.22	6.15
E6	732,878.93	9,819,338.20	3680		37.03	3.15
E5	732,747.88	9,819,258.49	3640		156.62	4.50
E4	732,536.99	9,819,676.66	3560	Lower reach	58.92	5.17
E3	732,358.40	9,819,590.67	3520		137.03	1.33
E2	731,928.45	9,819,425.31	3440		10.32	5.08
E1	731,693.62	9,819,352.54	3352	Outlet	566.04	59.06

summarizes the spatial attributes of the monitoring stations. Appendix A.2 (Figure A3) presents the hypsometric curve of the Quinllunga watershed.

The morphometric characteristics of the Diablo Sacha River catchment are presented in

Table 2. Morphometric characteristics of the Diablo Sacha River catchment.

Parameter	Value	Unit
Basin area	566.04	ha
Perimeter	59.06	km
Main channel length	4.3	km
Elevation range	3352–4100	m a.s.l.
Basin orientation	East–West	—
Horton form factor (Rf)	0.3	—
Elongation ratio	0.5–0.8	—
Stream types	Perennial and intermittent	—
Total drainage network length	8.4	km
Perennial streams	5.04 (60.04%)	km
Intermittent streams	3.36 (39.96%)	km
Stream order classification	Horton method	—
Drainage density	<0.5	km/km2
Annual hydrological deficit	145	mm

to support the interpretation of spatial hydrochemical variability. Appendix A.2 (Figure A2) also includes photographs of the ten monitoring stations (E1–E10), showing variations in channel morphology, riparian vegetation, flow conditions, and surrounding páramo cover from the downstream outlet to the upper headwaters.

Appendix A.2 (Figure A1) shows the ecosystem is dominated by high-Andean páramo formations, including grass páramo (pajonales), herbaceous páramo communities, and remnants of evergreen páramo shrublands. These environments are characterized by organic-rich Andisols with high porosity and water retention capacity, which regulate runoff generation, enhance infiltration, and sustain baseflow throughout the hydrological year [19]. The climatic regime exhibits marked seasonality, with well-defined wet and dry periods. Thermal variability is mainly controlled by altitude, seasonal precipitation, and persistent wind exposure, all of which are typical of páramo environments and directly influence evapotranspiration rates, hydrological response, and biogeochemical cycling.

From a geological perspective, the basin comprises three main lithostratigraphic units that influence hydrogeochemical processes: the Pallatanga Unit (KPa), the Apagua Formation (PcEA), and the Gallo Rumi Formation (PcEGR) [Figure 1A].

The central-western sector is dominated by the Pallatanga Unit, which is mainly composed of tholeiitic oceanic basalts with mid-ocean ridge basalt (MORB) affinity from the Middle Cretaceous. The Apagua Formation, widely distributed across the central and eastern sectors, consists of fine- to coarse-grained sandstones and siltstones with high quartz content, influencing silica availability and bicarbonate formation through silicate weathering. The easternmost portion includes the Gallo Rumi Formation, characterized by conglomerates, pebbly sandstones, bedded quartz, mudstones, cherts, and volcanic lithic fragments, which introduce additional lithological heterogeneity. The spatial distribution of these units along the fluvial corridor creates contrasting water–rock interaction environments, supporting longitudinal variations in major ionic composition.

The monitoring network consisted of ten stations (E1–E10) distributed longitudinally along the Diablo Sacha River to capture the altitudinal, geomorphological, lithological, hydrological, and land-use gradients of the catchment. For the spatial analysis, the stations were grouped into three river reaches: lower reach (3352–3560 m a.s.l.; E2–E4), middle reach (3640–3880 m a.s.l.; E5–E7), and upper reach (3900–4100 m a.s.l.; E8–E10). Station E1 was analyzed separately as a downstream outlet station, representing the integrated hydrochemical response of the catchment outside the QWPA boundaries. This classification is consistent with the spatial structure used for PCA interpretation and PERMANOVA analysis.

The lower reach and outlet sector are relatively more accessible and may therefore be more influenced by diffuse anthropogenic pressures, whereas the upper reaches represent high-altitude headwater conditions with comparatively lower direct human influence [1]. However, these spatial categories were not interpreted as fixed pollution classes, but rather as geomorphological and ecohydrological units used to evaluate longitudinal hydrochemical gradients and localized anthropogenic signals. Overall, the combined geomorphological, geological, hydrological, and ecological gradients of the QWPA provide an appropriate natural setting for assessing the relative influence of geogenic controls, seasonal hydrological variability, and localized anthropogenic pressures on water quality dynamics in a protected high-Andean headwater basin.

2.2. Hydrological and Water Quality Monitoring

A longitudinal spatiotemporal sampling design was implemented along the Diablo Sacha River to evaluate seasonal and spatial variability in water quality within the QWPA. Ten monitoring stations, designated as E1–E10, were distributed along the main river corridor to capture changes in altitude, geomorphology, hydrological connectivity, land use, and diffuse anthropogenic influence associated with livestock activity, small-scale agriculture, and scattered rural settlements.

The stations were grouped into three spatial sectors according to altitude and geomorphological characteristics: lower reach, from 3352 to 3560 m a.s.l. E2–E4; middle reach, from 3640 to 3880 m a.s.l. E5–E7; and upper reach, from 3900 to 4100 m a.s.l. E8–E10. Station E1 was analyzed separately as a downstream outlet station, representing the integrated hydrochemical response of the catchment outside the QWPA boundaries.

Monitoring was conducted bimonthly over one hydrological year, from March 2024 to January 2025, resulting in six sampling campaigns: March, May, July, September, November, and January. Based on regional Andean hydrological seasonality and observed streamflow dynamics, the sampling campaigns were classified into three hydrological seasons: wet season, including March and January; transitional season, including May and November; and dry season, including July and September.

The monitoring program generated 60 spatiotemporal observations per parameter, corresponding to 10 stations sampled across six monitoring campaigns. This design enabled the evaluation of longitudinal hydrochemical gradients, seasonal ecohydrological variability, and localized anthropogenic signals within a high-Andean headwater river system.

2.3. Field Measurements and Sample Collection

Field measurements and water sample collection were conducted following the Ecuadorian technical standard NTE INEN 2176:2013 [20] for water quality sampling, preservation, and handling procedures. During each monitoring campaign, in situ measurements of pH, electrical conductivity, dissolved oxygen and temperature were performed using a portable multiparameter meter (HI9829, Hanna Instruments, Woonsocket, RI, USA), while water depth, channel width, and flow velocity were measured using calibrated hydrometric equipment. The descriptive summary of the main physical parameters measured during the monitoring campaigns is provided in

Table A1. Mean values ± standard deviation of physical parameters (electrical conductivity, total dissolved solids, total suspended solids, temperature, and pH) recorded in the lower, middle, and upper zones of the Diablo Sacha River during six monitoring campaigns.

Parameters	Unit	River Reaches	M1	M2	M3	M4	M5	M6
Depth	cm	Upper	0.218 ± 0.032	0.218 ± 0.032	5.040 ± 3.615	12.345 ± 7.640	3.100 ± 0.441	2.367 ± 0.698
		Middle	0.250 ± 0.150	0.254 ± 0.157	14.119 ± 13.753	6.958 ± 0.762	3.456 ± 1.272	5.963 ± 4.227
		Lower	0.263 ± 0.138	0.432 ± 0.062	22.500 ± 6.455	6.978 ± 1.041	5.709 ± 1.351	4.629 ± 2.295
Velocity	m/s	Upper	0.233 ± 0.058	0.233 ± 0.058	0.817 ± 0.503	1.868 ± 0.574	0.325 ± 0.017	0.271 ± 0.048
		Middle	0.167 ± 0.058	0.178 ± 0.038	3.392 ± 3.991	1.197 ± 0.129	0.567 ± 0.459	0.566 ± 0.522
		Lower	0.125 ± 0.050	0.525 ± 0.157	0.375 ± 0.150	0.727 ± 0.460	0.701 ± 0.356	0.728 ± 0.497
Electrical Conductivity (EC)	µS/cm	Upper	120.500 ± 17.	142.200 ± 25.673	635.860 ± 45.658	164.967 ± 36.248	233.667 ± 44.859	153.667 ± 20.502
		Middle	142.717 ± 16.753	148.511 ± 8.300	537.167 ± 361.533	241.667 ± 30.925	310.000 ± 49.000	204.000 ± 65.643
		Lower	136.325 ± 4.563	130.692 ± 14.706	142.575 ± 6.883	213.000 ± 8.042	265.000 ± 41.239	186.500 ± 19.122
Total Dissolved Solids (TDS)	mg/L	Upper	0.170 ± 0.047	0.096 ± 0.082	0.193 ± 0.179	0.074 ± 0.052	0.193 ± 0.179	0.099 ± 0.008
		Middle	0.172 ± 0.053	0.086 ± 0.033	0.094 ± 0.070	0.147 ± 0.044	0.119 ± 0.028	0.098 ± 0.006
		Lower	0.114 ± 0.021	0.122 ± 0.009	0.060 ± 0.010	0.124 ± 0.038	0.173 ± 0.088	0.197 ± 0.153
Total Suspended Solids (TSS)	mg/L	Upper	0.017 ± 0.008	0.002 ± 0.002	0.008 ± 0.003	0.004 ± 0.002	0.008 ± 0.002	0.002 ± 0.002
		Middle	0.109 ± 0.151	0.004 ± 0.005	0.004 ± 0.005	0.005 ± 0.003	0.030 ± 0.038	0.002 ± 0.000
		Lower	0.059 ± 0.076	0.028 ± 0.019	0.007 ± 0.005	0.002 ± 0.001	0.024 ± 0.022	0.007 ± 0.005
Temperature	ºC	Upper	8.328 ± 0.155	8.080 ± 0.919	8.205 ± 0.366	7.100 ± 0.721	8.233 ± 0.907	8.567 ± 0.306
		Middle	8.580 ± 1.143	7.567 ± 0.666	7.699 ± 0.786	9.380 ± 3.170	8.600 ± 0.436	8.467 ± 1.401
		Lower	9.500 ± 0.271	9.533 ± 0.448	8.963 ± 0.826	9.700 ± 1.236	10.250 ± 0.420	8.125 ± 0.512
ph	(−) *	Upper	8.580 ± 0.419	8.633 ± 0.231	8.191 ± 0.219	8.003 ± 0.090	7.890 ± 0.121	7.847 ± 0.656
		Middle	8.680 ± 0.217	7.567 ± 0.635	8.383 ± 0.500	8.037 ± 0.152	7.597 ± 0.117	8.170 ± 0.130
		Lower	8.110 ± 0.196	7.075 ± 0.364	8.565 ± 0.281	7.792 ± 0.230	7.155 ± 0.315	7.372 ± 0.554
Dissolved Oxygen (DO)	mg/L	Upper	7.400 ± 0.100	7.700 ± 0.100	8.813 ± 1.502	15.367 ± 8.025	6.767 ± 1.060	6.833 ± 0.473
		Middle	7.400 ± 0.265	7.633 ± 0.306	8.987 ± 0.776	20.000 ± 0.000	6.767 ± 2.540	6.633 ± 0.462
		Lower	6.950 ± 0.100	7.125 ± 0.096	7.758 ± 0.096	11.475 ± 6.254	7.500 ± 1.294	7.375 ± 2.021
Sodium (Na+)	mg/L	Upper	0.912 ± 0.168	1.035 ± 0.390	0.960 ± 0.087	0.932 ± 0.484	0.960 ± 0.087	0.972 ± 0.067
		Middle	1.081 ± 0.567	1.036 ± 0.153	1.130 ± 0.306	1.001 ± 0.452	1.062 ± 0.347	1.057 ± 0.293
		Lower	6.682 ± 11.711	1.100 ± 0.462	1.026 ± 0.279	1.183 ± 0.570	2.498 ± 2.932	1.452 ± 0.745
Potassium (K+)	mg/L	Upper	2.176 ± 0.195	2.427 ± 0.335	2.096 ± 0.062	1.683 ± 0.044	2.096 ± 0.062	2.075 ± 0.116
		Middle	2.064 ± 0.205	2.066 ± 0.122	1.914 ± 0.017	1.797 ± 0.140	1.960 ± 0.077	1.934 ± 0.046
		Lower	2.726 ± 0.936	1.812 ± 0.457	1.705 ± 0.143	1.917 ± 0.255	2.040 ± 0.291	1.869 ± 0.138
Calcium (Ca2+)	mg/L	Upper	50.063 ± 18.322	29.051 ± 10.385	42.416 ± 19.142	48.135 ± 59.859	42.416 ± 19.142	40.505 ± 22.821
		Middle	80.726 ± 24.615	207.099 ± 255.939	121.140 ± 89.943	80.707 ± 32.260	122.418 ± 88.703	132.841 ± 105.096
		Lower	72.651 ± 10.212	50.558 ± 3.953	56.857 ± 6.475	89.794 ± 70.223	67.465 ± 15.229	66.169 ± 20.670
Magnesium (Mg2+)	mg/L	Upper	32.255 ± 10.591	17.484 ± 13.427	24.400 ± 2.309	23.460 ± 8.177	35.290 ± 5.250	32.605 ± 10.268
		Middle	33.787 ± 26.875	22.599 ± 13.929	39.550 ± 8.323	40.317 ± 9.720	38.188 ± 10.936	37.501 ± 16.987
		Lower	42.292 ± 18.619	21.649 ± 12.478	51.610 ± 34.255	45.568 ± 23.437	39.772 ± 14.546	23.802 ± 8.245
Iron (Fe)	mg/L	Upper	0.277 ± 0.020	0.212 ± 0.013	0.197 ± 0.030	0.103 ± 0.095	0.197 ± 0.030	0.178 ± 0.042
		Middle	0.207 ± 0.069	0.121 ± 0.067	0.140 ± 0.057	0.097 ± 0.072	0.141 ± 0.057	0.125 ± 0.054
		Lower	0.165 ± 0.065	0.132 ± 0.106	0.076 ± 0.033	0.045 ± 0.041	0.105 ± 0.031	0.089 ± 0.028
Manganese (Mn)	mg/L	Upper	0.006 ± 0.005	0.008 ± 0.005	0.006 ± 0.003	0.007 ± 0.004	0.005 ± 0.003	0.005 ± 0.004
		Middle	0.005 ± 0.002	0.001 ± 0.001	0.003 ± 0.002	0.003 ± 0.004	0.004 ± 0.003	0.003 ± 0.002
		Lower	0.006 ± 0.004	0.004 ± 0.004	0.003 ± 0.002	0.002 ± 0.002	0.003 ± 0.003	0.001 ± 0.000
Copper (Cu)	µg/L	Upper	0.009 ± 0.009	0.002 ± 0.000	0.003 ± 0.001	0.003 ± 0.001	0.002 ± 0.000	0.001 ± 0.000
		Middle	0.255 ± 0.336	0.003 ± 0.001	0.053 ± 0.067	0.003 ± 0.001	0.002 ± 0.000	0.002 ± 0.000
		Lower	0.001 ± 0.001	0.004 ± 0.001	0.002 ± 0.001	0.003 ± 0.001	0.002 ± 0.001	0.002 ± 0.000
Zinc (Zn)	mg/L	Upper	0.005 ± 0.001	0.099 ± 0.125	0.024 ± 0.026	0.007 ± 0.006	0.002 ± 0.001	0.007 ± 0.007
		Middle	0.008 ± 0.002	0.020 ± 0.011	0.040 ± 0.038	0.021 ± 0.008	0.009 ± 0.003	0.021 ± 0.012
		Lower	0.007 ± 0.006	0.012 ± 0.010	0.013 ± 0.005	0.014 ± 0.007	0.008 ± 0.005	0.004 ± 0.003
Cadmium (Cd)	µg/L	Upper	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000
		Middle	0.009 ± 0.004	0.006 ± 0.000	0.007 ± 0.001	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000
		Lower	0.005 ± 0.002	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000	0.006 ± 0.000
Nickel (Ni)	µg/L	Upper	0.006 ± 0.001	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000
		Middle	0.169 ± 0.270	0.005 ± 0.000	0.038 ± 0.054	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000
		Lower	0.005 ± 0.002	0.006 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000
Mercury (Hg)	µg/L	Upper	0.012 ± 0.000	0.013 ± 0.000	0.013 ± 0.000	0.013 ± 0.001	0.013 ± 0.000	0.012 ± 0.000
		Middle	0.015 ± 0.005	0.013 ± 0.000	0.013 ± 0.001	0.012 ± 0.000	0.014 ± 0.000	0.013 ± 0.000
		Lower	0.010 ± 0.005	0.013 ± 0.000	0.013 ± 0.000	0.012 ± 0.000	0.014 ± 0.000	0.013 ± 0.000
Lead (Pb)	µg/L	Upper	0.006 ± 0.001	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000
		Middle	0.029 ± 0.031	0.005 ± 0.000	0.010 ± 0.006	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000
		Lower	0.004 ± 0.001	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000	0.005 ± 0.000
Arsenic (As)	µg/L	Upper	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000
		Middle	0.025 ± 0.028	0.007 ± 0.000	0.011 ± 0.006	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000
		Lower	0.006 ± 0.002	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000
Cobalt (Co)	µg/L	Upper	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000
		Middle	0.013 ± 0.010	0.007 ± 0.000	0.008 ± 0.002	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000
		Lower	0.006 ± 0.002	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000	0.007 ± 0.000
Selenium (Se)	µg/L	Upper	0.322 ± 0.026	0.329 ± 0.011	0.292 ± 0.003	0.356 ± 0.011	0.293 ± 0.021	0.161 ± 0.009
		Middle	0.318 ± 0.011	0.329 ± 0.006	0.307 ± 0.012	0.330 ± 0.005	0.342 ± 0.011	0.195 ± 0.006
		Lower	0.249 ± 0.164	0.322 ± 0.008	0.327 ± 0.004	0.331 ± 0.011	0.380 ± 0.008	0.231 ± 0.023
Bicarbonate + Carbonate (HCO3− + CO32−)	mg/L	Upper	73.167 ± 5.965	67.333 ± 10.563	80.373 ± 13.113	86.167 ± 17.395	106.667 ± 23.240	68.533 ± 9.650
		Middle	113.167 ± 40.652	90.333 ± 21.455	104.273 ± 21.165	112.500 ± 6.265	126.500 ± 6.144	91.567 ± 24.613
		Lower	98.345 ± 6.119	58.625 ± 28.733	92.875 ± 2.287	103.625 ± 3.449	105.875 ± 2.428	91.125 ± 4.740
Nitrite (NO2−)	mg/L	Upper	0.005 ± 0.004	0.003 ± 0.001	0.003 ± 0.001	0.002 ± 0.001	0.000 ± 0.001	0.005 ± 0.001
		Middle	0.024 ± 0.027	0.003 ± 0.003	0.004 ± 0.002	0.003 ± 0.003	0.001 ± 0.002	0.005 ± 0.001
		Lower	0.011 ± 0.004	0.002 ± 0.002	0.006 ± 0.003	0.006 ± 0.002	0.002 ± 0.001	0.003 ± 0.001
Nitrate (NO3−)	mg/L	Upper	2.630 ± 3.635	2.678 ± 0.979	6.775 ± 1.247	6.557 ± 1.069	20.108 ± 4.548	22.242 ± 9.295
		Middle	3.746 ± 2.470	2.250 ± 0.814	4.381 ± 3.277	3.975 ± 0.788	7.948 ± 8.270	19.498 ± 18.547
		Lower	2.738 ± 0.769	1.193 ± 1.485	0.645 ± 0.156	3.854 ± 1.016	0.154 ± 0.162	0.639 ± 0.449
Sulfate (SO42−)	mg/L	Upper	3.607 ± 0.476	2.292 ± 0.796	6.227 ± 1.652	10.467 ± 4.071	10.196 ± 8.600	4.574 ± 1.715
		Middle	9.232 ± 7.437	5.523 ± 5.035	8.572 ± 5.200	9.573 ± 6.167	8.793 ± 2.574	10.966 ± 9.419
		Lower	5.937 ± 1.932	6.316 ± 2.007	5.764 ± 2.603	8.805 ± 3.777	8.439 ± 2.807	6.139 ± 2.626
Chemical Oxygen Demand (COD)	mg/L	Upper	10.667 ± 10.066	0.000 ± 0.000	4.889 ± 2.776	4.000 ± 4.000	4.889 ± 2.776	3.444 ± 1.503
		Middle	9.333 ± 6.110	8.000 ± 6.928	3.111 ± 2.776	4.000 ± 0.000	6.111 ± 2.589	5.306 ± 1.717
		Lower	13.000 ± 10.520	13.000 ± 10.520	2.000 ± 2.309	2.000 ± 2.309	7.500 ± 4.796	6.125 ± 3.406
Biochemical Oxygen Demand (BOD5)	mg/L	Upper	0.700 ± 0.265	1.333 ± 0.153	3.219 ± 1.322	10.767 ± 7.559	1.960 ± 1.086	1.333 ± 0.493
		Middle	1.200 ± 0.608	1.167 ± 0.513	2.907 ± 1.584	15.833 ± 1.155	2.033 ± 2.491	0.733 ± 0.379
		Lower	0.700 ± 0.668	0.650 ± 0.058	1.108 ± 0.017	6.200 ± 6.585	3.175 ± 1.245	1.775 ± 1.297
Oil and Grease	mg/L	Upper	0.006 ± 0.005	0.031 ± 0.014	0.015 ± 0.011	0.006 ± 0.006	0.003 ± 0.003	0.027 ± 0.033
		Middle	0.001 ± 0.001	0.003 ± 0.002	0.011 ± 0.011	0.002 ± 0.001	0.003 ± 0.004	0.045 ± 0.056
		Lower	0.009 ± 0.006	0.003 ± 0.002	0.002 ± 0.001	0.006 ± 0.001	0.003 ± 0.003	0.009 ± 0.004
Fecal Coliforms	NMP/100 mL	Upper	2.633 ± 4.136	2.067 ± 2.714	4.367 ± 3.525	0.667 ± 0.289	11.985 ± 12.245	7.283 ± 1.007
		Middle	3.633 ± 2.928	4.167 ± 2.797	16.344 ± 19.460	36.133 ± 59.043	9.586 ± 0.523	12.691 ± 7.019
		Lower	37.225 ± 26.695	14.100 ± 15.256	3.725 ± 2.367	7.125 ± 6.968	12.228 ± 3.295	9.855 ± 3.867
Total Coliforms	NMP/100 mL	Upper	41.800 ± 65.120	71.700 ± 16.416	71.522 ± 45.279	101.067 ± 66.413	72.887 ± 8.823	71.418 ± 10.021
		Middle	1.867 ± 1.950	74.733 ± 52.474	64.372 ± 46.370	133.100 ± 32.486	74.076 ± 16.377	77.699 ± 19.972
		Lower	56.175 ± 42.473	25.100 ± 27.052	10.425 ± 7.027	293.175 ± 80.817	96.732 ± 31.662	81.112 ± 19.171

Note(s): * Dimensionless.

.

Flow velocity was measured using a calibrated universal current meter equipped with a Type 1 propeller (HyQuest Solutions Pty Ltd., Warwick, Queensland, Australia; operational range: 0.025–10 m s⁻¹; accuracy: ±1%), following standard hydrometric procedures and manufacturer recommendations. Measurements were taken at representative channel cross-sections following standard hydrometric procedures. Water depth and channel width were determined using a graduated staff.

Hydraulic measurements were performed under relatively stable flow conditions, avoiding extreme rainfall events whenever possible. This allowed the characterization of local hydrodynamic variability influencing hydrochemical transport and seasonal ecohydrological responses.

Water samples were collected in pre-cleaned polyethylene bottles and preserved according to parameter-specific requirements. Samples intended for physicochemical, hydrochemical, and microbiological analyses were refrigerated and transported to the laboratory under controlled conditions for subsequent analysis.

Field quality assurance and quality control procedures included instrument calibration prior to each campaign, standardized sampling protocols, duplicate field measurements whenever possible, and appropriate sample preservation during transport and storage.

2.4. Laboratory Analysis

Physicochemical, hydrochemical, and microbiological analyses were conducted in the Instrumental Chemistry Laboratory of the Escuela Superior Politécnica de Chimborazo (ESPOCH), according to Standard Methods for the Examination of Water and Wastewater [21] The analytical parameters, methods, techniques, and equipment used for laboratory determinations are summarized in

Table A2. Analytical methods and techniques applied for physicochemical and microbiological parameter determination.

Property	Assay	Unit	Method/Technique Used (APHA (APHA et al., 2017) [17])	Equipment
Physical	Temperature	(°C)	Temperature, multiparameter model SX751 (2550 B)	Multiparameter SX751
	Total Suspended Solids (TSS)	(mg/L)	Total Suspended Solids Dried at 103–105 °C (2540 D)
	Electrical Conductivity (EC)	(µS/cm)	Electrical Conductivity (2510 B)
Chemical	Total Dissolved Solids (TDS)	(mg/L)	Total Solids Dried at 103–105 °C (2540 C)
	pH	(−) *	pH Value * (4500-H* B)	Multiparameter SX751
	Oil and Grease	(mg/L)	Liquid–Liquid, Partition-Gravimetric Method (5520 B)	Rotavapor RE100-Pro
	Alkalinity (HCO3− + CO32−)	(mg/L)	Alkalinity by Titration (2320 B)	HACH Titration
	Dissolved Oxygen (DO)	(mg/L)	Optical-Probe Method (4500-O G)	Multiparameter SX751
	COD	(mg/L)	COD HACH, Closed Reflux, Colorimetric Method (5220 D)
	BOD5	(mg/L)	5-Day BOD Test (5210 B)	Multiparameter SX751
	SO42−	(mg/L)	SulfaVer®4, Turbidimetric Method (4500-SO42− E)	UV-VIS Spectrophotometer Thermo Electron Corporation/Helios b
	NO3−	(mg/L)	NitraVer®5; Cadmium Reduction Method (4500-NO3− B)
	NO2−	(mg/L)	Colorimetric Method (4500-NO2− B)
	Ca2+, Mg2+, Na+ + K+, Fe, Mn, Zn	(mg/L)	Direct Air-Acetylene Flame Method (3111 B)	Spectrophotometer Thermo Scientific/iCE 3000 AA05170304 v1.30
	Cu, Cd, Ni, Hg, Pb, As, Co, Se	(µg/L)	Inductively Coupled Plasma–Mass Spectrometry (ICP–MS) Method (3125 B)	Analytik Jena/Plasma Quant MSQ
Microbiological	Fecal and Total Coliforms	NMP/100 mL	Enzyme Substrate Test, Indexx Colilert (9223 B)

Note(s): * Dimensionless.

.

The analyzed parameters included physical variables, major ions, nutrients, organic matter indicators, trace metals, and microbiological indicators. Field parameters, including temperature, pH, electrical conductivity, and dissolved oxygen, were measured in situ using a multiparameter probe, and their descriptive summary is provided in Table A1. Total suspended solids were determined using APHA Method 2540 D, corresponding to total suspended solids dried at 103–105 °C, while total dissolved solids were determined using APHA Method 2540 C, corresponding to total dissolved solids dried at 180 °C.

Alkalinity was determined by titration and expressed in terms of bicarbonate/carbonate species when appropriate. Chemical oxygen demand was measured using the closed reflux colorimetric method, whereas biochemical oxygen demand was determined using the 5-day BOD test. Sulfates, nitrates, and nitrites were analyzed using spectrophotometric methods. Major cations, including Ca²⁺, Mg²⁺, Na⁺, K⁺, Fe, Mn, and Zn, were determined by atomic absorption spectrophotometry. Trace metals, including Cu, Cd, Ni, Hg, Pb, As, Co, and Se, were analyzed by inductively coupled plasma–mass spectrometry. Total and fecal coliforms were determined using the IDEXX Colilert method and expressed as MPN/100 mL.

Quality assurance and quality control procedures included calibration with certified standards, reagent blanks, duplicate analyses, and verification of analytical precision according to APHA recommendations. Samples with anomalous values or analytical inconsistencies were reanalyzed when necessary.

Detection limits varied according to the analytical technique and parameter evaluated. Major ions and nutrients generally had detection limits within the mg/L range, whereas trace metals analyzed by ICP–MS had detection limits within the µg/L range, according to instrument specifications and APHA recommendations. Approximate detection limits were 0.01 mg/L for NO₂⁻ and NO₃⁻, 0.1 mg/L for major ions, and between 0.01 and 1 µg/L for trace metals determined by ICP–MS.

The reliability of hydrochemical analyses was evaluated using ionic balance error calculations. Samples with ionic balance errors within ±5% were considered analytically acceptable for subsequent hydrochemical interpretation.

Trace metals with concentrations near detection limits or limited spatial variability were excluded from PCA and PERMANOVA analyses to avoid introducing statistical noise and masking the dominant hydrogeochemical and anthropogenic gradients. However, these variables were retained for descriptive interpretation when analytically reliable.

2.5. Hydrogeochemical Analysis

A hydrogeochemical analysis was conducted based on the relationships between major cations (Ca²⁺, Mg²⁺, Na⁺, and K⁺) and anions (HCO₃⁻ + CO₃²⁻, SO₄²⁻, and Cl⁻) represented in a Piper diagram, with data expressed in (% meq/L) [22]. Ion concentrations originally expressed in mg/L were converted to milliequivalents per liter (meq/L) using the equivalent weight of each ion and were subsequently transformed into percentage milliequivalents (% meq/L) for graphical representation. This conversion allowed the relative dominance of major cations and anions to be compared among stations and hydrological periods.

This graphical approach is widely used to identify hydrochemical facies, interpret hydrogeochemical processes, assess geological controls, and evaluate mineral dissolution reactions that influence water chemistry [22]. In this study, the Piper diagram was used to classify dominant hydrochemical facies, evaluate longitudinal hydrochemical gradients, and support the interpretation of mineralization and water–rock interaction processes along the Diablo Sacha River.

Only samples with ionic balance errors within ±5% were considered analytically acceptable for hydrogeochemical interpretation. This criterion was applied to ensure consistency between major cation and anion concentrations and to increase confidence in facies classification [9].

The Piper diagram and related hydrochemical analyses were performed in RStudio Version 2025.09.2+418 using the hydrogeo, tidyverse, readxl, and cowplot packages. This analysis was also used to assess whether the hydrochemical composition remained relatively stable across monitoring stations and hydrological seasons, and whether downstream changes in ionic composition were consistent with progressive mineralization and lithological influence.

2.6. Statistical Analysis

The statistical analysis of the dataset was conducted using three complementary approaches: descriptive statistics, bivariate analysis, and multivariate analysis. The descriptive statistics were used to characterize the general behavior of the data, summarize measures of central tendency and dispersion, identify outliers and potential anomalies, and assess the consistency and quality of the dataset collected throughout the monitoring period.

Subsequently, bivariate analysis was performed using Spearman’s rank correlation to assess monotonic associations among physicochemical parameters. This non-parametric test is appropriate when datasets contain extreme values or violate normality assumptions, as indicated by the Shapiro–Wilk test (p < 0.05), making it suitable for surface water studies characterized by heterogeneous concentrations and variables measured at different scales [22,23]. Variables with concentrations close to analytical detection limits, very low variability, or limited environmental interpretability were excluded from correlation and multivariate analyses to reduce spurious associations and statistical noise [24].

The multivariate statistical methods were applied to evaluate spatial and temporal patterns in water quality. Principal component analysis (PCA) was performed separately on two predefined groups of variables to distinguish natural hydrogeochemical gradients from potential anthropogenic signals [24]. This separation was adopted to avoid masking weaker anthropogenic gradients by the stronger geogenic and hydrological variability typically observed in high-Andean headwater systems.

The hydrogeochemical PCA included electrical conductivity, total dissolved solids, pH, HCO₃⁻, SO₄²⁻, Ca²⁺, Mg²⁺, Na⁺, K⁺, Fe, Mn, discharge, flow velocity, and water depth. These variables were selected because they reflect mineralization, lithological influence, water–rock interaction, and seasonal hydrological modulation. The anthropogenic PCA included NO₃⁻, NO₂⁻, BOD₅, COD, total suspended solids, oils and grease, total coliforms, and fecal coliforms. These variables were selected because they represent nutrient enrichment, organic matter inputs, suspended material transport, and microbiological signals associated with localized land-use pressures.

Prior to multivariate analysis, all variables were transformed using log10(x + 1) and subsequently standardized using z-score normalization. The log10(x + 1) transformation was applied to reduce skewness and minimize the influence of extreme values, whereas z-score standardization was used to remove the effect of different measurement units and ensure comparability among variables expressed in different units and concentration ranges. This strategy enabled separate interpretation of natural and anthropogenic processes, avoiding confusion between environmental signals of different nature. In particular, the independent PCA structure was used to prevent strong geogenic gradients from masking weaker but environmentally relevant anthropogenic signals. The suitability of each PCA dataset was evaluated using the Kaiser–Meyer–Olkin index and Bartlett’s test of sphericity. PCA was interpreted as an exploratory ordination method for identifying dominant ecohydrological gradients rather than as a causal model.

Trace metals were excluded from PCA and PERMANOVA analyses when they presented concentrations close to analytical detection limits, limited spatial variability, or high collinearity with other variables (|ρ| > 0.70). This criterion was applied to reduce redundancy, minimize statistical noise, and avoid the disproportionate influence of variables with limited contribution to the dominant hydrogeochemical and anthropogenic gradients. However, trace metals were retained for descriptive interpretation when analytically reliable [24].

Spatiotemporal variation in water quality was evaluated using a two-way PERMANOVA based on Euclidean distances and 999 permutations. Prior to analysis, variables were transformed and standardized as described above. The model included spatial effects represented by river reach (River_Reaches), temporal effects represented by hydrological season (Season), and their interaction (River_Reaches × Season). To account for the repeated sampling structure and reduce potential bias associated with non-independent observations from the same monitoring stations, permutations were constrained within each sampling station using the strata argument [24]. Homogeneity of multivariate dispersions was assessed using PERMDISP for both River_Reaches and Season to evaluate whether significant PERMANOVA results could be attributed to differences in group centroids rather than differences in within-group dispersion. When PERMDISP was significant, PERMANOVA results were interpreted cautiously, as significant differences may partially reflect dispersion heterogeneity [25].

To verify the reliability of the hydrochemical data, ionic balance error (IBE) was calculated for each sample after converting major cation and anion concentrations from mg/L to meq/L. The ionic balance error was calculated as: IBE (%) = [(Σ cations − Σ anions)/(Σ cations + Σ anions)] × 100. Samples with ionic balance errors within ±5% were considered analytically acceptable for hydrogeochemical interpretation and Piper diagram classification. This criterion was applied to ensure consistency between major cation and anion concentrations.

2.7. Use of Generative AI and AI-Assisted Technologies

ChatGPT (OpenAI, San Francisco, CA, USA; GPT-5.5 version) was used only to support language editing, grammar correction, and textual clarity. It was not used to generate data, conduct analyses, create results, or replace the authors’ interpretation.

3. Results

3.1. Physicochemical Parameter Analysis

Table 3. Descriptive statistics of physicochemical and chemical parameters of surface water quality.

Parameter	Unit	Mean	Min	Max	SD	Variance	CV (%)	Skewness
Velocity	m/s	0.264	0.1	0.633	0.127	0.016	48.06	1.145
Depth	m	0.213	0.05	0.45	0.091	0.008	42.91	0.563
Width	m	1.98	0.96	3.32	0.831	0.69	41.95	0.344
Discharge	L/s	78.38	3.117	445.022	91.015	8283.679	116.12	2.544
EC	µS/cm	190.7	101.8	359	64.887	4210.277	34.03	0.839
TDS	mg/L	129.777	13.5	422	80.717	6515.292	62.2	2.126
TSS	mg/L	11.331	1	74.5	14.107	199.004	124.5	2.586
Temperature	°C	8.678	6.5	13	1.182	1.398	13.62	0.882
pH	-	7.95	6.63	8.95	0.578	0.334	7.27	−0.368
HCO3	mg/L	92.727	28	160	22.533	507.714	24.3	0.07
Na	mg/L	1.069	0.424	2.204	0.351	0.123	32.88	0.852
K	mg/L	1.985	1.143	2.787	0.253	0.064	12.77	0.056
Ca	mg/L	64.559	0.075	149.769	30.593	935.955	47.39	0.876
Mg	mg/L	33.851	8.125	101.406	16.891	285.323	49.9	1.372
Fe	mg/L	0.141	0.004	0.299	0.074	0.006	52.81	0.2
Mn	mg/L	0.004	0	0.013	0.003	0	82.55	1.022
Cu	µg/L	0.008	0.001	0.127	0.024	0.001	290.93	4.5
Zn	mg/L	0.015	0.001	0.121	0.019	0	129.69	3.765
Cd	µg/L	0.006	0.005	0.008	0	0	5.25	0.692
Ni	µg/L	0.006	0.002	0.01	0.001	0	18.87	2.251
Hg	µg/L	0.013	0.012	0.014	0.001	0	4.71	1.144
Pb	µg/L	0.005	0.002	0.009	0.001	0	16.33	1.915
As	µg/L	0.008	0.002	0.028	0.003	0	40.88	5.613
Co	µg/L	0.007	0.002	0.012	0.001	0	15.5	1.969
Se	µg/L	0.305	0.154	0.387	0.056	0.003	18.19	−1.306
NO2	mg/L	0.005	0.001	0.025	0.004	0	93.6	2.636
NO3	mg/L	3.584	0.017	9.158	2.835	8.039	79.11	0.374
DO	mg/L	7.727	5	12.1	1.433	2.052	18.54	0.588
SO4	mg/L	7.261	0.764	21.842	4.381	19.195	60.34	1.529
BOD5	mg/L	2.295	0.1	8.25	2.29	5.245	99.8	1.541
COD	mg/L	5.392	4	12	2.3	5.289	42.65	1.456
Oils & Grease	mg/L	0.008	0	0.065	0.011	0	138.12	3.155
Total Coliforms	CFU/100 mL	73.66	0.5	208.8	53.076	2817.103	72.06	0.564
Fecal Coliforms	CFU/100 mL	10.195	0	58.3	13.016	169.414	127.67	2.238

Note(s): * Dimensionless.

and Appendix A.1 (Table A1) summarizes the descriptive statistics (mean ± standard deviation) of the principal physicochemical parameters measured along the Diablo Sacha River during the monitoring period. The results revealed marked spatial and temporal variability associated with the altitudinal gradient, hydrological seasonality, and longitudinal hydrochemical evolution within the catchment.

Water temperature ranged from 6.5 to 13.0 °C (8.68 ± 1.18 °C), showing a progressive decrease toward the upper reaches, consistent with the thermal gradient characteristic of high-Andean ecosystems [26,27]. In contrast, electrical conductivity (EC) and total dissolved solids (TDS) exhibited substantial variability, with mean values of 190.70 ± 64.89 µS/cm and 129.78 ± 80.72 mg/L, respectively. The highest EC and TDS values were predominantly observed in middle and lower reaches during dry and transitional hydrological periods, suggesting progressive mineralization associated with reduced dilution, longer water residence times, and enhanced water–rock interaction along the river continuum [9]. These patterns were likely reinforced by the influence of volcanic and pyroclastic materials, which may favor solute enrichment under low-flow conditions [28]. Similar longitudinal increases in mineralization have been reported in other high-Andean páramo catchments, where lithological weathering and subsurface flow connectivity regulate hydrochemical evolution [6,29].

The pH remained relatively stable throughout the monitoring period (7.95 ± 0.58), indicating circumneutral to moderately alkaline hydrochemical conditions. Together with elevated bicarbonate concentrations (92.73 ± 22.53 mg/L) and the predominance of Ca²⁺ (64.56 ± 30.59 mg/L) and Mg²⁺ (33.85 ± 16.89 mg/L), these results suggest hydrochemical regulation primarily controlled by natural geogenic processes, including lithological weathering, silicate mineral dissolution, and subsurface hydrological connectivity [18,29]. The predominance of bicarbonate and alkaline-earth cations is also consistent with longer hydrological residence times, shallow subsurface flow pathways, and progressive water–rock interaction within the catchment [9]. Spatially, middle and lower reaches exhibited comparatively greater ionic enrichment, whereas upper reaches maintained lower mineralization levels characteristic of less disturbed headwater environments.

Hydrological variables also exhibited considerable variability. Discharge ranged from 3.12 to 445.02 L/s (78.38 ± 91.01 L/s), reflecting marked temporal fluctuations associated with wet, transitional, and dry hydrological conditions, antecedent moisture, and delayed water release from páramo soils [30]. Flow velocity varied between 0.10 and 0.63 m/s (0.26 ± 0.13 m/s), while water depth ranged from 0.05 to 0.45 m (0.21 ± 0.09 m), indicating substantial spatial heterogeneity in hydraulic conditions throughout the river network. The upper reaches generally exhibited faster hydraulic responses associated with steeper slopes and narrower channels, whereas middle and lower reaches showed greater hydraulic variability linked to changes in channel morphology and subsurface contributions. The combination of steep slopes, short concentration times, and soil-water regulation by páramo soils likely contributed to the rapid yet partially buffered hydrological responses observed across the basin [31,32].

Most nutrient and organic matter indicators remained at relatively low concentrations throughout the monitoring period. NO₃⁻ concentrations averaged 3.58 ± 2.84 mg/L, whereas NO₂⁻ concentrations remained comparatively low (0.005 ± 0.004 mg/L). Likewise, BOD₅ (2.30 ± 2.29 mg/L) and COD (5.39 ± 2.30 mg/L) generally indicated limited organic contamination at the watershed scale [33]. However, localized increases in nutrients, organic matter, and microbiological indicators were detected during specific sampling campaigns, particularly within middle and lower reaches influenced by livestock activity and diffuse runoff inputs. These increases were more evident during wet and transitional hydrological periods, when enhanced surface runoff and hydrological connectivity likely promoted nutrient and organic matter mobilization toward the fluvial network [26,27].

Dissolved oxygen concentrations remained relatively high (7 7.73 ± 1.43 mg/L), reflecting favorable oxygenation conditions typical of turbulent high-Andean headwaters [10,34]. In contrast, total suspended solids (TSS) showed considerable variability (11.33 ± 14.11 mg/L), particularly during wet periods, likely associated with enhanced runoff generation, sediment mobilization, and increased hydrological connectivity during rainfall events [34]. Similarly, microbiological indicators exhibited localized temporal variability, suggesting intermittent inputs associated with surface runoff, livestock activity, and hydrological transport processes operating during periods of increased connectivity.

Overall, the observed physicochemical variability reflects the combined influence of lithological controls, hydrological seasonality, subsurface connectivity, and longitudinal hydrochemical evolution characteristic of high-Andean páramo headwaters [11]. Although natural hydrogeochemical regulation processes dominated the system, localized increases in nutrients, organic matter, and microbiological indicators suggest the presence of spatially restricted anthropogenic influences under specific hydrological conditions [10,11]. These patterns indicate the coexistence of dominant natural hydrogeochemical controls and secondary anthropogenic signals within the catchment, providing the basis for subsequent multivariate analyses aimed at differentiating geogenic gradients from localized anthropogenic influences.

3.2. Hydrochemical Classification of Water (Piper Diagram)

The Piper diagram (Figure 2) revealed a predominance of Ca–Mg–HCO₃ hydrochemical facies throughout the Diablo Sacha River system, indicating hydrochemical conditions primarily controlled by natural geogenic processes. The limited dispersion of samples within the Piper fields suggests relatively homogeneous hydrochemical composition across monitoring stations and hydrological periods, reflecting the strong influence of lithological weathering, groundwater contributions, and subsurface water–rock interaction processes characteristic of high-Andean headwater systems [22].

The river system was dominated by bicarbonate waters associated with Ca²⁺ and Mg²⁺ ions, suggesting predominant geogenic influence related to silicate weathering and progressive mineralization processes [13]. Hydrochemical facies showed gradual downstream evolution along the river continuum. Upper reach stations generally exhibited lower electrical conductivity, lower total dissolved solids, and reduced ionic concentrations, reflecting shorter residence times, stronger dilution by recent precipitation, and limited mineralization processes typical of high-altitude headwater environments [12,13]. In contrast, middle and lower reaches showed slight increases in bicarbonate concentrations and alkaline-earth cations, accompanied by higher EC and TDS values, consistent with enhanced mineralization and cumulative solute enrichment during downstream transport [34].

This longitudinal hydrochemical evolution was likely influenced by the spatial distribution of lithostratigraphic units within the catchment. Volcanic and pyroclastic materials, together with sandstone and siltstone formations rich in silicate minerals, may have contributed to the progressive release of dissolved ions through weathering processes [10,34]. Furthermore, increased subsurface connectivity and longer water residence times in downstream sectors likely promoted greater hydrochemical stabilization and ion accumulation [10,34].

Despite seasonal hydrological variability, hydrochemical facies remained relatively stable between rainy and dry periods, showing only minor displacement within the Piper diagram. This limited seasonal variability suggests partial hydrochemical buffering associated with groundwater contributions and subsurface flow regulation processes characteristic of high-Andean páramo ecosystems [1,5]. During wet periods, slightly lower EC and reduced ionic concentrations were observed, likely reflecting dilution effects associated with increased runoff and precipitation inputs. Conversely, transitional and dry periods were characterized by moderately higher ionic concentrations and enhanced mineralization, reflecting reduced dilution, increased groundwater influence, and greater water–rock interaction under low-flow conditions [16,17].

The absence of sulfate- and chloride-dominated facies indicates limited influence of strong anthropogenic contamination sources and supports the predominance of natural hydrogeochemical controls within the catchment [6,7]. Nevertheless, localized hydrochemical deviations were observed at some monitoring stations and sampling periods, particularly in downstream sectors where nutrients, organic matter indicators, and microbiological parameters occasionally increased. These localized variations may indicate spatially restricted anthropogenic influence associated with diffuse runoff, livestock activity, and seasonal hydrological connectivity [34].

Overall, the hydrochemical facies of the Diablo Sacha River reflect a hydrogeochemically dynamic but relatively stable high-Andean headwater system, where lithology, hydrological seasonality, subsurface flow processes, and longitudinal connectivity collectively regulate solute generation, transport, and downstream evolution [13,34,35]. These results suggest that hydrochemical variability within the catchment is primarily governed by natural hydrogeochemical and ecohydrological controls, while localized anthropogenic signals emerge under conditions of increased hydrological connectivity and downstream accumulation.

3.3. Correlation Analysis of Physicochemical Parameters

The Spearman correlation matrix (Figure 3) revealed significant interactions among hydrochemical, hydrological, and water quality variables, reflecting the combined influence of lithological weathering, hydrological seasonality, subsurface connectivity, and localized anthropogenic inputs within the Diablo Sacha River catchment. The correlation structure showed the coexistence of dominant hydrogeochemical gradients and secondary anthropogenic signals modulated by seasonal hydrological dynamics.

3.4. Principal Component Analysis (PCA)

Moderate to strong positive correlations (|ρ| ≥ 0.40; p < 0.05) were observed among EC, TDS, HCO₃⁻, Ca²⁺, Mg²⁺, and SO₄²⁻, indicating that dissolved ion concentrations were primarily influenced by natural mineralization and water–rock interaction processes [34]. These associations suggest that bicarbonates and alkaline earth cations constitute the dominant hydrochemical signature of the fluvial system, consistent with silicate weathering processes and progressive downstream mineralization characteristic of high-Andean sedimentary systems [10,36]. Positive associations among HCO₃⁻, SO₄²⁻, and EC reflect ionic enrichment processes linked to longer water residence times and enhanced subsurface connectivity along the river continuum. Likewise, the positive correlations observed among Fe, Mn, and K suggest possible common lithological controls and geochemical mobilization processes associated with mineral dissolution and subsurface transport [24,25].

EC and TDS also exhibited inverse relationships with discharge and flow velocity, particularly during dry and transitional periods, suggesting concentration effects under low-flow conditions. This pattern indicates that reduced dilution and longer residence times favored the accumulation of dissolved solutes, whereas wet periods promoted dilution through increased runoff and hydrological connectivity. Similarly, discharge showed negative correlations with several ionic parameters, reinforcing the influence of seasonal dilution processes during periods of higher precipitation and flow [24,25,37]. On the other hand, the strong positive associations among depth, velocity, and discharge reflect the hydraulic coherence of the fluvial system and the influence of seasonal hydrological dynamics on flow conditions within the catchment [37].

The correlation matrix also revealed a secondary cluster composed of BOD₅, COD, total suspended solids (TSS), total coliforms, and fecal coliforms, suggesting localized relationships among organic matter inputs, nutrient transport, and microbiological contamination. Positive correlations among these variables likely reflect diffuse anthropogenic contributions associated with livestock activity, surface runoff, and hydrological transport of organic materials during precipitation events [38]. These associations were more evident in middle and lower reaches during wet and transitional hydrological periods characterized by greater runoff connectivity [39,40]. Furthermore, the positive correlation between TSS and COD suggests the joint transport of sediments and organic matter during runoff events, indicating that hydrological processes play an important role in the mobilization of particulate materials and organic compounds within the fluvial system [25,38]. Nevertheless, the generally low concentrations observed for these variables indicate that anthropogenic influence remained spatially restricted and secondary relative to the dominant natural hydrogeochemical controls.

Dissolved oxygen exhibited negative correlations with temperature and some organic matter indicators, suggesting the combined influence of thermal conditions and oxygen consumption processes associated with organic decomposition [41,42]. However, positive associations were observed between dissolved oxygen and BOD₅, likely related to the intense turbulence and natural aeration characteristic of steep high-Andean streams, where high oxygen concentrations can be maintained even under moderate organic matter inputs [27]. Likewise, the positive associations between dissolved oxygen and flow velocity reflect enhanced aeration under turbulent hydraulic conditions typical of mountain river systems [35].

Total suspended solids showed positive correlations with discharge and wet-season conditions, indicating that runoff generation and increased hydrological connectivity favored the mobilization and downstream transport of sediments during precipitation periods. These patterns are consistent with the steep slopes, erosive geomorphology, and rapid hydrological response characteristic of the catchment [38].

In contrast, several trace metals exhibited weak correlations with the principal hydrochemical and organic matter variables, suggesting limited anthropogenic metal contamination and low spatial variability during the monitoring period. The absence of strong associations among trace metals further supports the predominance of natural hydrogeochemical controls within the fluvial system [43].

In this context, the observed correlation structure suggests that the hydrochemical variability of the Diablo Sacha River is predominantly governed by natural geogenic and hydrological processes, whereas localized anthropogenic signals emerge under conditions of enhanced hydrological connectivity and diffuse runoff transport [43,44]. These relationships support the interpretation of the catchment as a hydrogeochemically dynamic yet relatively buffered high-Andean páramo system, where lithology, groundwater regulation, flow dynamics, and longitudinal ecohydrological connectivity jointly control water quality variability. Furthermore, the observed correlation patterns provide a consistent multivariate basis for subsequent PCA and PERMANOVA interpretation, allowing differentiation between dominant hydrogeochemical gradients and secondary anthropogenic signals within the fluvial system [38,45].

Spearman’s correlation coefficients were interpreted according to their absolute magnitude. Correlations with |ρ| ≥ 0.40 and p < 0.05 were considered meaningful for hydrochemical interpretation, whereas |ρ| ≥ 0.70 was considered strong and indicative of high association or potential collinearity among variables.

Principal component analysis (PCA) was applied to identify the dominant hydrogeochemical and anthropogenic gradients controlling water quality variability in the Diablo Sacha River [46]. Separate PCAs were performed for hydrogeochemical variables and anthropogenic indicators in order to differentiate natural geogenic processes from diffuse anthropogenic influences [24,47]. Prior to analysis, variables were log-transformed and standardized using z-score normalization to minimize the influence of different measurement scales and non-normal distributions [22,24,25].

The suitability of the datasets for PCA was evaluated using the Kaiser–Meyer–Olkin (KMO) index and Bartlett’s test of sphericity. The hydrogeochemical dataset showed moderate sampling adequacy (KMO = 0.54), whereas the anthropogenic dataset showed acceptable adequacy (KMO = 0.63). In both cases, Bartlett’s test was highly significant (p < 0.001), confirming the existence of sufficient multivariate correlation structure for exploratory PCA. The relatively moderate KMO values likely reflect the environmental heterogeneity and multifactorial ecohydrological controls characteristic of high-Andean river systems.

3.4.1. Hydrogeochemical PCA

The hydrogeochemical PCA (Figure 4) explained 52.75% of the total variance in the first two principal components (PC1 = 30.83%; PC2 = 21.92%), revealing clear hydrochemical gradients associated with mineralization processes, hydrological connectivity, and longitudinal ecohydrological dynamics within the catchment.

PC1 was positively associated with EC, HCO₃⁻, SO₄²⁻, and Ca²⁺, suggesting that this component reflects a hydrogeochemical gradient related to mineralization and progressive water–rock interaction processes [48,49]. The positive loadings of these variables suggest progressive downstream ionic enrichment associated with lithological weathering, subsurface flow contributions, and longer water residence times along the river continuum. Conversely, discharge and water depth showed negative relationships with PC1, reflecting dilution effects under higher flow conditions and reduced solute concentrations during periods of increased hydrological connectivity [50].

PC2 was mainly associated with flow velocity and discharge, while Fe, Mn, K, and pH exhibited negative loadings. This component appears to represent hydrodynamic variability and differential hydrochemical mobilization associated with seasonal hydrological conditions. The inverse relationships between hydraulic variables and some dissolved constituents suggest that changes in flow conditions influence sediment mobilization, metal transport, and subsurface contributions within the river system [51].

Spatially, the PCA revealed partial differentiation among river reaches. Middle and lower reaches tended to cluster toward positive PC1 values, reflecting higher mineralization and greater ionic concentrations, whereas upper reaches were generally associated with lower mineralization conditions characteristic of recent precipitation inputs and shorter hydrological residence times in high-Andean headwaters [39]. The outlet station exhibited partial overlap with downstream reaches, suggesting integrated hydrochemical responses produced by cumulative longitudinal transport and catchment-scale hydrological connectivity [40,52].

Overall, the hydrogeochemical PCA suggests that water quality variability in the Diablo Sacha River is primarily controlled by natural geogenic and ecohydrological processes, including lithological weathering, groundwater contributions, seasonal hydrological variability, and longitudinal solute transport.

To further support the geological interpretation of the hydrogeochemical gradient, mean Ca²⁺ and Mg²⁺ concentrations were evaluated across monitoring stations (Figure A3). The results showed clear spatial variability in the main cations, with higher Ca²⁺ concentrations at E6, E7, and E3 and more moderate variation in Mg²⁺ along the longitudinal gradient. This pattern provides additional spatial evidence linking the hydrogeochemical PCA gradient with changes in major cation composition. Although this analysis does not by itself prove direct lithological control, it is consistent with cumulative water–rock interaction, residence time, and lithological heterogeneity along the monitored river corridor.

3.4.2. Anthropogenic PCA

The anthropogenic PCA (Figure 5), explained 61.77% of the total variance within the first two components (PC1 = 41.45%; PC2 = 20.33%), indicating coherent multivariate relationships among variables associated with nutrients, organic matter, suspended solids, and microbiological indicators [53].

PC1 was positively associated with NO₃⁻, oils and grease, and total coliforms, whereas TSS and COD showed negative relationships. This component appears to represent a diffuse anthropogenic influence gradient associated with livestock activity, surface runoff, and seasonal transport of nutrients and microbiological contaminants. The association between nitrate concentrations and microbiological indicators suggests that diffuse hydrological connectivity may facilitate the mobilization of organic and nutrient-rich materials during specific hydrological conditions [53,54].

PC2 was primarily associated with total coliforms and COD, reflecting variability linked to organic matter dynamics and microbiological transport processes. The distribution of samples along this axis suggests temporal fluctuations in organic inputs and hydrological mobilization mechanisms throughout the monitoring period [12].

Seasonally, the PCA revealed partial differentiation among hydrological periods. Dry-season samples tended to cluster toward positive PC1 values, indicating greater associations with nitrate enrichment and oils and grease, likely due to reduced dilution and increased solute concentration under lower flow conditions. Transitional periods showed greater association with COD variability, whereas wet-season samples exhibited broader dispersion patterns, reflecting increased hydrological heterogeneity and runoff-driven transport processes during periods of higher precipitation and hydrological connectivity [12].

Although anthropogenic signals were detected, their influence remained secondary relative to the dominant natural hydrogeochemical controls identified in the hydrogeochemical PCA. Nevertheless, the observed multivariate patterns indicate that diffuse anthropogenic activities, particularly livestock-related runoff and seasonal hydrological transport, contribute to localized water quality variability within the high-Andean river system [55].

3.5. Spatiotemporal Multivariate Analysis (PERMANOVA)

The PERMANOVA (

Table 4. Results of the PERMANOVA for hydrogeochemical and anthropogenic variables.

	Degrees of Freedom	Sum of Squares	R2	Pseudo-F	p-Value
Hydrogeochemical variables
River Reaches	3	108.41	0.167	4.017	0.002 **
Season	2	47.82	0.074	2.658	0.002 **
River Reaches × Season	6	61.02	0.094	1.131	0.048 *
Residual	48	431.75	0.665
Total	59	649	1
Anthropogenic variables
River Reaches	3	43.93	0.149	3.396	0.076
Season	2	19.39	0.066	2.248	0.009 **
River Reaches × Season	6	24.67	0.084	0.954	0.408
Residual	48	207.01	0.702
Total	59	295	1

Note(s): (*) Significant at p < 0.05; (**) significant at p < 0.01. PERMANOVA was performed using Euclidean distances and 999 permutations with station-constrained permutations (strata = station).

) results revealed significant spatiotemporal differences in hydrogeochemical composition along the Diablo Sacha River, indicating that both longitudinal gradients and hydrological seasonality significantly influence water quality dynamics within the catchment [25,38].

For hydrogeochemical variables, significant differences were detected among river reaches (Pseudo-F = 4.02, p = 0.002) and hydrological seasons (Pseudo-F = 2.66, p = 0.002). In addition, the interaction between river reach and season was also significant (Pseudo-F = 1.13, p = 0.048), suggesting that the magnitude and direction of hydrochemical variability change spatially according to seasonal hydrological conditions. River reach explained 16.7% of the total multivariate variance, whereas season explained 7.4%, indicating that longitudinal hydrochemical gradients constituted the principal source of variability within the river system [38,56].

These results support the interpretation that lithological weathering, subsurface connectivity, mineralization processes, and seasonal hydrological fluctuations collectively regulate hydrochemical composition along the river continuum. Upper reaches were generally associated with lower mineralization and recently recharged waters, whereas middle and lower reaches exhibited progressive ionic enrichment linked to downstream solute transport and enhanced water–rock interaction [56,57].

PERMDISP analyses indicated no significant differences in multivariate dispersion among river reaches (p = 0.58) or seasons (p = 0.12), confirming that the PERMANOVA results primarily reflect differences in centroid location rather than heterogeneity of dispersion. This supports the robustness of the detected hydrogeochemical spatiotemporal patterns [25,58].

For anthropogenic variables, PERMANOVA detected significant seasonal differences (Pseudo-F = 2.25, p = 0.009), whereas differences among river reaches were only marginally significant (p = 0.076). The interaction between river reach and season was not significant (p = 0.408), suggesting that anthropogenic influences were more strongly controlled by seasonal hydrological variability than by consistent longitudinal spatial gradients [38,59].

These findings indicate that diffuse anthropogenic signals, including nutrients, organic matter, oils and grease, and microbiological indicators, are likely mobilized through temporally variable hydrological processes such as runoff generation and seasonal hydrological connectivity. The absence of strong spatial structuring suggests that anthropogenic impacts remain localized and intermittent rather than spatially persistent throughout the catchment [55,59].

However, PERMDISP results for anthropogenic variables revealed significant heterogeneity of multivariate dispersion among seasons (p = 0.008). Therefore, the detected seasonal differences should be interpreted cautiously, as part of the observed variability may be associated with differences in within-group dispersion rather than exclusively with centroid separation [25,58].

Overall, the multivariate analyses demonstrate that hydrochemical variability in the Diablo Sacha River is primarily governed by natural ecohydrological and geogenic controls, while anthropogenic influences emerge seasonally through diffuse hydrological transport and localized runoff processes [57].

4. Discussion

The hydrogeochemical composition of the Diablo Sacha River is predominantly controlled by natural processes characteristic of high-Andean páramo ecosystems. The dominance of calcium–bicarbonate and magnesium–bicarbonate facies (Figure 2), identified through the Piper diagram, indicates that water chemistry is primarily governed by water–rock interaction, mineral weathering, and geochemical equilibrium between water, soil CO₂, and substrate minerals [22,29,60]. These mechanisms have been widely recognized as the main controls on solute generation and mobilization in high-Andean catchments, where lithology, water residence time, and soil properties regulate the hydrogeochemical evolution of the system [1,3].

The observed stability in pH (7.95 ± 0.58) and the moderate electrical conductivity values (190.70 ± 64.89 µS/cm) suggest the presence of natural buffering mechanisms associated with the high organic matter content and the elevated water retention capacity of páramo soils, which regulate subsurface flow pathways and contribute to maintaining stable chemical conditions despite seasonal hydrological variability [1,3]. This interpretation is consistent with the predominance of Ca–Mg–HCO₃ facies and the limited dispersion of samples within the Piper diagram, indicating a relatively stable hydrochemical background rather than severe hydrochemical alteration.

Spatial variability along the altitudinal gradient reflects the combined influence of lithological, geomorphological, and hydrological factors on the dynamics of the system. Increased mineralization in middle and lower sectors suggests longer water residence time and differential interaction with the substrate. This longitudinal pattern is intrinsically linked to the complex geological architecture of the QWPA.

Along the monitored river corridor, the upper, middle, lower, and outlet sectors intersect different lithological domains, including the Apagua Formation, the Pallatanga Unit, and the Gallo Rumi Formation. This spatial arrangement creates contrasting water–rock interaction environments that may explain the observed variability in Ca²⁺, Mg²⁺, HCO₃⁻, SO₄²⁻, and total ionic content. Therefore, the progressive enrichment of major ions toward middle and lower reaches should be interpreted as the result of cumulative mineralization, longer residence time, subsurface connectivity, and lithological heterogeneity rather than as direct evidence of anthropogenic contamination. This interpretation is further supported by the spatial analysis of mean Ca²⁺ and Mg²⁺ concentrations (Figure A2) which showed marked differences among monitoring stations and was consistent with water–rock interaction, residence time, and lithological heterogeneity.

This succession of water-rock interaction environments confirms what has been documented in high Andean river systems, where geogenic control and hydrological connectivity determine the spatial distribution of solutes [18,60].

The hydrological dataset showed marked discharge variability, ranging from 3.12 to 445.02 L/s, which indicates strong temporal changes in dilution capacity and hydrological connectivity. At the seasonal scale, EC increased from 162.70 ± 46.45 µS/cm during wet periods to 212.63 ± 64.69 µS/cm during dry periods, while transitional periods showed intermediate-to-high EC values of 196.77 ± 73.22 µS/cm. This pattern supports the interpretation that higher hydrological connectivity and dilution capacity partially reduced dissolved ion concentrations, whereas reduced dilution and longer residence times favored mineralization and solute accumulation.

This behavior is consistent with the ecohydrological functioning of páramo ecosystems, where precipitation patterns and gradual water storage and release regulate solute transport [61]. The high storage capacity of organic soils contributes to buffering hydrogeochemical fluctuations and maintaining relatively stable water quality conditions throughout the year. However, seasonality should not be interpreted only as a dilution–concentration mechanism. During wet and transitional periods, increased surface runoff and hydrological connectivity may also mobilize suspended solids, nutrients, organic matter, and microbiological indicators from riparian areas, livestock zones, and small-scale disturbed surfaces. The high storage capacity of organic soils contributes to buffering hydrogeochemical fluctuations and maintaining relatively stable water quality conditions throughout the year [6,8].

However, seasonality should not be interpreted only as a dilution–concentration mechanism. During wet and transitional periods, increased surface runoff and hydrological connectivity may also mobilize suspended solids, nutrients, organic matter, and microbiological indicators from riparian areas, livestock zones, and small-scale disturbed surfaces. Thus, seasonal hydrology simultaneously regulates natural solute dilution and the detectability of localized anthropogenic signals [56,59,62].

Multivariate analysis allowed for a robust differentiation between natural hydrogeochemical gradients and signals associated with land use. PERMANOVA results demonstrated that both hydrogeochemical and anthropogenic variables significantly contribute to the spatiotemporal variability of the system, confirming that water quality reflects the interaction between geogenic controls and anthropogenic pressures modulated by hydrological dynamics and surface and subsurface connectivity. This behavior highlights the ecohydrological complexity of páramo ecosystems, where natural processes structure hydrogeochemical composition, while human activities generate detectable signals that contribute to system variability [25].

For hydrogeochemical variables, the significant effects of river reach and season, together with non-significant PERMDISP results, support the interpretation that the observed differences mainly reflect changes in multivariate centroid location rather than dispersion artifacts. In contrast, for anthropogenic variables, seasonal differences should be interpreted more cautiously because PERMDISP indicated significant heterogeneity of dispersion among seasons. Therefore, anthropogenic patterns represent temporally variable and spatially localized signals rather than persistent basin-wide degradation [24,25].

The independent application of principal component analyses enabled clear separation between background hydrogeochemical variability and anthropogenic signals. The hydrogeochemical PCA identified a mineralization gradient dominated by bicarbonates, sulfates, and major cations, reflecting lithological control and the influence of water residence time, as widely documented in high-Andean systems [53,63]. In contrast, the anthropogenic PCA revealed an independent gradient associated with nitrates, organic matter, and microbiological indicators, suggesting localized inputs related to livestock activities or low-intensity human settlements (Figure 4). Similar patterns have been reported in Andean catchments, where anthropogenic pressures modify nutrient and organic matter dynamics without significantly altering dominant hydrogeochemical facies [31]. This separation is methodologically important because it reduces the risk that strong geogenic gradients mask weaker but environmentally relevant anthropogenic signals. In this sense, the independent PCA structure supports the central hypothesis of the study: the Diablo Sacha River maintains a dominant natural hydrogeochemical signature, while anthropogenic pressures appear as secondary, localized, and seasonally modulated signals.

Temporal variability also influenced the expression of anthropogenic signals. During the rainy season, increased surface runoff promoted the mobilization of nutrients, organic matter, and coliforms, whereas during the dry season, reduced dilution capacity enhanced the expression of localized sources. This behavior highlights the role of hydrological connectivity in contaminant transport, regulated by interactions among organic soils, vegetation, and precipitation regimes [39,40,64]. The presence of elevated concentrations of BOD₅, nitrates, and coliforms in specific sectors suggests that even low-intensity disturbances may constitute early indicators of ecosystem pressure [49]. Nevertheless, these signals did not modify the dominant Ca–Mg–HCO₃ hydrochemical facies, indicating that anthropogenic influence remains secondary relative to the natural hydrogeochemical structure of the system. This distinction is important for management because localized organic, nutrient, suspended-solid, oil-and-grease, and microbiological signals may represent early-warning indicators before structural hydrochemical degradation becomes evident [53].

Trace metals showed limited contribution to the dominant multivariate gradients, consistent with their low concentrations, limited spatial variability, and weak association with the main hydrogeochemical and anthropogenic indicators. Because the exclusion criteria were defined in the methodology, these elements were not emphasized in the multivariate interpretation. This reinforces the interpretation that major ions, nutrients, organic matter indicators, suspended solids, oils and grease, and microbiological variables were more relevant for explaining water quality variability in the Diablo Sacha River.

Overall, the results demonstrate that water quality in the Diablo Sacha River is primarily structured by natural hydrogeochemical controls, while anthropogenic pressures act as secondary factors modulated by seasonal hydrological dynamics. The coexistence of hydrochemical stability and localized anthropogenic signals suggests that the system still maintains functional ecohydrological resilience. However, this resilience should not be interpreted as absence of vulnerability. Instead, it indicates that the natural buffering capacity of páramo soils, vegetation, and subsurface flow pathways can partially attenuate disturbance signals, while cumulative land-use pressures may progressively reduce this capacity over time [1,49].

From a management perspective, these results support a preventive monitoring strategy for the QWPA. Because the dominant Ca–Mg–HCO₃ hydrochemical facies remained relatively stable, management actions should not focus only on detecting severe hydrochemical degradation, but also on identifying early-warning indicators such as localized increases in NO₃⁻, BOD₅, COD, TSS, oils and grease, and coliforms. Monitoring programs should prioritize middle and lower reaches, transitional and wet periods, and areas with greater livestock access or diffuse runoff potential [30,45,53]. These results can support protected-area management by guiding riparian buffer conservation, livestock exclusion near stream channels, control of diffuse organic inputs, and the design of seasonal water-quality surveillance protocols. At the policy level, the findings provide scientific support for strengthening water-protection-area management plans, establishing site-specific monitoring indicators, and integrating water-quality surveillance with land-use control, páramo conservation, and community-based watershed management [9].

Study Limitations and Future Research

Although this study provides relevant evidence on the factors controlling water quality in the Diablo Sacha River, the monitoring period was limited to one hydrological year. Therefore, the observed spatial and seasonal patterns should be interpreted as first-order ecohydrological responses rather than long-term trends. The six bimonthly sampling campaigns captured wet, transitional, and dry conditions; however, they do not fully represent interannual hydroclimatic variability, extreme rainfall events, prolonged droughts, or multi-year land-use changes. Consequently, interpretations related to ecosystem resilience, hydrochemical stability, and anthropogenic pressure should be understood as conditional on the monitored period. Future studies should incorporate multi-year monitoring and, where available, regional historical rainfall and discharge records to place local water-quality variability within a broader hydroclimatic context [9,65].

Future research should integrate multi-year and high-frequency monitoring with isotopic tracers, hydrochemical end-member analysis, and conceptual or distributed hydrological modeling to better identify residence times, flow pathways, and solute sources. The incorporation of water quality indices could also help translate complex hydrochemical and microbiological information into management-oriented indicators. In addition, hydrological models such as SWAT or other process-based tools could be used to evaluate land-use scenarios, runoff generation, sediment transport, and nutrient export under changing climate and management conditions. These approaches would strengthen the capacity to distinguish natural hydrogeochemical variability from anthropogenic pressures and support the design of adaptive management strategies for protected high-Andean watersheds.

5. Conclusions

Water quality in the Diablo Sacha River is predominantly structured by natural hydrogeochemical processes characteristic of high-Andean páramo ecosystems, where water–rock interaction, mineral weathering, and water residence time control solute generation and distribution. These processes determine the predominance of calcium–bicarbonate and magnesium–bicarbonate facies, reflecting dominant geogenic control and contributing to the physicochemical stability of the system.

Hydrological seasonality acts as a key modulating factor of hydrogeochemical variability. Dilution processes during the rainy season, associated with increased discharge and meteoric water inputs, contrast with stronger geochemical control during the dry season, when longer residence times enhance water–rock interaction and mineralization.

The independent application of multivariate analyses enabled robust differentiation between natural hydrogeochemical controls and anthropogenic signals. PERMANOVA results demonstrated that both hydrogeochemical variables (R² = 0.191; p < 0.05) and anthropogenic variables (R² = 0.203; p < 0.05) significantly contribute to the spatiotemporal variability of the system. These findings confirm that, although geogenic control dominates the hydrogeochemical structure, land-use-related pressures generate detectable signals modulated by seasonal hydrological dynamics.

Organic, sanitary, and nutrient-related variables exhibited greater sensitivity to surface runoff processes and hydrological connectivity, constituting early indicators of ecosystem pressure, particularly in the middle and lower sections of the catchment. This behavior highlights the vulnerability of these systems to land-use changes.

Overall, the results confirm the role of the páramo as a key ecohydrological regulator that maintains water quality stability through natural processes of storage, filtration, and gradual release. However, the detection of anthropogenic signals underscores the sensitivity of these ecosystems to environmental disturbances, emphasizing the need to implement long-term monitoring programs and adaptive management strategies aimed at preserving ecosystem services and ensuring water security under current and future environmental pressures.

This study provides key insights for sustainable water resource management in high-Andean protected areas, emphasizing the importance of preserving ecohydrological processes that regulate water quality. The identification of early anthropogenic signals supports the implementation of adaptive management strategies aimed at maintaining ecosystem resilience and ensuring long-term water security.

From a practical management perspective, conservation actions in the QWPA should prioritize: maintaining and restoring riparian buffer zones; limiting livestock access to stream channels and springs; controlling diffuse organic, nutrient, and microbiological inputs from grazing areas and rural settlements; and strengthening seasonal water-quality monitoring, especially during wet and transitional periods. Monitoring programs should include early-warning indicators such as NO₃⁻, BOD₅, COD, TSS, oils and grease, and coliforms, in addition to major ions and electrical conductivity. These measures would support adaptive management, páramo conservation, and long-term water security in protected high-Andean ecosystems.