3.4.2 Dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand

DO levels were generally high throughout the year, reaching their maximum (7.80 ± 0.28 mg L⁻¹) in winter. As dissolved oxygen generally increases with low temperature, the high level in winter can be explained by the water's low temperature. In comparison, the lower values observed in summer reflect higher water temperatures and greater metabolic activity of aquatic organisms.

On the other hand, BOD levels were persistently low across all seasons (0.70-1.00 mg L⁻¹). A low BOD level means there is little organic pollution and that the oxygen needs of aerobic bacteria and animals in water bodies are met, indicating good water quality. During the monsoon season, an increase to 1.00 ± 0.06 mg L⁻¹ was detected, which may be due to surface runoff carrying organic matter into the river.

COD values were characteristically low to moderate and lower during the monsoon, at 8.00 ± 0.40 mg L⁻¹, likely due to dilution effects. In contrast, the summer and winter months showed higher concentrations due to low flow conditions (Figure 5).

3.4.3 Total dissolved solids, hardness, and alkalinity

The maximum value recorded for TDS was 72.00 ± 0.90 mg L⁻¹, observed in the monsoon season. This increase is likely related to enhanced runoff and soil erosion in the catchment. Summer TDS is lower, suggesting little external input and less surface flow.

Total hardness values during the monsoon season ranged from 44.00 ± 0.70 mg L⁻¹ to 54.95 ± 0.60 mg L⁻¹ during summer. Rainwater dilution during the monsoon might account for lower hardness, whereas high summer values may be attributed to evaporation concentration of calcium and magnesium ions. Hence, the water is soft to moderately hard.

Total alkalinity (TA) also varied significantly seasonally, with maximum values of 38.30 ± 0.50 mg L⁻¹ in summer and minimum values of 31.60 ± 0.60 mg L⁻¹ in winter, and a value during the monsoon period at its lowest, around 18.70 ± 0.55 mg L⁻¹, which reflects seasonal variation in bicarbonate concentrations depending on degrees of hydrological dilution and groundwater mixing (Figure 6).

3.4.4 Major ions and nutrient concentrations

Chloride concentration was uniformly low, recorded at 2.61 ± 0.29 mg L⁻¹ in summer and 2.97 ± 0.33 mg L⁻¹ during the monsoon season. A marginal increase in the level of these elements during the monsoon season can be associated with runoff from adjacent land use. The low chloride values further indicate drilling east of the basin, where little domestic or industrial salt contamination of surface waters is present.

Winter nitrate-nitrogen concentrations (2.43 ± 0.35 mg L⁻¹) were similar to monsoon levels (2.67 ± 0.20 mg L⁻¹). The high levels during the monsoon are possibly due to agricultural runoff and soil leaching. Nonetheless, these concentrations were under key limits, meaning that dangerous nutrient pollution isn’t an issue.

The phosphate concentration showed a strong seasonal variation, with extremely low concentrations in summer (0.0058 ± 0.0010 mg L⁻¹) and higher concentrations during the monsoon (0.0463 ± 0.0013 mg L⁻¹) and winter (0.0580 ± 0.0017 mg L⁻¹). This increase in phosphate concentrations during the monsoon and winter months can most likely be attributed to runoff, sediment release, and reduced biological assimilation. Such factors can increase primary productivity, as shown in Figure 7.

These variations in temperature, dissolved oxygen, organic load, total dissolved solids (TDS), hardness, alkalinity, and nutrient levels were observed over time during the physicochemical characterization of Sankh River upper catchment. In all, the deviation of all parameters in this long-duration study from the acceptable range was not notable throughout the seasons and suggests good water quality with the least anthropogenic impact. Understanding chemical dynamics in this catchment has heretofore been limited by the scarcity of time-series data, presenting an important shortcoming given the role of forested headwater landscapes in maintaining lowland (upstream-downstream) land-use practices on the Sankh River seasonally.

3.5 Statistical analysis of seasonal variability

3.5.1 Seasonal variation in physico-chemical parameters (MANOVA)

PCA is necessary before applying Multivariate Analysis of Variance (MANOVA) when classifying data where the number of parameters equals or outweighs the observations. In this case, even having only nine samples from three seasons and a set of water quality variables is complex enough that a standard MANOVA would produce a rank-deficient variance-covariance matrix. This deficiency would make the test statistically invalid and would forestall the computation of test statistics. By implementing PCA first, this collapses the original correlated parameters into a small number of orthogonal Principal Components (PCs). These factors detect maximal data variance while minimizing the number of variables to a level feasible within the limitations imposed by available degrees of freedom.

However, there are two major justifications for choosing MANOVA over multiple ANOVAs: the inflation of Type I error and keeping correlations between variables. Performing separate ANOVAs increases the possibility of detecting significant differences based on random chance alone, an effect known as multiplicity. More critically, water quality parameters like pH, temperature, and dissolved oxygen are inherently interdependent. MIC calculates the correlation between chemicals, and MANOVA does so across all seasons simultaneously as one big multivariate vector. It allows for the detection of subtle shifts across all parameters at once, information that individual ANOVAs (which would analyze each variable more or less independently) were likely to miss.

3.5.2 PCA dimensionality reduction and variance apportionment

The PCA reduced the detailed water-quality dataset into two orthogonal axes that together contained 91.65% of the total environmental variance. PC1 becomes the first dimension of change extracted, accounting for 60.05% of the observed variation within the data, as noted in the loadings part of Table 3. This component is marked by a clear divide between mineral-based properties and elements associated with organic enrichment. The dominant positive loadings for Total Alkalinity (0.371), Total Hardness (0.367), and Chemical Oxygen Demand (0.354) suggest a defining axis of ionic concentration and chemical oxidation potential. Total Dissolved Solids (-0.371), Sulphate (-0.365), and Chloride (-0.362) have much higher negative loadings than all other variables, which suggests that the first principal component (PC1) captured an important, large gradient in dissolved mineral inputs to Lake Merritt, which may vary with hydrological conditions during sampling periods.

PC2 captures an additional 31.6% of the variance in the system and acts as a primary indicator of physicochemical and thermal seasonality. Additionally, as indicated in Table 3, this component is positively affected by Temperature (0.479) and Nitrate-Nitrogen (0.279); however, it exhibits negative loadings at a notable extent for pH (-0.478), Dissolved Oxygen (-0.466), and Phosphate (-0.420). The significant inverse relationship between Temperature and Dissolved Oxygen on this axis reflects a known theory that higher thermal energy decreases gas solubility. Defaulting Nitrate and Temperature against Dissolved Oxygen and pH extremes, with respect to PC2, suggests that it expresses the biological and physical "pulse" of the waterbody correlated with the variations in nutrient availability as well as lower levels of oxygen in hotter seasons.

3.5.3 Multivariate significance of seasonal variation (MANOVA)

To determine if the visual patterns seen in Figure 9 represent statistically significant differences rather than stochastic noise, a Multivariate Analysis of Variance (MANOVA) was performed on the PC scores in Table 4. The analysis showed a Pillai's Trace of 1.9631 with an approximate F-statistic of 159.8 and a p-value of 2.697e-10. These results indicate significant multivariate differences among the sampled seasons. The near-zero p-value gives us the confidence to reject our null hypothesis, which proposes that each season would share the same hydrochemical "fingerprint".

The use of PC scores as the dependent variables in this model was a critical methodological step to ensure the analysis remained statistically identified, given the small sample size (n=9) relative to the original number of parameters (d=12).

3.5.4 Interpretation of seasonal clustering and biplot analysis

The findings are visually synthesized in Figure 8, which provides a seasonal division of the sampling units into three domains, which is further corroborated by the coordinate values found in the Scores section (Table 4). Samples from the Summer (Points 1-3) appear in the upper-right quadrant of Figure 1 with high positive scores on both PC1 and PC2, coinciding with vectors describing Temperature, Total Alkalinity, and COD. This trend suggests that the warmer months present a period with high organic loads and thermal stress. While the Monsoon samples are all grouped in the upper-left quadrant (Points 4-6) of the plot, and have a strong correlation with TDS, Sulphate, and Chloride vectors. Indicating that the monsoon has an important role in mineral and dissolved solids transport, probably due to the contribution from surface runoff into the aquatic system.

Lastly, the Winter samples, which are (Points 7~9), are located in the lower-right quadrant of Figure 8, showing a strong association with increased Dissolved Oxygen and pH levels, as can be seen from their negative PC2 score in Table 4. Related to this positioning, it shows the oxygen-rich and colder water conditions of winter, which is considered the season with the best physical indicators of quality in aquatic waters over the year. The spatial separation of these clusters in Figure 9 demonstrates that seasonal transitions exert a more profound influence on water chemistry than variations among individual sampling sites, as evidenced by the tight grouping of replicates within each seasonal territory.

3.5.5 Pearson correlation analysis

The Pearson correlation matrix provides a comprehensive quantitative assessment of the linear dependencies between the continuous water quality parameters monitored during this work. One of the matrix features is the expected tight inverse relationship between temperature and dissolved oxygen (DO) with a correlation coefficient r = -0.94. The fact that the solubility anomaly from the dataset agrees with the general principle that a warmer aquatic environment has lower solubility reinforces the physical reliability of this aspect of the study. In addition to that, a very strong positive correlation cluster is also detected between TA, TH, and COD with a high coefficient value of 0.99. This observation suggests geochemical or anthropogenic commonalities in terms of a source for these parameters, and that mineral concentration/organic loading changes in sets.

Conversely, the matrix shows extremely strong negative correlation between alkaline-mineral group (TA, TH) with dissolved solids group (TDS, Sulphate, and Chloride) as seen by correlation values from -0.94 to -1.00. This opposite dynamic implies an essential transformation in the chemical regime of the water, which may oscillate between a mineral-dominant and a runoff-dominated, high-salinity state. The presence of high positive correlations in the dissolved solids group (Chlorine, TDS, and Sulphate; r≥0.94) is interpreted as an inflow mechanism that acts similarly through surface runoff during the monsoon period. Moreover, Phosphate shows a strong positive correlation (r=0.91) with DO and is inversely correlated with Temperature (r=-0.97), reaching peaks in the colder, well-oxygenated winter samples. Such complex interdependencies are reminders that water quality is functionally dependent not on single variables but rather on coupled physicochemical cycles that differ greatly in response to seasonal context (Figure 9).

3.6 Water quality index calculation

Analysis of the calculated water quality index (WQI) values shows that upper catchment, indicating upper Sankh river has good water quality throughout the year with only marginal fluctuations during monsoon season. The modest seasonal differences in WQI may reflect variations in nutrient availability, runoff, and dilution during the sampling periods.; however, these values still remain within excellent range. The consistently low values of WQI indicate its minimal influences by human activities in a forested headwater catchment, which supports the findings from physicochemical analyses (Table 5).

3.7 Hierarchical clustering analysis

The natural groupings of the sampling units based on their cumulative physiochemical similarities are displayed in the Global Dendrogram of Seasonal Water Quality (Figure 10). Using Ward's minimum variance method with Euclidean distance, the clustering analysis grouped the nine samples into three distinct clusters that corresponded with the three sampled seasons. The Monsoon (4, 5, and 6) samples occupy a parasitic primary branch that indicates the monsoon cluster showed a distinct chemical pattern that is consistent with increased runoff-related mineral inputs. compared to other seasons.

The remaining samples are grouped into Summer (1, 2, and 3) and Winter (7, 8, and 9) clades; the Summer cluster shows the shortest internal distance between its members. The high level of internal homogeneity suggests that summer water quality is highly stable across replicates. Moreover, the dendrogram (Figure 10) independently confirms the seasonal clusters from MANOVA and PCA (Figure 8), providing further confidence that transitions between seasons are the key determinants of water quality variance well beyond stochastic sampling noise.

3.8 Ichthyofaunal diversity and functional structure

A total of seven fish species representing six families and four orders were recorded from upper catchment of the Sankh River based on pooled data from all three seasonal surveys. This suggests a low level of species richness, but the assemblage is functionally structured and indicative of a minimally disturbed fluvial system that is sourced from plateau systems (Figure 11).

3.8.1 Diversity indices (based on N = 140)

Total species (S) = 7

Total individuals (N) = 140

Species Proportions (pi)

 Shannon-Wiener Diversity Index (H′)

H′ = 1.78

Maximum Diversity (H_max)

Pielou’s Evenness (J′)

J′ = 0.91

The Shannon index value of 1.78 indicated moderate richness of species, resembling the tropical headwater stream with complex structural patterns. The evenness score, which equals 0.91, supports the absence of a dominant species in the community and indicates that habitat resources within the researched areas are stable and available (Table 6).

3.8.2 Functional guild classification

The most abundant group was rheophilic taxa at 42.8%, followed by moderate proportions of benthic and pool-associated species. The distribution emphasizes the effect of flow regime and substrate complexity as dominant controls on assemblage structure in upper catchment. These patterns are indicative of the ecohydrological dynamics dominant in a minimally disturbed forested catchment setting (Table 7).

1 Dominance of rheophilic taxa (42.8%) confirms high dissolved oxygen (>7 mg L⁻¹) and coarse lateritic substrate.

2 Moderate representation of benthic species reflects structural complexity.

3 Pool-associated species indicate seasonal habitat heterogeneity during low-flow conditions.

4 Absence of exotic taxa suggests minimal anthropogenic disturbance.

3.8.3 Functional structure pattern

The dominance of rheophilic taxa and an even distribution between benthic and pool-dwelling guilds support the perspective that flow regime and substrate complexity govern the assemblage structure in the upper Sankh River. Such a finding is consistent with the ecohydrological regulation principles identified among only slightly perturbed tropical headwater systems (Table 8).

4 Discussion

The present study demonstrates that the ecological integrity of the upper Sankh River headwater system is primarily regulated by the interaction of hydrogeomorphic setting, seasonal monsoonal hydrology, and extensive forest cover. Although seasonal fluctuations in hydrochemical characteristics were evident, the consistently excellent water quality, distinct multivariate seasonal separation, and structurally stable fish assemblages indicate that the upper catchment remains a minimally disturbed tropical plateau river ecosystem. Similar relationships between catchment geomorphology, hydrological regime, water chemistry, and ecological functioning have been reported for relatively undisturbed headwater rivers worldwide, where natural environmental controls dominate over anthropogenic influences (Poff et al., 1997; Bunn and Arthington, 2002; Meybeck, 2003; Allan, 2004). The present findings therefore reinforce the importance of integrating hydrogeomorphological, hydrochemical, biological, and landscape-level information when evaluating the ecological integrity and long-term conservation of tropical headwater systems.

The hydrochemical characteristics indicate that natural geogenic processes are the dominant factors driving the system, with a minor anthropogenic influence. The key attributes include low ionic strength, near-neutral to weakly alkaline pH, and high dissolved oxygen concentrations. Such characteristics are typical of forested headwater streams, where rock weathering and soil-water interactions control solute proportions (Gibbs, 1970; Wetzel, 2001; Meybeck, 2003). Similar trends have been noted even in relatively pristine catchments, where land cover types play an important role in protecting water quality (Allan, 2004; Li et al., 2008).

Monsoonal hydrology drives dilution, runoff, and solute transport processes resulting in seasonal variation in water chemistry. In the monsoon season, runoff is high with increased dissolved solids and nutrients, while in summer and winter, low-flow conditions cause concentration effects. This seasonal hydrogeochemical behavior has been described in heterogeneous river systems (Singh and Hasnain, 1999; Singh et al., 2004; Shrestha and Kazama, 2007; Setia et al., 2021). Both biological and physical processes drive these dynamics, which affect key parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD), and major ion concentrations (Pejman et al., 2009; Garizi et al., 2011; Bojarczuk et al., 2018). Therefore, the observed inverse temperature-DO relationship in this study would also show consistency with established physicochemical and biological principles dictating oxygen solubility (Wetzel, 2001).

A multivariate statistical analysis elucidated the dominant factors that are impacting water quality. Application of Principal Component Analysis (PCA) shows that hydrochemical variability is structured along two primary axes: lithogenic and seasonal physicochemical gradients. Nonetheless, caution is needed when interpreting PCA as statistical association does not mean direct causation (Shrestha and Kazama, 2007; Hair et al., 2019). The measured correlations between alkalinity, hardness, COD, and dissolved solids imply that such conditions fluctuate between mineral-dominated pathways versus runoff-influenced pathways as previously described by models of weathering-controlled river chemistry (Meybeck, 2003; Li et al., 2008).

The clear separation of seasonal clusters in PCA space, supported by MANOVA results (p < 0.001), further suggests that temporal variance holds greater influence on hydrochemistry than spatial differences within the study area. This concurrent consistency among PCA, correlation analysis, and hierarchical clustering provides strong evidence that seasonal discharge patterns are the major drivers of water quality variation.

The socio-ecological characteristics of the upper catchment further support the observed ecological integrity. Indigenous settlements remain closely associated with protected Sal forests and predominantly rain-fed agriculture, resulting in relatively low land-use intensity. This landscape configuration likely contributes to reduced sediment disturbance, enhanced infiltration, and maintenance of water quality through continued ecosystem functioning. Similar relationships between forest cover, traditional land use, and river ecosystem health have been reported in tropical headwater catchments where intact forests regulate runoff, nutrient retention, and aquatic habitat quality (Allan, 2004; Foley et al., 2005; Berkes, 2012).

Ecological observations support these findings. The ichthyofaunal assemblage was moderately diverse (H′ = 1.78) and highly even (J′ = 0.91), with a dominance of rheophilic species, indicating well-oxygenated, low-disturbance flowing-water conditions (Matthews, 1998; Sarkar et al., 2011). The composition of the assemblage corresponds to the River Continuum Concept, which hypothesizes a functional organization of aquatic communities along many environmental gradients (Vannote et al., 1980).

WQI values were consistently good in all seasons, which further strengthens the very low anthropogenic pressure results of the upper catchment. The comparison of specific physicochemical parameters with WQI also helps in assessing the river's overall health (Tyagi et al., 2020; Ali et al., 2024).

At the catchment scale, forest cover is critical to runoff regulation as well as erosion control and aquatic biodiversity. The sal-pine silvopastoral to forest-agriculture mosaic observed in the upper Sankh basin ensures hydrological stability and promotes nutrient retention which is essential for hydrochemical balance (Allan, 2004; Foley et al., 2005; Rodríguez-Romero et al., 2018). Sustainable land use practices and participatory local resource management are crucial to maintaining the ecological integrity of these headwater systems (Berkes, 2012).

Overall, the upper Sankh River provides a minimally disturbed headwater system dominated by lithogenic processes that work in conjunction with seasonal monsoonal hydrology to shape hydrochemistry and ecology. The insights forged herein can be further illuminated in future work with greater temporal frequency, broader spatial coverage, and long-term monitoring to advance understanding of ecohydrological processes for watershed management.

Study Limitations

There are certain limitations to consider when interpreting this study's findings. The hydrochemical and statistical assessments are based on a small sample size (n = 9) derived from composite seasonal sampling. Although this approach captures some integrated conditions, it limits spatial generalization and the capacity to observe fine-scale heterogeneity. In addition, multivariate methods like PCA and MANOVA identify statistical relationships but not causal connections. Though based on available observational data, the ichthyofaunal assessment is preliminary. Studies with additional spatial replication, higher sampling frequencies, and long-term monitoring would allow a more complete exploration of the dynamics within our system.

5 Conclusion

The upper Sankh River represents an undisturbed lateritic plateau headwater system strongly impacted by lithology and monsoonal hydrology. Hydrochemical variability is seasonal and follows known dilution-concentration models, while changes through time account for more than spatial differences according to multivariate analyses. The combined implementation of Principal Component Analysis (PCA) and Multivariate Analysis of Variance (MANOVA) successfully identified key environmental gradients influencing water quality. The makeup of fish species, in addition, highlights the stability and well-oxygenated state of the ecosystem, a characteristic feature of intact headwater streams. Low values of the Water Quality Index confirmed the good quality of the water and its little anthropogenic impact. The strong association between forest cover and hydrological regulation highlights the ecological importance of this catchment area.

Author Contributions

BB: Conceptualization, field investigation, data collection, socio-ecological surveys, ichthyofaunal assessment, data curation, and manuscript drafting. DG: Statistical analysis, data interpretation, multivariate analysis, visualization, and critical revision of the manuscript. SD: GIS Mapping, socio-ecological surveys. SKV: data interpretation, manuscript review, SG: Supervision, conceptual guidance, methodological oversight, interpretation of findings, manuscript review, and final revision.

Acknowledgements

The authors sincerely thank Miss Margaret Kujur for her valuable assistance and cooperation during the field investigations and data collection.

Data availability statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Ali M.S., Begum S., Rabbi F.M., Sumaia Hasan M.K., Rahman M.A., Rahman M.M., and Rahaman M.H., 2024, Multivariate analysis of water quality in the Dhaleshwari River, Bangladesh: Identifying pollution sources and environmental implications, Water Practice and Technology, 19(10): 4128-4147.

https://doi.org/10.2166/wpt.2024.251

Allan J.D., 2004, Landscapes and riverscapes: The influence of land use on stream ecosystems, Annual Review of Ecology, Evolution, and Systematics, 35: 257-284.

https://doi.org/10.1146/annurev.ecolsys.35.120202.110122

Allan J.D., and Castillo M.M., 2007, Stream ecology: Structure and function of running waters, 2nd ed., Springer.

American Public Health Association, 2017, Standard methods for the examination of water and wastewater, 23rd ed., APHA.

Benesty J., Chen J., Huang Y., and Cohen I., 2009, Pearson correlation coefficient, In: Springer topics in signal processing, Springer, pp. 1-4.

https://doi.org/10.1007/978-3-642-00296-0_5

Berkes F., 2012, Sacred ecology, 3rd ed., Routledge.

https://doi.org/10.4324/9780203123843

Bojarczuk A., Jelonkiewicz Ł., and Lenart-Boroń A., 2018, The effect of anthropogenic and natural factors on the prevalence of physicochemical parameters of water and bacterial water quality indicators along the river Białka, southern Poland, Environmental Science and Pollution Research, 25(10): 10102-10114.

https://doi.org/10.1007/s11356-018-1212-2

Boyero L., Pearson R.G., Dudgeon D., Graça M.A.S., Gessner M.O., Albariño R.J., Ferreira V., Yule C.M., Boulton A.J., Arunachalam M., Callisto M., Chauvet E., Ramírez A., Chará J., Moretti M.S., Gonçalves J.F., Helson J.E., Chará-Serna A.M., Encalada A.C., and Pringle C.M., 2011, Global distribution of a key trophic guild contrasts with common latitudinal diversity patterns, Ecology, 92(9): 1839-1848.

https://doi.org/10.1890/10-2244.1

Brown R.M., McClelland N.I., Deininger R.A., and O'Connor M.F., 1973, A water quality index-crossing the psychological barrier, Water and Sewage Works, 117(10): 339-343.

Buffington J.M., and Montgomery D.R., 1997, A systematic analysis of eight decades of incipient motion studies, with special reference to gravel-bedded rivers, Water Resources Research, 33(8): 1993-2029.

https://doi.org/10.1029/96WR03190

Bunn S.E., and Arthington A.H., 2002, Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity, Environmental Management, 30(4): 492-507.

https://doi.org/10.1007/s00267-002-2737-0

Champion H.G., and Seth S.K., 1968, A revised survey of the forest types of India, Government of India Press.

Colvin S.A.R., Sullivan S.M.P., Shirey P.D., Colvin R.W., Winemiller K.O., Hughes R.M., Fausch K.D., Infante D.M., Olden J.D., and Bestgen K.R., 2019, Headwater streams and wetlands are critical for sustaining fish, fisheries, and ecosystem services, Fisheries, 44(2): 73-91.

https://doi.org/10.1002/fsh.10229

Daniels R.J.R., 2002, Freshwater fishes of peninsular India, Universities Press.

Foley J.A., DeFries R., Asner G.P., Barford C., Bonan G., Carpenter S.R., Chapin F.S., Coe M.T., Daily G.C., Gibbs H.K., Helkowski J.H., Holloway T., Howard E.A., Kucharik C.J., Monfreda C., Patz J.A., Prentice I.C., Ramankutty N., and Snyder P.K., 2005, Global consequences of land use, Science, 309(5734): 570-574.

https://doi.org/10.1126/science.1111772

Freeman M.C., Pringle C.M., and Jackson C.R., 2007, Hydrologic connectivity and the contribution of stream headwaters to ecological integrity at regional scales, Journal of the American Water Resources Association, 43(1): 5-14.

https://doi.org/10.1111/j.1752-1688.2007.00002.x

Fricke R., Eschmeyer W.N., and Van der Laan R., 2024, Eschmeyer's catalog of fishes, California Academy of Sciences.

Gabriel K.R., 1971, The biplot graphic display of matrices with application to principal component analysis, Biometrika, 58(3): 453-467.

https://doi.org/10.1093/biomet/58.3.453

Garizi A.Z., Sheikh V., and Sadoddin A., 2011, Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods, International Journal of Environmental Science and Technology, 8(3): 581-592.

https://doi.org/10.1007/BF03326244

Gibbs R.J., 1970, Mechanisms controlling world water chemistry, Science, 170(3962): 1088-1090.

https://doi.org/10.1126/science.170.3962.1088

Gupta N., Singh S., Sharma R., Kumar A., and Verma P., 2022, Spatial and temporal disparity of fish assemblages and their association with environmental factors in Himalayan rivers of Jammu and Kashmir, Current World Environment, 17(3): 620-632.

https://doi.org/10.12944/CWE.17.3.13

Hair J.F. Jr., Black W.C., Babin B.J., and Anderson R.E., 2019, Multivariate data analysis, 8th ed., Cengage.

Horton R.E., 1945, Erosional development of streams and their drainage basins, Geological Society of America Bulletin, 56(3): 275-370.

https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2

Horton R.K., 1965, An index number system for rating water quality, Journal of the Water Pollution Control Federation, 37: 300-306.

Jolliffe I.T., 2002, Principal component analysis, 2nd ed., Springer.

Kale V.S., 2014, Landscapes and landforms of India, Springer.

https://doi.org/10.1007/978-94-017-8029-2

Lakra W.S., Sarkar U.K., Gopalakrishnan A., and Kathirvelpandian A., 2010, Threatened freshwater fishes of India, National Bureau of Fish Genetic Resources.

Legendre P., and Legendre L., 2012, Numerical ecology, 3rd ed., Elsevier.

Li S., Gu S., Liu W., Han H., and Zhang Q., 2008, Water quality in relation to land use and land cover in the upper Han River basin, China, CATENA, 75(2): 216-222.

https://doi.org/10.1016/j.catena.2008.06.005

Lowe W.H., and Likens G.E., 2005, Moving headwater streams to the head of the class, BioScience, 55(3): 196-197.

https://doi.org/10.1641/0006-3568(2005)055[0196:MHSTTH]2.0.CO;2

Matthews W.J., 1998, Patterns in freshwater fish ecology, Springer.

https://doi.org/10.1007/978-1-4615-4066-3

Meybeck M., 2003, Global analysis of river systems: From Earth system controls to Anthropocene syndromes, Philosophical Transactions of the Royal Society B: Biological Sciences, 358(1440): 1935-1955.

https://doi.org/10.1098/rstb.2003.1379

Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being: Synthesis, Island Press.

Montgomery D.R., 1999, Process domains and the river continuum, Journal of the American Water Resources Association, 35(2): 397-410.

https://doi.org/10.1111/j.1752-1688.1999.tb03598.x

Murtagh F., and Legendre P., 2014, Ward's hierarchical agglomerative clustering method: Which algorithms implement Ward's criterion?, Journal of Classification, 31(3): 274-295.

https://doi.org/10.1007/s00357-014-9161-z

Naiman R.J., Décamps H., and McClain M.E., 2005, Riparia: Ecology, conservation, and management of streamside communities, Elsevier.

https://doi.org/10.1016/B978-012663315-3/50010-1

Narasimmarajan K., Atkore V., Palanivel S., Mathai M.T., and Johnson J.A., 2025, Fish diversity and habitat preference of endemic and threatened freshwater fishes of the Moyar River, Western Ghats, India, Tropical Ecology, 66(4): 611-621.

https://doi.org/10.1007/s42965-025-00405-9

Pejman A.H., Nabi Bidhendi G.R., Karbassi A.R., Mehrdadi N., and Esmaeili Bidhendi M., 2009, Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques, International Journal of Environmental Science and Technology, 6(3): 467-476.

https://doi.org/10.1007/BF03326086

Pielou E.C., 1966, The measurement of diversity in different types of biological collections, Journal of Theoretical Biology, 13: 131-144.

https://doi.org/10.1016/0022-5193(66)90013-0

Poff N.L., Allan J.D., Bain M.B., Karr J.R., Prestegaard K.L., Richter B.D., Sparks R.E., and Stromberg J.C., 1997, The natural flow regime, BioScience, 47(11): 769-784.

https://doi.org/10.2307/1313099

Rencher A.C., and Christensen W.F., 2012, Methods of multivariate analysis, 3rd ed., Wiley.

https://doi.org/10.1002/9781118391686

Rodríguez-Romero A.J., Rico-Sánchez A.E., Mendoza-Martínez E., Gómez-Ruiz A., Sedeño-Díaz J.E., and López-López E., 2018, Impact of changes of land use on water quality, from tropical forest to anthropogenic occupation: A multivariate approach, Water, 10(11): 1518.

https://doi.org/10.3390/w10111518

Root R.B., 1967, The niche exploitation pattern of the blue-gray gnatcatcher, Ecological Monographs, 37(4): 317-350.

https://doi.org/10.2307/1942327

Sarkar U.K., Pathak A.K., Sinha R.K., Sivakumar K., Pandian A.K., Pandey A., Dubey V.K., and Lakra W.S., 2011, Freshwater fish biodiversity in the River Ganga (India): Changing pattern, threats and conservation perspectives, Reviews in Fish Biology and Fisheries, 22(1): 251-272.

https://doi.org/10.1007/s11160-011-9218-6

Schumm S.A., 1956, Evolution of drainage systems, Geological Society of America Bulletin, 67(5): 597-646.

https://doi.org/10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2

Setia R., Lamba S., Chander S., Kumar V., Singh R., Litoria P.K., Singh R.P., and Pateriya B., 2021, Spatio-temporal variations in water quality and hydrochemistry of a perennial river in India, Applied Water Science, 11(169).

https://doi.org/10.1007/s13201-021-01504-3

Shannon C.E., and Weaver W., 1949, The mathematical theory of communication, University of Illinois Press.

Shrestha S., and Kazama F., 2007, Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji River basin, Japan, Environmental Modelling and Software, 22(4): 464-475.

https://doi.org/10.1016/j.envsoft.2006.02.001

Singh A.K., and Hasnain S.I., 1999, Environmental geochemistry of the Damodar River basin, India, Environmental Geology, 37(1-2): 124-136.

https://doi.org/10.1007/s002540050369

Singh K.P., Malik A., Mohan D., and Sinha S., 2004, Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of the Gomti River (India): A case study, Water Research, 38(18): 3980-3992.

https://doi.org/10.1016/j.watres.2004.06.011

Strahler A.N., 1952, Hypsometric analysis of erosional topography, Geological Society of America Bulletin, 63(11): 1117-1142.

https://doi.org/10.1130/0016-7606(1952)63[1117:HAAOET]2.0.CO;2

Talwar P.K., and Jhingran A.G., 1991, Inland fishes of India and adjacent countries, Vols. 1-2, Oxford and IBH Publishing.

Tyagi S., Sharma B., Singh P., and Dobhal R., 2020, Water quality assessment using water quality index, American Journal of Water Resources, 1(3): 34-38.

https://doi.org/10.12691/ajwr-1-3-3

Valdiya K.S., 2016, The making of India: Geodynamic evolution, Springer.

https://doi.org/10.1007/978-3-319-25029-8

Vannote R.L., Minshall G.W., Cummins K.W., Sedell J.R., and Cushing C.E., 1980, The river continuum concept, Canadian Journal of Fisheries and Aquatic Sciences, 37(1): 130-137.

https://doi.org/10.1139/f80-017

Ward J.H., 1963, Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 58(301): 236-244.

https://doi.org/10.1080/01621459.1963.10500845

Wetzel R.G., 2001, Limnology: Lake and river ecosystems, 3rd ed., Academic Press.