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Article

Intense Chemical Weathering at Glacial Meltwater-Dominated Hailuogou Basin in the Southeastern Tibetan Plateau

1
College of Hydrology and Water Resource/State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2
State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
5
School of Resource Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
6
Hydrology and Water Resource Survey Bureau of Qinghai Province, Xining 810001, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(6), 1209; https://doi.org/10.3390/w11061209
Received: 5 March 2019 / Revised: 15 May 2019 / Accepted: 31 May 2019 / Published: 10 June 2019

Abstract

:
Climate warming has caused rapid shrinkage of glaciers in the Tibetan Plateau (TP), but the impact of glacier retreat on the chemical denudation rate remains largely unknown at the temperate glacial basins. The chemical weathering processes were examined at a temperate glacial basin (HLG) in the southeastern TP based on comprehensive data from the supraglacial meltwater, proglacial river water, precipitation and groundwater over two glacier melt seasons in 2008 and 2013. The concentrations of major ions and suspended sediments in river water exhibit a pronounced seasonality and display a close relationship with river discharge, suggesting a strong hydrological control on the chemical and physical weathering processes. Runoff chemistry is dominated by carbonate weathering and sulfide oxidation. HCO3, Ca2+, and/or SO42− are the dominant ions in meltwater, river water, precipitation and groundwater. For river water, HCO3 and Ca2+ primarily come from calcite weathering, and SO42− is mainly derived from pyrite oxidation. Both solute and sediment fluxes are positively related to river discharge (r = 0.69, p < 0.01 for sediments). The solute flux and yields are 18,095–19,435 t·year−1 and 225–241 t·km−2·year−1, and the sediment load and yields are 126,390 t·year−1 and 1570 t·km−2·year−1, respectively. The solute yields, cationic denudation rate (CDR; 2850–3108 Σ*meq+ m−2·year−1) and chemical weathering intensity (CWI; 616–711 Σ*meq+ m−3·year−1) at HLG are higher than those at most basins irrespective of the lithology, suggesting more intense weathering in the TP in comparison to other glacial basins worldwide.

1. Introduction

Climate change has resulted in rapid shrinkage of glaciers in the Tibetan Plateau (TP) [1,2,3,4,5,6,7,8,9]. Such changes in ice reserves are expected to alter the magnitude and timing of meltwater discharge and the species transport therein [10,11,12,13,14]. Meltwater runoff, affected substantially by climate warming, provides the headwaters for some large rivers (e.g., Indus, Brahmaputra, and Yangtze Rivers, which are most susceptible to the reductions of meltwater) in Asia, which potentially affect the seasonal water availability and food security of over 60 million people downstream [3]. Runoff chemistry enables us to better understand the influence of melting glaciers on the chemical denudation, biogeochemical cycling and aqueous ecosystems downstream [15,16,17]. As a result, some major and minor and trace solutes (e.g., Ca2+, HCO3, SO42−, Fe, Si, P, Pb, and Hg) in meltwater runoff are of particular interest due to their importance in the assessment of chemical weathering process, solute provenances, water quality and aquatic environments at glacial basins (e.g., [18,19,20,21]).
Meltwater runoff chemistry has been studied at many glacial basins in Asia. In the Muztag Ata Mountains, meltwater discharge was slightly alkaline and mainly affected by air temperature and precipitation, and the concentrations of major ions had an inverse relationship with meltwater discharge and were dominated by the water-rock interaction at Kartamak Glacier (KAG) [22]. In the Tianshan Mountains, the concentrations of major ions were influenced by many factors such as precipitation, glaciation, snow melt, frozen soil and water-rock interaction at Urumqi Glacier No.1 (UG1) [23,24]. At Koxkar Glacier (KOG), pH values in different samples followed the order: proglacial river water > supraglacial meltwater > precipitation [25]. In the Qilian Mountains, major ions, Li, Sr, and Ba, were exported predominately as the mobile monovalent or divalent ions at Qiyi Glacier (QG) [26]. In the Tanggula Mountains, the dominant ions and elements were HCO3, Ca2+, Fe and Sr at Dongkemadi Glacier (DG) [19]. In this basin, the electrical conductivity (EC) displayed a strong seasonality, with higher values during the early and late melt seasons in comparison to the peak flow season [27]. This can be attributed to the evolution of glacial drainage system (which is closely related to the potential for solute acquisition in glacial environments) over the glacier melt season [15,28,29]. The distribution of metal elements was featured by the mixture of the soluble metal and non-metal ligand complexes [19]. In the Himalaya Mountains, major ions were enriched after meltwater passes through glaciers at Chhota-Shigri Glacier (CSG) [30]. HCO3 and Ca2+ were the dominant ions, and major ion concentrations followed the orders of Ca2+ > Mg2+ > Na+ > K+ for cations and HCO3 > SO42− > Cl > NO3 for anions, and runoff chemistry was related to the natural (mainly carbonate weathering) and anthropogenic inputs at Bilare Banga Glacier [31]. Additionally, the concentrations of major ions and some minor elements (e.g., Li, Sr, and Ba) exhibited an inverse relationship with meltwater discharge at KAG, QG and DG [19,22,26,32,33], but some elements (e.g., Fe, Al, and Cr) were not correlated with discharge at QG and DG [19,26] where major ion fluxes are positively related to discharge [32,33]. In general, meltwater runoff chemistry was dominated by carbonate weathering, sulphide oxidation, and/or sulfate and evaporates dissolution at QG, DG, Dokriani Glacier and Dudu Glacier [19,26,32,34,35], and the flushing of rainfall may have enhanced the moraine weathering at Dokriani Glacier [35]. In addition, the sediment concentration was primarily related to discharge and temperature, and the sediment yield was 411 t·km−2·year−1 at Shaune Garang basin [36]. The Karakoram and western Himalayan glaciers produced lower sediment yields than the central Himalayan glaciers, and the hydro meteorological conditions and sediment delivery for Chorabari Glacier provided insight on the meltwater generation processes and sediment transport patterns during the Indian monsoon seasons [37]. Glacier retreat may lead to the occurrence of debris flows, which is closely related to increasing meltwater, degraded moraine, loose sediments and continuous rainfall, and should be investigated intensively in the Himalaya regions [38].
However, few studies have been conducted on the meltwater hydrochemistry and the sediment delivery at the temperate glacial basins in the TP, where more solutes and sediments are expected to be largely released into downstream aquatic ecosystems due to the enhanced physical and chemical weathering in the subglacial and proglacial regions as a result of increasing meltwater discharge [9,10,11,12,13,14,16,29,30,31,32,33,34]. This study aims to elucidate the hydrochemical characteristics of meltwater runoff, and then determine the seasonal controls on the runoff chemistry, solute and sediment export, and chemical weathering at the Hailuogou basin in the southeastern TP over two melt seasons. This will help to better evaluate the influence of glacial retreat on the physical and chemical denudation rates, and downstream hydrochemistry in a warming environment.

2. Materials and Methods

2.1. Study Area

The Hailuogou basin is located on the eastern slope of the Gongga Mountains in the southeastern TP (Figure 1). Hailuogou Glacier (HLG; 29°34′ N, 101°59′ E) is composed of seven glaciers, which cover 45.3% of basin area (80.5 km2) [39]. About 8.2% of the glacier area is covered by supraglacial debris [40], of which the thickness increases from several millimeters of patchy cover in the upper part of the ablation zone to over one meter at the glacier terminus [41]. The climate is dominated by the southwest monsoon in summer and the westerly jet in winter [39,42]. At the Gongga Alpine Ecosystem Observation and Research Station (GAEORS), 0.5 km east of HLG, the annual mean temperature was 4.6 °C and mean annual precipitation was 1881 mm during the period 2000 to 2008. The glacial drainage system forms by the incision of supraglacial streams, and the perennial subglacial channels are well developed [42]. As long as the conduits remain connected to the drainage network, they continue a three-dimensional meandering towards glacier bed. When the meltwater supply to an englacial conduit terminates, the ice deformation closes the conduit and any sediment deposits at the conduit floor form a sorted debris band [42]. At this basin, the bedrock is mainly composed of deep-metamorphic schist accompanied with quartzite, marble, slate, and crystalline limestone of the Permian [43]. The mineralogy of bedrock includes illite, quartz, potassium feldspar, hornblende, plagioclase, clay, pyrite, augite, calcite, kaolinite and chlorite (Table 1; Figure 1) [44].

2.2. Sample Collection

In 2013, the supraglacial meltwater (n = 26) was collected from 8 April to 24 November, groundwater (n = 15) was sampled from a spring lateral in the proglacial areas from 8 April to 31 July, and precipitation (n = 142) was collected at the GAEORS from 8 February to 24 November (Figure 1). The proglacial river water was sampled at a site ~1.1 km from the glacier terminus from 1 May to 30 September in 2008 (n = 144) and from 1 April to 30 November in 2013 (n = 379) (Figure 1). Field sampling was performed once daily at high flow in 2008, and twice daily at approximately minimum (09:00 h) and maximum (18:00 h) flows in 2013. River samples were immediately filtered through 0.45 μm pore size Whatman cellulose nitrate membranes using a polysulphone filter and a manual pump. After drying, the sediments on the filters in 2013 was weighed in order to calculate the suspended sediment concentrations (SSC). All samples were kept in a refrigerator, and then were transported to the State Key Laboratory of Cryospheric Sciences (SKLCS) at the Chinese Academy of Sciences (CAS) and kept in a cold room at 4 °C until laboratory analysis. The glacial deposits were collected at different sites in the proglacial area (Figure 1) and then transported to the Geochemistry Department of Test Center of Lanzhou Branch for the mineral composition analysis. Data of air temperature and precipitation were continuously observed at the GAEORS (Figure 1).

2.3. Laboratory Analysis

pH and EC/total dissolved solids (TDS) were determined by Eutech PH310 and CON410 portable meters respectively. δ18O and δ2H were measured by a LGR-DLT-100 mass spectrometer with a precision of ±0.06%. The cations (Na+, K+, Ca2+, and Mg2+) were determined by a Dionex-600 ion chromatograph using an IonPac CS-12A-HC column, 20 mM MSA eluent and CSRS-ULTRA-II suppressor, and anions (Cl, NO3, and SO42−) were determined by a Dionex-2500 ion chromatograph using an IonPac AS11-HC column, 25 mM KOH eluent and ASRSULTRA-II suppressor [5,19,26]. The detection limits were ~1 μg·L−1 and the precision was ±1% based on repeated measurement of a certified reference solution. HCO3 was determined by ion charge balance. The mineral composition was determined by an X-ray diffraction (XRD) in the Lanzhou Center for Oil and Gas Resources at the CAS [19,26]. The identified species and relative component contents were assessed using X’Pert High Score Plus software, and trace minerals were excluded from these analyses because semiquantitative XRD estimates involve an uncertainty of 5%. Gibbs plots and the stoichiometry and chemical reactions (e.g., [45,46,47]) were used to identify the weathering process. Principal component analysis (PCA) was performed on meltwater runoff chemistry using CANOCO 4.5 [48] to determine whether individual dissolved species were significantly controlled by meltwater discharge and to determine what species are likely derived from the same process or source.

2.4. Water and Solute Flux Estimates

River discharge was estimated by a glacio-hydrological model developed by Zhang and others [39]. In short, discharge from glacier area was estimated using a surface energy-/mass-balance model, which is identical to that developed by Zhang and others [40]. The model consists of two components: a surface model that computes the energy available for melting from the exchange of energy between the debris-covered/debris-free surfaces and the atmosphere, and a subsurface model that treats processes occurring in the subsurface after meltwater percolates into underlying layers [39]. The temperature and precipitation time series for each elevation band were interpolated according to the mean elevation using the temperature lapse rate and precipitation gradient [39,40]. Glaciers were separated into the debris-covered and debris-free surfaces based completely on the spatial distribution of the thermal resistance of the debris layer derived from remotely sensed data [40]. Discharge from glacier-free area was estimated by the US Soil Conservation System (SCS) curve number method [49] and the mass balance equation of snowpack [39]. The model was forced by local weather observations since 1988. The forcing data required by the model included the daily air temperature, precipitation, wind speed, relative humidity and incoming solar radiation, which is monitored by the GAEORS. Modeled discharge was calibrated and validated using observed discharge at a gauging section (Figure 1), ~1.5 km downstream to the glacier terminus.
A full description of the model components, associated optimal model parameters, and the evaluation of the model performance was provided by Zhang and others [39]. As shown in Table 2, the results of model calibration demonstrate a close agreement between simulated and observed monthly discharge, yielding the Nash–Sutcliffe efficiency (NSE) value of 0.86 and the per cent bias (PBIAS) value of 5%. For the validation period, although the model yields a slight lower NSE value compared to the calibration period, the model generally reproduces the observed discharge well and captures the long-term trend. Overall, this model successfully replicates all key glacio-hydrological processes, with a rating of ‘very good’ on the scale of Moriasi and others [50].
Daily fluxes of solutes and sediments were calculated by daily solute/sediment concentrations multiplied by discharge. During the absent sampling days, daily solute/sediment concentrations were estimated by the best-fit regressions between the measured solute/sediment concentrations and modeled river discharge (for solutes: 0.22 < R2 < 0.74, p < 0.01; for sediments: R2 = 0.57, p < 0.01).

3. Results

River discharge exhibited an obvious seasonality, which showed higher variability during the high flow season (June to September) in comparison to the low flow seasons (April to May, and October to November) (Figure 2a). The temperature and precipitation displayed a similar seasonality to river discharge. Stream δ2H and δ18O values were highest during April to May, decreased to the lowest values during early August, and increased again to higher values during November (Figure 2b). pH demonstrated higher values during April to July, decreased to lower values during August or September, and increased again to higher values during November (Figure 2c). EC displayed higher values during April to May and September to November and lower values during June to August, in which its temporal variation is relatively stable (Figure 2d).
HCO3 was the dominant anion, followed by SO42−, NO3 or Cl in precipitation, groundwater and river water, and followed by Cl, SO42− and NO3 in supraglacial meltwater (Table 3). The dominant cation was Ca2+, followed by K+, Mg2+ or Na+ in meltwater, precipitation, groundwater and river water (Table 3). The concentrations or values of EC, HCO3 and cations followed the sequence: groundwater (G) > river water (R) > meltwater (M) > precipitation (P). The sequences of pH, δ2H, Cl, SO42− and NO3 were R > G > M > P, P > G > R > M, G > M > R > P, G > R > P > M, and G > P > R > M, respectively (Table 3). The concentrations of major ions and suspended sediments (SS) in river water exhibited a pronounced seasonality (Figure 3). Major ions (except for NO3) displayed an inverse relationship with river discharge exhibiting higher concentrations during the low flow seasons (R2 > 0.45, p < 0.01), and SS had a direct relationship with river discharge exhibiting the highest concentration during the high flow season (R2 = 0.33, p < 0.01) in 2008 and 2013 (Figure 4).
Daily fluxes of most ions and SS also exhibited a strong seasonality, with higher values during high flow season and lower values during low flow seasons (Figure 5). Daily fluxes were positively related to river discharge (r > 0.48 and p < 0.01 for ions, and r = 0.69 and p < 0.01 for sediments), and the cumulative fluxes and discharge increased sharply during the high flow season (not shown). Total discharge was 3.52 ± 0.18 × 108 m3 in 2008, which transported an estimated 19,435 ± 972 t solutes, with 50.2% from HCO3, 21.0% from SO42−, 19.1% from Ca2+, 8.8% from Na+, K+ and Mg2+, and 1.0% from Cl and NO3 (Table 4). In comparison, total discharge (3.73 ± 0.19 × 108 m3) in 2013 transported 18,095 ± 905 t solutes, with 52.6% from HCO3, 19.5% from SO42−, 19.2% from Ca2+, 8.4% from Na+, K+ and Mg2+, and 0.3% from Cl and NO3 (Table 4). This equated to the melt season area weighted solute yields (the solute flux divided by the drainage contributing area) of 241 ± 12.1 t∙km−2 in 2008 and 225 ± 11.2 t∙km−2 in 2013. In addition, total discharge in 2013 transported 126,390 ± 6320 t suspended sediments, with area weighted sediment yields of 1570 ± 78.5 t∙km−2 (Table 4).

4. Discussion

4.1. Controls on Runoff Chemistry

River discharge is primarily dependent on the meteorological factors, with higher discharge occurring as a result of increases in air temperature and/or precipitation (Figure 2a). The isotopic signature reflects the seasonal changes in the proportions of meltwater, precipitation and groundwater during the glacier melt season [19]. For example, the decrease of δ18O values in river water from May to August indicates the increasing proportion of meltwater and the decreasing proportion of precipitation, and vice versa for the period from August to November (Figure 6). River water is supplied by precipitation, groundwater and meltwater. This is supported by isotopic values in river water (−112.6‰ for δ2H and −15.9‰ for δ18O in 2013), which fall between meltwater (−119.3‰ and −17.2‰) and precipitation (−73.6‰ and −11.4‰)/groundwater (−93.7‰ and −13.8‰) (Table 3; Figure 6). Changes in EC highlight a temporal pattern of solute acquisition at glacial basins. The glacial drainage system evolves from a slow-inefficient distributed drainage system (delayed flow) with elevated EC during the early melt season (April to May) to a fast-efficient channelized system (fast flow) with lower EC during the peak flow season (June to September) and then to a delayed flow drainage system during the late melt season (October to November) with elevated EC again [28,29]. This limits or encourages the potential for solute acquisition in glacial environments.
The enrichment of cations between supraglacial meltwater and river water reflects the solute liberation by the subglacial bio-geochemical weathering processes as meltwater is routed through the subglacial environments, as documented at other glacial basins [26,35,55,56,57]. In addition, the concentrations of HCO3 and/or Ca2+, Mg2+, SO42− at HLG, as well as at Gangotri Glacier (GG) and/or CSG in the Himalaya Mountains and at Haut Glacier d’Arolla (HGA) in Switzerland, were lower than those at UG1 in the Tianshan Mountains (Figure 1; Table 3). For example, the seasonal mean concentrations of Ca2+, Mg2+ and SO42− from UG1 [51] are 1.2 to 4.9 times higher than those from HLG, CSG [53] and HGA [54]. This may be related to the intense dilution process at glacial basins which are controlled by the oceanic climate (more precipitation and higher discharge), in comparison to glacial basins which are controlled by the continental climate (less precipitation and lower discharge). The seasonal patterns of major ion concentrations at HLG (Figure 3) are similar to those at GG [52], CSG [53], HGA [54], and Longyearbreen Glacier in Svalbard [58]. However, the inter-annual variations of ionic concentrations slightly differed at HLG. For example, the discharge-weighted mean Ca2+ and SO42− concentrations during 2013 were 1.1 times higher than those during 2008. This may be related to the longer duration of field sampling during the low flows (with enriched solutes) in 2013 in comparison to the short duration in 2008 (Figure 3 and Figure 4).
The relationships between solute concentrations and discharge (Figure 4) indicate a combined influence of solute acquisition and meltwater dilution. The inverse correlations suggest a strong hydrological control on meltwater runoff chemistry. The fast flow transports meltwater rapidly through a channelized drainage system in the ice-walled conduits, while the delayed flow transports meltwater slowly through a distributed drainage system in the ice-bedrock interface [15,28]. Thus, the fast flow limits, and the delayed flow encourages, the potential for solute acquisition in subglacial environments (e.g., [15,28,29]). During low flow seasons at HLG, meltwater is routed through a distributed drainage system, and the longer residence time promotes the protracted and intimate contact of meltwater with the supra-, sub- and pro-glacial sediments, debris and deposits. This leads to a higher concentration of major ions in river water. During high flow season at HLG, meltwater is routed through a channelized drainage system. When meltwater enters into the subglacial environments, the dilution of delayed flow and the duration of water-sediments/debris interaction declines. This results in lower concentrations of major ions in river water at HLG.

4.2. Chemical Weathering

The meltwater runoff chemistry is primarily related to bedrock mineralogy, and the evolution of glacial drainage system (e.g., [15,54,59]). The Gibbs plots showed that the river water and groundwater are dominated by rock weathering at HLG (Figure 7). This is consistent with previous studies for river water at UG1 [51,60] and KOG [25] in the Tianshan Mountains, at DG in the Tanggula Mountains [19], and at QG in the Qilian Mountains [26] (Figure 1). However, the clusters of precipitation and supraglacial meltwater lay between the rock weathering and precipitation end-members (Figure 7). This is reasonable because the supraglacial meltwater chemistry is dominated by the atmospheric deposition and water-debris interaction, and the precipitation chemistry may be related to surrounding crustal materials carried by wind and human activity due to busy glacier tourism at HLG [61]. This further confirmed the availability of Gibbs plots in assessing the controlling mechanisms of runoff chemistry at glacial basins. In addition, the Na-normalized ratios of Ca2+ and Mg2+ are often used to evaluate the relative importance of the carbonate, silicate and evaporate weathering (e.g., [19,62,63]). The clusters of river water and groundwater lay between carbonate and silicate end members but are close to the carbonate end member (Figure 8). This suggests that runoff chemistry may be related to a mixture of carbonate and silicate weathering products. However, the clusters of precipitation and meltwater support an influence of crustal materials, and also display an influence of precipitation and meltwater on runoff chemistry at HLG (Figure 8).
Previous studies have shown that meltwater chemical composition is typically controlled by carbonate weathering at glacial basins (e.g., [15,47,64]). Even if the bedrock is dominated by silicate minerals, trace carbonates are still preferentially weathered [47], resulting in enhanced concentration of (Ca2+ + Mg2+) versus (Na+ + K+) [65]. This is supported by high molar contribution of (Ca2+ + Mg2+) to total cations during 2008 (75.6%) and 2013 (72.6%) at HLG. The ratio of (Ca2+ + Mg2+) to (Na+ + K+) has traditionally been used to indicate the amount of silicate weathering versus carbonate weathering [65,66,67]. Elevated concentrations of (Na+ + K+) versus (Ca2+ + Mg2+) are associated with enhanced silicate weathering [68]. The molar (Ca2+ + Mg2+)/(Na+ + K+) ratio increased with the seasonal evolution of glacier drainage system during 2008 and 2013 (Figure 9a). This suggests that (Na+ + K+) concentrations decrease in relation to (Ca2+ + Mg2+) concentrations and thus silicate weathering is a negligible source at HLG. Reynolds and Johnson [69] and Brown and others [55] showed that the C-ratio [HCO3/(HCO3 + SO42−)] signifies the weathering of carbonate or sulfide or both coupled. Namely, the 1:1 line signifies the carbonation reactions and 1:2 signifies the coupled sulfide and carbonate weathering. The ratios lay between 0.5 and 1 during 2008 (ranging 0.60 to 0.91) and 2013 (0.67 to 0.93) (Figure 9b), indicating the coupled carbonate and sulfide weathering at HLG. This is supported by higher molar loading of SO42− (10.8% during 2008 and 12.0% during 2013) only second to HCO3 (48.4% and 47.2%) and Ca2+ (27.3% and 26.6%). However, molar ratios of HCO3 to SO42− are far larger than 2 (Figure 9c), suggesting carbonate rather than sulfide dominates. Thus, the carbonate dissolution and sulfide oxidation primarily affects runoff chemistry at HLG.
At HLG, HCO3 and Ca2+ are primarily derived from calcite weathering (Table 1). This is confirmed by the correlations between Ca2+ and HCO3 (R2 > 0.80, p < 0.01) and the ratios of Ca2+ to HCO3 (0.41 to 0.88) that are distributed around 1:2 (which denotes calcite dissolution [52,53]) during 2008 and 2013 (Figure 9d). The Ca-Mg carbonate also contributes HCO3 and Ca2+, which is supported by relatively excess of Ca2+ (the ratios above the 1:2 line) during calcite weathering (Figure 9d) and the ratios of (Ca2+ + Mg2+) to HCO3 that are close to 1:2 (which denotes Ca-Mg carbonate dissolution [53]) during part of the periods (Figure 9e). Another source of HCO3 is oxidation of organic carbon (OC; i.e., OC + O2 + H2O → HCO3), which characterizes the solutes derived from the decomposed biota and microbial activity [70,71]. Previous studies have shown that the glacially-derived OC primarily comes from in situ primary production in the supraglacial, subglacial and proglacial environments and from the external terrestrial (e.g., soils and plant) and anthropogenic sources (e.g., [13,14,72]). However, attempting to quantify the contribution of OC to HCO3 is hampered by other sources, such as the carbonate dissolution or atmospheric sequestration of CO2 (e.g., CaCO3 + CO2 + H2O → Ca2+ + HCO3 + OH) [70]. Plagioclase, hornblende, augite and clay (e.g., montmorillonite, vermiculite) may also contribute Ca2+ to runoff chemistry (Table 1). Thus, C1 in Figure 10 reflects the geochemical reactivity of ubiquitous minerals at HLG.
SO42− mainly results from pyrite (Table 1) because pyrite is the most common sulfide mineral at glacial basins (e.g., [52,58,73]). The sulphate minerals may also contribute SO42−, which is confirmed by the close relationships between (Ca2+ + Mg2+) and SO42− during 2008 and 2013 (R2 > 0.66, p < 0.01) (Figure 9f). The molar ratios of cations to Cl is usually applied to distinguish solutes from atmospheric source after Sharp and others [59]. Compared to the molar ratios in sea water (Na/Cl = 1, K/Cl = 0.2), higher molar ratios of Na/Cl and K/Cl from HLG during 2008 (4.8 for Na/Cl and 17.8 for K/Cl) and 2013 (9.2 and 27.4) indicate less contribution of atmospheric source. In addition, the clusters of Cl, NO3 and SO42− imply an influence of human activity (C2; Figure 10). Recent studies showed that the atmospheric environment in the TP is closely related to the combustion of fossil fuel and biomass from the Indo-Gangetic Plain [74]. Additionally, at Baishui Glacier No.1 basin in the southeastern TP, NO3 and SO42− in snowpack are mainly derived from the wet deposition and anthropogenic input [61]. This source is supported by PCA (Figure 10), where significant correlations exist between Cl, SO42− and NO3 during 2008 (0.37 < R2 < 0.39, p < 0.01) and 2013 (0.19 < R2 < 0.62, p < 0.01), because NO3 has been suggested mainly from the input of acid aerosols (e.g., [58,71,75]). Thus, C2 in Figure 10 represents the contributions of the sulfide oxidation and atmosphere and/or human activity.

4.3. Solute/Sediment Export

During the high flow season, although solute concentrations are lower, solute load is higher at HLG. The temporal pattern of solute exports can largely be attributed to meltwater flux. Namely, the changes in the daily and cumulative solute fluxes are consistent with the variations in the daily and cumulative discharge respectively (not shown). This suggests that solute export is dominated by meltwater runoff at HLG. The solute yields at HLG ranges from 225 to 241 t·km−2·year−1 during 2008 and 2013 (Table 4), which is far larger than those from most glacial basins worldwide (Table 5; [76,77,78,79,80,81,82,83,84,85]). For example, the yields from HLG are 2.3 to 16.0 times higher than Midre Lovénbreen (41,000–47,000 kg·km−2·year−1) in Arctic [76], Kuannersuit (15,900 kg·km−2·year−1) in Greenland [70,71], Tungufljót (98,000 kg·km−2·year−1) in Icelandic [78], Worthington Glacier (15,000 kg·km−2·year−1) in North American [79], Chhota Shigri Glacier (17,400 kg·km−2·year−1) in Himalayan [30,53,66] and Haut Glacier d’Arolla (50,000–61,000 kg·km−2·year−1) in Alpine [59]. Although the cation denudation rate (CDR) at HLG (H-CDR; 2850 to 3108 Σ*meq+·m−2·year−1) is within the global range of currently published CDR (94–4200 Σ*meq+·m−2·year−1; Table 5), H-CDR is larger than those from almost all glacial basins except for Dokriani Glacier (462–4200 Σ*meq+·m−2·year−1) in the Himalaya Mountains (Table 5). If we normalize H-CDR by annual specific discharge (4.37 m in 2008, and 4.63 m in 2013), the chemical weathering intensity (CWI) at HLG (H-CWI) ranges from 616 to 711 Σ*meq+·m−3·year−1, which is also larger than those at most glacial basins worldwide irrespective of the lithology (Table 5). The higher yields, CDR and CWI at HLG may be caused by greater abundance of carbonate (e.g., calcite with the proportion of 1.5% in glacial deposits; Table 1), in comparison to other basins such as Haut Glacier d’Arolla (Switzerland) with 0.6% of calcite [59], suggesting that chemical weathering at glacial basins in the TP is far higher than that at other glacial basins worldwide. Given limited field data, further estimates of solute export from HLG would be helpful to better quantify the variability in the catchment-scale hydrodynamics and solute acquisition processes. Noticeably, the CDR at alpine basins (185–4200 Σ*meq+·m−2·year−1) is larger than those at polar basins (except for Icelandic; 38–850 Σ*meq+·m−2·year−1) (Table 5). This should be related to higher melt rates and more precipitation as well as the polythermal- or warm-based drainage system for alpine glaciers [51,82,83,84,85], in comparison to lower rates and less precipitation as well as cold-based drainage systems for studied polar glaciers [70,71,76]. Both daily mean suspended sediment load and yields from HLG are higher than from Chhota Shigri Glacier (135 t·day−1 for SSL and 3.0 t·km2·day1 for SSY; [86]), Dunagiri Glacier (47 t·day−1 and 2.6 t·km2·day1; [87]), Changme Khangpu Glacier (18 t·day−1 and 4.0 t·km2·day1; [88]) and Shaune Garang Glacier (43 t·day−1 and 1.1 t·km2·day1; [37]) and lower than from Dokriani Glacier (447 t·day−1 and 28 t·km2·day1; [89]) and Gangotri Glacier (11,673 t·day−1 and 21 t·km2·day1 [52]) in the Himalaya Mountains (Table 6). The difference between basins is related to many factors such as river discharge, glacierized area, rock debris, basin geology, weather conditions and sampling methods. For example, the suspended sediment concentrations and fluxes are generally controlled by river discharge at many glacial basins [30,36,38,52], and field sampling only during the high flow season will lead to the high sediment fluxes and yields at a glacial basin (Figure 4).
To investigate the temporal behavior of the delivery of solutes and sediments, the dates corresponding to 10%, 50% and 90% delivery of major ions and sediments were calculated (Table 7). In general, the 10% delivery of all ions was 25 to 98 days later than that of discharge in both 2008 and 2013. However, the 50% delivery for all ions in 2008 and for Na+, K+ and NO3 in 2013 were 3 to 57 days later, and for HCO3, Ca2+, Mg2+, SO42− and Cl in 2013 were 3 to 19 days earlier than that of discharge. Additionally, the 90% delivery of all ions were 11 to 58 days earlier than discharge in both 2008 and 2013 (Table 7). In addition, the 10% delivery of sediments was 27 days earlier than that of discharge, but the 50% and 90% deliveries were 12 and 22 days later than discharge, respectively. This is consistent with the delivery patterns of sediments from Gornergletscher Glacier in Switzerland [90] and from Gangotri Glacier in the Himalaya [91]. The later delivery suggests that the solute and sediment exports are dominated by meltwater, and the early delivery indicates that their exports are controlled not only by meltwater but also by others factors (e.g., precipitation). After glacier meltwater enters downstream, it is often used for the irrigation and drinking in the arid and/or semiarid regions (e.g., [91,92,93]). Thus, the delivery pattern of some solutes (e.g., N, P, Fe, As, Cd, Pb, and Th) adsorbed on the sediment surface [20] has important implications for the planning and management of water resources and water quality in human settlements downstream [18,19,20,21].

5. Conclusions

The chemical weathering processes and downstream solute and sediment delivery were examined at the Hailuogou Glacier basin (HLG) in the southeast Tibetan Plateau (TP) over two glacier melt seasons in 2008 and 2013. The dominant ions were HCO3, Ca2+ and SO42− in supraglacial meltwater (M), proglacial river water (R) and groundwater (G), but in precipitation (P) the dominant ions were HCO3 and SO42−. Concentrations of HCO3 and cations followed the sequence: G > R > M > P, while the sequence of SO42− was G > R > P > M. Major ions and suspended sediment concentrations in river water exhibited a pronounced seasonality, with higher values during low flow for major ions and during high flow for suspended sediments. Concentrations of major ions and suspended sediments exhibited negative and positive relationships with river discharge respectively, suggesting a strong hydrological control on meltwater runoff chemistry and suspended sediment transport. Runoff chemistry was dominated by carbonate weathering and sulfide oxidation, with less influence of oxidation of organic carbon and anthropogenic input. HCO3 and Ca2+ in river water were mainly derived from calcite weathering, and SO42− primarily came from pyrite oxidation. The solute and sediment fluxes also exhibited a strong seasonality and were positively related to river discharge. Solute flux ranged from 18,095 to 19,435 t·year−1, with 50–53% from HCO3, 20–21% from SO42− and 19% from Ca2+, and solute yields were 225–241 t·km−2·year−1. The sediment load and yields were 126,390 t·year−1 and 1570 t·km−2·year−1, respectively. The cationic denudation rate (CDR) ranged from 2850 to 3108 Σ*meq+·m−2·year−1, and chemical weathering intensity (CWI) was 616–711 Σ*meq+·m−3·year−1. The solute yields, CDR and CWI at HLG were higher than those at most glacial basins worldwide, suggesting an intense chemical weathering at monsoon-dominated glacial basins in the TP.

Author Contributions

Conceptualization, X.L. and Y.D.; Methodology, X.L. and Y.Z.; Software, X.L. and Y.Z.; Validation, Y.D. and X.L.; Investigation, X.L., T.H., Z.J., S.L. and Q.L. (Qiao Liu); Resources, X.L., Q.L. (Qiao Liu) and Y.Z.; Data curation, X.L. and Y.Z.; Writing—original draft preparation, X.L.; Writing—review and editing, Q.L. (Qiao Liu) and Y.Z.; Visualization, X.L.; Supervision, Y.D. and Z.Y.; Project administration, X.L., Y.D. and Q.L. (Qijiang Li); Funding acquisition, X.L., Y.D., T.H. and Z.J.

Funding

This research was funded by National Natural Science Foundation of China (No. 41671053 and 91647102), Fundamental Research Funds for the Central Universities (No. 2018B10214), National Natural Science Foundation of China (No. 41771040, 41730751 and 41671071), Open Foundation of State Key Laboratory of Cryospheric Sciences (No. SKLCS-OP-2019-04), and Open Foundation of State Key Laboratory of Frozen Soil Engineering (No. SKLFSE201411).

Acknowledgments

We thank the Gongga Alpine Ecosystem Observation and Research Station (GAEORS) for data sharing and field assistance.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bolch, T.; Kulkarni, A.; Kääb, A.; Huggel, C.; Paul, F.; Cogley, G.; Frey, H.; Kargel, J.S.; Fujita, K.; Scheel, M.; et al. The state and fate of Himalayan glaciers. Science 2012, 336, 310–314. [Google Scholar] [CrossRef] [PubMed]
  2. Gardelle, J.; Berthier, E.; Arnaud, Y.; Kääb, A. Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011. Cryosphere 2013, 7, 1263–1286. [Google Scholar] [CrossRef]
  3. Immerzeel, W.W.; van Beek, L.P.H.; Bierkens, M.F.P. Climate change will affect the Asian water towers. Science 2010, 328, 1382–1385. [Google Scholar] [CrossRef] [PubMed]
  4. Kang, S.; Wang, F.; Morgenstern, U.; Zhang, Y.; Grigholm, B.; Kaspari, S.; Schwikowski, M.; Ren, J.; Yao, T.; Qin, D.; et al. Dramatic loss of glacier accumulation area on the Tibetan Plateau revealed by ice core tritium and mercury records. Cryosphere 2015, 9, 1213–1222. [Google Scholar] [CrossRef][Green Version]
  5. Li, X.; Ding, Y.; Yu, Z.; Mika, S.; Liu, S.; Shangguan, D.; Lu, C. An 80-year summer temperature history from the Xiao Dongkemadi ice core in the central Tibetan Plateau and its association with atmospheric circulation. J. Asian Earth Sci. 2015, 98, 285–295. [Google Scholar] [CrossRef]
  6. Singh, D.; Swain, D.L.; Mankin, J.S.; Horton, D.E.; Thomas, L.N.; Rajaratnam, B.; Diffenbaugh, N.S. Recent amplification of the North American winter temperature dipole. J. Geophys. Res. Atmos. 2016, 121, 9911–9928. [Google Scholar] [CrossRef] [PubMed][Green Version]
  7. Yang, K.; Wu, H.; Qin, J.; Lin, C.; Tang, W.; Chen, Y. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review. Glob. Planet. Chang. 2014, 112, 79–91. [Google Scholar] [CrossRef]
  8. Yao, T.; Thompson, L.G.; Yang, W.; Yu, W.; Gao, Y.; Gao, X.; Yang, X.; Duan, K.; Zhao, H.; Xu, B.; et al. Different glacier status with atmospheric circulations in TP and surroundings. Nat. Clim. Chang. 2012, 2, 663–667. [Google Scholar] [CrossRef]
  9. Zhang, Y.; Enomoto, H.; Ohata, T.; Kitabata, H.; Kadota, T.; Hirabayashi, Y. Glacier mass balance and its potential impacts in the Altai Mountains over the period 1990–2011. J. Hydrol. 2017, 553, 662–677. [Google Scholar] [CrossRef]
  10. Hawkings, J.R.; Wadham, J.L.; Benning, L.G.; Hendry, K.R.; Tranter, M.; Tedstone, A.; Nienow, P.; Raiswell, R. Ice sheets as a missing source of silica to the polar oceans. Nat. Commun. 2017, 8, 14198. [Google Scholar] [CrossRef]
  11. Hawkings, J.R.; Wadham, J.L.; Tranter, M.; Raiswell, R.; Benning, L.G.; Statham, P.J.; Tedstone, A.; Nienow, P.; Lee, K.; Telling, J. Ice sheets as a significant source of highly reactive nanoparticulate iron to the oceans. Nat. Commun. 2014, 5, 3929. [Google Scholar] [CrossRef]
  12. Hawkings, J.R.; Wadham, J.L.; Tranter, M.; Telling, J.; Bagshaw, E.; Beaton, A.; Simmons, S.-L.; Chandler, D.; Tedstone, A.; Nienow, P. The Greenland Ice Sheet as a hot spot of phosphorus weathering and export in the Arctic. Glob. Biogeochem. Cycles 2016, 30, 191–210. [Google Scholar] [CrossRef][Green Version]
  13. Hood, E.; Battin, T.J.; Fellman, J.; O’Neel, S.; Spencer, R.G.M. Storage and release of organic carbon from glaciers and ice sheets. Nat. Geosci. 2015, 8, 91–96. [Google Scholar] [CrossRef]
  14. Li, X.; Ding, Y.; Xu, J.; He, X.; Han, T.; Kang, S.; Wu, Q.; Mika, S.; Yu, Z.; Li, Q. Importance of mountain glaciers as a source of dissolved organic carbon. J. Geophys. Res. Earth Surf. 2018, 123, 2123–2134. [Google Scholar] [CrossRef]
  15. Brown, G.H. Glacier meltwater hydrochemistry. Appl. Geochem. 2002, 17, 855–883. [Google Scholar] [CrossRef]
  16. Tranter, M.; Skidmore, M.; Wadham, J. Hydrological controls on microbial communities in subglacial environments. Hydrol. Process. 2005, 19, 995–998. [Google Scholar] [CrossRef]
  17. Wadham, J.L.; Bottrell, S.; Tranter, M.; Raiswell, R. Stable isotope evidence for microbial sulphate reduction at the bed of a polythermal high Arctic glacier. Earth Planet. Sci. Lett. 2004, 219, 341–355. [Google Scholar] [CrossRef]
  18. Fortner, S.K.; Mark, B.G.; McKenzie, J.M.; Bury, J.; Trierweiler, A.; Burns, P.J.; Munk, L.A. Elevated stream trace and minor element concentrations in the foreland of receding tropical glaciers. Appl. Geochem. 2011, 26, 1792–1801. [Google Scholar] [CrossRef]
  19. Li, X.; He, X.; Kang, S.; Mika, S.; Ding, Y.; Han, T.; Wu, Q.; Yu, Z.; Qin, D. Diurnal dynamics of minor and trace elements in stream water draining Dongkemadi Glacier on the Tibetan Plateau and its environmental implications. J. Hydrol. 2016, 541, 1104–1118. [Google Scholar] [CrossRef]
  20. Mitchell, A.; Brown, G.H. Diurnal hydrological-physicochemical controls and sampling methods for trace elements in an Alpine glacial hydrological system. J. Hydrol. 2007, 332, 123–143. [Google Scholar] [CrossRef]
  21. Qu, B.; Zhang, Y.; Kang, S.; Sillanpää, M. Water quality in the Tibetan Plateau: Major ions and trace elements in rivers of the “Water Tower of Asia”. Sci. Total Environ. 2019, 649, 571–581. [Google Scholar] [CrossRef]
  22. Zhao, H.; Yao, T.; Xu, B. Preliminary results on hydrological and hydrochemical features of Kartamak Glacier area in Mt. Muztag Ata. J. Mt. Sci. 2007, 4, 77–85. [Google Scholar] [CrossRef]
  23. Liu, F.; Williams, M.W.; Sun, J.; Zhu, S.; Hood, E.; Cheng, G. Hydrochemical process and hydrological separation at the headwaters of the Urumqi River, Tianshan Mountains, China. J. Glaciol. Geocryol. 1999, 21, 362–370. [Google Scholar]
  24. Li, C.; Hou, S.; Qin, D. Spatial differences of hydrochemical and its control factors of the headwater runoff in the Urumqi River, Tianshan Mountains. J. Glaciol. Geocryol. 2003, 25, 72–76. [Google Scholar]
  25. Wang, J.; Ding, Y.; Xu, J.; Han, H. Hydrochemical characteristic analysis of melting water flow in Koxkar Glacier, Tianshan (West) Mountains. Environ. Sci. 2006, 27, 1305–1311. [Google Scholar]
  26. Li, X.; Qin, D.; Jing, Z.; Li, Y.; Wang, N. Diurnal hydrological controls and non-filtration effects on minor and trace elements in stream water draining the Qiyi Glacier, Qilian Mountain. Sci. China Earth Sci. 2013, 56, 81–92. [Google Scholar] [CrossRef]
  27. Wang, J.; Aihemaiti, A.; Ding, Y.; Liu, S.; Wu, J. Variations of pH value and electrical conductivity in the Dongkemadi basin, Tanggula Range. Environ. Sci. 2007, 28, 2031–2037. [Google Scholar]
  28. Brown, G.H.; Sharp, M.; Tranter, M.; Gurnell, A.M.; Nienow, P. Impact of post mixing chemical reactions on the major ion chemistry of bulk meltwaters draining the Haut Glacier d’Arolla, Valais, Switzerland. Hydrol. Process. 1994, 8, 465–480. [Google Scholar] [CrossRef]
  29. Tranter, M.; Brown, G.H.; Raiswell, R.; Sharp, M.; Gurnell, A. A conceptual model of solute aqusistion by Alpine glacial meltwaters. J. Glaciol. 1993, 39, 573–581. [Google Scholar] [CrossRef]
  30. Hasnain, S.I.; Subramanian, V.; Dhanpal, K. Chemical characteristics and suspended sediment load of meltwaters from a Himalayan glacier in India. J. Hydrol. 1989, 106, 99–108. [Google Scholar] [CrossRef]
  31. Kumar, R.; Kumar, R.; Singh, A.; Singh, S.; Bhardwaj, A.; Kumari, A.; Sinha, R.K. Hydro-geochemical analysis of meltwater draining from Bilare Banga glacier, Western Himalaya. Acta Geophys. 2019, 67, 651–660. [Google Scholar] [CrossRef]
  32. Wu, X.; Li, Q.; Song, G.; He, J.; Jiang, X. Hydrochemical characteristics and evolution of runoff at Qiyi Glacier, Qilian Mts. Environ. Sci. 2008, 29, 613–618. [Google Scholar]
  33. Wu, X.; Wang, N.; Li, Q. Diurnal variation of meltwater chemistry in the Qiyi Glacier during the late ablation period. J. Glaciol. Geocryol. 2009, 31, 1080–1085. [Google Scholar]
  34. Ahmad, S.; Hasnain, S.I. Chemical characteristics of stream draining from Dudu Glacier: An alpine meltwater stream in Ganga Headwater, Garhwal Himalaya. J. China Univ. Geosci. 2001, 12, 75–83. [Google Scholar]
  35. Hasnain, S.I.; Thayyen, R.J. Controls on the major-ion chemistry of the Dokriani glacier meltwaters, Ganga basin, Garhwal Himalaya, India. J. Glaciol. 1999, 45, 87–92. [Google Scholar] [CrossRef]
  36. Kumar, R.; Kumar, R.; Singh, S.; Singh, A.; Bhardwaj, A.; Kumari, A.; Randhawa, S.S.; Saha, A. Dynamics of suspended sediment load with respect to summer discharge and temperatures in Shaune Garang glacierized catchment, Western Himalaya. Acta Geophys. 2018, 67, 1109–1120. [Google Scholar] [CrossRef]
  37. Kumar, A.; Verma, A.; Gokhale, A.A.; Bhambri, R.; Misra, A.; Sundriyal, S.; Dobhal, D.P.; Kishore, N. Hydrometeorological assessments and suspended sediment delivery from a central Himalayan glacier in the upper Ganga basin. Int. J. Sediment Res. 2018, 33, 493–509. [Google Scholar] [CrossRef]
  38. Kumar, A.; Bhambri, R.; Tiwari, S.K.; Verma, A.; Gupta, A.K.; Kawishwar, P. Evolution of debris flow and moraine failure in the Gangotri Glacier region, Garhwal Himalaya: Hydro-geomorphological aspects. Geomorphology 2019, 333, 152–166. [Google Scholar] [CrossRef]
  39. Zhang, Y.; Hirabayashi, Y.; Liu, Q.; Liu, S. Glacier runoff and its impact in a highly glacierized catchment in the southeastern Tibetan Plateau: Past and future trends. J. Glaciol. 2015, 61, 713–730. [Google Scholar] [CrossRef]
  40. Zhang, Y.; Hirabayashi, Y.; Liu, S. Catchment-scale reconstruction of glacier mass balance using observations and global climate data: Case study of the Hailuogou basin, south-eastern Tibetan Plateau. J. Hydrol. 2012, 444–445, 146–160. [Google Scholar] [CrossRef]
  41. Zhang, Y.; Fujita, K.; Liu, S.; Liu, Q.; Nuimura, T. Distribution of debris thickness and its effect on ice melt at Hailuogou Glacier, southeastern Tibetan Plateau, using in situ surveys and ASTER imagery. J. Glaciol. 2011, 57, 1147–1157. [Google Scholar] [CrossRef]
  42. Liu, Q.; Liu, S.; Zhang, Y.; Wang, X.; Zhang, Y.; Guo, W.; Xu, J. Recent shrinkage and hydrological response of Hailuogou glacier, a monsoon temperate glacier on the east slope of Mount Gongga, China. J. Glaciol. 2010, 56, 215–224. [Google Scholar] [CrossRef][Green Version]
  43. Lü, R. The physiognomy and ecological and environmental resources in Hailuogou catchment. Res. Trends Ecol. Environ. Netw. 1996, 7, 32–39. [Google Scholar]
  44. Liu, G.; Zhang, Y.; Fu, H.; Chen, Y.; Shi, L. Sedimentary characteristics and subglacial processes of the glacial deposits in Hailuogou Glacier, Gongga Mountain. J. Glaciol. Geocryol. 2009, 31, 68–74. [Google Scholar]
  45. Deuerling, K.M.; Martin, J.B.; Martin, E.E.; Scribner, C.A. Hydrologic exchange and chemical weathering in a proglacial watershed near Kangerlussuaq, west Greenland. J. Hydrol. 2018, 556, 220–232. [Google Scholar] [CrossRef]
  46. Scribner, C.A.; Martin, E.E.; Martin, J.B.; Deuerling, K.M.; Collazo, D.F.; Marshall, A.T. Exposure age and climate controls on weathering in deglaciated watersheds of western Greenland. Geochim. Cosmochim. Acta 2015, 170, 157–172. [Google Scholar] [CrossRef]
  47. Tranter, M. Geochemical Weathering in Glacial and Proglacial Environments; Drever, I.J., Ed.; Elsevier: Amsterdam, The Netherlands, 2003; pp. 189–205. [Google Scholar]
  48. Ter Braak, C.J.F.; Smilauer, P. CANOCO Reference Manual and CanoDraw for Windows User’s Guide; Software for Canonical Community Ordination Version 4.5.; Biometris; Wageningen and Ceske Budejovice: Ithaca, NY, USA, 2002. [Google Scholar]
  49. Soil Conservation Service (SCS). Hydrology. In National Engineering Handbook; Soil Conservation Service; US Department of Agriculture: Washington, DC, USA, 1972. [Google Scholar]
  50. Moriasi, D.N.; Arnold, J.; Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. Am. Soc. Agric. Biol. Eng. 2007, 50, 885–900. [Google Scholar]
  51. Feng, F.; Li, Z.; Jin, S.; Dong, Z.; Wang, F. Hydrochemical characteristics and solute dynamics of meltwater runoff of Urumqi Glacier No.1, eastern Tianshan, northwest China. J. Mt. Sci. 2012, 9, 472–482. [Google Scholar] [CrossRef]
  52. Singh, V.B.; Ramanathan, A.L.; Pottakkal, J.G.; Kumar, M. Seasonal variation of the solute and suspended sediment load in Gangotri glacier meltwater, central Himalaya, India. J. Asian Earth Sci. 2014, 79, 224–234. [Google Scholar] [CrossRef]
  53. Singh, V.B.; Ramanathan, A.L. Hydrogeochemistry of the Chhota Shigri glacier meltwater, Chandra basin, Himachal Pradesh, India: Solute acquisition processes, dissolved load and chemical weathering rates. Environ. Earth Sci. 2017, 76, 223. [Google Scholar] [CrossRef]
  54. Mitchell, A.C.; Brown, G.H.; Fuge, R. Minor and trace element export from a glacierized Alpine headwater basin (Haut Glacier d’Arolla, Switzerland). Hydrol. Process. 2001, 15, 3499–3524. [Google Scholar] [CrossRef]
  55. Brown, G.H.; Tranter, M.; Sharp, M. Subglacial chemical erosion-seasonal variations in solute provenance, Haut Glacier d’Arolla, Switzerland. Ann. Glaciol. 1996, 22, 25–31. [Google Scholar] [CrossRef]
  56. Hodson, A.J.; Tranter, M.; Vatne, G. Contemporary rates of chemical denudation and atmospheric CO2 sequestration in glacier basins: An Arctic perspective. Earth Surf. Proc. Land. 2000, 25, 1447–1471. [Google Scholar] [CrossRef]
  57. Tranter, M.; Huybrechts, P.; Munhoven, G.; Sharp, M.; Brown, G.H.; Jones, I.W.; Hodson, A.J.; Hodgkins, R.; Wadhama, J.L. Direct effect of ice sheets on terrestrial bicarbonate, sulphate and base cation fluxes during the last glacial cycle: Minimal impact on atmospheric CO2 concentrations. Chem. Geol. 2002, 190, 33–44. [Google Scholar] [CrossRef]
  58. Yde, J.C.; Riger-Kusk, M.; Christiansen, H.H.; Knudsen, N.T.; Humlum, O. Hydrochemical characteristics of bulk meltwater from an entire ablation season, Longyearbreen, Svalbard. J. Glaciol. 2008, 54, 259–272. [Google Scholar] [CrossRef][Green Version]
  59. Sharp, M.; Tranter, M.; Brown, G.H.; Skidmore, M. Rates of chemical denudation and CO2 drawdown in a glacier-covered alpine basin. Geology 1995, 23, 61–64. [Google Scholar] [CrossRef]
  60. Williams, M.W.; Yang, D.; Liu, F.; Turk, J.; Melack, J.M. Controls on the major ion chemistry of the Urumqi River, Tian shan, People’s Republic of China. J. Hydrol. 1995, 172, 209–229. [Google Scholar] [CrossRef]
  61. Wang, S.; Shi, X.; Cao, W.; Pu, T. Seasonal variability and evolution of glaciochemistry at an alpine temperate glacier on the southeastern Tibetan Plateau. Water 2018, 10, 114. [Google Scholar] [CrossRef]
  62. Gaillardet, J.; Dupré, B.; Louvat, P.; Allègre, C.J. Global silicate weathering and CO2 consumption rates deduced from the chemistry of large rivers. Chem. Geol. 1999, 159, 3–30. [Google Scholar] [CrossRef]
  63. Yde, J.C.; Knudsen, N.T.; Hasholt, B.; Mikkelsen, A.B. Meltwater chemistry and solute export from a Greenland Ice Sheet catchment, Watson River, West Greenland. J. Hydrol. 2014, 519, 2165–2179. [Google Scholar] [CrossRef]
  64. Bhatia, M.P.; Kujawinski, E.B.; Das, S.B.; Breier, C.F.; Henderson, P.B.; Charette, M.A. Greenland meltwater as a significant and potentially bioavailable source of iron to the ocean. Nat. Geosci. 2013, 6, 274–278. [Google Scholar] [CrossRef]
  65. Tranter, M.; Sharp, M.J.; Lamb, H.R.; Brown, G.H.; Hubbard, B.P.; Willis, I.C. Geochemical weathering at the bed of Haut Glacier d’Arolla, Switzerland—A new model. Hydrol. Process. 2002, 16, 959–993. [Google Scholar] [CrossRef]
  66. Hodson, A.; Porter, P.; Lowe, A.; Mumford, P. Chemical denudation and silicate weathering in Himalayan glacier basins: Batura Glacier, Pakistan. J. Hydrol. 2002, 262, 193–208. [Google Scholar] [CrossRef]
  67. Wadham, J.L.; Tranter, M.; Skidmore, M.; Hodson, A.J.; Priscu, J.; Lyons, W.B.; Sharp, M.; Wynn, P.; Jackson, M. Biogeochemical weathering under ice: Size matters. Glob. Biogeochem. Cycles 2010, 24. [Google Scholar] [CrossRef][Green Version]
  68. Hatton, J.E.; Hendry, K.R.; Hawkings, J.R.; Wadham, J.L.; Kohler, T.J.; Stibal, M.; Beaton, A.D.; Bagshaw, E.A.; Telling, J. Investigation of subglacial weathering under the Greenland Ice Sheet using silicon isotopes. Geochim. Cosmochim. Acta 2019, 247, 191–206. [Google Scholar] [CrossRef][Green Version]
  69. Reynolds, R.C.; Johnson, N.M. Chemical weathering in the temperate glacial environment of the Northern Cascade Mountains. Geochim. Cosmochim. Acta 1972, 36, 537–554. [Google Scholar] [CrossRef]
  70. Yde, J.C.; Knudsen, N.T. The importance of oxygen isotope provenance in relation to solute content of bulk meltwaters at Imersuaq Glacier, West Greenland. Hydrol. Process. 2004, 18, 125–139. [Google Scholar] [CrossRef]
  71. Yde, J.C.; Knudsen, N.T.; Nielsen, O.B. Glacier hydrochemistry, solute provenance, and chemical denudation at a surge-type glacier in Kuannersuit Kuussuat, Disko Island, West Greenland. J. Hydrol. 2005, 300, 172–187. [Google Scholar] [CrossRef]
  72. Stibal, M.; Wadham, J.L.; Lis, G.P.; Telling, J.; Pancost, R.D.; Dubnick, A.; Sharp, M.; O’Donnell, E.; Butler, C.E.H.; Hasan, F.; et al. Methanogenic potential of Arctic and Antarctic subglacial environments with contrasting organic carbon sources. Glob. Chang. Biol. 2012, 18, 3332–3345. [Google Scholar] [CrossRef]
  73. Wadham, J.L.; Hodgkins, R.; Cooper, R.J.; Tranter, M. Evidence for seasonal subglacial outburst events at a polythermal glacier, Finsterwalderbreen, Svalbard. Hydrol. Process. 2001, 15, 2259–2280. [Google Scholar] [CrossRef]
  74. Li, C.; Bosch, C.; Kang, S.; Andersson, A.; Chen, P.; Zhang, Q.; Cong, Z.; Chen, B.; Qin, D. Sources of black carbon to the Himalayan-Tibetan Plateau glaciers. Nat. Commun. 2016, 7, 12574. [Google Scholar] [CrossRef]
  75. Hodgkins, R.; Tranter, M.; Dowdeswell, J.A. Solute provenance, transport and denudation in a High-Arctic glacierised basin. Hydrol. Process. 1997, 11, 1813–1832. [Google Scholar] [CrossRef]
  76. Hodson, A.; Heaton, T.; Langford, H.; Newsham, K. Chemical weathering and solute export by meltwater in a maritime Antarctic glacier basin. Biogeochemistry 2010, 98, 9–27. [Google Scholar] [CrossRef]
  77. Church, M. On the quality of some waters on Baffin Island, Northwest Territories. Can. J. Earth Sci. 1974, 11, 1676–1688. [Google Scholar] [CrossRef]
  78. Gíslason, S.; Arnorsson, S.; Armannsson, H. Chemical weathering in southwest Iceland: Effects of runoff, age of rocks and vegetative/glacial cover. Am. J. Sci. 1996, 296, 837–907. [Google Scholar] [CrossRef]
  79. Anderson, S.P.; Drever, J.I.; Humphrey, N.F. Chemical weathering in glacial environments. Geology 1997, 25, 399–402. [Google Scholar] [CrossRef]
  80. Eyles, N.; Sasseville, D.R.; Slatt, R.M.; Rogerson, R.J. Geochemical denudation rates and solute transport mechanisms in a maritime temperate glacier basin. Can. J. Earth Sci. 1982, 19, 1570–1581. [Google Scholar] [CrossRef]
  81. Axtmann, E.V.; Stallard, R.F. Chemical weathering in the South Cascade Glacier Basin, comparison of subglacial and extra-glacial weathering. IAHS Publ. 1995, 228, 431–439. [Google Scholar]
  82. Collins, D.N.; Lowe, A.T.; Boult, S. Solute fluxes in meltwaters draining from glacierised high mountain basins. In Fourth International Symposium on The Geochemistry of the Earth’s Surface; Bottrell, S.H., Ed.; University of Leeds: Ilkley, UK, 1996; pp. 728–732. [Google Scholar]
  83. Hasnain, S.I.; Thayyen, R.J. Sediment transport and solute variation in meltwaters of Dokriani Glacier Bamak, Garwhal Himalaya. J. Geol. Soc. India 1996, 47, 731–739. [Google Scholar]
  84. Collins, D.N. Solute yield from a glacierised high mountain basin. IAHS Publ. 1983, 141, 41–50. [Google Scholar]
  85. Li, X.; He, X.; Kang, S.; Mika, S.; Ding, Y.; Han, T.; Wu, Q.; Yu, Z. Corrigendum to ‘‘Diurnal dynamics of minor and trace elements in stream water draining Dongkemadi Glacier on the Tibetan Plateau and its environmental implications”. J. Hydrol. 2017, 555, 995. [Google Scholar] [CrossRef]
  86. Vohra, K. Sediment Load of Chhota Shigri Glacier; Technical Report on Multi Disciplinary Glacier Expedition to Chhota Shigri; Department of Science and Technology: New Delhi, India, 1991; Volume 4, pp. 75–90.
  87. Puri, V.M.K. Glaciohydrological and Suspended Sediment Load Studies in the Melt Water Channel of Changme Khangpu Glacier, Mangam District, Sikkim; Symposium on Snow, Ice and Glaciers-Himalayan Prospective: Lucknow, India, 1999; p. 1. [Google Scholar]
  88. Singh, P.; Ramasastri, K.S. Project Report on Dokriani Glacier; National Institute of Hydrology: Roorkee, India, 1999; p. 143.
  89. Srivastava, D.; Swaroop, S.; Mukerji, S.; Gautam, C.K.; Roy, D. Suspended Sediment Yield and Its Variation in Dunagiri Glacier Melt Stream, Garhwal Himalaya; Symposium on Snow, Ice and Glaciers a Himalayan Perspective: Lucknow, India, 1999; p. 45. [Google Scholar]
  90. Collins, D.N. Seasonal and annual variations of suspended sediment transport in meltwaters draining from an Alpine glacier. IAHS Publ. 1990, 193, 439–446. [Google Scholar]
  91. Haritashya, U.K.; Singh, P.; Kumar, N.; Gupta, R.P. Suspended sediment from the Gangotri Glacier: Quantification, variability and associations with discharge and air temperature. J Hydrol. 2006, 321, 116–130. [Google Scholar] [CrossRef]
  92. Butz, D. The agricultural use of meltwater in Hopar settlement, Pakistan. Ann. Glaciol. 1989, 13, 35–39. [Google Scholar] [CrossRef]
  93. Bratt, G. The Bisses of Valais: Man-made Watercourses in Switzerland; Amadeus Press: Huddersfield, UK, 1995. [Google Scholar]
Figure 1. Location map showing the sampling sites, weather station and gauging section at the Hailuogou basin (HLG) in the southeastern Tibetan Plateau, as well as at Kartamak Glacier (KAG) in the Muztag Ata Mountains, at Koxkar Glacier (KOG) and Urumqi Glacier No.1 (UG1) in the Tianshan Mountains, at Shaune Garang Glacier (SGG), Bilare Banga Glacier (BBG), Chhota Shigri Glacier (CSG) and Gangotri Glacier (GG) in the Himalaya Mountains, at Dongkemadi Glacier (DG) in the Tanggula Mountains, and at Qiyi Glacier (QG) in the Qilian Mountains in Asia.
Figure 1. Location map showing the sampling sites, weather station and gauging section at the Hailuogou basin (HLG) in the southeastern Tibetan Plateau, as well as at Kartamak Glacier (KAG) in the Muztag Ata Mountains, at Koxkar Glacier (KOG) and Urumqi Glacier No.1 (UG1) in the Tianshan Mountains, at Shaune Garang Glacier (SGG), Bilare Banga Glacier (BBG), Chhota Shigri Glacier (CSG) and Gangotri Glacier (GG) in the Himalaya Mountains, at Dongkemadi Glacier (DG) in the Tanggula Mountains, and at Qiyi Glacier (QG) in the Qilian Mountains in Asia.
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Figure 2. Seasonal variations in river discharge (Q), air temperature (T) and precipitation (P) (a), as well as δ18O (b), pH (c) and electrical conductivity (EC; (d) in river water at the Hailuogou basin over two melt seasons in 2008 and 2013. Note that δ18O represents δ2H (not shown) in 2013.
Figure 2. Seasonal variations in river discharge (Q), air temperature (T) and precipitation (P) (a), as well as δ18O (b), pH (c) and electrical conductivity (EC; (d) in river water at the Hailuogou basin over two melt seasons in 2008 and 2013. Note that δ18O represents δ2H (not shown) in 2013.
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Figure 3. Seasonal variations in the concentrations of major ions and suspended sediments (SS) in river water at the Hailuogou basin over two melt seasons in 2008 and 2013.
Figure 3. Seasonal variations in the concentrations of major ions and suspended sediments (SS) in river water at the Hailuogou basin over two melt seasons in 2008 and 2013.
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Figure 4. Relationships between river discharge and concentrations of major ions and suspended sediments (SS) in river water at the Hailuogou basin over two melt seasons in 2008 and 2013.
Figure 4. Relationships between river discharge and concentrations of major ions and suspended sediments (SS) in river water at the Hailuogou basin over two melt seasons in 2008 and 2013.
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Figure 5. Seasonal variations in the proportions of daily (FLU) and cumulative (CFL) fluxes for major ions and suspended sediments (SS) in river water, in comparison to the proportions of daily (DIS) and cumulative (CDI) discharge at the Hailuogou basin over two melt seasons in 2008 and 2013.
Figure 5. Seasonal variations in the proportions of daily (FLU) and cumulative (CFL) fluxes for major ions and suspended sediments (SS) in river water, in comparison to the proportions of daily (DIS) and cumulative (CDI) discharge at the Hailuogou basin over two melt seasons in 2008 and 2013.
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Figure 6. The levels of δ18O values in river water (R), in comparison to those in precipitation (P), meltwater (M) and groundwater (G) at the Hailuogou basin over two melt seasons in 2008 and 2013.
Figure 6. The levels of δ18O values in river water (R), in comparison to those in precipitation (P), meltwater (M) and groundwater (G) at the Hailuogou basin over two melt seasons in 2008 and 2013.
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Figure 7. The Gibbs plots of total dissolved solids (TDS) versus molar Na/(Na + Ca) (a) and Cl/(Cl + HCO3) (b) for river water (R), precipitation (P), meltwater (M) and groundwater (G) at the Hailuogou basin over two melt seasons in 2008 and 2013.
Figure 7. The Gibbs plots of total dissolved solids (TDS) versus molar Na/(Na + Ca) (a) and Cl/(Cl + HCO3) (b) for river water (R), precipitation (P), meltwater (M) and groundwater (G) at the Hailuogou basin over two melt seasons in 2008 and 2013.
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Figure 8. Variations in molar Mg/Na versus Ca/Na (a) in river water (R), precipitation (P), meltwater (M) and groundwater (G) at the Hailuogou basin over two melt seasons in 2008 and 2013, in comparison to the end-members of carbonate, silicate and evaporate weathering.
Figure 8. Variations in molar Mg/Na versus Ca/Na (a) in river water (R), precipitation (P), meltwater (M) and groundwater (G) at the Hailuogou basin over two melt seasons in 2008 and 2013, in comparison to the end-members of carbonate, silicate and evaporate weathering.
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Figure 9. Variations in molar (Ca + Mg)/(Na + K) ratios (a), as well as in HCO3 versus HCO3 + SO4 (b), HCO3 versus SO4 (c), Ca versus HCO3 (d), Ca + Mg versus HCO3 (e) and Ca + Mg versus SO4 (f) for river water at the Hailuogou basin over two melt seasons in 2008 and 2013.
Figure 9. Variations in molar (Ca + Mg)/(Na + K) ratios (a), as well as in HCO3 versus HCO3 + SO4 (b), HCO3 versus SO4 (c), Ca versus HCO3 (d), Ca + Mg versus HCO3 (e) and Ca + Mg versus SO4 (f) for river water at the Hailuogou basin over two melt seasons in 2008 and 2013.
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Figure 10. Principle component analysis (PCA) for species in river water at the Hailuogou basin over two melt seasons in 2008 (a) and 2013 (b). Variables are ions, δ18O, pH, electrical conductivity (EC), total dissolved solids (TDS), air temperature (T), precipitation (P) and discharge (Q). The similar arrow direction indicates that variables exhibit a positive correlation, the opposite direction indicates a negative correlation, and the perpendicular direction denotes no correlation.
Figure 10. Principle component analysis (PCA) for species in river water at the Hailuogou basin over two melt seasons in 2008 (a) and 2013 (b). Variables are ions, δ18O, pH, electrical conductivity (EC), total dissolved solids (TDS), air temperature (T), precipitation (P) and discharge (Q). The similar arrow direction indicates that variables exhibit a positive correlation, the opposite direction indicates a negative correlation, and the perpendicular direction denotes no correlation.
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Table 1. The mineral composition (%) of glacial deposits at the Hailuogou basin.
Table 1. The mineral composition (%) of glacial deposits at the Hailuogou basin.
SampleQuartzPotash FeldsparPlagioclaseCalcitePyriteHornblendeAugiteClay
HLG127.519.424.8N/D2.516.83.85.2
HLG232.66.115.4N/D9.025.86.64.5
HLG330.49.212.21.85.022.96.711.8
HLG430.812.916.31.46.015.76.410.5
HLG531.88.914.21.48.316.26.812.4
Average30.611.316.61.56.219.56.18.9
N/D indicates not been detected in the laboratory.
Table 2. The model calibration and validation results for the Hailuogou basin [39].
Table 2. The model calibration and validation results for the Hailuogou basin [39].
PeriodNSEPBIAS (%)
Calibration (1994–2000)0.865.0
Validation (2001–2007)0.83−7.0
Overall (1994–2007)0.85−4.5
Table 3. Median (Med), maximum (Max) and minimum (Min) concentrations or values of pH, electrical conductivity (EC); μS·cm−1), total dissolved solids (TDS; mg·L−1), δ2H and δ18O (‰), major ions (μM), and/or suspended sediment concentrations (SSC; mg·L−1) in precipitation (P), supraglacial meltwater (M), groundwater (G) and river water (R) at the Hailuogou basin (HLG) over two melt seasons in 2008 and 2013, in comparison to Urumqi Glacier No.1 (UG1) in the Tianshan Mountains, Gangotri Glacier (GG) and Chhota Shigri Glacier (CSG) in the Himalaya Mountains, and Haut Glacier d’Arolla (HGA) in Switzerland. N/A denotes data are not available.
Table 3. Median (Med), maximum (Max) and minimum (Min) concentrations or values of pH, electrical conductivity (EC); μS·cm−1), total dissolved solids (TDS; mg·L−1), δ2H and δ18O (‰), major ions (μM), and/or suspended sediment concentrations (SSC; mg·L−1) in precipitation (P), supraglacial meltwater (M), groundwater (G) and river water (R) at the Hailuogou basin (HLG) over two melt seasons in 2008 and 2013, in comparison to Urumqi Glacier No.1 (UG1) in the Tianshan Mountains, Gangotri Glacier (GG) and Chhota Shigri Glacier (CSG) in the Himalaya Mountains, and Haut Glacier d’Arolla (HGA) in Switzerland. N/A denotes data are not available.
HLGUG1 [51]GG [52]CSG [53]HGA [54]
PMGRRRRRR
201320132013Seasonal (2008)Seasonal (2013)SeasonalSeasonalSeasonalSeasonal
MedMedMedMinMaxMedSt devMinMaxMedSt devAverageAverageAverageMed
N1422615144144144144379379379379217N/AN/A132
SSCN/AN/AN/AN/AN/AN/AN/A36.03608193346N/AN/AN/AN/A
pH5.907.727.896.779.268.470.686.819.268.150.317.61–7.746.646.68.1
EC8.5811.70153.930.3090.6059.1014.0045.30178.695.4030.3375.4–93.678.736.8N/A
TDS4.335.9476.6015.1545.3029.557.0023.0090.3048.3015.2848.3–59.951.5N/AN/A
δ2H−73.61−119.3−93.66N/AN/AN/AN/A−130.8−93.96−112.68.74N/AN/AN/AN/A
δ18O−11.42−17.21−13.78−18.22−13.57−15.931.37−18.79−13.69−15.911.13N/AN/AN/AN/A
Na+0.5826.10995.965.64347.9116.058.3047.17488.3927.4018.1826.9648.4337.9116.09
K+0.5898.336228.731.59140.861.3822.6124.46173.777.4432.3123.3354.3330.799.487
Mg2+0.2551.871152.211.8653.7524.408.6254.17990.1324.9516.1345.00–47.08110.237.0816.67
Ca2+3.45316.07586.9122.0414.3215.154.1560.28529.1252.3103.4309.8–364.8129.575.00165.0
Cl0.4513.2538.7141.11315.773.4222.1770.58014.702.7663.00817.78–19.1715.3315.892.444
SO42−3.6461.135397.728.76259.285.0436.3519.38296.9113.864.73177.7–199.8227.556.5054.17
NO32.8450.8689.5230.2239.0792.1271.8710.1264.6271.6851.04315.81–18.065.9193.5006.452
HCO316.0348.21999.7148.9689.6381.797.46111.81021448.0157.6370.3–439.3217.0216.0278.7
Table 4. The flux and yields (with an estimated 5% error due to the bias between modeled and observed discharge) of major ions and suspended sediments (SS) in river water draining Hailuogou Glacier over two melt seasons in 2008 and 2013. N/A denotes data are not available.
Table 4. The flux and yields (with an estimated 5% error due to the bias between modeled and observed discharge) of major ions and suspended sediments (SS) in river water draining Hailuogou Glacier over two melt seasons in 2008 and 2013. N/A denotes data are not available.
Species2008 2013
FluxErrorYieldsErrorFluxErrorYieldsError
t·year−1 t·km−2·year−1 t·year−1 t·km−2·year−1
SSN/A N/A 126,3906320157078.5
Na+25112.63.120.1622511.32.80.14
K+117458.714.60.73107253.613.30.67
Mg2+28314.23.510.1821610.82.680.13
Ca2+371218646.12.31348217443.32.17
Cl88.14.411.090.0540.22.010.50.03
SO42−407320450.62.53352317643.82.19
NO31025.101.270.0620.61.030.260.01
HCO397524881216.0595164761185.90
Dissolved solutes19,43597224112.118,09590522511.2
Table 5. The solute yields (kg·km−2·year−1), cationic denudation rate (CDR; Σ*meq+·m−2·year−1) and chemical weathering intensity (CWI; Σ*meq+·m−3·year−1) at the Hailuogou basin, in comparison to other glacial basins worldwide. N/A denotes data are not available.
Table 5. The solute yields (kg·km−2·year−1), cationic denudation rate (CDR; Σ*meq+·m−2·year−1) and chemical weathering intensity (CWI; Σ*meq+·m−3·year−1) at the Hailuogou basin, in comparison to other glacial basins worldwide. N/A denotes data are not available.
BasinTypeSolute YieldsCDRCWIReference
Tuva GlacierAntarcticN/A163308[76]
Austre BroggerbreenArctic23,000–28,000240–270208–286[56]
ErdmannbreenArctic16,000190235[56]
ErikbreenArctic31,000320627[56]
HannabreenArctic30,000320400[56]
FinsterwalderbreenArcticN/A210–440250–1257[73]
Scott TurnerbreenArctic16,000160308[75]
LongyearbreenArctic23,535322940[58]
Midre LovénbreenArctic41,000–47,000450–560300–431[76]
Lewis RiverArcticN/A94132[77]
KuannersuitGreenland15,900680–850272–340[70,71]
MittivakkatGreenlandN/A27083[76]
Watson RiverGreenlandN/A38–56100–138[63]
TungufljótIcelandic98,000720343[78]
Hvítá-S/WIcelandic80,000–123,000650–1100361–524[78]
Worthington GlacierN American15,0001600208[79]
Berendon GlacierN AmericanN/A947256[80]
S Cascade GlacierN American14,100676–930173–282[69,79,81]
Batura GlacierHimalayanN/A1460730[66,82]
Chhota Shigri GlacierHimalayan17,400750–935214[30,53,66]
DokrianiHimalayan9700462–4200385–646[62,83]
Gornergletscher GlacierAlpineN/A450321[84]
Haut Glacier d’ArollaAlpine50,000–61,00064–685298–376[59]
Dongkemadi GlacierAlpine14,911185189[19,85]
Glacier No.1Alpine43,240–52,329577–703N/A[51]
Hailuogou GlacierAlpine231,700–241,4282850–3108616–711This study
Table 6. Daily mean suspended sediment load (SSL) and yields (SSY) from Hailuogou Glacier in comparison to nearby Himalayan glaciers. N/A denotes data are not available.
Table 6. Daily mean suspended sediment load (SSL) and yields (SSY) from Hailuogou Glacier in comparison to nearby Himalayan glaciers. N/A denotes data are not available.
GlacierBasin Area (km2)Glacierized Area (%)SSL (t∙day−1)SSY (t·km−2·day−1)Reference
Shaune Garang38.1N/A431.1[37]
Gangotri55651.411,67321.0[52]
Chhota Shigri45251353.0[86]
Dunagiri17.9N/A472.6[87]
Changme Khangpu4.5N/A184.0[88]
Dokriani16.14544727.8[89]
Hailuogou80.545.33464.3This study
Table 7. Comparison of the dates of cumulative percentage distribution of major ion and suspended sediment (SS) fluxes with discharge (DIS) at the Hailuogou basin over two melt seasons in 2008 (before the slash) and 2013 (after the slash). Negative and positive values denote the days for solutes and SS later and earlier than discharge delivery, respectively. N/A denotes data are not available.
Table 7. Comparison of the dates of cumulative percentage distribution of major ion and suspended sediment (SS) fluxes with discharge (DIS) at the Hailuogou basin over two melt seasons in 2008 (before the slash) and 2013 (after the slash). Negative and positive values denote the days for solutes and SS later and earlier than discharge delivery, respectively. N/A denotes data are not available.
Dates for Cumulative Percentage of Species Flux and DischargeLead Days of Species Flux Relative to Discharge
10%50%90%10%50%90%
DIS5 May/7 May28 Jul/26 Jul6 Oct/2 OctNA/NANA/NANA/NA
SSNA/3 JunNA/14 JulNA/10 SepNA/+27NA/−12NA/−22
Na+5 Feb/12 Mar29 Jun/23 Jul26 Nov/26 Oct−90/−56−29/−3+51/+24
K+3 Mar/4 Apr18 Jul/23 Jul6 Nov/13 Oct−63/−33−10/−3+31/+11
Mg2+29 Feb/20 Mar19 Jul/29 Jul8 Nov/21 Oct−66/−48−9/+3+33/+19
Ca2+16 Mar/1 Apr23 Jul/2 Aug30 Oct/19 Oct−50/−36−5/+7+24/+17
Cl4 Feb/18 Feb27 Jun/7 Jul27 Nov/10 Nov−91/−78−31/+19+52/+39
SO42−24 Feb/5 Mar19 Jul/7 Aug10 Nov/3 Nov−71/−63−9/+15+35/+32
NO328 Jan/19 Feb15 Jun/30 May3 Dec/13 Nov−98/−77−43/−57+58/+42
HCO321 Mar/12 Apr23 Jul/29 Jul28 Oct/14 Oct−45/−25−5/+3+22/+12

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Li, X.; Ding, Y.; Liu, Q.; Zhang, Y.; Han, T.; Jing, Z.; Yu, Z.; Li, Q.; Liu, S. Intense Chemical Weathering at Glacial Meltwater-Dominated Hailuogou Basin in the Southeastern Tibetan Plateau. Water 2019, 11, 1209. https://doi.org/10.3390/w11061209

AMA Style

Li X, Ding Y, Liu Q, Zhang Y, Han T, Jing Z, Yu Z, Li Q, Liu S. Intense Chemical Weathering at Glacial Meltwater-Dominated Hailuogou Basin in the Southeastern Tibetan Plateau. Water. 2019; 11(6):1209. https://doi.org/10.3390/w11061209

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Li, Xiangying, Yongjian Ding, Qiao Liu, Yong Zhang, Tianding Han, Zhefan Jing, Zhongbo Yu, Qijiang Li, and Sha Liu. 2019. "Intense Chemical Weathering at Glacial Meltwater-Dominated Hailuogou Basin in the Southeastern Tibetan Plateau" Water 11, no. 6: 1209. https://doi.org/10.3390/w11061209

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