Multivariate statistical analysis and Geospatial approach for evaluating Hydro-geochemical characteristics of meltwater from Shaune Garang Glacier, Himachal Pradesh, India

Multivariate statistical analysis and Geospatial approach for evaluating Hydro-geochemical characteristics of meltwater from Shaune Garang Glacier, Himachal Pradesh, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivariate statistical analysis and Geospatial approach for evaluating Hydro-geochemical characteristics of meltwater from Shaune Garang Glacier, Himachal Pradesh, India Ramesh Kumar, Rajesh Kumar, Anshuman Bhardwaj, Atar Singh, Shaktiman Singh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1440542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The study focuses on the hydro-geochemistry of Shaune Garang glacier's meltwater concerning glacial geomorphology. Seventy-nine water samples (53 in 2016 and 26 in 2017) of ablation season were analyzed. The cations were dominant in the order Ca 2+ > Mg 2+ > Na + > K + , and the anions in the order HCO 3 − > SO 4 2− > Cl − > NO 3 − . Ca 2+ . The result demonstrated that HCO 3 − were the abundant ions, accounting for 41.03 and 34.84% of the total ionic budget (TZ). The high ionic proportions of (Ca 2+ +Mg 2+ ) versus TZ + and (Ca 2+ +Mg 2+ ) versus (Na + +K + ) were identified as the primary factors influencing dissolved ion chemistry in meltwater. Piper diagram shows that Ca 2+ – HCO 3 − type water is the most common, followed by Mg 2+ – HCO 3 − . In addition, a remote sensing approach has been used to find the possible source of the chemical constituents in the meltwater. The catchment geology has been mapped on various scales, including diverse rocks and unconsolidated surface materials containing "quartz and carbonate minerals". Layered silicates (LS) and "hydroxyl-bearing minerals" are not as common as they used to be, but their availability varies greatly in the area where they are found. The distribution of Layered silicate (LS) minerals within the catchment are majorly found at lower altitudes, which implies the weathering mechanism due to the interaction of meltwater and parental rock. Multivariate analysis revealed that CO 3 and SiO 2 weathering, sulphate dissolution, and pyrite oxidation dominate dissolved ion concentrations. Chemometric analysis of meltwater hydro-geochemistry through Principal Component Analysis (PCA) explains 72.1% of the total variance of four PCs. PCs 1, 2, 3 and 4 explain 39.21%, 12.91%, 10.24% and 9.74% of variance respectively in 2016. Similarly, in 2017, four PCs explain 69.9.1% of the total variance. PC 1, 2, 3 and 4 can explain 26.62%, 20.12%, 12.64% and 10.52% of variance. Hydro-geochemistry Geological mapping Chemometric analysis Shaune Garang glacier Hydroxyl-bearing minerals Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction The Hindu Kush Himalayan Mountain ranges and the Tibetan Plateau extend across several Asian countries (Bolch et al., 2012) and are recognized as the cryospheric region beyond the polar latitudes (Bolch et al., 2019; Wood et al., 2020). The Hindu Kush Himalayas, the source of many essential river systems in Asia, are the highest mountain range (Kaser et al., 2010; Singh et al., 2020; Immerzeel et al., 2020), which provide a source of livelihood for millions of people living in this region. The Hindu Kush Himalaya, also known as the 'Third Pole' and the 'water tower of Asia,' provides ecosystem services, particularly water, the lifeblood of all organisms. The ecosystem services produced in the region provide support for 240 million people who live in the hills and mountains and approximately 1.7 billion people who live downstream in the major river basins. The increasing metropolitan infrastructure in the Himalayan region in the last three decades has put additional strain on the region's limited water resources (Tiwari et al., 2018). Other than natural forces, the primary causal variables in the Himalayas include extraordinary urbanization, increasing tourism, shifting land use patterns, garbage disposal, and agricultural runoff are all factors to consider (Thakur et al., 2019; Kumar et al., 2019). Snow/glaciers meltwater is a vital water source for domestic use in the higher Himalayas. At the same time, it is essential for irrigation, industrial use, and hydroelectric power generation for the downstream population (Singh et al., 2008). When the other water sources are low in supply, the glacier discharge plays a more prominent role in sustaining the demand (Kumar et al., 2016; Singh et al., 2022). The meltwater drains from the glaciers meet the various soil particles and are subsequently influenced by the various mineral components. According to recent research, climate and land-use/cover changes caused by human endeavours significantly impact the chemical composition of Himalayan freshwater (Pant et al., 2021; Thapa et al., 2020). Hydrogeochemical characterization of the glacier meltwater varies between glaciers due to differential lithology (Collins, 1979). When it comes to glacier meltwater chemistry and, consequently, the quality of meltwater, the geology of the catchment is one of the most important factors to consider. Such studies also improve our understanding of the interactions of meltwater with the underlying geological strata and provide insights into the hydro-climatic regime of a region. There is a need to conduct more research in these areas to create a robust database to determine hydrochemistry confidently. The widely varying geology makes the hydro-chemical study of Himalayan glaciers even more exciting. Earlier studies on the chemistry of the major ions in the Himalayas engrossed in recognizing chemical input in glacial meltwater. Various studies of Himalayan glaciers have suggested that water transients over sub-glacial waterways encounter rock substrate experiencing a significant chemical change in meltwater (Singh et al., 2015, 2017). Some studies specify that the high degree of differential erosion in Himalayan glaciated areas is the consequence of the long interaction time of meltwater through bedrock (Haritashya et al., 2010; Singh et al., 2015). Investigating hydrochemistry in the Himalayas is highly important, and several researchers (Haritashya et al., 2010; Singh et al., 2015; Kumar et al., 2019) have contributed to this regard. All the previous studies in the Himalayan region on water chemistry have focused only on the chemical characterization of meltwater from glaciers. Documentation of such investigations substantially allows precise lithological mapping and erosion rate estimation in these high-mountain areas. This helps in understandig large-scale links between glacio-hydrology and geochemistry. Considering possible sources and their differential erosion rates can give us a better idea about the possible future trajectory of meltwater chemistry in these high-mountain catchments. Photogrammetry, high-resolution terrain modelling and hyperspectral imaging are used in lithological mapping and estimation of erosion rates in high mountains. Further, it helps establish large-scale links between glacio-hydrology and geochemistry on a global scale. In the present study, the remote sensing approach has been used to identify chemical species' sources of origin in meltwater through the catchment scale lithological mapping, mineral detection, and many other perspectives. Also, ASTER-SWIR data were used to delineate types of rocks in the Shaune Garang catchment to interpret and measure minerals and the geochemical composition of debris on the glacial surfaces. This study validates several features of ASTER (SWIR and TIR) data and their processing techniques; analysis has been used to map several minerals containing indices. These exploration approaches are suitable for low-cost identification and mapping of minerals containing indices such as layered silicates (LS), Calcite (CA), Hydroxyl bearing (OH), Alunite (AL) on the SWIR band and carbonate, quartz, and mafic index on TIR band in ASTER data. In this research, we focus on identifying (i) major ion concentrations, chemical characteristics, and their variability; (ii) hydro-geochemical processes and solute sources during the study period; and (iii) lithological mapping of the Shaune Garang catchment. Hence, the results of this study enrich the catchment findings in terms of glacio-hydrology and geochemistry. Furthermore, this would be a significant step towards a more precise interpretation of weathering and hydrological processes in the catchment. This research includes applying various chemometric analyses such as principal component analysis (PCA) and factor analysis to understand better the dominant weathering process in the Shaune Garang catchment. Study Area The hydro-geochemical analysis has been performed in the Shaune Garang glacier catchment, located in Himachal Pradesh. The glacial meltwater of this catchment merges to the Baspa River, a stream of the Sutlej River and finally joins the Indus River system. The study area has been presented in Figure 1, where locations of the instruments like the Automatic Weather Station (AWS) discharge gauge have also been shown. The watershed and glaciated area in the catchment have also been presented along with the contour map. This catchment receives precipitation from the winter westerlies and the monsoon in summer (Singh et al. 2018), but summer precipitation dominates winter precipitation. The winter precipitation in this region is received through the Western Disturbance (WD) (Dimri 2004). This region experiences ablation from May to September (Kumar et al., 2016). It covers an area of 38.13 km 2 above the discharge site. The hypsometric distribution of the catchment is presented in Figure 2. Hypsometry distribution clearly shows that 75% of the catchment has a non-glaciated area while 25% is glaciated. In the glaciated area, the debris-covered glacier has its share of 5%, while the debris-free glacier has 20%. The rocks of this region resemble the Higher Himalayan Crystalline. It contains pelitic and psammopelitic meta-sediments having acid and basic intrusive. Granite and gneiss rocks in the Himalayan region have a familiar presence of late-stage pegmatitic veins feldspar (Kumar et al., 1987). Rohtang gneiss is the principal constituent of the Himalayan glaciers. Chalcopyrites' presence is also noticed in lateral morainic deposits of the Himalayas. The morphology of the Shaune Garang catchment describing the area vs aspect and area vs slope has been shown in Figure 3. The aspect ratio suggests that most of the part of the catchment falls in the East and South, Southwest and West direction and receives higher solar insolation, making it prone to weathering. Further, the primary area falls in the middle slope zone of 24° to 40° and the higher slope zone 40° to 56°, which transfers the weathered materials very quickly to the glacier. Sampling and analysis protocol In the higher Himalayas, the physical accessibility is limited to the summer season. Water samplings were performed during the summer seasons of 2016 and 2017 at the selected locations of the channel (Figure 1). Selected cations and anions of glacial meltwater were examined in 2016 and 2017. Fifty-three water samples in 2016 and 26 water samples in 2017 were collected during ablation season. The sampling sites were chosen as per the geology, altitude, terrain, and convergence of tributaries. Polyethylene sample bottles (250 ml) were rinsed with distilled water after being cleaned with nitric acid for accuracy in data. Water samples were taken from 20 cm deep to have a well-mixed concentration. Sampled glacial meltwater was filtered on 0.45 µm Whatman filter paper. The conductivity and pH measurements have been performed in the field by a handheld multi-parameter instrument (HANNA, model No. HI9829). The essential cations (Ca 2+ , Mg 2+ , K + and Na + ) analyses were performed using Atomic Absorption Spectroscopy. The instrument has a precession of 0.05 parts per million (ppm) for Ca 2+ , Mg 2+ , K + , Na + , and 0.01 parts per million (ppm) for the remaining parameters. Ion chromatography was used to analyze the anions (Cl − , SO 4 2− and NO 3 − ). The bicarbonate concentration was based on the results of the titration method (APHA 2005) and the charge balance method. The precautions were made as per the specified norms, and we used non-powder vinyl cleanroom gloves and masks for sampling and analysis. A new standard of identified concentration and procedural blank was analyzed for every analytical run. During the investigation, no detectable contamination was obtained. The data from physio-chemical analysis of the meltwater samples were subjected to multivariate statistical analysis. The study used multivariate statistical tests to examine relationships among multiple variables in the data set. Excel add-on XLSTAT was used for the analysis of normalized data under PCA. Bartlett's sphericity test was applied to both years' melting season data, and a correlation matrix was prepared. The principal component analysis has been analyzed to understand problems under different measurement scales of the original variable, which is avoided by diagonalizing the correlation matrix. The dissolved ions composition of the meltwater from Shaune Garang glacier were subjected for the error analysis in the charge balance, which has been computed through the given formula: Equation 1 Where, (TZ + ) = total cations and (TZ - ) = total anions The errors of TZ + and TZ - were < 10 % for two consecutive melting periods (2016 and 2017), which is indicative of the good quality of data. Remote sensing data and characteristics Air temperature of glacier surface was measured in ablation season (June to October) during the study period. Satellite data has been validated by the ground-based temperature records of snow, ice, and debris-covered glacier at twenty sites during the satellite passes at 10:30 AM over the study area. ASTER data were used in this investigation to obtain lithological and mineralogical evidence from the Shaune Garang catchment. A subset of the high-resolution and cloud-free images (ASTER) were used throughout the study to determine the precise lithological mapping of the exposed rock present in the catchment. The imagery of nine bands was used in this study, with band-1 being blue (0.43 - 0.45 µm), band-2 being blue (0.45 - 0.51µm), band-3 being green (0.53 - 0.59 µm), band-4 being red (0.64 - 0.67 µm), band-5 being near-infrared (0.85 - 0.88 µm), band-6 being shortwave infrared (1.57 - 1.65 m), band-7: shortwave infrared (2.11 - 2.29 µm), band-8: panchromatic (0.50 - 0.68 µm), and band-9: cirus (1.36 - 1.38 µm) were used. Respective bandwidths and subsystems characteristics are presented in Table1. Result And Discussion Hydrogeochemistry of glacial meltwater The physical analysis of meltwater indicates a little alkaline with pH ranging from 6.86-8.56 with an average value of 7.45 ±0.48 and 7.45 ±0.45 for 2016 and 2017, respectively (Table 2). Higher pH indicates that the process of dissolution is higher due to the more considerable contact period with rock, soil and rainwater. These would have imparted alkalinity to the meltwater (Kumar et al. 2014). Electrical conductivity indirectly measures the mineralization that explains the ionic strength of water (Kumar et al., 2019). The standard value of electrical conductivity was 86.14 ±16.96 μS/cm in 2016 and 91.25 ±16.62 μS/cm in 2017. The higher EC must result from the weathering, evaporation, and crystallization processes. Furthermore, the lesser conductivity in glacier discharge is influenced by increased precipitation making higher discharge and decreased influence of the evaporite dissolution process. The measured value of EC suggests that hydrochemistry of location is regulated through the interface of water and rock and depends on the weathering of rocks. Different rocks and their solubility influence the proportional concentration of ions in glacial meltwater (Pant et al. 2021). Table 2 displays the distribution of dissolved ionic concentrations with standard deviation in the meltwater discharge of the Shaune Garang glacier. The results presented in Table 2 indicate that Ca 2+ contributes 39.57% and 42.53 % in the total cationic budget in both the consecutive study periods 2016 and 2017. However, Ca 2+ + Mg 2+ contributes 82.10 % and 71.02 % of the total cationic budget in the catchment. The other two cations, Na + and K + , contribute only 16.78 % and 17.57 %, respectively, during the study period 2016 and 2017. Bicarbonate (HCO 3 - ) is the most dominant anion contributing 62.18 % and 54.44 %, respectively, in the total anionic budget of the ablation period of 2016 and 2017. Its average concentration was observed as 369.65 ±79.41 µeq/l, and 316.73 ±83.23 µeq/l in the consecutive study period. Sulphate (31.10% and 38.11 %) was the second most dominant anion, followed by chloride (5.58% and 6.64 %) and Nitrate (1.18% and 0.79 %) during consecutive years' observation. The dominance of bicarbonate in Shaune Garang Catchment is due to the silicate dominating geomorphology of the catchment. According to the findings, weathering of silicate minerals is less visible than carbonate minerals. Figure 4 displays the concentration of different anions and cations and electrical conductivity for the glacier's meltwater at different parts of the Indian Himalaya. The concentration of cations and anions varies as per the morphology of rocks in the catchments and weather system. Meltwater draining from the Himalayan region shows the dominance of Ca 2+ and HCO 3 - , whereas Bagni, Chaturangi, Gangotri and Dudu glaciers demonstrate the dominance of SO 4 2- in their catchment. The dominating presence of silicate-bearing rocks is the important factor for the higher concentration of bicarbonate (HCO 3 - ) in the meltwater of the Himalayan glacier. In addition, the dominancy of SO 4 2- in the Bagni, Chaturangi, Gangotri and Dudu glaciers could be due to pyrite oxidation that enhances sulphate concentration. Cl − and SO 4 2- ' domination is influenced by halite and sulfide oxidation and weathering of soft sulphate minerals such as gypsum (Thomas et al., 2015). The chemical composition analysis reflects the dominance of bicarbonate (HCO 3 - ) as an anion in most of the glacial meltwater in the Himalayan region due to the dissolution of atmospheric carbon dioxide and carbonate (Sharma et al., 2013; Kumar et al., 2014; Singh et al., 2015). Concentration of cations in the meltwater of Dokariani, Bara Shigri and Gangotri glaciers follows a trend like Ca 2+ > Mg 2+ > K + > Na + while it follows a trend of Ca 2+ > Mg 2+ > Na + > K + for meltwater of Kafni and Chhota Shigri, the only alteration in the concentration of Na + and K + for different basins. However, the Bagni glacier meltwater showed the Potassium ion as the second most abundant. In the Bara Shigri glacial, meltwater concentration of cations varied as Ca 2+ > Mg 2+ > Na + > K + like the Chhota Shigri of its vicinity and the Kafni of Kumanyu Himalaya whereas anions concentration followed the pattern of HCO 3 - > SO 4 2- > NO 3 - . It has been observed from the comparative analysis in Figure 4 that the central Indian Himalayan glacier's meltwater has the highest concentration of Ca 2+ cation, and a similar observation is from the present study. Interestingly, the graph shows a higher concentration of anions and cations in discharge from glaciers located in the central part of the Indian Himalayan region than the glaciers in the western part. Hydro‑geochemical process in the glacial catchment Dissolved ions in the glacial meltwater are generally contributed through rock weathering, precipitation, anthropogenic influence, and atmospheric conditions (Jeelani 2011; Kumar et al. 2019). Generally, the chemical composition of glacial meltwater is governed by the chemical weathering between the interaction of water and bedrock beneath the glacier (Kumar et al., 2009; Kumar et al., 2014; Singh et al., 2017). The cation and anion in the glacier discharge are elucidated concerning the nature of rock and its weathering processes. Dissolved solute particles present in the glacial melt are determined by the processes involved in the glacial environment. The interrelationship between the physical and chemical parameters are presented through a scatter plot (Figure 5) of (Ca 2+ +Mg 2+ ) and (Na + +K + ) against total cation (TZ + ). A positive correlation is observed between (Ca 2+ +Mg 2+ ) and TZ + . It further shows a ratio ranging from 0.75 to 0.85 with an average equivalent value of 0.75 ±0.05 during the study period. The result demonstrates that the impact of Ca 2+ +Mg 2+ in the glacial meltwater is comparatively high compared to the total cation TZ + . The ratio of Ca 2+ and Mg 2+ determines the input source of calcium and magnesium ions in water. Ca 2+ /Mg 2+ ≤ 1 (Table 3) indicates a process of dolomite dissolution, and a value >1 recommends the dominance of silicate weathering in water (Kumar and Singh 2015). As a result, silicate weathering could be a factor in the dominant concentration of Ca 2+ and Mg 2+ among cations in the Shaune Garang glacial discharge. The scatter plot (Figure 5) among Na + +K + and TZ + displays a small contribution of Na + +K + in total dissolved ion with 0.24 ±0.04 and 0.25 ±0.08 during both ablation years. The results reveal carbonate weathering as a leading factor in the glacial meltwater ionic characteristics of the Shaune Garang catchment. The large equivalent ratios 3.23 ±0.75 and 3.28 ±1.14 for (Ca 2+ +Mg 2+ )/(Na + +K + ) in the consecutive melting period of 2016 and 2017 further strengthen the understanding of carbonate weathering dominance in the catchment. High ratio of (Ca 2+ +Mg 2+ )/(Na + +K + ) and (Ca 2+ +Mg 2+ )/TZ + in the glacial meltwater demonstrate that hydro-geochemistry of the meltwater of Shaune Garang catchment is mainly administrated by CO 3 weathering with a minor contribution of SiO 2 . However, the evaporation process enhances the TDS concentration in water (Prasanna et al. 2010; Xing et al. 2013). The average ratio of Na + /Cl - was measured to be 4.77 ±2.27 and 3.83 ±1.90 in 2016 and 2017 (Table 3). The Na + /Cl - ratio indicates a minor contribution of atmospheric constituents in the chemical characterization of meltwater of the catchment. The ion exchange process in the water is mainly defined because of (Ca 2+ +Mg 2+ ) versus (HCO 3 − +SO 4 2− ) (Srinivasamoorthy et al., 2008). Dominant dissolution process of calcite, dolomite, and gypsum ion exchange may shift the points rightward owing to excess of (HCO 3 − +SO 4 2− ). Further, the reverse ion exchange process turns leftward due to a surplus (Ca 2+ +Mg 2+ ). (Ca 2+ +Mg 2+ ) versus (HCO 3 − +SO 4 2− ) indicates carbonate and silicate weathering with ion exchange as leading geochemical processes in the catchment (Figure 6). The diagram displays that contribution of (HCO 3 − +SO 4 2− ) to the total ionic concentration in the glacial meltwater is greater than the (Ca 2+ +Mg 2+ ), indicating an excess of (HCO 3 − +SO 4 2− ) which is contributed by silicate weathering. The dominance of calcium and magnesium ions is calculated through Ca 2+ /Na + and Mg 2+ /Na + ratios, a product of weathering of CO 3 and SiO 2 . The Ca 2+ /Na + ratio was observed as 2.57 ±0.80 and 2.64 ±1.06, while Mg 2+ /Na + was respectively 2.16 ±0.67 and 2.07 ±0.81 in the meltwater of Shaune Garang glacier (Table 3). This ratio shows the dominance of Ca 2+ and Mg 2+ over Na + . The result further confirms that hydro-geochemistry is governed by weathering of CO 3 minerals in the catchment. Hydrogen ion availability is responsible for rapid CO 3 weathering (Das and Kaur 2001). Na + normalizes Ca 2+ and HCO 3 - concentration and determines the effect of SiO 2 weathering, evaporative dissolution or CO 3 weathering in meltwater (Kumar et al., 2015). To understand the chemical weathering, sulphate mass fraction (SMF), and the ratio of sulphate (SO 4 2− ) to (SO 4 2− + HCO 3 − ) have been calculated in the study catchment. The chemical characteristics of meltwater show the importance of carbonation if the SMF value is (<0.5). The SMF value indicates the chemical attributes of meltwater affected by sulfide oxidation and the termination of CO 3 (Tranter et al. 1993). In the Shaune Garang catchment, an average SMF value of 0.33 ± 0.07 and 0.41± 0.09, respectively, during the study period 2016 and 2017 indicates the dissolution of carbonate and sulfide oxidation. In Addition, C-ratio (HCO 3 − / HCO 3 − +SO 4 2− ) has also been calculated to find the significance of proton-producing effects necessary for the chemical weathering of carbonate rocks. During 2016 and 2017, the C-ratios were 0.67 ±0.07 and 0.59 ±0.09, demonstrating the domination of the carbonate and sulphate weathering processes. Mineral mapping The Short Wavelength Infra-Red (SWIR) and Thermal Infrared (TIR) spectral resolution agree for mapping surface mineralogy. These spectral bands are available in (ASTER) and have been used to map the distribution of minerals on supraglacial debris. Minerals are mapped through the band indices like "SWIR indices", "TIR indices", and "TIR emissivity silica weight per cent" in the Shaune Garang catchment. The mineral measurement reflects the primary presence of quartz, feldspar, carbonate, and mica. High altitude glacier debris reflected the fact of "quartz, feldspar as calcium albite, and mica as biotite". The debris on the Shaune Garang glacier is dominated by muscovite (mica), calcium albite (feldspar) and quartz. Though in lesser quantity, the presence of calcite has also been noticed. To create thematic mineral abundance maps and quantitative estimation of minerals, "SWIR and TIR indices" have also been used (Ninomiya, 2004). SWIR indices Short Wavelength Infrared (SWIR) mineral indices were used to wavelength-dependent absorption patterns in estimating minerals in the catchment. The SWIR mineral indices were used to evaluate the mineral's dominance in the catchment. Equations 1, 2, 3 and 4 have been used respectively for understanding the dominance of layered silicate (LS), calcite (CA), hydroxyl-bearing (OH), and Alunite (AL). Where, AST n is band number ( n) related to ASTER The varying indices are related to the variable absorption properties, which helps measure the types of minerals. A sensor, "radiance band ratios", can reduce the influence of the atmosphere and the topography of a region and the variation in illuminance (Abrams et al., 1983; Mather, 1987). The evidence also indicates the nonsignificant evidence in "single band or three-band true or false colour composite imageries". It is also helpful in having the quantitative estimation of mineral abundances. In this study, images of the 4-shortwave infrared (SWIR) mineral indices are displayed in Figure 7, reflecting the relative dominance of minerals and their presence on the surface. Alunite has been most dominant and abundant in higher altitudes up to the accumulation zone. "Layered silicates" and "hydroxyl-bearing minerals" are less productive, while "calcite and hydroxyl-bearing minerals" varies location-wise. Alunite index displays little white patches in the higher region with high abundance. Figure 7 shows kinematics and pulse flow movements of layered silicate debris, which can be understood through their variability and abundance. The evidence of Alunite at a higher altitude might be due to its formation mechanism. The formation of Alunite through the reaction of sulfuric acids with potassium-rich feldspars is called "alunitization". Layered silicates and "hydroxyl-bearing minerals" are in short supply, but "calcite and hydroxyl-bearing" minerals vary significantly within the catchment. Layered silicate consists of octahedral layers bound to the tetrahedral and primary component of soil. Its distribution within the catchment at lower altitudes implies the weathering mechanism due to meltwater and parental rock interaction. They have been the excellent water trapping mechanism held between layers. The essential minerals in layered silicates are kaolinite, nacrite and dickite. TIR indices To evaluate various minerals in the Shaune Garang catchment area, Thermal infrared (TIR) mineral indices of Carbonate, Quartz and Mafic were used. The thermal spectrum is instrumental in distinguishing the geology of earthy minerals, where TIR satellite spatial resolution is noticeably lesser than VNIR or SWIR (VNIR 15 m, SWIR 30 m, TIR 90 m). However, TIR is exclusive in targeting the profusion of carbonate, quartz, and silicate minerals. Band ratios derived from TIR estimate carbonate, quartz and silica bearing lithology (Figure 8). Equations 5, 6 and 7 were used for Carbonate Index (CI), Quartz Index (QI), and Mafic Index (MI), respectively. Where, "ASTn is band number (n) based on the properties of ASTER spectral The CI is better used to detect primary carbonate minerals such as "calcite and dolomite". These two carbonates mineral have higher absorption features, which indicates the availability of calcite and dolomite. The absorption features of calcite are about 11.4 to 11.2 μm in the case of dolomite minerals. Minerals of carbonates with "hydrothermal origin" are very challenging to identify through a CI map due to an inadequate percentage of carbonate presence. However, calcite-bearing propylitic alteration is accredited as the "ASTER TIR" feature. The chlorite and epidote had lower emissivity between TIR bands 11 and 13 but slightly higher between 13 and 14 (Salisbury et al. 1992). Aspects of the spectrum with these characteristics resemble the mafic index minerals. Mafic and quartz index minerals found uneven distribution within the catchment, but carbonate minerals were found at lower altitudes along the riverside. The reason behind the occurrence of carbonate minerals along river channels might be due to the higher weathering across the river. Chemometric Analysis Principal component and factor analysis Excel add-on XLSTAT was used for the analysis of normalized data under PCA. The sphericity test of Bartlett was performed on the data of both years. The Bartlett sphericity test shows that observed χ2 (342.85) is considerably more significant than the critical χ2 (85.96) in 2016 and χ2 (observed) = 125.25 larger than the critical value χ2 (critical) = 48.3 in 2017. The principal component analysis helped to understand problems under different measurement scales of the original variable avoided by diagonalizing the correlation matrix. Table 4 demonstrates the PC value of more than 1, which explains 72.1 % of the total variance of four PCs. PCs 1, 2, 3 and 4 are capable of explaining 39.21%, 12.91%, 10.24% and 9.74% of variance in 2016. Similarly, in 2017 scree plot (Figure 9) shows four PCs, which explains 69.9.1% of the total variance. PC 1, 2, 3 and 4 can explain 26.62 %, 20.12 %, 12.64% and 10.52 % of variance. Table 4 further depicts the Eigenvalues, the percentage of variance calculated through varimax rotation matrix with Kaiser Normalization and rotated factor, and the percentage of variance in each PC. High SO 4 2− and K + loadings are observed in both the years, indicating silicate weathering dominance in the catchment. Moderately high loading values of Ca 2+ , Mg 2+ and Na + in both years indicate the dominance of the process, which is prevailing in factor 1. Table 5 indicates that factor 4 shows the negative pH in both the years and the acceptable value of Cl - in the consecutive study period. The first two principal component loading are presented to understand the grouping and relationship of all chemical parameters. The PC loading has been calculated to understand correlations among variables and know the most influential variables. It could result from the minerals present in the soil (Yakubo et al., 2009). Throughout the study, higher loadings in Na + and Mg 2+ may be accredited to the ionic conversation between water through dissolution minerals containing sodium. Statistically, the coefficient of determination (R 2 ) indicates one variable's level of statistical agreement with another. Here, it is applied among the hydrogeochemical parameters and represented in (Table 6) during the study period. Water chemistry parameters such as EC and Na + are highly interrelated with Ca 2+ , Mg 2+ , and HCO 3 − . Similarly, a decent relationship among (Ca 2+ - Mg 2+ ), (Ca 2+ - HCO 3 − ) and (Mg 2+ - HCO 3 − ) has been observed. The above parameters have a good positive correlation (R 2 > 0.5) and are an indicator of control by these parameters in the solute chemistry of the study region. The strong correlation between parameters such as Ca 2+ , Mg 2+ , (Ca 2+ - HCO 3 − , (Mg 2+ - HCO 3 − ) indicates strong carbonate weathering (Singh et al., 2017). In the case of sulphate (SO 4 2- ) ion concentration, it shows a good relationship with Ca 2+ and Mg 2+ , indicating the sulphate mineral's high dissolution in the glacier's catchment. Hydro-geochemical facies of the glacial meltwater Hydrogeochemical facies of the meltwater helps interpret the dominant anions and cations, which have been determined through a Piper plot (Figure 10). It is used to find similarities and dissimilarities among all water types, where the analogous water qualities fall together (Todd, 2001). In the cation plot, it can be seen that most of the water is concentrated in a trilinear pattern in the middle, indicating that it is a mixed water type. The calcium ions predominate in the glacial discharge. The hydro-geochemical cations in the bottom left triangle of Shaune Garang glacial discharge prove calcium ions' dominance. It substantiates the conclusions reached in the sections on hydro-geochemistry and hydro-geochemical processes, both included in the previous section. Slightly elevated sodium and potassium ion concentrations in several samples confirm the presence of sources at the various sampling locations of the Shaune Garang catchment. This must be contributing to the overall cation concentration (Karim and Veizer, 2000; Ravikumar, 2017). The Piper plot aids in the understanding of the fact, Ca 2+ , Mg 2+ and HCO 3 - are the most prevalent ions in the Shaune Garang catchment. The average percentage value of (Ca 2+ + Mg 2+ ) is about 77% and 81%, respectively in the years 2016 and 2017; however, for the (Na + + K + ) it showed about 33 % and 29%, respectively, demonstrating that alkaline earth metals are prevailing over alkali metals (Singh et al., 2017). It further supports the dominance of dolomitic limestone containing gypsum and pyrite in the region (Figures 7 and 8). The piper diagram demonstrates that carbonate type weathering has been more instrumental in governing hydro-geochemistry in this catchment. The figure indicates the presence of the (Ca 2+ – HCO 3 - ) type of water with little influence from (Ca 2+ – SO 4 2- ) type. Significant ions and TDS were found in higher concentrations in this catchment, indicating more significant interactive processes between rock materials and water having influential weathering due to moisture. Apart from the Piper plot, Gibb's diagram (Gibbs 1970) was applied to understand how hydro-geochemical techniques such as precipitation, rock-water interface, and vaporization impact the environment's hydrogeology. According to the Gibbs diagram (Figure 11), the chemical weathering of rock minerals and a minimal extent of evaporation crystallisation are the essential variables to consider the meltwater quality in the Shaune Garang catchment. Gibb's diagram advocates the higher rock-water interaction resulting in higher ionic concentration in meltwater. Chemical weathering, carbonate dissolution and ionic exchange between water and clay indicate the rock-water interaction processes (Kumar et al., 2014). Increased evaporation, chemical weathering and anthropogenetic actions raise the total dissolved solids. Furthermore, the findings show that water contamination from poor sanitation has increased Na + and Cl - ions and increased total dissolved solids (TDS) (Kumar et al., 2014). Conclusion The current study focused on the hydro-chemical analysis of meltwater from the Shaune Garang catchment, located within the Baspa Basin. According to the findings, meltwater is slightly alkaline with Ca 2+ and HCO 3 - the most prevalent ions during the study. Ca 2+ and Mg 2+ were the dominant cations constituting (41.03%, 42.53%) and (34.84%, 32.89%) of the total cationic budget in the consecutive study period. The predominance of carbonate weathering is indicated by the ratio of (Ca 2+ +Mg 2+ )/(Na + +K + ) and (Ca 2+ +Mg 2+ )/(TZ + ) and a strong positive association between Ca 2+ – Mg 2+ , Ca 2+ – HCO 3 - and Mg 2+ – HCO 3 − . Piper plot demonstrated that alkaline earth metal dominates alkali metal, and weak acid exceeds strong acid. This plot also indicated that the Ca 2+ – HCO 3 − is most influential, assessed by Mg 2+ –HCO 3 − type of water in this area. The Gibbs plot also revealed that rock corrosion is the primary process regulating meltwater concentration. The values in the chloro-alkaline indices in this study were negative, indicating the conversation of Ca 2+ and Mg 2+ ions by Na + and K + ions of rock material. The geological mapping of the catchment has been done on varying scales, including diversified rocks and unconsolidated surface materials that possess "quartz and carbonate minerals". The results were verified through the geological, stratigraphic, and structural maps in the multifaceted lithological terrain of the region. Based on the lithological map of the Shaune Garang catchment, Layered silicates and "hydroxyl-bearing minerals" are less abundant. However, "calcite and hydroxyl-bearing minerals" have significantly varying availability in the catchment. The distribution of Layered silicate minerals within the catchment is majorly found at lower altitudes, which implies the weathering mechanism due to the interaction of meltwater and parental rock. The chemometric analysis includes a principal component (PC), eigenvalues, the percentage of variance calculated through varimax rotation matrix with Kaiser Normalization and rotated factor, and the percentage of variance in each PC. High SO 4 2− and K + loadings are observed in both years, indicating silicate weathering dominance in the catchment. Moderately high loading values of Ca 2+ , Mg 2+ and Na + in both years indicate the dominance of the process, which is prevailing in factor 1. The strong correlation between parameters such as Ca 2+ , Mg 2+ , (Ca 2+ - HCO 3 − , (Mg 2+ - HCO 3 − ) indicates intense carbonate weathering (Singh et al. 2017). In the case of sulphate (SO 4 2- ) ion concentration, it shows a good relationship with Ca 2+ and Mg 2+ , indicating the sulphate mineral's high dissolution in the glacier's catchment. Declarations Acknowledgement The present support of the Department of Science and Technology as a research project (DST/CCP/NHC/159/2018(G)) became instrumental in utilising the human resources for the analysis of the results. The financial support of the USAID project (CHARIS) for carrying out fieldwork is thankfully acknowledged. The analysis is made at the DST supported FIST Lab in the Department of Environmental Science, Central University of Rajasthan. The support is thankfully acknowledged. Declaration of interests The authors declare no competing financial or personal interests that influenced their work. References Abrams MJ, Brown D, Lepley L, Sadowski R (1983) Remote sensing for porphyry copper deposits in southern Arizona. Economic Geol 78(4):591–604. https://doi.org/10.2113/gsecongeo.78.4.591 APHA (2005) Standard methods for the examination of water and wastewater (p. 1220). American Public Health Association Bishwakarma K, Pant RR, Pal KB, Ghimire A, Thapa LB, Saud P, Joshi S, Panthi KP (2019) Water quality and land use/cover changes in the Phewa Watershed, Gandaki Province, Nepal. Nepal J Environ Sci 7:31–39. https://doi.org/10.3126/njes.v7i0.34473 Bolch T, Kulkarni AV, Kääb A, Huggel C, Paul F, Cogley JG, Frey H, Kargel JS, Fujita K, Scheel M, Bajracharya S, Stoffel M (2012) The state and fate of Himalayan glaciers. Science 336(6079):310–314. DOI: 10.1126/science.1215828 Brown GH, Sharp MJ, Tranter M, Gurnell AM, Nienow PW (1994) The impact of post-mixing chemical reactions on the major ion chemistry of bulk meltwaters draining the Haut Glacier d'Arolla, Valais, Switzerland. Hydrol Process 8:465–480. https://doi.org/10.1002/hyp.3360080509 Chidambaram S, Karmegam U, Prasanna MV, Sasidhar P, Vasanthavigar M (2011) A study on the hydro-chemical elucidation of coastal groundwater in and around Kalpakkam region, Southern India. Environ Earth Sci 64(5):1419–1431. https://doi.org/10.1007/s12665-011-0966-3 Das BK, Kaur P (2001) Major ion chemistry of Renuka Lake and weathering processes, Simaur District, Himachal Pradesh, India. Environ Geol 40:908–917. https://doi.org/10.1007/s002540100268 Dimri AP (2004) Impact of horizontal model resolution and orography on the simulation of a western disturbance and its associated precipitation. Meteorological Application 11:115–127. DOI: 10.1017/S1350482704001227 Dutta S, Mujtaba SAI, Saini HS, Chunchekar R, Kumar P(2017) Geomorphic evolution of glacier-fed Baspa Valley, NW Himalaya: record of Late Quaternary climate change, monsoon dynamics and glacial fluctuations. In: Pant NC, Ravindra R, Srivastava D, Thompson LG (eds) The Himalayan cryosphere: past and present, Special Publications, 462. Geological Society, London. https://doi.org/10.1144/SP462.5 Fu B, Chou X (1998) Thermal infrared spectra and TIMS imagery features of sedimentary rocks in the Kalpin Uplift, Tarim Basin, China. Geocarto Int 13:69–73. https://doi.org/10.1080/10106049809354630 Gabr S, Ghulam A, Kusky T (2010) Detecting areas of high-potential gold mineralisation using ASTER data. Ore Geol Reviews 38:59–69. https://doi.org/10.1016/j.oregeorev.2010.05.007 Gabr SS, Hassan SM, Sadek MF (2015) Prospecting for new gold-bearing alteration zones at El-Hoteib area, South Eastern Desert, Egypt, using remote sensing data analysis. Ore Geol Reviews 71:1–13. https://doi.org/10.1016/j.oregeorev.2015.04.021 Gad S, Kusky T (2007) ASTER spectral ratioing for lithological mapping in the Arabian–Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Res 11(3):326–335. https://doi.org/10.1016/j.gr.2006.02.010 Galy A, France-Lanord C (1999) Weathering processes in the GangesBrahmaputra basin and the riverine alkalinity budget. Chem Geol 159:31–60. https://doi.org/10.1016/S0009-2541(99)00033-9 Gibbs RJ (1970) Mechanisms controlling world water chemistry. Science 170:1088–1090. https://doi.org/10.1126/science.170.3962.1088 Han H, Liu S, Wang J, Wang Q, Xie C (2010) Glacial runoff characteristics of the Koxkar Glacier, Tuomuer-Khan Tengri Mountain Ranges, China. Environ Earth Sci 61:665–674. https://doi.org/10.1007/s12665-009-0378-9 Haritashya UK, Kumar A, Singh P (2010) Particle size characteristics of suspended sediment transported in meltwater from the Gangotri Glacier, central Himalaya — An indicator of subglacial sediment evacuation. Geomorphology 122(1–2):140–152. https://doi.org/10.1016/j.geomorph.2010.06.006 Jeelani GH, Bhat NA, Shivanna K, Bhat MY (2011) Geochemical characterisation of surface water and spring water in SE Kashmir Valley, western Himalaya: implications to water–rock interaction. J Earth Syst Sci 120(5):921–932. https://www.ias.ac.in/article/fulltext/jess/120/05/0921-0932 Karim A, Veizer J (2000) Weathering processes in the Indus River Basin: implications from riverine carbon, sulfur, oxygen, and strontium isotopes. Chem Geol 170:153–177 Kaser G, Grobhauser M, Marzeion B(2010) Contribution potential of glaciers to water availability in different climate regimes. Proceedings of the National Academy of Science, USA. 107 (47), 20223–20227. https://doi.org/10.1073/pnas.1008162107 Khadka UR, Ramanathan AL (2012) Major ion composition and seasonal variation in the Lesser Himalayan lake: case of Begnas Lake of the Pokhara Valley, Nepal. Arab J Geosci 6:4191–4206. https://doi.org/10.1007/s12517-012-0677-4 Kumar R, Kumar R, Singh A, Singh S, Bhardwaj A, Kumari A, Sinha RK, Gupta A (2019) Hydro-geochemical analysis of meltwater draining from Bilare Banga glacier, Western Himalaya. Acta Geophys 67:651. https://doi.org/10.1007/s11600-019-00262-w Kumar R, Kumar R, Singh A, Sinha RK, Kumari A (2018) Nanoparticles in glacial meltwater. Mater Today Proc 5(3P1):9161–9166. https://doi.org/10.1016/j.matpr.2017.10.037 Kumar R, Kumar R, Singh A, Sinha RK, Kumari A, Gupta A, Singh J (2019) Distribution of trace metal in Shaune Garang catchment: evidence from particles and nanoparticles. Mater Today Proc 15(3):586–594. https://doi.org/10.1016/j.matpr.2019.04.125 Kumar R, Kumar R, Singh S, Singh A, Bhardwaj A, Kumari A, Randhawa SS, Saha A (2018) Dynamics of suspended sediment load with respect to summer discharge and temperatures in Shaune Garang glacierised catchment, Western Himalaya. https://doi.org/10.1007/s11600-018-0184-4 . Acta Geophysica Kumar R, Singh S, Kumar R, Singh A, Bhardwaj A, Sam L, Randhawa SS, Gupta A (2016) Development of a Glacio hydrological model for discharge and mass balance reconstruction. J Water Resource Management. https://doi.org/10.1007/s11269-016-1364-0 Kumar A, Singh CK (2015) Characterisation of Hydro geochemical Processes and Fluoride Enrichment in Groundwater of South-Western Punjab. Water Qual Exposure Health 7(3):373–387. https://doi.org/10.1007/s12403-015-0157-7 Kumar A, Verma A, Dobhal DP, Mehta M, Kesarwani K (2014) Climatic control on extreme sediment transfer from Dokriani Glacier during monsoon, Garhwal Himalaya (India). J Earth Syst Sci 123:109–120. https://doi.org/10.1007/s12040-013-0375-y Kumar K, Miral MS, Joshi S, Pant N, Joshi V, Joshi LM (2009) Solute dynamics of meltwater of Gangotri glacier, Garhwal Himalaya, India. Environ Geol 58:1151–1159. https://doi.org/10.1007/s00254-008-1592-6 Kumar R, Kumar R, Singh S, Singh A, Bhardwaj A, Chaudhary H(2019) Hydro–geochemical characteristics of glacial meltwater from Naradu Glacier catchment, Western Himalaya. Environmental Earth Sciences (2019) 78:683. https://doi.org/10.1007/s12665-019-8687-0 Kumar S, Rai H, Purohit KK, Rawat BRS, Mundepi AK(1987) Chhota Shigri glacier. New Delhi: Department of Science and Technology, Technical Report 1, 1–29 Mather PM(1987) Computer Processing of Remotely Sensed Images. An Introduction, 1st Edition, Wiley, Chichester Mortatti J, Probst JL (2003) Silicate rock weathering and atmospheric/soil CO2 uptake in the Amazon basin estimated from river water geochemistry: seasonal and spatial variations. Chem Geol 197:177–196. http://dx.doi.org/10.1016/S0009-2541(02)00349-2 Ninomiya Y(2004) Lithologic mapping with multispectral ASTER TIR and SWIR data. Proceeding, SPIE, 5234, 180–190. https://doi.org/10.1117/12.511902 Pant RR, Bishwakarma K, Basnet BB, Pal KB, Karki L, Dhital YP, Bhatta YR, Pant BR, Thapa LB (2021) Distribution and risk appraisal of dissolved trace elements in Begnas Lake and Rupa Lake, Gandaki Province, Nepal. SN Appl Sci 3:1–13. https://doi.org/10.1007/s42452-021-04516-5 Pant RR, Qaiser FUR, Wang G, Adhikari S, Bishwakarma K, Baral U, Rimal B, Bhatta YR, Rijal K (2021) Hydrochemical appraisal and solute acquisitions in Seti River Basin, Central Himalaya, Nepal. Environ Monit Assess 193:656. https://doi.org/10.1007/s10661-021-09437-9 Piper AM (1944) A graphic procedure in the geochemical interpretation of water analyses. EOS Trans Am Geophys Union 25(6):914–928. https://doi.org/10.1029/TR025i006p00914 Prasanna MV, Chidambaram S, Srinivasamoorthy K (2010) Statistical analysis of the hydro geochemical evolution of groundwater in hard and sedimentary aquifers system of Gadilam river basin, South India. J King Saud Univ – Sci 22:133–145. https://doi.org/10.1016/j.jksus.2010.04.001 Quinton WL, Pomeroy JW (2006) Transformations of runoff chemistry in the Arctic tundra. Northwest Territories Canada Hydrological Process 20:2901–2919. https://doi.org/10.1002/hyp.6083 Ravikumar P Somashekar RK(2017) (2017) Principal component analysis and hydro-chemical facies characterisation to evaluate groundwater quality in Varahi river basin, Karnataka state, India. Appl Water Sci 7:745–755. https://doi.org/10.1007/s13201-015-0287-x Rowan LC, Mars JC (2003) Lithologic mapping in the Mountain Pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sens Environ 84(3):350–366. https://doi.org/10.1016/S0034-4257(02)00127-X Sabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev 14(3):157–183. https://doi.org/10.1016/S0169-1368(99)00007-4 Sadashivaiah C, Ramakrishnaiah CR Ranganna G(2008) (2008) Hydrochemical Analysis and Evaluation of Groundwater Quality in Tumkur Taluk, Karnataka State, India. Int J Environ Res Public Health 5(3):158–164. https://doi.org/10.3390/ijerph5030158 Salisbury JW, D'Aria DM (1992) Emissivity of terrestrial materials in the 8–14 µm atmospheric window. Remote Sens Environ 42(2):83–106. https://doi.org/10.1016/0034-4257(92)90092-X Sharma P, Ramanathan AL, Pottakkal J (2013) Study of solute sources and evolution of hydrogeochemical processes of the Chhota Shigri Glacier meltwaters, Himachal Himalaya, India. Hydrol Sci J 58(5):1128–1143. https://doi.org/10.1080/02626667.2013.802092 Sharma P, Ramanathan AL, Pottakkal JG (2013) Study of solute sources and evolution of hydrogeochemical processes of the Chhota Shigri glacier meltwaters, Himachal Pradesh, India. Hydrol Sci J 58(5):1128–1143. https://doi.org/10.1080/02626667.2013.802092 Singh S, Kumar R, Bhardwaj A, Kumar R, Singh A (2018) Changing climate and glacio-hydrology: a case study of Shaune Garang basin, Himachal Pradesh. Int J Hydrol Sci Technol. https://doi.org/10.1504/IJHST.2018.10010353 Singh S, Kumar R, Bhardwaj A, Sam L, Shekhar M, Singh A, Kumar R, Gupta A (2016c) Changing climate and glacio-hydrology in Indian Himalayan Region: a review. Wiley Interdiscip Rev Clim Chang 7(3):393–410. https://doi.org/10.1002/wcc.39 Singh CK, Kumar A, Shashtri S, Kumar A, Kumar P, Mallick J (2017) Multivariate statistical analysis and geochemical modeling for geochemical assessment of groundwater of Delhi, India". J Geochemical Explor 175:59–71. https://doi.org/10.1016/j.gexplo.2017.01.001 Singh P, Haritashya UK, Kumar N (2008) Modelling and estimation of different components of streamflow for Gangotri Glacier basin, Himalayas. Hydrol Sci J 53(2):309–322. https://doi.org/10.1623/hysj.53.2.309 Singh VB, Ramanathan AL (2017) Hydro-geochemistry of the Chhota Shigri glacier meltwater, Chandra basin, Himachal Pradesh, India: solute acquisition processes, dissolved load, and chemical weathering rates. Environ Earth Sci 76:223. https://doi.org/10.1007/s12665-017-6465-4 Singh VB, Ramanathan AL, Kuriakose T (2015) Hydrogeochemical assessment of meltwater quality using major ion chemistry: a case study of Bara Shigri glacier, Western Himalaya, India. Natl Acad Sci Letter 38(2):147–151. https://doi.org/10.1007/s40009-014-0310-z Srinivasamoorthy K, Chidambaram S, Prasanna MV (2008) Identification of major sources controlling Groundwater Chemistry from a hard rock terrain- A case study from Mettur taluk, Salem district, Tamilnadu, India. J Earth Syst Sci 117(1):49–58. https://doi.org/10.1007/s12040-008-0012-3 Tangestani MH, Jaffari L, Vincent RK, Sridhar BM (2011) Spectral characterisation and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran. Remote Sens Environ 115(9):2243–2254. https://doi.org/10.1016/j.rse.2011.04.023 Thakur N, Rishi M, Keesari T, Sharma DA, Sinha UK (2019) Assessment of recharge source to springs in upper Beas basin of Kullu region, Himachal Pradesh, India using isotopic signatures. J Radioanalytical NuclearChemistry 1–9. https://doi.org/10.1007/s10967-019-06617-3 Thapa B, Pant RR, Thakuri S, Pond G (2020) Assessment of spring water quality in Jhimruk River Watershed, Lesser Himalaya, Nepal. Environ Earth Sci 79:1–14. https://doi.org/10.1007/s12665-020-09252-4 Thomas J, Joseph S, Thrivikramji KP (2015) Hydrochemical variations of a tropical mountain river system in a rain shadow region of the southern Western Ghats, Kerala, India. Appl Geochem 63:456–471. https://doi.org/10.1016/j.apgeochem.2015.03.018 Tiwari PC, Tiwari A, Joshi B (2018) Urban growth in Himalaya: understanding the process and options for sustainable development. J Urban Reg Stud Contemp India 4(2):15–27. http://doi.org/10.15027/45582 Todd DK (2001) Groundwater hydrology. Wiley, Hoboken Tranter M, Brown G, Raiswell R, Sharp M, Gurnell A (1993) A Conceptual Model of Solute Acquisition by Alpine Glacier Meltwaters. J Glaciol 39:573–581. https://doi.org/10.3189/S0022143000016464 Van der Meer FD, Van der Werff HM, van Ruitenbeek FJ, Hecker CA, Bakker WH, Noomen MF, Woldai T (2012) Multi-and hyperspectral geologic remote sensing: A review. Int J Appl Earth Obs Geoinf 14(1):112–128. https://doi.org/10.1016/j.jag.2011.08.002 Wulf H, Bookhagen B, Scherler D (2010) Seasonal precipitation gradients and their impact on fluvial sediment flux in the Northwest Himalaya. Geomorphology 118:13–21. https://doi.org/10.1016/j.geomorph.2009.12.003 Xing L, Guom H, Zhan Y (2013) Groundwater hydro-chemical characteristics and processes along flow paths in the North China Plain. J Asian Earth Sci 70–71:250–264. https://doi.org/10.1016/j.jseaes.2013.03.017 Yakubo BB, Yidana SM, Nti E (2009) Hydrochemical analysis of groundwater using multivariate statistical methods—the Volta region, Ghana. KSCE J Civil Eng 13(1):55–63. https://doi.org/10.1007/s12205-009-0055-2 Bolch T, Shea JM, Liu S, Azam FM, Gao Y, Gruber S et al (2019) Status and Change of the Cryosphere in the Extended Hindu Kush Himalaya Region. In: Wester P, Mishra A, Mukherji A, Shrestha A (eds) The Hindu Kush Himalaya Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-92288-1_7 . Wood LR, Neumann K, Nicholson KN, Bird BW, Dowling CB, Sharma S (2020) Melting Himalayan Glaciers Threaten Domestic Water Resources in the Mount Everest Region. Nepal Front Earth Sci 8:128. doi: 10.3389/feart.2020.00128 Singh R, Kumar R, Bahuguna IM, Kumar R (2020) Grain Size Analysis of Dune and Bar Sediments of the Shyok River between Khalsar and Hunder Village, Karakoram Range, Ladakh, India. J Geol Soc India 95:183–189. https://doi.org/10.1007/s12594-020-1408-1 Singh R, Kumar R, Latief SU, Kumar R, Shekhar M (2022) Recession of Gaglu Glacier, Chandra Basin, Western Indian Himalaya. In: Rani S, Kumar R (eds) Climate Change. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-030-92782-0_5 . Immerzeel WW, Lutz AF, Andrade M, Bahl A, Biemans H, Bolch T, Hyde S, Brumby S, Davies BJ, Elmore AC, Emmer A, Feng M, Fernández A, Haritashya U, Kargel JS, Koppes M, Kraaijenbrink PDA, Kulkarni AV, Mayewski PA, Nepal S, Pacheco P, Painter TH, Pellicciotti F, Rajaram H, Rupper S, Sinisalo A, Shrestha AB, Viviroli D, Wada Y, Xiao C, Yao T(2020) Baillie, J. E. M Importance and vulnerability of the world's water towers. Nature 2020, 577, 364–369 Tables Table1: Characteristics feature of ASTER data used for lithological mapping. Band VNIR spectral resolution(mm) Band SWIR Spectral resolution(mm) Band TIR spectral resolution (mm) 1 (nadir) 0.52–0.60 4 1.600–1.700 10 8.125–8.475 2 (nadir) 0.63–0.69 5 2.145–2.185 11 8.475–8.825 3 (nadir) 0.76–0.86 6 2.185–2.225 12 8.925–9.275 3 (back ward) 0.76–0.86 7 2.235–2.285 13 10.25–10.95 8 2.295–2.365 14 10.95–11.65 9 2.360–2.430 Technology detector Push broom Si Push broom PtSi:Si Whiskbroom Hg: Cd:Te Spatial resolution (m) 15 x 15 30 x 30 90 x 90 Swath width 60 km 60 km 60 km Quantization 8 bits 8 bits 8 bits Table 2: Distribution of dissolved ionic concentrations in Shaune Garang glacier meltwater (unit: EC in μS/cm; TZ+, TZ− in µeq/l). Year 2016 (Water Sample 53) 2017 (Water Sample 26) Parameters Min Max Mean SD Min Max Mean SD pH 6.86 8.56 7.45 0.48 6.86 8.43 7.45 0.45 EC 51.00 108.00 86.14 16.96 51.00 110.00 91.25 16.62 Na+ 89.80 191.40 131.34 30.57 89.00 200.10 128.38 37.17 K + 32.00 98.00 57.32 15.02 32.00 78.00 51.10 10.23 Ca 2+ 150.00 480.00 321.06 70.82 178.00 510.00 310.72 87.74 Mg 2+ 200.00 456.50 272.65 71.33 141.01 389.40 240.25 56.62 Cl - 14.62 51.14 32.74 13.93 11.52 53.11 38.63 12.79 SO 4 2- 104.15 310.30 185.04 49.34 147.30 356.00 221.74 60.16 NO 3 - 2.60 8.98 7.04 1.88 2.12 8.98 4.61 2.26 HCO 3 - 201.00 478.00 369.65 79.41 201.00 523.00 316.73 83.23 TZ + 570.80 998.23 782.37 109.38 606.50 987.00 730.46 110.39 TZ - 348.94 791.11 594.47 103.28 429.37 791.11 581.71 94.55 Table 3: The relationship between different ionic concentrations in meltwater from the Shaune Garang glacier catchment during the ablation period of 2016 and 2017. (Unit: TZ+, TZ− in µeq/l). Year 2016 2017 Parameters Min Max Mean SD Min Max Mean SD Ca 2+ / Mg 2+ 0.53 2.30 1.25 0.41 0.82 2.35 1.31 0.34 Na + / K + 0.98 5.85 2.46 0.95 1.56 5.85 2.59 0.90 HCO 3 - / SO 4 2- 0.93 3.89 2.12 0.71 0.69 2.76 1.53 0.58 Ca 2+ + Mg 2+ / TZ + 0.63 0.85 0.76 0.04 0.57 0.84 0.75 0.08 Ca 2+ + Mg 2+ / Na + + K + 1.73 5.72 3.23 0.75 1.31 5.33 3.28 1.14 Na + + K + / TZ + 0.15 0.37 0.24 0.04 0.16 0.43 0.25 0.08 Ca 2+ / Na + 1.15 4.60 2.57 0.80 0.89 4.68 2.64 1.06 Mg 2+ / Na + 1.11 3.89 2.16 0.67 0.74 3.82 2.07 0.81 HCO 3 − / Na + 1.39 4.96 2.96 0.94 1.40 5.13 2.65 1.01 Na + / Cl - 1.96 11.23 4.77 2.27 1.90 8.85 3.83 1.90 K + / Cl - 0.66 5.57 2.22 1.34 0.78 4.77 1.59 0.97 C-Ratio 0.48 0.80 0.67 0.07 0.41 0.73 0.59 0.09 SMF 0.20 0.52 0.33 0.07 0.27 0.59 0.41 0.09 Table 4: Principal component (PC), Eigenvalues and the percentage of variance calculated through varimax rotation matrix with Kaiser Normalization and rotated factor, and the percentage of variance. Total Variance explained Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings Total Variance (%) Cumulative (%) Total Variance (%) Cumulative (%) Total Variance (%) Cumulative (%) 2016 1 4.71 39.21 39.21 4.71 39.21 39.21 4.41 36.78 36.78 2 1.55 12.91 52.12 1.55 12.91 52.12 1.10 9.13 45.91 3 1.23 10.24 62.37 1.23 10.24 62.37 4 1.17 9.74 72.11 1.17 9.74 72.11 5 0.82 6.81 78.91 6 0.71 5.94 84.85 7 0.46 3.85 88.70 8 0.42 3.50 92.20 9 0.35 2.91 95.12 10 0.29 2.42 97.53 11 0.22 1.86 99.40 12 0.07 0.60 100.00 2017 1 3.19 26.62 26.62 3.19 26.62 26.62 2.90 24.16 24.16 2 2.41 20.12 46.74 2.41 20.12 46.74 2.22 18.52 42.68 3 1.51 12.64 59.38 1.51 12.64 59.38 1.14 9.51 52.19 4 1.26 10.52 69.91 1.26 10.52 69.91 5 0.93 7.80 77.72 6 0.82 6.83 84.55 7 0.63 5.24 89.80 8 0.57 4.76 94.56 9 0.37 3.10 97.66 10 0.13 1.11 98.78 11 0.09 0.75 99.53 12 0.05 0.46 100.00 Table 5: Factor analysis of hydro-chemical variables during the melting period of 2016 and 2017. 2016 2017 Variables F1 F2 F3 F4 Communality F1 F2 F3 F4 Communality pH -0.09 0.07 0.06 -0.12 0.03 0.12 0.03 0.10 -0.33 0.14 E C 0.92 0.05 0.02 0.35 0.97 0.76 -0.15 -0.03 -0.14 0.62 Na + 0.84 -0.09 -0.11 0.00 0.73 -0.57 -0.29 0.04 0.06 0.41 K + -0.06 0.76 -0.28 0.04 0.66 -0.19 0.42 0.13 0.02 0.24 Ca 2+ -0.91 -0.07 -0.11 -0.13 0.86 0.77 -0.23 0.02 -0.02 0.65 Mg 2+ -0.91 0.12 0.20 0.34 1.00 0.76 0.33 -0.03 0.27 0.76 Cl - 0.03 0.10 -0.55 0.24 0.37 0.16 -0.27 -0.29 -0.46 0.39 SO 4 2- 0.08 0.65 0.20 -0.14 0.48 0.75 -0.20 -0.01 -0.01 0.60 NO 3 - -0.04 -0.05 -0.38 -0.15 0.17 0.14 0.64 -0.57 -0.18 0.79 HCO 3 - -0.91 -0.03 -0.09 0.10 0.84 0.98 0.07 0.11 0.11 0.99 Table 6: Correlation matrix of the melting period of the year 2016 and 2017 Variables pH EC Na + K + Ca 2+ Mg 2+ Cl - SO 4 2- NO 3 - HCO 3 - pH 1 E C 0.106 1 Na + -0.078 -0.389 1 K + -0.01 -0.207 0.038 1 Ca 2+ 0.063 0.622 -0.488 -0.357 1 Mg 2+ 0.009 0.493 -0.514 0.027 0.482 1 Cl - 0.147 0.268 -0.033 -0.167 0.146 -0.069 1 SO 4 2- 0.075 0.568 -0.316 -0.192 0.631 0.522 0.216 1 NO 3 - 0.039 0.044 -0.325 0.153 -0.041 0.268 0.091 -0.009 1 HCO 3 - 0.103 0.733 -0.554 -0.151 0.735 0.789 0.046 0.71 0.105 1 pH 1 E C -0.219 1 Na + 0.208 -0.583 1 K + 0.019 -0.357 0.236 1 Ca 2+ 0.095 -0.122 -0.398 -0.124 1 Mg 2+ -0.211 0.116 -0.662 -0.167 0.545 1 Cl - 0.135 0.036 0.250 0.054 -0.022 -0.411 1 SO 4 2- 0.326 -0.075 0.545 0.158 -0.250 -0.452 0.281 1 NO 3 - 0.169 -0.194 0.170 0.356 0.345 -0.317 0.220 0.191 1 HCO 3 - 0.030 -0.417 -0.115 -0.094 0.814 0.556 -0.091 -0.217 0.248 1 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 May, 2022 Reviewers invited by journal 26 May, 2022 Editor assigned by journal 26 May, 2022 First submitted to journal 24 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1440542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":110486372,"identity":"10684701-968c-4841-888c-68c6ebe6f0d3","order_by":0,"name":"Ramesh Kumar","email":"","orcid":"","institution":"Central University of Rajasthan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"","lastName":"Kumar","suffix":""},{"id":110486373,"identity":"e3f929b6-38e4-4617-be63-0118cc542150","order_by":1,"name":"Rajesh Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYHCCBAbGBgYDMPMDmDwAIiSI08I4g0gtDHAtzDzEuMq8/cDDhz93MBgbXDv87LNNzeF8/sYDjB9+MFjk4dIicyYh2Zj3DIOZwe0049k5xw5bzjhwgFmyh0GiGJcWCYaENGnGNgYbg9sJxsy5DYcNGA4cYJAGSiQ24NLC/yD950+wlvTPzJZALfJAW37j1SKRkMbA2wZyWI4xMyNQi8GBA2z4bZF4kCzN2yZhLHk7p5ix51i6geGBg22WPQb4HJaT+PFnm41h3+30zQw/aqwN5G4cPnzjR0UdTi0MDDwJDKgRJ3EQqNgAp3ogYD+AJsCP2/hRMApGwSgYmQAAuVNWn8rouwkAAAAASUVORK5CYII=","orcid":"","institution":"Central University of Rajasthan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"","lastName":"Kumar","suffix":""},{"id":110486374,"identity":"a572b0c6-2cfc-4c1f-bc6d-924e176b09bb","order_by":2,"name":"Anshuman Bhardwaj","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anshuman","middleName":"","lastName":"Bhardwaj","suffix":""},{"id":110486375,"identity":"45b63731-d9e9-4a94-8933-5d6b6bcc7eaa","order_by":3,"name":"Atar Singh","email":"","orcid":"","institution":"Central University of Rajasthan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Atar","middleName":"","lastName":"Singh","suffix":""},{"id":110486376,"identity":"b1c57777-7ca9-4418-87aa-392d06ba42ac","order_by":4,"name":"Shaktiman Singh","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaktiman","middleName":"","lastName":"Singh","suffix":""},{"id":110486377,"identity":"74592e00-430b-4c42-8c26-c4f114a21beb","order_by":5,"name":"Anupma Kumari","email":"","orcid":"","institution":"Patna University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anupma","middleName":"","lastName":"Kumari","suffix":""},{"id":110486378,"identity":"2d6d287c-ee7e-476e-93b3-a7aab2cbb57e","order_by":6,"name":"Ravindra Kumar Sinha","email":"","orcid":"","institution":"Patna University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ravindra","middleName":"Kumar","lastName":"Sinha","suffix":""}],"badges":[],"createdAt":"2022-03-11 05:16:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1440542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1440542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22244774,"identity":"973e7fd9-b8f7-48a8-8a9c-94c8a0b98275","added_by":"auto","created_at":"2022-06-03 22:43:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271791,"visible":true,"origin":"","legend":"\u003cp\u003eShaune Garang glacier showing the glacial boundary, river, and water sampling sites. Sampling locations are numbered in ascending order from higher altitudes to lower altitudes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/8aedf4d3a71fdb77fd0b23d3.jpeg"},{"id":22244556,"identity":"57a493be-f4d5-47c7-b39d-df6695f13f89","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10061,"visible":true,"origin":"","legend":"\u003cp\u003eHypsometry of the Shaune Garang catchment derived from the Landsat 8 image (Kumar et al., 2016)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig02.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/0ed7e3fb0f4132984c48eb84.png"},{"id":22244560,"identity":"9eb63aba-ffb5-4c2f-89d6-e75d6037fe23","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13111,"visible":true,"origin":"","legend":"\u003cp\u003eThe basin's area (%) vs aspect and slope distribution (Kumar et al., 2018).\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig03.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/4ec46235b14dee6c099e59e7.png"},{"id":22244776,"identity":"3a4b1d9d-8b97-439f-8cf2-92ce59acc987","added_by":"auto","created_at":"2022-06-03 22:43:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209888,"visible":true,"origin":"","legend":"\u003cp\u003eThe average hydro-geochemical characteristics of glacial meltwater from Shaune Garang Glacier and its comparison with the other Himalayan glaciers.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/7f39ab7b31472cbc70a7f46b.png"},{"id":22244558,"identity":"1fa851cc-19ed-4a97-a3cc-501ac9b70808","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45375,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) against TZ\u003csup\u003e+ \u003c/sup\u003eand (Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) against total cation (TZ\u003csup\u003e+\u003c/sup\u003e) during the study period of 2016 and 2017 from Shaune Garang glacier catchment.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig05.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/39f77845a19aa0450eb7a39c.png"},{"id":22244562,"identity":"ac689d13-a3bc-4d74-9a7a-4e2189dfa659","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57282,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) against (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e−\u003c/sup\u003e+SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2−\u003c/sup\u003e) and (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) against SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2−\u003c/sup\u003e during the study period of 2016 and 2017 from Shaune Garang glacier catchment.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig06.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/474fa655996150fddafe139f.png"},{"id":22244775,"identity":"312cac95-a05b-43c6-b9f0-1c398e179907","added_by":"auto","created_at":"2022-06-03 22:43:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":580432,"visible":true,"origin":"","legend":"\u003cp\u003eMineralogical maps based on the radiance properties of ASTER SWIR of Shaune Garang catchment. The indexes are represented through LS, CA, AL, and OH for layered silicate, calcite, Alunite, and hydroxyl bearing materials.\u003c/p\u003e","description":"","filename":"Fig07.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/8d7fcadcf13e97735fd8644f.png"},{"id":22244563,"identity":"3b881ddd-1b6e-4009-b5ba-b146e8a04de4","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":262578,"visible":true,"origin":"","legend":"\u003cp\u003eMineralogy maps based on the \"ASTER TIR\" of Shaune Garang catchment's radiance properties. The indexes are \"Carbonate Index (CI), Quartz Index (QI), and Mafic Index (MI)\".\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig08.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/bc69bc64e2bd3c8059dcc702.png"},{"id":22244554,"identity":"4dccabc5-3ad9-4568-8fc2-09dba8da42be","added_by":"auto","created_at":"2022-06-03 22:38:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":29756,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot graph between eigenvalue and factors for 2016 and 2017.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig09.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/405d85d2593abdf8cb5de2e8.png"},{"id":22244564,"identity":"4f8befc0-a538-46a7-9cbe-9a8c46616cff","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":154356,"visible":true,"origin":"","legend":"\u003cp\u003ePiper plot for concentration of ions during study period 2016 and 2017 in the Shaune Garang catchment.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/2ad8c86738282383ebaafcd2.png"},{"id":22244565,"identity":"d8ff3e62-8814-41db-9507-adc9cd8be28d","added_by":"auto","created_at":"2022-06-03 22:38:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":46582,"visible":true,"origin":"","legend":"\u003cp\u003eGibb's diagram represents controlling factors of meltwater quality.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/d464ef3d3a96a31d7fb55a43.png"},{"id":22244777,"identity":"1ca3ccd7-228c-4a68-b755-e16b3ff12ed3","added_by":"auto","created_at":"2022-06-03 22:43:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1718967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1440542/v1/8c9ffa38-f710-4086-ae95-d17e3926798d.pdf"}],"financialInterests":"","formattedTitle":"Multivariate statistical analysis and Geospatial approach for evaluating Hydro-geochemical characteristics of meltwater from Shaune Garang Glacier, Himachal Pradesh, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Hindu Kush Himalayan Mountain ranges and the Tibetan Plateau extend across several Asian countries (Bolch et al., 2012) and are recognized as the cryospheric region beyond the polar latitudes (Bolch et al., 2019; Wood et al., 2020). The Hindu Kush Himalayas, the source of many essential river systems in Asia, are the highest mountain range (Kaser et al., 2010; Singh et al., 2020; Immerzeel et al., 2020), which provide a source of livelihood for millions of people living in this region. The Hindu Kush Himalaya, also known as the \u0026apos;Third Pole\u0026apos; and the \u0026apos;water tower of Asia,\u0026apos; provides ecosystem services, particularly water, the lifeblood of all organisms. The ecosystem services produced in the region provide support for 240 million people who live in the hills and mountains and approximately 1.7 billion people who live downstream in the major river basins. The increasing metropolitan infrastructure in the Himalayan region in the last three decades has put additional strain on the region\u0026apos;s limited water resources (Tiwari et al., 2018). Other than natural forces, the primary causal variables in the Himalayas include extraordinary urbanization, increasing tourism, shifting land use patterns, garbage disposal, and agricultural runoff are all factors to consider (Thakur et al., 2019; Kumar et al., 2019). Snow/glaciers meltwater is a vital water source for domestic use in the higher Himalayas. At the same time, it is essential for irrigation, industrial use, and hydroelectric power generation for the downstream population (Singh et al., 2008). When the other water sources are low in supply, the glacier discharge plays a more prominent role in sustaining the demand (Kumar et al., 2016; Singh et al., 2022). The meltwater drains from the glaciers meet the various soil particles and are subsequently influenced by the various mineral components. According to recent research, climate and land-use/cover changes caused by human endeavours significantly impact the chemical composition of Himalayan freshwater (Pant et al., 2021; Thapa et al., 2020). Hydrogeochemical characterization of the glacier meltwater varies between glaciers due to differential lithology (Collins, 1979). When it comes to glacier meltwater chemistry and, consequently, the quality of meltwater, the geology of the catchment is one of the most important factors to consider. Such studies also improve our understanding of the interactions of meltwater with the underlying geological strata and provide insights into the hydro-climatic regime of a region. There is a need to conduct more research in these areas to create a robust database to determine hydrochemistry confidently. The widely varying geology makes the hydro-chemical study of Himalayan glaciers even more exciting. Earlier studies on the chemistry of the major ions in the Himalayas engrossed in recognizing chemical input in glacial meltwater. Various studies of Himalayan glaciers have suggested that water transients over sub-glacial waterways encounter rock substrate experiencing a significant chemical change in meltwater (Singh et al., 2015, 2017). Some studies specify that the high degree of differential erosion in Himalayan glaciated areas is the consequence of the long interaction time of meltwater through bedrock (Haritashya et al., 2010; Singh et al., 2015). Investigating hydrochemistry in the Himalayas is highly important, and several researchers (Haritashya et al., 2010; Singh et al., 2015; Kumar et al., 2019) have contributed to this regard.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the previous studies in the Himalayan region on water chemistry have focused only on the chemical characterization of meltwater from glaciers. Documentation of such investigations substantially allows precise lithological mapping and erosion rate estimation in these high-mountain areas. This helps in understandig large-scale links between glacio-hydrology and geochemistry.\u0026nbsp;Considering possible sources and their differential erosion rates can give us a better idea about the possible future trajectory of meltwater chemistry in these high-mountain catchments. Photogrammetry, high-resolution terrain modelling and hyperspectral imaging are used in lithological mapping and estimation of erosion rates in high mountains. Further, it helps establish large-scale links between glacio-hydrology and geochemistry on a global scale.\u003c/p\u003e\n\u003cp\u003eIn the present study, the remote sensing approach has been used to identify chemical species\u0026apos; sources of origin in meltwater through the catchment scale lithological mapping, mineral detection, and many other perspectives. Also, ASTER-SWIR data were used to delineate types of rocks in the Shaune Garang catchment to interpret and measure minerals and the geochemical composition of debris on the glacial surfaces. This study validates several features of ASTER (SWIR and TIR) data and their processing techniques; analysis has been used to map several minerals containing indices. These exploration approaches are suitable for low-cost identification and mapping of minerals containing indices such as layered silicates (LS), Calcite (CA), Hydroxyl bearing (OH), Alunite (AL) on the SWIR band and carbonate, quartz, and mafic index on TIR band in ASTER data. In this research, we focus on identifying (i) major ion concentrations, chemical characteristics, and their variability; (ii) hydro-geochemical processes and solute sources during the study period; and (iii) lithological mapping of the Shaune Garang catchment. Hence, the results of this study enrich the catchment findings in terms of glacio-hydrology and geochemistry. Furthermore, this would be a significant step towards a more precise interpretation of weathering and hydrological processes in the catchment. This research includes applying various chemometric analyses such as principal component analysis (PCA) and factor analysis to understand better the dominant weathering process in the Shaune Garang catchment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hydro-geochemical analysis has been performed in the Shaune Garang glacier catchment, located in Himachal Pradesh. The glacial meltwater of this catchment merges to the Baspa River, a stream of the Sutlej River and finally joins the Indus River system. The study area has been presented in Figure 1, where locations of the instruments like the Automatic Weather Station (AWS) discharge gauge have also been shown. The watershed and glaciated area in the catchment have also been presented along with the contour map. This catchment receives precipitation from the winter westerlies and the monsoon in summer (Singh et al. 2018), but summer precipitation dominates winter precipitation. The winter precipitation in this region is received through the Western Disturbance (WD) (Dimri 2004). This region experiences ablation from May to September (Kumar et al., 2016). It covers an area of 38.13 km\u003csup\u003e2\u003c/sup\u003e above the discharge site. The hypsometric distribution of the catchment is presented in Figure 2.\u003c/p\u003e\n\u003cp\u003eHypsometry distribution clearly shows that 75% of the catchment has a non-glaciated area while 25% is glaciated. In the glaciated area, the debris-covered glacier has its share of 5%, while the debris-free glacier has 20%. The rocks of this region resemble the Higher Himalayan Crystalline. It contains pelitic and psammopelitic meta-sediments having acid and basic intrusive. Granite and gneiss rocks in the Himalayan region have a familiar presence of late-stage pegmatitic veins feldspar (Kumar et al., 1987). Rohtang gneiss is the principal constituent of the Himalayan glaciers. Chalcopyrites\u0026apos; presence is also noticed in lateral morainic deposits of the Himalayas. The morphology of the Shaune Garang catchment describing the area vs aspect and area vs slope has been shown in Figure 3. The aspect ratio suggests that most of the part of the catchment falls in the East and South, Southwest and West direction and receives higher solar insolation, making it prone to weathering. Further, the primary area falls in the middle slope zone of 24\u0026deg; to 40\u0026deg; and the higher slope zone 40\u0026deg; to 56\u0026deg;, which transfers the weathered materials very quickly to the glacier.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling and analysis protocol\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the higher Himalayas, the physical accessibility is limited to the summer season. Water samplings were performed during the summer seasons of 2016 and 2017 at the selected locations of the channel (Figure 1). Selected cations and anions of glacial meltwater were examined in 2016 and 2017. Fifty-three water samples in 2016 and 26 water samples in 2017 were collected during ablation season. The sampling sites were chosen as per the geology, altitude, terrain, and convergence of tributaries. Polyethylene sample bottles (250 ml) were rinsed with distilled water after being cleaned with nitric acid for accuracy in data. Water samples were taken from 20 cm deep to have a well-mixed concentration. Sampled glacial meltwater was filtered on 0.45 \u0026micro;m Whatman filter paper. The conductivity and pH measurements have been performed in the field by a handheld multi-parameter instrument (HANNA, model No. HI9829). The essential cations (Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e) analyses were performed using Atomic Absorption Spectroscopy. The instrument has a precession of 0.05 parts per million (ppm) for Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, and 0.01 parts per million (ppm) for the remaining parameters. Ion chromatography was used to analyze the anions (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e). The bicarbonate concentration was based on the results of the titration method (APHA 2005) and the charge balance method. The precautions were made as per the specified norms, and we used non-powder vinyl cleanroom gloves and masks for sampling and analysis. A new standard of identified concentration and procedural blank was analyzed for every analytical run. During the investigation, no detectable contamination was obtained. The data from physio-chemical analysis of the meltwater samples were subjected to multivariate statistical analysis. The study used multivariate statistical tests to examine relationships among multiple variables in the data set. Excel add-on XLSTAT was used for the analysis of normalized data under PCA. Bartlett\u0026apos;s sphericity test was applied to both years\u0026apos; melting season data, and a correlation matrix was prepared. The principal component analysis has been analyzed to understand problems under different measurement scales of the original variable, which is avoided by diagonalizing the correlation matrix. The dissolved ions composition of the meltwater from Shaune Garang glacier were subjected for the error analysis in the charge balance, which has been computed through the given formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1654281369.PNG\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Equation 1\u003c/p\u003e\n\u003cp\u003eWhere, (TZ\u003csup\u003e+\u003c/sup\u003e) = total cations and (TZ\u003csup\u003e-\u003c/sup\u003e) = total anions\u003c/p\u003e\n\u003cp\u003eThe errors of TZ\u003csup\u003e+\u003c/sup\u003e and TZ\u003csup\u003e-\u0026nbsp;\u003c/sup\u003ewere \u0026lt; 10 % for two consecutive melting periods (2016 and 2017), which is indicative of the good quality of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRemote sensing data and characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAir temperature of glacier surface was measured in ablation season (June to October) during the study period. Satellite data has been validated by the ground-based temperature records of snow, ice, and debris-covered glacier at twenty sites during the satellite passes at 10:30 AM over the study area. ASTER data were used in this investigation to obtain lithological and mineralogical evidence from the Shaune Garang catchment. A subset of the high-resolution and cloud-free images (ASTER) were used throughout the study to determine the precise lithological mapping of the exposed rock present in the catchment. The imagery of nine bands was used in this study, with band-1 being blue (0.43 - 0.45 \u0026micro;m), band-2 being blue (0.45 - 0.51\u0026micro;m), band-3 being green (0.53 - 0.59 \u0026micro;m), band-4 being red (0.64 - 0.67 \u0026micro;m), band-5 being near-infrared (0.85 - 0.88 \u0026micro;m), band-6 being shortwave infrared (1.57 - 1.65 m), band-7: shortwave infrared (2.11 - 2.29 \u0026micro;m), band-8: panchromatic (0.50 - 0.68 \u0026micro;m), and band-9: cirus (1.36 - 1.38 \u0026micro;m) were used. Respective bandwidths and subsystems characteristics are presented in Table1.\u003c/p\u003e"},{"header":"Result And Discussion","content":"\u003cp\u003e\u003cstrong\u003eHydrogeochemistry of glacial meltwater\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe physical analysis of meltwater indicates a little alkaline with pH ranging from 6.86-8.56 with an average value of 7.45 \u0026plusmn;0.48 and 7.45 \u0026plusmn;0.45 for 2016 and 2017, respectively (Table 2). Higher pH indicates that the process of dissolution is higher due to the more considerable contact period with rock, soil and rainwater. These would have imparted alkalinity to the meltwater (Kumar et al. 2014). Electrical conductivity indirectly measures the mineralization that explains the ionic strength of water (Kumar et al., 2019). The standard value of electrical conductivity was 86.14 \u0026plusmn;16.96 \u0026mu;S/cm in 2016 and 91.25 \u0026plusmn;16.62 \u0026mu;S/cm in 2017. The higher EC must result from the weathering, evaporation, and crystallization processes. Furthermore, the lesser conductivity in glacier discharge is influenced by increased precipitation making higher discharge and decreased influence of the evaporite dissolution process. The measured value of EC suggests that hydrochemistry of location is regulated through the interface of water and rock and depends on the weathering of rocks.\u0026nbsp;Different rocks and their solubility influence the proportional concentration of ions in glacial meltwater (Pant et al. 2021). Table 2 displays the distribution of dissolved ionic concentrations with standard deviation in the meltwater discharge of the Shaune Garang glacier.\u003c/p\u003e\n\u003cp\u003eThe results presented in Table 2 indicate that Ca\u003csup\u003e2+\u003c/sup\u003e contributes 39.57% and 42.53 % in the total cationic budget in both the consecutive study periods 2016 and 2017. However, Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e contributes 82.10 % and 71.02 % of the total cationic budget in the catchment. The other two cations, Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e,\u003csup\u003e\u0026nbsp;\u003c/sup\u003econtribute only 16.78 % and 17.57 %, respectively, during the study period 2016 and 2017. Bicarbonate (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) is the most dominant anion contributing 62.18 % and 54.44 %, respectively, in the total anionic budget of the ablation period of 2016 and 2017. Its average concentration was observed as 369.65 \u0026plusmn;79.41 \u0026micro;eq/l, and 316.73 \u0026plusmn;83.23 \u0026micro;eq/l in the consecutive study period. Sulphate (31.10% and 38.11 %) was the second most dominant anion, followed by chloride (5.58% and 6.64 %) and Nitrate (1.18% and 0.79 %) during consecutive years\u0026apos; observation. The dominance of bicarbonate in Shaune Garang Catchment is due to the silicate dominating geomorphology of the catchment. According to the findings, weathering of silicate minerals is less visible than carbonate minerals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4 displays the concentration of different anions and cations and electrical conductivity for the glacier\u0026apos;s meltwater at different parts of the Indian Himalaya. The concentration of cations and anions varies as per the morphology of rocks in the catchments and weather system.\u0026nbsp;Meltwater draining from the Himalayan region shows the dominance of Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003eand HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, whereas Bagni, Chaturangi, Gangotri and Dudu glaciers demonstrate the dominance of SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u0026nbsp;\u003c/sup\u003ein their catchment.\u0026nbsp;The dominating presence of silicate-bearing rocks is the important factor for the higher concentration of bicarbonate (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) in the meltwater of the Himalayan glacier. In addition, the dominancy of\u0026nbsp;SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e in the Bagni, Chaturangi, Gangotri and Dudu glaciers could be due to pyrite oxidation that enhances sulphate concentration. Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u0026apos; domination is influenced by halite and sulfide oxidation and weathering of soft sulphate minerals such as gypsum (Thomas et\u0026nbsp;al., 2015). The chemical composition analysis reflects the dominance of bicarbonate (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003e)\u0026nbsp;as an anion in most of the glacial meltwater in the Himalayan region due to the dissolution of atmospheric carbon dioxide and carbonate (Sharma et al., 2013; Kumar et al., 2014; Singh et al., 2015). Concentration of cations in the meltwater of Dokariani, Bara Shigri and Gangotri glaciers follows a trend like Ca\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Mg\u003csup\u003e2+\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e \u0026gt; Na\u003csup\u003e+\u003c/sup\u003e while it follows a trend of Ca\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Mg\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e for meltwater of Kafni and Chhota Shigri, the only alteration in the concentration of Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u0026nbsp;\u003c/sup\u003efor different basins. \u0026nbsp;However, the Bagni glacier meltwater showed the Potassium ion as the second most abundant. In the Bara Shigri glacial, meltwater concentration of cations varied as Ca\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Mg\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e like the Chhota Shigri of its vicinity and the Kafni of Kumanyu Himalaya whereas anions concentration followed the pattern of HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e \u0026gt; SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u0026nbsp;\u003c/sup\u003e\u0026gt; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e. It has been observed from the comparative analysis in Figure 4 that the central Indian Himalayan glacier\u0026apos;s meltwater has the highest concentration of Ca\u003csup\u003e2+\u003c/sup\u003e cation, and a similar observation is from the present study. Interestingly, the graph shows a higher concentration of anions and cations in discharge from glaciers located in the central part of the Indian Himalayan region than the glaciers in the western part. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHydro‑geochemical process in the glacial catchment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDissolved ions in the glacial meltwater are generally contributed through rock weathering, precipitation, anthropogenic influence, and atmospheric conditions (Jeelani 2011; Kumar et al. 2019). Generally, the chemical composition of glacial meltwater is governed by the chemical weathering between the interaction of water and bedrock beneath the glacier (Kumar et al., 2009; Kumar et al., 2014; Singh et al., 2017). The cation and anion in the glacier discharge are elucidated concerning the nature of rock and its weathering processes. Dissolved solute particles present in the glacial melt are determined by the processes involved in the glacial environment.\u0026nbsp;The interrelationship between the physical and chemical parameters are presented through a scatter plot (Figure 5) of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) and (Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) against total cation (TZ\u003csup\u003e+\u003c/sup\u003e). A positive correlation is observed between (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) and TZ\u003csup\u003e+\u003c/sup\u003e. It further shows a ratio ranging from 0.75 to 0.85 with an average equivalent value of 0.75 \u0026plusmn;0.05 during the study period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe result demonstrates that the impact of Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e in the glacial meltwater is comparatively high compared to the total cation TZ\u003csup\u003e+\u003c/sup\u003e. The ratio of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e determines the input source of calcium and magnesium ions in water. Ca\u003csup\u003e2+\u003c/sup\u003e/Mg\u003csup\u003e2+\u003c/sup\u003e \u0026le; 1 (Table 3) indicates a process of dolomite dissolution, and a value \u0026gt;1 recommends the dominance of silicate weathering in water (Kumar and Singh 2015). As a result, silicate weathering could be a factor in the dominant concentration of Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e among cations in the Shaune Garang glacial discharge. The scatter plot (Figure 5) among Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e and TZ\u003csup\u003e+\u003c/sup\u003e displays a small contribution of Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e in total dissolved ion with 0.24 \u0026plusmn;0.04 and 0.25 \u0026plusmn;0.08 during both ablation years. The results reveal carbonate weathering as a leading factor in the glacial meltwater ionic characteristics of the Shaune Garang catchment. The large equivalent ratios 3.23 \u0026plusmn;0.75 and 3.28 \u0026plusmn;1.14 for (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)/(Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) in the consecutive melting period of 2016 and 2017 further strengthen the understanding of carbonate weathering dominance in the catchment. High ratio of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)/(Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) and (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)/TZ\u003csup\u003e+\u003c/sup\u003e in the glacial meltwater demonstrate that hydro-geochemistry of the meltwater of Shaune Garang catchment is mainly administrated by CO\u003csub\u003e3\u003c/sub\u003e weathering with a minor contribution of SiO\u003csub\u003e2\u003c/sub\u003e. However, the evaporation process enhances the TDS concentration in water (Prasanna et al. 2010; Xing et al. 2013). The average ratio of Na\u003csup\u003e+\u003c/sup\u003e/Cl\u003csup\u003e-\u003c/sup\u003e was measured to be 4.77 \u0026plusmn;2.27 and 3.83 \u0026plusmn;1.90 in 2016 and 2017 (Table 3). The Na\u003csup\u003e+\u003c/sup\u003e/Cl\u003csup\u003e-\u003c/sup\u003e ratio indicates a minor contribution of atmospheric constituents in the chemical characterization of meltwater of the catchment.\u003c/p\u003e\n\u003cp\u003eThe ion exchange process in the water is mainly defined because of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) versus (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u0026nbsp;\u003c/sup\u003e+SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) (Srinivasamoorthy et al., 2008). Dominant dissolution process of calcite, dolomite, and gypsum ion exchange may shift the points rightward owing to excess of (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u0026nbsp;\u003c/sup\u003e+SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e). Further, the reverse ion exchange process turns leftward due to a surplus (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e). (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) versus (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u0026nbsp;\u003c/sup\u003e+SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) indicates carbonate and silicate weathering with ion exchange as leading geochemical processes in the catchment (Figure 6).\u0026nbsp;The diagram displays that contribution of (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e +SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) to the total ionic concentration in the glacial meltwater is greater than the (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e), indicating an excess of (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e+SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) which is contributed by silicate weathering.\u0026nbsp;The dominance of calcium and magnesium ions is calculated through Ca\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e ratios, a product of weathering of CO\u003csub\u003e3\u003c/sub\u003e and SiO\u003csub\u003e2\u003c/sub\u003e. The Ca\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e ratio was observed as 2.57 \u0026plusmn;0.80 and 2.64 \u0026plusmn;1.06, while Mg\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e was respectively 2.16 \u0026plusmn;0.67 and 2.07 \u0026plusmn;0.81 in the meltwater of Shaune Garang glacier (Table 3). This ratio shows the dominance of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e over Na\u003csup\u003e+\u003c/sup\u003e. The result further confirms that hydro-geochemistry is governed by weathering of CO\u003csub\u003e3\u003c/sub\u003e minerals in the catchment. Hydrogen ion availability is responsible for rapid CO\u003csub\u003e3\u003c/sub\u003e weathering (Das and Kaur 2001). Na\u003csup\u003e+\u003c/sup\u003e normalizes Ca\u003csup\u003e2+\u003c/sup\u003e and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003econcentration and determines the effect of SiO\u003csub\u003e2\u003c/sub\u003e weathering, evaporative dissolution or CO\u003csub\u003e3\u003c/sub\u003e weathering in meltwater (Kumar et al., 2015). To understand the chemical weathering, sulphate mass fraction (SMF), and the ratio of sulphate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) to (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e+ HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) have been calculated in the study catchment. The chemical characteristics of meltwater show the importance of carbonation if the SMF value is (\u0026lt;0.5). The SMF value indicates the chemical attributes of meltwater affected by sulfide oxidation and the termination of CO\u003csub\u003e3\u003c/sub\u003e (Tranter et al. 1993). In the Shaune Garang catchment, an average SMF value of 0.33 \u0026plusmn; 0.07 and 0.41\u0026plusmn; 0.09, respectively, during the study period 2016 and 2017 indicates the dissolution of carbonate and sulfide oxidation. In Addition, C-ratio (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e/ HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u0026nbsp;\u003c/sup\u003e+SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) has also been calculated to find the significance of proton-producing effects necessary for the chemical weathering of carbonate rocks. During 2016 and 2017, the C-ratios were 0.67 \u0026plusmn;0.07 and 0.59 \u0026plusmn;0.09, demonstrating the domination of the carbonate and sulphate weathering processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMineral mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Short Wavelength Infra-Red (SWIR) and Thermal Infrared (TIR) spectral resolution agree for mapping surface mineralogy. These spectral bands are available in\u0026nbsp;(ASTER) and\u0026nbsp;have been used to map the distribution of minerals on supraglacial debris. Minerals are mapped through the band indices like \u0026quot;SWIR indices\u0026quot;, \u0026quot;TIR indices\u0026quot;, and \u0026quot;TIR emissivity silica weight per cent\u0026quot; in the Shaune Garang catchment. The mineral measurement reflects the primary presence of quartz, feldspar, carbonate, and mica. High altitude glacier debris reflected the fact of \u0026quot;quartz, feldspar as calcium albite, and mica as biotite\u0026quot;. The debris on the Shaune Garang glacier is dominated by muscovite (mica), calcium albite (feldspar) and quartz. Though in lesser quantity, the presence of calcite has also been noticed. To create thematic mineral abundance maps and quantitative estimation of minerals, \u0026quot;SWIR and TIR indices\u0026quot; have\u0026nbsp;also been used (Ninomiya, 2004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWIR indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShort Wavelength Infrared (SWIR) mineral indices were used to wavelength-dependent absorption patterns in estimating minerals in the catchment. The SWIR mineral indices were used to evaluate the mineral\u0026apos;s dominance in the catchment. Equations 1, 2, 3 and 4 have been used respectively for understanding the dominance of layered silicate (LS), calcite (CA), hydroxyl-bearing (OH), and Alunite (AL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1654281515.PNG\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;AST\u003cem\u003en\u0026nbsp;\u003c/em\u003eis band number (\u003cem\u003en)\u0026nbsp;\u003c/em\u003erelated to ASTER\u003c/p\u003e\n\u003cp\u003eThe varying indices are related to the variable absorption properties, which helps measure the types of minerals. A sensor, \u0026quot;radiance band ratios\u0026quot;, can reduce the influence of the atmosphere and the topography of a region and the variation in illuminance (Abrams et al., 1983; Mather, 1987). The evidence also indicates the nonsignificant evidence in \u0026quot;single band or three-band true or false colour composite imageries\u0026quot;. It is also helpful in having the quantitative estimation of mineral abundances. In this study, images of the 4-shortwave infrared (SWIR) mineral indices are displayed in Figure 7, reflecting the relative dominance of minerals and their presence on the surface. Alunite has been most dominant and abundant in higher altitudes up to the accumulation zone. \u0026quot;Layered silicates\u0026quot; and \u0026quot;hydroxyl-bearing minerals\u0026quot; are less productive, while \u0026quot;calcite and hydroxyl-bearing minerals\u0026quot; varies location-wise. Alunite index displays little white patches in the higher region with high abundance. Figure 7 shows kinematics and pulse flow movements of layered silicate debris, which can be understood through their variability and abundance. The evidence of Alunite at a higher altitude might be due to its formation mechanism. The formation of Alunite through the reaction of sulfuric acids with potassium-rich feldspars is called \u0026quot;alunitization\u0026quot;. Layered silicates and \u0026quot;hydroxyl-bearing minerals\u0026quot; are in short supply, but \u0026quot;calcite and hydroxyl-bearing\u0026quot; minerals vary significantly within the catchment. Layered silicate consists of octahedral layers bound to the tetrahedral and primary component of soil. Its distribution within the catchment at lower altitudes implies the weathering mechanism due to meltwater and parental rock interaction. They have been the excellent water trapping mechanism held between layers. The essential minerals in layered silicates are kaolinite, nacrite and dickite. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTIR indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate various minerals in the Shaune Garang catchment area, Thermal infrared (TIR) mineral indices of Carbonate, Quartz and Mafic were used. The thermal spectrum is instrumental in distinguishing the geology of earthy minerals, where TIR satellite spatial resolution is noticeably lesser than VNIR or SWIR (VNIR 15 m, SWIR 30 m, TIR 90 m). However, TIR is exclusive in targeting the profusion of carbonate, quartz, and silicate minerals. Band ratios derived from TIR estimate carbonate, quartz and silica bearing lithology (Figure 8). Equations 5, 6 and 7 were used for Carbonate Index \u0026nbsp;(CI), Quartz Index (QI), and \u0026nbsp;Mafic Index (MI), respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1654281529.PNG\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere, \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026quot;ASTn is band number (n) based on the properties of ASTER spectral\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CI is better used to detect primary carbonate minerals such as \u0026quot;calcite and dolomite\u0026quot;. These two carbonates mineral have higher absorption features, which indicates the availability of calcite and dolomite. The absorption features of calcite are about 11.4 to 11.2 \u0026mu;m in the case of dolomite minerals. Minerals of carbonates with \u0026quot;hydrothermal origin\u0026quot; are very challenging to identify through a CI map due to an inadequate percentage of carbonate presence. However, calcite-bearing propylitic alteration is accredited as the \u0026quot;ASTER TIR\u0026quot; feature. The chlorite and epidote had lower emissivity between TIR bands 11 and 13 but slightly higher between 13 and 14 (Salisbury et al. 1992). Aspects of the spectrum with these characteristics resemble the mafic index minerals. Mafic and quartz index minerals found uneven distribution within the catchment, but carbonate minerals were found at lower altitudes along the riverside. The reason behind the occurrence of carbonate minerals along river channels might be due to the higher weathering across the river.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChemometric Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal component and factor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExcel add-on XLSTAT was used for the analysis of normalized data under PCA. The sphericity test of Bartlett was performed on the data of both years. The Bartlett sphericity test shows that observed \u0026chi;2 (342.85) is considerably more significant than the critical \u0026chi;2 (85.96) in 2016 and \u0026chi;2 (observed) = 125.25 larger than the critical value \u0026chi;2 (critical) = 48.3 in 2017. The principal component analysis helped to understand problems under different measurement scales of the original variable avoided by diagonalizing the correlation matrix.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edemonstrates the PC value of more than 1, which explains 72.1 % of the total variance of four PCs. PCs 1, 2, 3 and 4 are capable of explaining 39.21%, 12.91%, 10.24% and 9.74% of variance in 2016. Similarly, in 2017 scree plot (Figure 9) shows four PCs, which explains 69.9.1% of the total variance. PC 1, 2, 3 and 4 can explain 26.62 %, 20.12 %, 12.64% and 10.52 % of variance. Table 4 further depicts the Eigenvalues, the percentage of variance calculated through varimax rotation matrix with Kaiser Normalization and rotated factor, and the percentage of variance in each PC. High SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e loadings are observed in both the years, indicating silicate weathering dominance in the catchment. Moderately high loading values of Ca\u003csup\u003e2+\u003c/sup\u003e\u003csub\u003e,\u0026nbsp;\u003c/sub\u003eMg\u003csup\u003e2+\u003c/sup\u003e and Na\u003csup\u003e+\u0026nbsp;\u003c/sup\u003ein both years indicate the dominance of the process, which is prevailing in factor 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5\u0026nbsp;indicates that factor 4 shows the negative pH in both the years and the acceptable value of Cl\u003csup\u003e-\u003c/sup\u003e in the consecutive study period. The first two principal component loading are presented to understand the grouping and relationship of all chemical parameters. The PC loading has been calculated to understand correlations among variables and know the most influential variables. It could result from the minerals present in the soil (Yakubo et al., 2009). Throughout the study, higher loadings in Na\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e may be accredited to the ionic conversation between water through dissolution minerals containing sodium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistically, the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) indicates one variable\u0026apos;s level of statistical agreement with another. Here, it is applied among the hydrogeochemical parameters and represented in (Table 6) during the study period. Water chemistry parameters such as EC and Na\u003csup\u003e+\u003c/sup\u003e are highly interrelated with Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003csub\u003e.\u003c/sub\u003e Similarly, a decent relationship among (Ca\u003csup\u003e2+\u003c/sup\u003e- Mg\u003csup\u003e2+\u003c/sup\u003e), (Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e- HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) and (Mg\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e- HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) has been observed. The above parameters have a good positive correlation (R\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.5) and are an indicator of control by these parameters in the solute chemistry of the study region. The strong correlation between parameters such as Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, (Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e- HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e , (Mg\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e- HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) indicates strong carbonate weathering (Singh et al., 2017). In the case of sulphate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e) ion concentration, it shows a good relationship with Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e, indicating the sulphate mineral\u0026apos;s high dissolution in the glacier\u0026apos;s catchment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHydro-geochemical facies of the glacial meltwater\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHydrogeochemical facies of the meltwater helps interpret the dominant anions and cations, which have been determined through a Piper plot (Figure 10). It is used to find similarities and dissimilarities among all water types, where the analogous water qualities fall together (Todd, 2001). In the cation plot, it can be seen that most of the water is concentrated in a trilinear pattern in the middle, indicating that it is a mixed water type. The calcium ions predominate in the glacial discharge. The hydro-geochemical cations in the bottom left triangle of Shaune Garang glacial discharge prove calcium ions\u0026apos; dominance. \u0026nbsp;It substantiates the conclusions reached in the sections on hydro-geochemistry and hydro-geochemical processes, both included in the previous section. Slightly elevated sodium and potassium ion concentrations in several samples confirm the presence of sources at the various sampling locations of the Shaune Garang catchment. This must be contributing to the overall cation concentration (Karim and Veizer, 2000; Ravikumar, 2017). The Piper plot aids in the understanding of the fact, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eare the most prevalent ions in the Shaune Garang catchment. The average percentage value of (Ca\u003csup\u003e2+\u003c/sup\u003e+ Mg\u003csup\u003e2+\u003c/sup\u003e) is about 77% and 81%, respectively in the years 2016 and 2017; however, for the (Na\u003csup\u003e+\u003c/sup\u003e+ K\u003csup\u003e+\u003c/sup\u003e) it showed about 33 % and 29%, respectively, demonstrating that alkaline earth metals are prevailing over alkali metals (Singh et al., 2017).\u0026nbsp;It further supports the dominance of dolomitic limestone containing gypsum and pyrite in the region (Figures 7 and 8). The piper diagram demonstrates that carbonate type weathering has been more instrumental in governing hydro-geochemistry in this catchment. The figure indicates the presence of the (Ca\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e)\u003csup\u003e\u0026nbsp;\u003c/sup\u003etype of water with little influence from (Ca\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e) type. Significant ions and TDS were found in higher concentrations in this catchment, indicating more significant interactive processes between rock materials and water having influential weathering due to moisture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApart from the Piper plot, Gibb\u0026apos;s diagram (Gibbs 1970) was applied to understand how hydro-geochemical techniques such as precipitation, rock-water interface, and vaporization impact the environment\u0026apos;s hydrogeology. According to the Gibbs diagram (Figure 11), the chemical weathering of rock minerals and a minimal extent of evaporation crystallisation are the essential variables to consider the meltwater quality in the Shaune Garang catchment. Gibb\u0026apos;s diagram advocates the higher rock-water interaction resulting in higher ionic concentration in meltwater. Chemical weathering, carbonate dissolution and ionic exchange between water and clay indicate the rock-water interaction processes (Kumar et al., 2014). Increased evaporation, chemical weathering and anthropogenetic actions raise the total dissolved solids. Furthermore, the findings show that water contamination from poor sanitation has increased Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e-\u003c/sup\u003e ions and increased total dissolved solids (TDS) (Kumar et al., 2014).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study focused on the hydro-chemical analysis of meltwater from the Shaune Garang catchment, located within the Baspa Basin. According to the findings, meltwater is slightly alkaline with Ca\u003csup\u003e2+\u003c/sup\u003e and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e the most prevalent ions during the study. Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e were the dominant cations constituting (41.03%, 42.53%) and (34.84%, 32.89%) of the total cationic budget in the consecutive study period. The predominance of carbonate weathering is indicated by the ratio of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)/(Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) and (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)/(TZ\u003csup\u003e+\u003c/sup\u003e) and a strong positive association between Ca\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; Mg\u003csup\u003e2+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. Piper plot demonstrated that alkaline earth metal dominates alkali metal, and weak acid exceeds strong acid. This plot also indicated that the Ca\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e is most influential, assessed by Mg\u003csup\u003e2+\u003c/sup\u003e\u0026ndash;HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e type of water in this area. The Gibbs plot also revealed that rock corrosion is the primary process regulating meltwater concentration. The values in the chloro-alkaline indices in this study were negative, indicating the conversation of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003eions by Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e ions of rock material. The geological mapping of the catchment has been done on varying scales, including diversified rocks and unconsolidated surface materials that possess \u0026quot;quartz and carbonate minerals\u0026quot;. The results were verified through the geological, stratigraphic, and structural maps in the multifaceted lithological terrain of the region. Based on the lithological map of the Shaune Garang catchment, Layered silicates and \u0026quot;hydroxyl-bearing minerals\u0026quot; are less abundant. However, \u0026quot;calcite and hydroxyl-bearing minerals\u0026quot; have significantly varying availability in the catchment. The distribution of Layered silicate minerals within the catchment is majorly found at lower altitudes, which implies the weathering mechanism due to the interaction of meltwater and parental rock. The chemometric analysis includes a principal component (PC), eigenvalues, the percentage of variance calculated through varimax rotation matrix with Kaiser Normalization and rotated factor, and the percentage of variance in each PC. High SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e loadings are observed in both years, indicating silicate weathering dominance in the catchment. Moderately high loading values of Ca\u003csup\u003e2+\u003c/sup\u003e\u003csub\u003e,\u0026nbsp;\u003c/sub\u003eMg\u003csup\u003e2+\u003c/sup\u003e and Na\u003csup\u003e+\u0026nbsp;\u003c/sup\u003ein both years indicate the dominance of the process, which is prevailing in factor 1. The strong correlation between parameters such as Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, (Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e- HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e , (Mg\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e- HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) indicates intense carbonate weathering (Singh et al. 2017). In the case of sulphate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e) ion concentration, it shows a good relationship with Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e, indicating the sulphate mineral\u0026apos;s high dissolution in the glacier\u0026apos;s catchment.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present support of the Department of Science and Technology as a research project (DST/CCP/NHC/159/2018(G)) became instrumental in utilising the human resources for the analysis of the results. The financial support of the USAID project (CHARIS) for carrying out fieldwork is thankfully acknowledged. The analysis is made at the DST supported \u0026nbsp;FIST \u0026nbsp;Lab in the Department of Environmental Science, Central University of Rajasthan. The support is thankfully acknowledged. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or personal interests that influenced their work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrams MJ, Brown D, Lepley L, Sadowski R (1983) Remote sensing for porphyry copper deposits in southern Arizona. Economic Geol 78(4):591\u0026ndash;604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2113/gsecongeo.78.4.591\u003c/span\u003e\u003cspan address=\"10.2113/gsecongeo.78.4.591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAPHA (2005) Standard methods for the examination of water and wastewater (p.\u0026nbsp;1220). American Public Health Association\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishwakarma K, Pant RR, Pal KB, Ghimire A, Thapa LB, Saud P, Joshi S, Panthi KP (2019) Water quality and land use/cover changes in the Phewa Watershed, Gandaki Province, Nepal. Nepal J Environ Sci 7:31\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/njes.v7i0.34473\u003c/span\u003e\u003cspan address=\"10.3126/njes.v7i0.34473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolch T, Kulkarni AV, K\u0026auml;\u0026auml;b A, Huggel C, Paul F, Cogley JG, Frey H, Kargel JS, Fujita K, Scheel M, Bajracharya S, Stoffel M (2012) The state and fate of Himalayan glaciers. Science 336(6079):310\u0026ndash;314. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1215828\u003c/span\u003e\u003cspan address=\"10.1126/science.1215828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown GH, Sharp MJ, Tranter M, Gurnell AM, Nienow PW (1994) The impact of post-mixing chemical reactions on the major ion chemistry of bulk meltwaters draining the Haut Glacier d'Arolla, Valais, Switzerland. Hydrol Process 8:465\u0026ndash;480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.3360080509\u003c/span\u003e\u003cspan address=\"10.1002/hyp.3360080509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChidambaram S, Karmegam U, Prasanna MV, Sasidhar P, Vasanthavigar M (2011) A study on the hydro-chemical elucidation of coastal groundwater in and around Kalpakkam region, Southern India. Environ Earth Sci 64(5):1419\u0026ndash;1431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-011-0966-3\u003c/span\u003e\u003cspan address=\"10.1007/s12665-011-0966-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas BK, Kaur P (2001) Major ion chemistry of Renuka Lake and weathering processes, Simaur District, Himachal Pradesh, India. Environ Geol 40:908\u0026ndash;917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s002540100268\u003c/span\u003e\u003cspan address=\"10.1007/s002540100268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimri AP (2004) Impact of horizontal model resolution and orography on the simulation of a western disturbance and its associated precipitation. Meteorological Application 11:115\u0026ndash;127. DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1350482704001227\u003c/span\u003e\u003cspan address=\"10.1017/S1350482704001227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDutta S, Mujtaba SAI, Saini HS, Chunchekar R, Kumar P(2017) Geomorphic evolution of glacier-fed Baspa Valley, NW Himalaya: record of Late Quaternary climate change, monsoon dynamics and glacial fluctuations. In: Pant NC, Ravindra R, Srivastava D, Thompson LG (eds) The Himalayan cryosphere: past and present, Special Publications, 462. Geological Society, London. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1144/SP462.5\u003c/span\u003e\u003cspan address=\"10.1144/SP462.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu B, Chou X (1998) Thermal infrared spectra and TIMS imagery features of sedimentary rocks in the Kalpin Uplift, Tarim Basin, China. Geocarto Int 13:69\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10106049809354630\u003c/span\u003e\u003cspan address=\"10.1080/10106049809354630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabr S, Ghulam A, Kusky T (2010) Detecting areas of high-potential gold mineralisation using ASTER data. Ore Geol Reviews 38:59\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.oregeorev.2010.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.oregeorev.2010.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabr SS, Hassan SM, Sadek MF (2015) Prospecting for new gold-bearing alteration zones at El-Hoteib area, South Eastern Desert, Egypt, using remote sensing data analysis. Ore Geol Reviews 71:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.oregeorev.2015.04.021\u003c/span\u003e\u003cspan address=\"10.1016/j.oregeorev.2015.04.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGad S, Kusky T (2007) ASTER spectral ratioing for lithological mapping in the Arabian\u0026ndash;Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Res 11(3):326\u0026ndash;335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gr.2006.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.gr.2006.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaly A, France-Lanord C (1999) Weathering processes in the GangesBrahmaputra basin and the riverine alkalinity budget. Chem Geol 159:31\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0009-2541(99)00033-9\u003c/span\u003e\u003cspan address=\"10.1016/S0009-2541(99)00033-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibbs RJ (1970) Mechanisms controlling world water chemistry. Science 170:1088\u0026ndash;1090. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.170.3962.1088\u003c/span\u003e\u003cspan address=\"10.1126/science.170.3962.1088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan H, Liu S, Wang J, Wang Q, Xie C (2010) Glacial runoff characteristics of the Koxkar Glacier, Tuomuer-Khan Tengri Mountain Ranges, China. Environ Earth Sci 61:665\u0026ndash;674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-009-0378-9\u003c/span\u003e\u003cspan address=\"10.1007/s12665-009-0378-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaritashya UK, Kumar A, Singh P (2010) Particle size characteristics of suspended sediment transported in meltwater from the Gangotri Glacier, central Himalaya \u0026mdash; An indicator of subglacial sediment evacuation. Geomorphology 122(1\u0026ndash;2):140\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geomorph.2010.06.006\u003c/span\u003e\u003cspan address=\"10.1016/j.geomorph.2010.06.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeelani GH, Bhat NA, Shivanna K, Bhat MY (2011) Geochemical characterisation of surface water and spring water in SE Kashmir Valley, western Himalaya: implications to water\u0026ndash;rock interaction. J Earth Syst Sci 120(5):921\u0026ndash;932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ias.ac.in/article/fulltext/jess/120/05/0921-0932\u003c/span\u003e\u003cspan address=\"https://www.ias.ac.in/article/fulltext/jess/120/05/0921-0932\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarim A, Veizer J (2000) Weathering processes in the Indus River Basin: implications from riverine carbon, sulfur, oxygen, and strontium isotopes. Chem Geol 170:153\u0026ndash;177\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaser G, Grobhauser M, Marzeion B(2010) Contribution potential of glaciers to water availability in different climate regimes. Proceedings of the National Academy of Science, USA. 107 (47), 20223\u0026ndash;20227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1008162107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1008162107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhadka UR, Ramanathan AL (2012) Major ion composition and seasonal variation in the Lesser Himalayan lake: case of Begnas Lake of the Pokhara Valley, Nepal. Arab J Geosci 6:4191\u0026ndash;4206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12517-012-0677-4\u003c/span\u003e\u003cspan address=\"10.1007/s12517-012-0677-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Kumar R, Singh A, Singh S, Bhardwaj A, Kumari A, Sinha RK, Gupta A (2019) Hydro-geochemical analysis of meltwater draining from Bilare Banga glacier, Western Himalaya. Acta Geophys 67:651. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11600-019-00262-w\u003c/span\u003e\u003cspan address=\"10.1007/s11600-019-00262-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Kumar R, Singh A, Sinha RK, Kumari A (2018) Nanoparticles in glacial meltwater. Mater Today Proc 5(3P1):9161\u0026ndash;9166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.matpr.2017.10.037\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2017.10.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Kumar R, Singh A, Sinha RK, Kumari A, Gupta A, Singh J (2019) Distribution of trace metal in Shaune Garang catchment: evidence from particles and nanoparticles. Mater Today Proc 15(3):586\u0026ndash;594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.matpr.2019.04.125\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2019.04.125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Kumar R, Singh S, Singh A, Bhardwaj A, Kumari A, Randhawa SS, Saha A (2018) Dynamics of suspended sediment load with respect to summer discharge and temperatures in Shaune Garang glacierised catchment, Western Himalaya. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11600-018-0184-4\u003c/span\u003e\u003cspan address=\"10.1007/s11600-018-0184-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acta Geophysica\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Singh S, Kumar R, Singh A, Bhardwaj A, Sam L, Randhawa SS, Gupta A (2016) Development of a Glacio hydrological model for discharge and mass balance reconstruction. J Water Resource Management. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11269-016-1364-0\u003c/span\u003e\u003cspan address=\"10.1007/s11269-016-1364-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Singh CK (2015) Characterisation of Hydro geochemical Processes and Fluoride Enrichment in Groundwater of South-Western Punjab. Water Qual Exposure Health 7(3):373\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12403-015-0157-7\u003c/span\u003e\u003cspan address=\"10.1007/s12403-015-0157-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Verma A, Dobhal DP, Mehta M, Kesarwani K (2014) Climatic control on extreme sediment transfer from Dokriani Glacier during monsoon, Garhwal Himalaya (India). J Earth Syst Sci 123:109\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-013-0375-y\u003c/span\u003e\u003cspan address=\"10.1007/s12040-013-0375-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar K, Miral MS, Joshi S, Pant N, Joshi V, Joshi LM (2009) Solute dynamics of meltwater of Gangotri glacier, Garhwal Himalaya, India. Environ Geol 58:1151\u0026ndash;1159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00254-008-1592-6\u003c/span\u003e\u003cspan address=\"10.1007/s00254-008-1592-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Kumar R, Singh S, Singh A, Bhardwaj A, Chaudhary H(2019) Hydro\u0026ndash;geochemical characteristics of glacial meltwater from Naradu Glacier catchment, Western Himalaya. Environmental Earth Sciences (2019) 78:683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-019-8687-0\u003c/span\u003e\u003cspan address=\"10.1007/s12665-019-8687-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Rai H, Purohit KK, Rawat BRS, Mundepi AK(1987) Chhota Shigri glacier. New Delhi: Department of Science and Technology, Technical Report 1, 1\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMather PM(1987) Computer Processing of Remotely Sensed Images. An Introduction, 1st Edition, Wiley, Chichester\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMortatti J, Probst JL (2003) Silicate rock weathering and atmospheric/soil CO2 uptake in the Amazon basin estimated from river water geochemistry: seasonal and spatial variations. Chem Geol 197:177\u0026ndash;196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/S0009-2541(02)00349-2\u003c/span\u003e\u003cspan address=\"10.1016/S0009-2541(02)00349-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNinomiya Y(2004) Lithologic mapping with multispectral ASTER TIR and SWIR data. Proceeding, SPIE, 5234, 180\u0026ndash;190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1117/12.511902\u003c/span\u003e\u003cspan address=\"10.1117/12.511902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePant RR, Bishwakarma K, Basnet BB, Pal KB, Karki L, Dhital YP, Bhatta YR, Pant BR, Thapa LB (2021) Distribution and risk appraisal of dissolved trace elements in Begnas Lake and Rupa Lake, Gandaki Province, Nepal. SN Appl Sci 3:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42452-021-04516-5\u003c/span\u003e\u003cspan address=\"10.1007/s42452-021-04516-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePant RR, Qaiser FUR, Wang G, Adhikari S, Bishwakarma K, Baral U, Rimal B, Bhatta YR, Rijal K (2021) Hydrochemical appraisal and solute acquisitions in Seti River Basin, Central Himalaya, Nepal. Environ Monit Assess 193:656. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-021-09437-9\u003c/span\u003e\u003cspan address=\"10.1007/s10661-021-09437-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiper AM (1944) A graphic procedure in the geochemical interpretation of water analyses. EOS Trans Am Geophys Union 25(6):914\u0026ndash;928. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/TR025i006p00914\u003c/span\u003e\u003cspan address=\"10.1029/TR025i006p00914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasanna MV, Chidambaram S, Srinivasamoorthy K (2010) Statistical analysis of the hydro geochemical evolution of groundwater in hard and sedimentary aquifers system of Gadilam river basin, South India. J King Saud Univ \u0026ndash; Sci 22:133\u0026ndash;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jksus.2010.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jksus.2010.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinton WL, Pomeroy JW (2006) Transformations of runoff chemistry in the Arctic tundra. Northwest Territories Canada Hydrological Process 20:2901\u0026ndash;2919. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.6083\u003c/span\u003e\u003cspan address=\"10.1002/hyp.6083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavikumar P Somashekar RK(2017) (2017) Principal component analysis and hydro-chemical facies characterisation to evaluate groundwater quality in Varahi river basin, Karnataka state, India. Appl Water Sci 7:745\u0026ndash;755. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13201-015-0287-x\u003c/span\u003e\u003cspan address=\"10.1007/s13201-015-0287-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowan LC, Mars JC (2003) Lithologic mapping in the Mountain Pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sens Environ 84(3):350\u0026ndash;366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0034-4257(02)00127-X\u003c/span\u003e\u003cspan address=\"10.1016/S0034-4257(02)00127-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev 14(3):157\u0026ndash;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0169-1368(99)00007-4\u003c/span\u003e\u003cspan address=\"10.1016/S0169-1368(99)00007-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadashivaiah C, Ramakrishnaiah CR Ranganna G(2008) (2008) Hydrochemical Analysis and Evaluation of Groundwater Quality in Tumkur Taluk, Karnataka State, India. Int J Environ Res Public Health 5(3):158\u0026ndash;164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph5030158\u003c/span\u003e\u003cspan address=\"10.3390/ijerph5030158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalisbury JW, D'Aria DM (1992) Emissivity of terrestrial materials in the 8\u0026ndash;14 \u0026micro;m atmospheric window. Remote Sens Environ 42(2):83\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0034-4257(92)90092-X\u003c/span\u003e\u003cspan address=\"10.1016/0034-4257(92)90092-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma P, Ramanathan AL, Pottakkal J (2013) Study of solute sources and evolution of hydrogeochemical processes of the Chhota Shigri Glacier meltwaters, Himachal Himalaya, India. Hydrol Sci J 58(5):1128\u0026ndash;1143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02626667.2013.802092\u003c/span\u003e\u003cspan address=\"10.1080/02626667.2013.802092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma P, Ramanathan AL, Pottakkal JG (2013) Study of solute sources and evolution of hydrogeochemical processes of the Chhota Shigri glacier meltwaters, Himachal Pradesh, India. Hydrol Sci J 58(5):1128\u0026ndash;1143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02626667.2013.802092\u003c/span\u003e\u003cspan address=\"10.1080/02626667.2013.802092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Kumar R, Bhardwaj A, Kumar R, Singh A (2018) Changing climate and glacio-hydrology: a case study of Shaune Garang basin, Himachal Pradesh. Int J Hydrol Sci Technol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1504/IJHST.2018.10010353\u003c/span\u003e\u003cspan address=\"10.1504/IJHST.2018.10010353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Kumar R, Bhardwaj A, Sam L, Shekhar M, Singh A, Kumar R, Gupta A (2016c) Changing climate and glacio-hydrology in Indian Himalayan Region: a review. Wiley Interdiscip Rev Clim Chang 7(3):393\u0026ndash;410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wcc.39\u003c/span\u003e\u003cspan address=\"10.1002/wcc.39\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh CK, Kumar A, Shashtri S, Kumar A, Kumar P, Mallick J (2017) Multivariate statistical analysis and geochemical modeling for geochemical assessment of groundwater of Delhi, India\". J Geochemical Explor 175:59\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gexplo.2017.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.gexplo.2017.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh P, Haritashya UK, Kumar N (2008) Modelling and estimation of different components of streamflow for Gangotri Glacier basin, Himalayas. Hydrol Sci J 53(2):309\u0026ndash;322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1623/hysj.53.2.309\u003c/span\u003e\u003cspan address=\"10.1623/hysj.53.2.309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh VB, Ramanathan AL (2017) Hydro-geochemistry of the Chhota Shigri glacier meltwater, Chandra basin, Himachal Pradesh, India: solute acquisition processes, dissolved load, and chemical weathering rates. Environ Earth Sci 76:223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-017-6465-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-017-6465-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh VB, Ramanathan AL, Kuriakose T (2015) Hydrogeochemical assessment of meltwater quality using major ion chemistry: a case study of Bara Shigri glacier, Western Himalaya, India. Natl Acad Sci Letter 38(2):147\u0026ndash;151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40009-014-0310-z\u003c/span\u003e\u003cspan address=\"10.1007/s40009-014-0310-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivasamoorthy K, Chidambaram S, Prasanna MV (2008) Identification of major sources controlling Groundwater Chemistry from a hard rock terrain- A case study from Mettur taluk, Salem district, Tamilnadu, India. J Earth Syst Sci 117(1):49\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-008-0012-3\u003c/span\u003e\u003cspan address=\"10.1007/s12040-008-0012-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTangestani MH, Jaffari L, Vincent RK, Sridhar BM (2011) Spectral characterisation and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran. Remote Sens Environ 115(9):2243\u0026ndash;2254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2011.04.023\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2011.04.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur N, Rishi M, Keesari T, Sharma DA, Sinha UK (2019) Assessment of recharge source to springs in upper Beas basin of Kullu region, Himachal Pradesh, India using isotopic signatures. J Radioanalytical NuclearChemistry 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10967-019-06617-3\u003c/span\u003e\u003cspan address=\"10.1007/s10967-019-06617-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapa B, Pant RR, Thakuri S, Pond G (2020) Assessment of spring water quality in Jhimruk River Watershed, Lesser Himalaya, Nepal. Environ Earth Sci 79:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-020-09252-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-020-09252-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas J, Joseph S, Thrivikramji KP (2015) Hydrochemical variations of a tropical mountain river system in a rain shadow region of the southern Western Ghats, Kerala, India. Appl Geochem 63:456\u0026ndash;471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apgeochem.2015.03.018\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeochem.2015.03.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari PC, Tiwari A, Joshi B (2018) Urban growth in Himalaya: understanding the process and options for sustainable development. J Urban Reg Stud Contemp India 4(2):15\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.15027/45582\u003c/span\u003e\u003cspan address=\"10.15027/45582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTodd DK (2001) Groundwater hydrology. Wiley, Hoboken\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTranter M, Brown G, Raiswell R, Sharp M, Gurnell A (1993) A Conceptual Model of Solute Acquisition by Alpine Glacier Meltwaters. J Glaciol 39:573\u0026ndash;581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/S0022143000016464\u003c/span\u003e\u003cspan address=\"10.3189/S0022143000016464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan der Meer FD, Van der Werff HM, van Ruitenbeek FJ, Hecker CA, Bakker WH, Noomen MF, Woldai T (2012) Multi-and hyperspectral geologic remote sensing: A review. Int J Appl Earth Obs Geoinf 14(1):112\u0026ndash;128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jag.2011.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2011.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWulf H, Bookhagen B, Scherler D (2010) Seasonal precipitation gradients and their impact on fluvial sediment flux in the Northwest Himalaya. Geomorphology 118:13\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geomorph.2009.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.geomorph.2009.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing L, Guom H, Zhan Y (2013) Groundwater hydro-chemical characteristics and processes along flow paths in the North China Plain. J Asian Earth Sci 70\u0026ndash;71:250\u0026ndash;264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jseaes.2013.03.017\u003c/span\u003e\u003cspan address=\"10.1016/j.jseaes.2013.03.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYakubo BB, Yidana SM, Nti E (2009) Hydrochemical analysis of groundwater using multivariate statistical methods\u0026mdash;the Volta region, Ghana. KSCE J Civil Eng 13(1):55\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12205-009-0055-2\u003c/span\u003e\u003cspan address=\"10.1007/s12205-009-0055-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolch T, Shea JM, Liu S, Azam FM, Gao Y, Gruber S et al (2019) Status and Change of the Cryosphere in the Extended Hindu Kush Himalaya Region. In: Wester P, Mishra A, Mukherji A, Shrestha A (eds) The Hindu Kush Himalaya Assessment. Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-92288-1_7\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-92288-1_7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood LR, Neumann K, Nicholson KN, Bird BW, Dowling CB, Sharma S (2020) Melting Himalayan Glaciers Threaten Domestic Water Resources in the Mount Everest Region. Nepal Front Earth Sci 8:128. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/feart.2020.00128\u003c/span\u003e\u003cspan address=\"10.3389/feart.2020.00128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh R, Kumar R, Bahuguna IM, Kumar R (2020) Grain Size Analysis of Dune and Bar Sediments of the Shyok River between Khalsar and Hunder Village, Karakoram Range, Ladakh, India. J Geol Soc India 95:183\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12594-020-1408-1\u003c/span\u003e\u003cspan address=\"10.1007/s12594-020-1408-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh R, Kumar R, Latief SU, Kumar R, Shekhar M (2022) Recession of Gaglu Glacier, Chandra Basin, Western Indian Himalaya. In: Rani S, Kumar R (eds) Climate Change. Springer Climate. Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-92782-0_5\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-92782-0_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImmerzeel WW, Lutz AF, Andrade M, Bahl A, Biemans H, Bolch T, Hyde S, Brumby S, Davies BJ, Elmore AC, Emmer A, Feng M, Fern\u0026aacute;ndez A, Haritashya U, Kargel JS, Koppes M, Kraaijenbrink PDA, Kulkarni AV, Mayewski PA, Nepal S, Pacheco P, Painter TH, Pellicciotti F, Rajaram H, Rupper S, Sinisalo A, Shrestha AB, Viviroli D, Wada Y, Xiao C, Yao T(2020) Baillie, J. E. M Importance and vulnerability of the world's water towers. Nature 2020, 577, 364\u0026ndash;369\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1:\u003c/strong\u003e Characteristics feature of ASTER data used for lithological mapping.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBand\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVNIR spectral resolution(mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWIR Spectral resolution(mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTIR spectral resolution (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e1 (nadir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e0.52\u0026ndash;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e1.600\u0026ndash;1.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e8.125\u0026ndash;8.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e2 (nadir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e0.63\u0026ndash;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e2.145\u0026ndash;2.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e8.475\u0026ndash;8.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e3 (nadir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e0.76\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e2.185\u0026ndash;2.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e8.925\u0026ndash;9.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e3 (back ward)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e0.76\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e2.235\u0026ndash;2.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e10.25\u0026ndash;10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e2.295\u0026ndash;2.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e10.95\u0026ndash;11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e2.360\u0026ndash;2.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003eTechnology detector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003ePush broom Si\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003ePush broom PtSi:Si\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003eWhiskbroom Hg: Cd:Te\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003eSpatial resolution (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e15 x 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e30 x 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e90 x 90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003eSwath width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e60 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e60 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e60 km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.469217970049918%\"\u003e\n \u003cp\u003eQuantization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e8 bits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.81863560732113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.632279534109816%\"\u003e\n \u003cp\u003e8 bits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.151414309484194%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.296173044925126%\"\u003e\n \u003cp\u003e8 bits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Distribution of dissolved ionic concentrations in Shaune Garang glacier meltwater (unit: EC in \u0026mu;S/cm; TZ+, TZ\u0026minus; in \u0026micro;eq/l).\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"15.754560530679933%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"42.12271973466003%\"\u003e\n \u003cp\u003e2016 (Water Sample 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"42.12271973466003%\"\u003e\n \u003cp\u003e2017 (Water Sample 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"15.806988352745424%\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e51.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e108.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e86.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e16.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e51.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e110.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e91.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eNa+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e89.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e191.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e131.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e89.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e200.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e128.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e37.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"15.806988352745424%\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e98.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e57.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e15.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e78.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e51.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e150.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e480.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e321.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e70.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e178.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e510.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e310.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e87.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e456.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e272.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e71.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e141.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e389.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e240.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e56.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eCl\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e51.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e32.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e13.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e11.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e53.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e38.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e12.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e104.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e310.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e185.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e49.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e147.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e356.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e221.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e60.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e201.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e478.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e369.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e79.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e201.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e523.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e316.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e83.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eTZ\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e570.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e998.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e782.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e109.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e606.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e987.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e730.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e110.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.806988352745424%\"\u003e\n \u003cp\u003eTZ\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e348.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e791.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e594.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e103.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e429.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e791.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.482529118136439%\"\u003e\n \u003cp\u003e581.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.64891846921797%\"\u003e\n \u003cp\u003e94.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e The relationship between different ionic concentrations in meltwater from the Shaune Garang glacier catchment during the ablation period of 2016 and 2017. (Unit: TZ+, TZ\u0026minus; in \u0026micro;eq/l).\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"38.46153846153846%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"36.45484949832776%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e/ Mg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e / K\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.08361204013378%\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e / SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e+ Mg\u003csup\u003e2+\u003c/sup\u003e/ TZ\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e/ Na\u003csup\u003e+\u003c/sup\u003e + K\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e + K\u003csup\u003e+\u003c/sup\u003e/ TZ\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e/ Na\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e/ Na\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e/ Na\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e/ Cl\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e/ Cl\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eC-Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eSMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.535117056856187%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.361204013377927%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.528428093645484%\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.698996655518394%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.866220735785953%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003ePrincipal component (PC), Eigenvalues and the percentage of variance calculated through varimax rotation matrix with Kaiser Normalization and rotated factor, and the percentage of variance.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003eTotal Variance explained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e\u0026nbsp;Initial eigenvalues \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Extraction sums of squared loadings \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Rotation sums of squared loadings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003eVariance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003eCumulative (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003eVariance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003eCumulative (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003eVariance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003eCumulative (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e39.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e39.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e39.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e39.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e36.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e36.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e12.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e52.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e12.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e52.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e45.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e62.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e62.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e72.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e72.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e78.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e84.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e88.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e92.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e95.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e97.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e99.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e26.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e26.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e26.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e26.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e24.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e24.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e42.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e59.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e59.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e52.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e10.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e69.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e10.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e69.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e77.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e84.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e89.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e94.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e97.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e98.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e99.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.032467532467533%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.98051948051948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.311688311688311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Factor analysis of hydro-chemical variables during the melting period of 2016 and 2017.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2016 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003eCommunality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003eCommunality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eE C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eCl\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.771704180064308%\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.234726688102894%\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.395498392282958%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eTable 6:\u003c/strong\u003e Correlation matrix of the melting period of the year 2016 and 2017\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCl\u003csup\u003e-\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eE C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.622\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.514\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eCl\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.568\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.631\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.522\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.733\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.554\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.151\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.735\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.789\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eE C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.583\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.662\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.545\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eCl\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e-0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.545\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e-0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e-0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.685337726523889%\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.814\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.896210873146623%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.556\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.390444810543658%\"\u003e\n \u003cp\u003e-0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.401976935749587%\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.214168039538714%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hydro-geochemistry, Geological mapping, Chemometric analysis, Shaune Garang glacier, Hydroxyl-bearing minerals","lastPublishedDoi":"10.21203/rs.3.rs-1440542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1440542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study focuses on the hydro-geochemistry of Shaune Garang glacier's meltwater concerning glacial geomorphology. Seventy-nine water samples (53 in 2016 and 26 in 2017) of ablation season were analyzed. The cations were dominant in the order Ca\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Mg\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e, and the anions in the order HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026gt; SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e \u0026gt; Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026gt; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. Ca\u003csup\u003e2+\u003c/sup\u003e. The result demonstrated that HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e were the abundant ions, accounting for 41.03 and 34.84% of the total ionic budget (TZ). The high ionic proportions of (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) versus TZ\u003csup\u003e+\u003c/sup\u003e and (Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) versus (Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) were identified as the primary factors influencing dissolved ion chemistry in meltwater. Piper diagram shows that Ca\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e type water is the most common, followed by Mg\u003csup\u003e2+\u003c/sup\u003e\u0026ndash; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. In addition, a remote sensing approach has been used to find the possible source of the chemical constituents in the meltwater. The catchment geology has been mapped on various scales, including diverse rocks and unconsolidated surface materials containing \"quartz and carbonate minerals\". Layered silicates (LS) and \"hydroxyl-bearing minerals\" are not as common as they used to be, but their availability varies greatly in the area where they are found. The distribution of Layered silicate (LS) minerals within the catchment are majorly found at lower altitudes, which implies the weathering mechanism due to the interaction of meltwater and parental rock. Multivariate analysis revealed that CO\u003csub\u003e3\u003c/sub\u003e and SiO\u003csub\u003e2\u003c/sub\u003e weathering, sulphate dissolution, and pyrite oxidation dominate dissolved ion concentrations. Chemometric analysis of meltwater hydro-geochemistry through Principal Component Analysis (PCA) explains 72.1% of the total variance of four PCs. PCs 1, 2, 3 and 4 explain 39.21%, 12.91%, 10.24% and 9.74% of variance respectively in 2016. Similarly, in 2017, four PCs explain 69.9.1% of the total variance. PC 1, 2, 3 and 4 can explain 26.62%, 20.12%, 12.64% and 10.52% of variance.\u003c/p\u003e","manuscriptTitle":"Multivariate statistical analysis and Geospatial approach for evaluating Hydro-geochemical characteristics of meltwater from Shaune Garang Glacier, Himachal Pradesh, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-03 22:38:23","doi":"10.21203/rs.3.rs-1440542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-05-27T08:54:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-26T06:46:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-26T04:06:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Geophysica","date":"2022-05-24T06:50:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f5e228f2-dd50-43d3-9da7-6745e1d1721f","owner":[],"postedDate":"June 3rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-06-03T22:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-03 22:38:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1440542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1440542","identity":"rs-1440542","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}