Water Discharge and Suspended Sediment Dynamics in the Chandra River, Western Himalaya

J. Earth Syst. Sci. (2020) 129:206 Ó Indian Academy of Sciences

https://doi.org/10.1007/s12040-020-01455-4 (0123456789().,-volV)(0123456789().,-volV)

Water discharge and suspended sediment dynamics in the Chandra River, Western Himalaya

1,2, 1 2 1 AJIT TSINGH * ,PARMANAND SHARMA ,CHIRAG SHARMA ,CMLALURAJ , 1 1 1,2 1 LAVKUSH PATEL ,BHANU PRATAP ,SUNIL OULKAR and MELOTH THAMBAN 1National Centre for Polar and Ocean Research (NCPOR), Ministry of Earth Sciences, Vasco-da-Gama, Goa 403 804, India. 2School of Earth, Ocean and Atmospheric Sciences, Goa University, Goa 403 206, India. *Corresponding author. e-mail: [email protected]

MS received 15 January 2020; revised 8 June 2020; accepted 8 June 2020

Glacier systems are important components of the hydrological cycle and a major source of meltwater and sediment Cux that controls the river ecology, water quality, and hydropower generation in the Indian Himalayan Region (IHR). Thus, understanding short- and long-term changes in water and suspended sediment (SS) dynamics is crucial in highly sensitive pro-glacial Himalayan Rivers. In the present study, the Chandra River basin in Western Himalaya was chosen to study river discharge, SS transport dynamics, physical erosion rate, and their governing factors for the 2017 melting season (May–September). The daily mean water discharge and SS concentration in the Chandra River was 260.7 m3sÀ1 and 775.5 mgLÀ1 with maximum discharge and SS Cux in the month of July. The air temperature showed significant relationship with the river discharge (R2 = 0.67; n = 156; p\0.001), which in turn controlled the SS export in the basin (R2 = 0.86; n = 130; p \ 0.001). An anticlockwise sediment-discharge hysteresis during peak Cow conditions suggest exhausted sediments or large distance of sediment transport ([100 km) from the upper glacierized region to the end of the basin. Statistical analysis of SS particle size showed poorly sorted immature grains with a dominance of silt particles (85%), followed by sand (8.5%) and clay (6.5%). The SS estimates revealed a total suspended sediment yield of 1285 tons kmÀ2 yrÀ1 and physical erosion rate of 0.47 mm yrÀ1. Considering the socio-economic importance of the Himalayan region, the present study will help to evaluate the water and sediment budget of the Chandra River, Western Himalaya and to establish their relationship to the meteorological conditions in the basin.

Keywords. River discharge; suspended sediment; hysteresis loop; grain size; Chandra River; Western Himalaya.

1. Introduction together contribute *6–10% of global sediment inCux to the ocean, resulting in the buildup of Himalayan glaciers are shrinking in the recent world’s two largest submarine fans in the Arabian decades, generating an increased volume of melt- Sea and the Bay of Bengal (Subramanian 1993). water and as a result, increased transportation of Amount of SS Cux in a river directly aAects its suspended sediment (SS) Cux (Immerzeel et al. water quality, which is necessary for sustaining 2012; Milner et al. 2017; Mao et al. 2019). The river ecology, agriculture, hydropower, and drink- Indus, Ganga, and Brahmaputra river basins ing water in the downstream region (Haritashya 206 Page 2 of 15 J. Earth Syst. Sci. (2020) 129:206 et al. 2006; Wulf et al. 2012; Kumar et al. 2018). 2. Study area and geology The sediment budget of the Himalayan rivers greatly varies owing to various factors such as The present study was conducted in the Chandra energy conditions, availability of sediments, River basin which is one of the major glacierized topography, snow cover, and meteorological con- basins in the Western Himalaya. The total basin ditions (Chakrapani and Saini 2009; Haritashya area is *2440 km2, out of which about 30% (703 et al. 2010; Iida et al. 2012; Kumar et al. 2016). km2) is occupied by glaciers (Tawde et al. 2017) Additionally, various studies have shown that the (Bgure 1). The basin elevation extends from 2800 water and sediment Cux from the glaciated catch- to 6600 masl with a mean slope of 21o. The Chan- ments may significantly alter as a result of climate dra River emerges from the Bara Lacha region, change (Wulf et al. 2012). Therefore, it is crucial to which is further joined by other snow and glacier- understand the long-term and short-term dynamics fed tributaries downstream. The river Cows for of hydrology and sediment export from highly *125 km from its point of origin to its conCuence sensitive glacierized regions (Mao et al. 2019). with the Bhaga River at Tandi forming the The SS Cux in the Himalayan rivers is mostly Chandra–Bhaga River, a major tributary of the comprised of sand (0.06–1.00 mm), silt (0.002–0.06 Chenab River of the Indus river system (Rao et al. mm) and clay (\0.002 mm) size particles, con- 1997)(Bgure 1). The precipitation in the study trolled by energy conditions and availability. The region is derived partly from the Indian Summer grain size of SS significantly aAects various down- Monsoon (ISM) and Western Disturbances (WD’s) stream processes. Increase in silt size particles where the WD contribute predominantly ([70%) makes agricultural land more productive. However, to the precipitation in the form of snow during they also increase the turbidity of water which November–April (Dimri et al. 2015; Pratap et al. reduces permeability to dissolved gases, nutrients, 2019; Singh et al. 2019). Major runoA contribution aAecting the primary productivity and macro-in- to the Chandra River is governed by the seasonal vertebrate communities (Davies Colley and Smith snow and glacier melt from major glaciers like Bara 2001; Descloux et al. 2013; Bona et al. 2016). A Shigri, Samudra Tapu, Sutri Dhaka, Batal, Chhota study by Bona et al.(2016) showed that the ben- Shigri and Hamtah, which are under retreating thic invertebrates are more sensitive to Bne sedi- phase over the last few decades (Azam et al. 2014; ments and thrive well in the presence of coarse Patel et al. 2016; Sharma et al. 2016; Tawde et al. sediments. The increasing magnitude of SS also 2017). The vegetation in the basin is sparse because aAects the hydropower projects in the Himalayan of semi-arid to arid conditions. The steep slope, rivers as the water quality degrades, and Bner high contribution of glacier meltwater and narrow sediments result in siltation of dams and abrasion valley of the Chandra Basin makes it a suit- of hydropower turbines (Dharmadhikary 2008; able place for hydropower generation. Hence, many Arora et al. 2014). future hydropower plans have been proposed in the Despite the hydrological and socio-economic basin with a potential power capacity of *1125 importance of Indian Himalayan Region (IHR), MW (https://sandrp.Bles.wordpress.com). small numbers of studies have been conducted to Geologically, the Chandra Basin lies in the estimate water and sediment budget of the pro- central crystalline zone of the Pir-Panjal Range of glacial streams and rivers of IHR (Rao et al. 1997; the Indian Himalaya. The geology of the basin is Haritashya et al. 2010; Arora et al. 2014; Kumar diverse and majorly comprises of rocks ranging et al. 2016; Kumar et al. 2018; Singh and Rama- from Proterozoic (2500–542 ma) to Cenozoic era nathan 2018). The major challenges in studying the (66 ma to present) (Kumar et al. 1987). Rocks in Himalayan rivers are due to poor accessibility, high this basin are composed of meso to keto-metazonal elevation, undulated terrain, logistic difBculties migmatites, metamorphites, gneisses and recent and harsh weather conditions in the region. In this sediment deposits. The basement rocks are com- paper, the Chandra River situated in the upper posed of granite, gneiss, and granitoid rocks from Indus Basin, Western Himalayan region was the proterozoic era, which is overlained with rocks studied for 2017 melting season (May–September) from Haimanta group after a tectonic break with an aim to understand short (daily) and long- (Kumar et al. 1987; Geological Survey of India term (monthly) changes in river water discharge (GSI) 2011). The Haimanta group of rocks is (Q), SS Cuxes and to establish their relationship to mainly composed of slates, phyllites, quartzite, and meteorological conditions. Bne-grained biotite-schists (Kumar et al. 1987). J. Earth Syst. Sci. (2020) 129:206 Page 3 of 15 206

Figure 1. Chandra River basin drainage and glaciers with the location of automatic weather station (AWS), discharge site and sampling location.

Most recent deposits are of glacial, alluvial, and interval using a YSI radar sensor (Nile series) with Cuvial origin resulting in the formation of mor- storm data logger having ±2 mm accuracy. The aines, hillwash, scree, and riverbank deposits. The discharge of the Chandra River was computed detailed geology and lithology of the study region using the area-velocity method. Excessive veloci- are provided in Bgure 2 and table 1. ties, depth, and rolling boulders at the river bed prohibited us from using current meter and depth proBler for velocity measurements. Hence, the 3. Material and methods surface velocity of the river was determined at different parts using wooden Coat whereas the Meteorological observations such as daily air tem- depth of the river was measured by using gauging perature, relative humidity and precipitation were rod (Haritashya et al. 2006; Srivastava et al. 2014; monitored using an automatic weather station Kumar et al. 2016, 2018; Singh et al. 2017). After (AWS, Campbell ScientiBc) installed at the calculating the river discharge, a stage (water Himansh Base Camp (HBC) in the Chandra Basin levels) vs. discharge relationship (rating curve) was (4080 masl) (Bgure 1). The AWS equipped with established (Bgure 3). The discharge measurement the automated air temperature sensor (Campbell of Himalayan stream and river involves inherent HC2S3, range: À 50 to +60°C; accuracy: ± 0.1°C), uncertainties associated with gauging the depth precipitation bucket (OTT Pluvio Model, range: and measuring stream velocity. Previous studies in 0–1800 mm/hr; accuracy: ± 0.05 mm) and data the Himalayan region have shown that the area- logger (Campbell ScientiBc–CR1000) were used for velocity based discharge estimate of Himalayan continuous data measurement and logging at rivers involves an uncertainty of *5–10% (Hari- 10-min interval. A hydrological station was estab- tashya et al. 2006; Srivastava et al. 2014; Kumar lished at Tandi towards the end of the Chandra et al. 2016). In our study, the Btting curve of stage Basin (catchment area of 2440 km2 up to the vs. discharge relationship has an accuracy of ±6%, gauging site) for discharge measurement and twice- which demonstrates the reliability discharge esti- daily water sampling (at 8:00 and 17:00 hrs) was mation. However, the bed topography of the made (Bgure 1). For collecting the hydrological Himalayan streams and rivers changes constantly, data during 2017 melting season, the continuous and regular monitoring of river cross-section is water level in the river was recorded at every 10 sec hazardous in furious high altitude rivers. 206 Page 4 of 15 J. Earth Syst. Sci. (2020) 129:206

Figure 2. Geological formation and mineral map of the Chandra River basin in Western Himalaya modiBed after available geology and mineral map of Himachal Pradesh provided by Geological Survey of India (GSI (2011) Geological and Mineral Map of Himachal Pradesh. Source: www.gsi.gov.in).

Table 1. Geological formations of the Chandra River basin and the mineral composition modiBed after available Geology and Mineral map of Himachal Pradesh provided by Geological survey of India (GSI (2011) Geological and Mineral Map of Himachal Pradesh. Source: www.gsi.gov.in).

Era Age Name Formation Group Composition Cenozoic Quaternary Qal Fluvial/Fluvio- – Moraines, hillwash, scree Glacial/Alluvial Paleozoic Triassic to TJI Und. Lilang Group Shale, dolomite and limestone Jurassic Carboniferous to PK, Ck Und. Lilang Group Shale, sandstone Permian Devonian Dsm Muth Formation Senugba Group Quartz arenite and slate Ordovician to Osth Thango Formation Haimanta Group Quartz arenite and shale Devonian Cambrian EhK Kunzamla Formation Haimanta Group Slate, phyllite, siltstone, limestone Neo-Proterozoic Pt3k Katargill Formation Proterozoic Pt3m Manjir Formation Siltstone, slate, conglomerate, and limestone Pt3c Chamba Formation Schist Pt3hb Batal Formation Haimanta Slate, phyllite, quartzite, schist Ptvc Cholathach Vaikrita Group Granite, gneiss, and granatoids Formation (Central Crystalline)

To measure the SS concentration, we carried out and sediment samples were collected on a bi-hourly high-frequency (i.e., twice daily at 08:00 and 17:00 basis for 24 hrs on the selected day of each month hrs) water sampling at the discharge site (Bgure 1) (May–September). The water samples were during May–September 2017 in pre-cleaned 500 ml immediately Bltered onsite using 0.45 lm cellulose HDPE bottles. Similarly, to understand the diurnal membrane Blter paper to separate SS particles. pattern in river discharge and SS dynamics, water Thereafter, the samples were properly cased and J. Earth Syst. Sci. (2020) 129:206 Page 5 of 15 206

Chandra Basin varied from 2347 to 987.1 km2 during April–September. During early spring (April–May), the entire basin was covered with winter snow (October–March) (Bgure 4b). In early spring, followed by summer, the winter snow melted and contributed to the river discharge. There- fore, a good relationship was observed between daily mean air temperature, snow cover and discharge in the Chandra Basin (Bgure 4). The daily mean discharge during the study period varied from 115 to 457 m3sÀ1 with an overall mean of 260.7 m3sÀ1. The daily mean discharge for May, June, July, August and September were 196, 259, 394, 306, and 160 m3sÀ1, respectively (Bgure 4c). The total precipitation reported in the Chandra Basin during 2017 ablation season was 206.3 mm. Figure 3. The Chandra River stage-discharge relationship at The monthly rainfall for May, June, July, August Tandi to estimate the discharge during melting season and September at Himansh were 41.6, 57.4, 25.7, (May–September 2017). 18.3, and 63.3 mm, respectively. Major precipitation events occurred in the month of May, June, and September while July and August months transported back for further laboratory analysis. showed relatively lower precipitation amount The suspended sediment concentration (SSC), (Bgure 4d). suspended sediment load (SSL), suspended sedi- The SS concentration in the river is mainly a ment yield (SSY), erosion rate (E ) and grain size ss function of discharge, snow cover, and temperature characteristics of monthly bulk SS were estimated (table 2). The daily mean SSC ranged from 308.9 after post-weighing the Blter paper (Udden 1914; to 1943.1 mgLÀ1 with a mean of 775.5 mgLÀ1 Folk and Ward 1957; Kumar et al. 2016). Because (Bgure 5). A significant correlation was observed of frozen conditions, the winter months (Octo- between mean daily discharge and SSC (R2 = 0.86; ber–March) contributed a negligible amount of n = 130; p \ 0.001) which is attributed to the meltwater and SS Cux from the glacierized areas to inCuence of varying energy conditions of river dis- the downstream region. Therefore, the present charge on SSC (Bgure 5a and b). Mean monthly study was considered only for melting/ablation SSC for May, June, July, August and September season (May–September). were 533.8, 685.4, 1125.8, 1071.9, and 527.2 mgLÀ1, The snow cover area of the Chandra Basin respectively. The mean monthly relationship (April–September 2017) was analyzed using the between discharge and SSC concentration of the MODIS/Terra Snow Cover Daily L3 Global 500 m Chandra River remained similar to a daily mean grid (MOD10A2) snow cover products available at relationship. Since temperature is the major gov- 500 m spatial resolution with eight-day interval erning factor for the discharge, it also showed a (Hall and Riggs 2016). The detailed description of good relationship with the SS concentration (R2 = the MOD10A2 dataset is provided in the NSIDC 0.64; n = 130; p \ 0.001) (Bgure 5c and d). A sig- website (https://nsidc.org/). The obtained tiles nificant positive correlation among temperature, were further processed for the Chandra Basin using discharge and SS also suggests an increase in ArcGIS 10 environment. mobilization of stored sediment by the glaciers during previous winter and sediment along the 4. Results and discussion river as the glacier melt raises the river discharge with an increase in temperature during summer The daily mean air temperature at the Himansh (May–September). Similarly, Rao et al.(1997) also Base Camp ranged from À5.1 to 13.4°C with a reported a strong correlation between sediment daily mean of 7.6°C. The mean monthly air tem- and discharge of the Chandra River (R2 [ 0.8). A perature for May, June, July, August and strong relationship between SS and discharge has September were 2.9, 7.4, 11.9, 10.8, and 6.5°C, also been demonstrated in other Western Hima- respectively (Bgure 4a). The snow-cover in the layan rivers such as Wanger, Baspa and Sutlej 206 Page 6 of 15 J. Earth Syst. Sci. (2020) 129:206

Figure 4. Daily distribution of (a) air temperature, (b) snow cover, (c) daily discharge, and (d) precipitation for the Chandra River basin during the 2017 melting season (May–September).

Table 2. Statistical distribution of different hydrometeorological datasets and suspended sediment for the 2017 melting season (May–September).

Parameters May June July August September May–September Temp (°C) Mean 3.0 7.4 12.0 10.9 6.5 7.7 SD 3.0 2.6 1.2 1.4 1.1 4.2 CV 1.01 0.34 0.10 0.13 0.16 0.54 Discharge (m3sÀ1) Mean 196.6 259.1 394.4 306.2 160.4 260.7 SD 51.3 60.9 36.8 71.6 33.0 99.6 CV 0.26 0.23 0.09 0.23 0.20 0.38 SSC (mgLÀ1) Mean 533.8 685.4 1125.8 1072 527.3 775.6 SD 136.5 190.8 297.8 305.1 275.6 362.0 CV 0.25 0.27 0.26 0.28 0.52 0.46 Total Meltwater Flux (MCM) Mean 526.4 671.6 1060.4 820.1 415.6 3536.7 Total SSC load (tons) Mean 27.7 9 104 49 9 104 121 9 104 92.1 9 103 23.5 9 104 313 9 104 Suspended sediment yield (tons kmÀ2) Mean 113.7 200.8 496.2 377.5 96.3 1284.6 Erosion rate (mm yrÀ1) Mean 0.47

River (Wulf et al. 2012) suggesting similar hydro- controlled by transport energy, unlike precipita- logical characteristics in snow and glacier domi- tion amount in the monsoon dominated regimes nated (nivo-glacial) regimes. A good correlation of (Kumar et al. 2016). SS with discharge also suggests that the SS in the A poor relationship (R2 \ 0.1) was observed Western Himalayan rivers is predominantly between daily rainfall events and SSC. Since the J. Earth Syst. Sci. (2020) 129:206 Page 7 of 15 206

Figure 5. Daily mean variation of SSC in relationship with discharge (a, b) and temperature (c, d) observed in the Chandra Basin, Western Himalaya during 2017 melting season (May–September). rainfall was mostly in the form of drizzles, no (*5200 masl) (Tawde et al. 2017). Most of the significant change in sediment export were winter snowpack were melted by the end of July observed during the rainfall events. However, lack and thereafter the major contribution of meltwater of immediate response of sediment export during and SS were sourced from glacier discharge. rainfall could also be due to the delay factor that The proportion of discharge and SSC from the occurs in the larger catchments like the Chandra basin varied significantly on a monthly basis. May Basin. A strong negative correlation was observed and June received 34% of total runoA and trans- between snow cover and SSC (R2 = 0.87; n = 17; ported 24.5% of the total sediment load. About p \ 0.001) from May to mid-August, which 54% of the total runoA was received in July and diminishes as the snow cover depletes and dis- August and transported 68% of the total sediment. charge starts declining with drop in temperature Whereas, September contributed only 12% to the (Bgure 6). Since the entire Chandra Basin area is total discharge and transported 7.5% of the total snow-covered in the month of April, and as the sediment load. CoefBcient of variance (CV) of daily snow melting commences in the early May, it SSC for May, June, July, August, and September mobilizes the sediment in the basin, raising SSC was 0.25, 0.27, 0.26, 0.28, and 0.52. A substantial concentration. As the temperature increases, the increase in the CV of SSC for September month melting of snow progresses until the snow cover could be due to a drastic drop in the SSC during reaches the equilibrium line altitude (ELA) the latter half of the month due to lack in the 206 Page 8 of 15 J. Earth Syst. Sci. (2020) 129:206

Figure 6. (a) Daily mean of snow cover area and suspended sediment concentration and (b) relationship between mean daily snow cover area and suspended sediment concentration (May–September 2017) observed in the Chandra Basin, Western Himalaya. availability of sediment and significant drop in yrÀ1. Previous literature has shown a rapid discharge. The CV for monthly discharge for May, retreat in glaciers of the Chandra Basin resulting in June, July, August and September were 0.26, 0.23, *18% overall increase in water loss (Tawde et al. 0.09, 0.23, and 0.20, respectively (table 2). A major 2017). Also, 67% loss of low altitude glaciers were change in the CV of discharge in July (0.09) could reported in the Chandra Basin from 1984 to 2012 be due to the transition from snowmelt to glacier- (Tawde et al. 2017). This may have significantly melt contribution to the river, where winter snow raised the discharge and in return increasing the cover diminishes and a major contribution with SSC, SSY and erosion rate of the Chandra Basin. stable discharge is delivered by the glacier melt. The overall discharge and SS Cux during the study The overall (May–September) CV for SSC and period were 3536 Million Cubic Meters (MCM) and discharge was 0.46 and 0.38. The daily SS load 3 million tons, respectively. A study conducted by (SSL) in the Chandra River varied from 3.3 9 103 Singh and Ramanathan (2018) on the Chhota to 72 9 103 tons dayÀ1. The daily mean of SSL for Shigri Glacier in the Chandra Basin showed a May, June, July, August and September were 9.7 higher SSY of *3000 tons kmÀ2 yrÀ1. Higher 9 103,169 103,399 103,299 103, and 7.8 9 103 denudation rate at the Chhota Shigri Glacier is tons dayÀ1, respectively. The mean monthly SS potentially attributed to higher physical denuda- Yield (SSY) during the months of May, June, July, tion due to closeness of study site at the glacier August and September was 113.8, 200.8, 496.2, snout and higher altitudinal variation. Similarly, 377.5, and 96.3 tons kmÀ2, respectively (Bgure 7 Singh et al.(2014) also reported much higher SSY and table 2). Since the discharge contribution (*7663 tons kmÀ2 yrÀ1) in the upstream region of beyond these months is considered to be\10% and the Gangotri Glacier, Central Himalaya inCuenced SSC reduces significantly during lean Cow condi- by the steepness of the river gradient, and inte- tions, it can be stated that the SSY for the grated eAect of the heavy rainfall from Indian complete year is *1285 tons kmÀ2 yrÀ1. Summer Monsoon (ISM) and glacier aAected The estimated SSY of the Chandra Basin is physical erosion (Kumar et al. 2016). However, significantly higher than the average global SSY downstream regions of the Ganga Basin at Alak- (*150 tons kmÀ2 yrÀ1) and the Indian subconti- nanda and Bhagirathi River showed reduced SSY nent (*327 tons kmÀ2 yrÀ1) (Jha et al. 1988). A of 863 and 907 tons kmÀ2 yrÀ1 as the intensity of previous study (Rao et al. 1997) of the Chandra glacier inCuenced physical erosion reduces in the River (from 1978 to 1995) shows an average sedi- downstream region (Chakrapani and Saini 2009). ment yield of 513 tons kmÀ2 yrÀ1, suggesting a Almost similar SSY of 865 tons kmÀ2 yrÀ1 were nearly two-fold higher sediment yield in the pre- reported for downstream region of the Brahmapu- sent study. The estimated modern erosion rate of tra Basin (Subramanian 1996). The erosion rate for the Chandra Basin in the present study is 0.47 mm Nepal Himalayas was reported between 0.1 and 2.8 J. Earth Syst. Sci. (2020) 129:206 Page 9 of 15 206

Figure 7. (a) Monthly mean temperature, discharge and suspended sediment concentration and (b) Meltwater Cux, suspended sediment load and suspended sediment yield from the Chandra River (May–September 2017). mm yrÀ1 with relatively lower rates (\ 0.5 mm heterogeneous pattern (non-linear) for lean dis- yrÀ1) for higher Himalayas (Andermann et al. charge months, while the clear anticlockwise 2012). The spatio-temporal variability in the pattern for July and August diurnal days (high amount of precipitation is one of the significant Cow conditions) (Bgure 8c and d). Similarly, the factors governing SSY and erosion rate across the monthly mean discharge and SSC shows an anti- Himalayan belt. A study conducted by Bookhagen clockwise hysteresis suggesting a clear pattern on et al.(2005) in Baspa Basin, Western Himalaya long term period (Bgure 8f). A clear anticlockwise showed significant increase in mean erosion rate hysteresis during July and August indicates from *0.7 ± 0.14 to 1.5 mm yrÀ1 triggered by exhaustion of the sediments due to open hydro- abnormally higher monsoonal rain. Besides pre- logical network in the glacierized region as the cipitation, variability in exhumation rate of rocks temperature and discharge increases (Tananaev across the Himalayan belt showed an inCuence on 2015). Additionally, anticlockwise hysteresis could the physical erosion, generating the variable also be a result of long distance sediment transport amount of SS (Fort 2000). from the upper glacierized region of the basin to The bi-hourly data collected on selected days the sampling point at the end of the basin. Con- representative of each month during May– sidering the maximum number of glaciers in the September revealed significant Cuctuation in tem- upstream region, the erosion process and supply of perature, SSC and discharge. Our data revealed sediment to the discharge site could be delayed by that the daily maximum temperature (Tmax) for several hours. Based on the short term sediment selected diurnal days was reported at 1200–1600 availability during the lean discharge period, the hrs and maximum discharge (Qmax) was reported basin showed heterogeneous/complex hysteresis at 2200–2400 hrs for selected days. A delayed characteristics. Heterogeneous hysteresis in response of discharge with respect to temperature glacierized region is due to low eDciency of the showed mean lag time *6–8 hrs, which could be hydrological system during the early ablation per- due to the delayed routing of meltwater from iod as the melt water is mostly sourced from the snow/glaciers to the discharge site. snow cover (Bgure 8a, b, and f) (Mao et al. 2019). At a short term scale (hourly), the sediment- During the initial melting period (April–May), discharge relationship is often non-linear showing snow cover obstructs the supra-glacial, subglacial variable trend during ascending and descending and englacial drainage from the glacierized region. limb resulting in a hysteresis loop (Mao et al. As the winter-deposited snow in the basin melts 2019). Sediment-discharge hysteresis loops are away, a major source of the meltwater is derived frequently used to understand the sediment from the well-routed glacier melt showing a clear dynamics and transport processes. A typical Q-SSC hysteresis. However, as the temperature clockwise hysteresis is a result of readily available drops in September, it results in refreezing and SS material. In the present study, most of the melting of the glacier melt resulting in less eDcient hysteresis reveal an anticlockwise hysteresis with a water routing from the basin (Łepkowska and 206 Page 10 of 15 J. Earth Syst. Sci. (2020) 129:206

Figure 8. (a–e) Diurnal hysteresis for selected days during study period for 2017 ablation period and (f) Monthly hysteresis based on monthly mean value for the study period.

Stachnik 2018). Furthermore, dynamic processes like bursting of glacial water pockets, rainstorms, emptying of ice/moraine dammed lakes and other mass movements which are common in high altitude arid terrain may add complexity to hysteresis of the mountainous stream (Tananaev 2015). The suspended load comprises of different size fractions. The particle size characteristic of SS is its fundamental property controlling the sediment entrainment, transport and deposition in a river. Therefore, grain size analysis of river sediment is significant in understanding sediment provinces and its transport history (Folk and Ward 1957; Haritashya et al. 2010). The monthly grain size distribution of SSs is shown in Bgure 9. Majority of SSs are of silt size particles (85%) followed by sand Figure 9. Particle size distribution of the suspended sediment (8.5%) and clay size particles (6.5 %). Among silt collected from the Chandra River during the 2017 melting size particles, medium silt is dominant (23.8 ± season (May–September). 2.1%) followed by coarse silt (21.4% ± 2.8), Bne silt (17± 1.6%), very coarse silt (13.6 ± 1.6%) and very Bne silt (9.1 ± 1.2%). Sand size particles showed month of September. The total Cux of silt, sand, the dominance of very Bne sand (7.1 ± 2.3%) fol- and clay size particles were 2650 9 103, 265 9 103 lowed by Bne sand (1.4 ± 2.0%). Clay comprised of and 200 9 103 tons yr À1, respectively. rest of the particle size (6.5% ± 0.9). The abun- The granulometric characteristic of SS of the dance of various grain sizes follows similar pattern Chandra River is shown in Bgure 10. Various SS for all months except for Bne sand size particles grain size based statistical parameters such as which shows relatively higher concentration in the inclusive graphical median, inclusive graphical month of August and completely absent in the mean, inclusive graphic standard deviation, J. Earth Syst. Sci. (2020) 129:206 Page 11 of 15 206

Figure 10. Bivariate plots showing monthly variations in grain size characteristics of Chandra River basin for the 2017 melting season (May–September): (a) Grain size distribution for different months, (b) Mean size vs. sorting, (c) Mean size vs. skewness, and (d) Mean size vs. Kurtosis. inclusive graphic skewness and inclusive graphic energy conditions during the ablation season in the kurtosis can be useful to describe sediment char- Chandra Basin. Inclusive graphic skewness varied acteristics, provenance of the sediments and energy from –0.01 to 0.10/ with an average value of 0.04/ conditions (Folk and Ward 1957). The graphic (Bgure 10c). A positive skewness value shows a mean size values for the Chandra River SSs ranged dominance of Bne-sized sediments. Previous studies from 6.0 to 6.5/, with a mean value of 6.2/ indi- have shown that if the sediments are of supragla- cating the dominance of medium-grained silt sedi- cial origin, they show a coarsely skewed pattern, ments (Bgure 10a). The graphic mean values are while Bnely skewed sediment suggests a subglacial similar to the graphic median value. The medium origin of sediments (Kumar et al. 2016). Inclusive silt-sized particles could be sourced from the sub- graphic kurtosis ranged from 0.96 to 1.06 with an glacial environment as observed in Chorabari average value of 1.02/ (Bgure 10d). These values Glacier (Kumar et al. 2016) and Gangotri Glacier show equal sorting of tails, and the central part (Haritashya et al. 2010). Inclusive graphic standard reCects the mesokurtic distribution of sediments. deviation (sorting) values varied from 1.5 to 1.9/ The monthly value remains almost similar with an average graphic standard deviation value throughout the study period. Overall grain size of 1.68/,reCecting poor sorting of sediments statistics suggest that the SS grains of the Chandra (Bgure 10b). Such poor sorting values indicate that River are poorly sorted medium silt grain size with the sediments were evacuated because of variable symmetrical skewness and mesokurtic distribution. 206 Page 12 of 15 J. Earth Syst. Sci. (2020) 129:206

Present study showed a variable pattern of of grain size in the Chandra Basin comprises of Bne sediment grain size and percentage change along with particle size (\ 0.075 mm) followed by medium discharge (table 3). The coarse grain size may (0.2–0.075 mm) and coarse grain size ([0.2 mm) increase with discharge in some rivers, while in others with percentage contribution of 58, 23.6 and 18.2%, Bne grain size showed positive relationship with dis- respectively. In comparison to this classiBcation, charge (Walling and Moorehead 1989). However, the contribution of Bne, medium and coarse grain SS grain size in some rivers remain consistent with particles in our study were 94, 4.9 and 0.02%, variable discharge (Walling and Moorehead 1989). In respectively. A significant increase in Bne sized the present study, Principle Component Analysis sediment is reported in the present study, which (PCA) was performed on the monthly dataset of could be linked to a significant rise in glacier-melt discharge, air temperature, snow cover area and and rise in river discharge during the past two dec- various grain size classes. PCA analysis shows two ades (Tawde et al. 2017). In the present study, major components. In component 1 (D1) the Bner absence of coarse size particles could be due to lack of particle size such as Bne silt, very Bne silt and clay their availability. Additionally, significant increase sized particles showed a positive aDnity with the in Bne grain size particle could be implied to the temperature and discharge while snow cover, very comparatively high energy conditions. Most rivers coarse silt, coarse silt showed negative relationship around the world have shown elevated concentra- with the discharge explaining altogether 54% vari- tion of Bne sediment as a result of anthropogenic ability in the data. A negative relationship of coarse activities, deforestation, agriculture, construction silt size particle with discharge suggests that they are and mining activities (Owens et al. 2005). In the dominant in the low Cow conditionspossibly whenthe Chandra Basin, a large project like one of longest major contribution to the river is derived from the road tunnel of India ‘Atal Tunnel’ is under con- subglacial drainage (Kumar et al. 2016). The com- struction since 2010. Furthermore, significant ponent D2, explaining 39% variability, consists of increase in agricultural activities has also been relatively coarser particles like Bne sand, very Bne reported in the Lahaul and Spiti district covering the sand, coarse silt and medium silt. A poor aDnity of Chandra-Bhaga Basin (Oinam et al. 2005). Thus, second component with discharge, snow cover and the impact of anthropogenic activity in promoting temperature suggests that they are not governed by the elevated levels of the Bne sediment concentration energy or meteorological conditions, but controlled in the Chandra River runoA seems to be significant. by the availability sediments in the system. A previous study in Chandra River basin by Rao et al.(1997) used a different classiBcation method for 5. Conclusions grain size analysis. They reported that the majority Considering the socio-economic importance of the Table 3. Principal Component Analysis of monthly mean Himalayan region and future warming scenario, the discharge, temperature and SS particle sizes for the 2017 present study attempts to assess the water and sus- melting season (May–September). The factor loading [0.5 pended sediment (SS) budget of the Chandra River, (bold) indicates a significant association in two principle Western Himalaya. The mean water discharge and components. SS concentration during ablation period (May– Parameter D1 D2 September 2017) in the Chandra River were 260.7 m3sÀ1 and 775.5 mgLÀ1 with a total water discharge Discharge 0.88 0.29 Temperature 0.83 0.42 and SS Cux of 3536 MCM and 3 million tons, Snow cover À0.67 0.37 respectively. In comparison to the previous study Fine sand À0.07 0.99 conducted from 1978 to 1995, we observed a two-fold V. Bne sand À0.02 0.98 increase in the SS Cux. Heterogeneous hysteresis V. coarse silt À0.92 0.30 between river discharge and SS were more common Coarse silt À0.63 À0.77 during lean Cow conditions while a clear anticlock- Medium silt 0.01 À0.99 wise hysteresis was observed during peak Cow per- Fine silt 0.88 À0.44 iod. A heterogeneous hysteresis suggests complex V. Bne silt 0.99 À0.15 drainage through the basin while clear anticlockwise Clay 0.97 0.19 hysteresis indicates open drainage with exhausted Variability (%) 54.0 39.0 sediments or large distance of sediment transport. Cumulative (%) 54.0 93.0 Overall, the SS were composed of silt size particles J. Earth Syst. Sci. (2020) 129:206 Page 13 of 15 206

(85%) followed by sand (8.5%) and clay size (6.5%) References particles. The SS estimates revealed a total sus- 2 1 pended sediment yield of 1285 tons kmÀ yrÀ and Andermann C, Crave A, Gloaguen R, Davy P and Bonnet physical erosion rate of 0.47 mm yrÀ1. A significant S 2012 Connecting source and transport: Suspended relationship between discharge, snow cover area, sediments in the Nepal Himalayas; Earth Planet. Sci. temperature and SS in the present study suggests Lett. 351 158–170, https://doi.org/10.1016/j.epsl.2012. 06.059. that in a future warming scenario, the glacierized Arora M, Kumar R, Kumar N and Malhotra J 2014 Assess- Himalayan region may report higher water ment of suspended sediment concentration and load from a discharge and SS export. large Himalayan glacier; Hydrol. Res. 45(2) 292–306, This study provides a high resolution baseline https://doi.org/10.2166/nh.2013.129. dataset of water discharge, sediment Cuxes and aids Azam M F, Wagnon P, Vincent C, Ramanathan A L, Favier V, Mandal A and Pottakkal J G 2014 Processes governing to understand their dynamics with respect to mete- the mass balance of Chhota Shigri Glacier (Western orological conditions in the Chandra Basin. There- Himalaya, India) assessed by point-scale surface energy fore, considering the present study as a baseline balance measurements; Cryosphere 8(6) 2195–2217, dataset, in future, Beld and model-based approach https://doi.org/10.5194/tc-8-2195-2014. can be utilized for long-term assessment of the water Pratap B, Sharma P, Patel L, Singh A T, Gaddam V K, resources in the Chandra Basin. The hydro-climatic Oulkar S and Thamban M 2019 Reconciling high glacier surface melting in summer with air temperature in the conditions in the Himalayas region show variable semi-arid zone of Western Himalaya; Water 11(8) 1561, changes, and therefore more studies are required to https://doi.org/10.3390/w11081561. estimate the discharge, erosion rate and sediment Bona F, Doretto A, Falasco E, La Morgia V, Piano E, Ajassa characteristics which will be useful in understanding R and Fenoglio S 2016 Increased sediment loads in alpine the SS cycling from the Himalayan region and to streams: An integrated Beld study; River Res. Appl. 32(6) build robust models for future projections. 1316–1326, https://doi.org/10.1002/rra.2941. Bookhagen B, Thiede R C and Strecker M R 2005 Abnormal monsoon years and their control on erosion and sediment Cux in the high, arid northwest Himalaya; Earth Planet. Acknowledgements Sci. Lett. 231(1–2) 131–146, https://doi.org/10.1016/j.epsl. 2004.11.014. We thank the Director, National Centre for Polar Chakrapani G J and Saini R K 2009 Temporal and spatial and Ocean Research for his encouragement and the variations in water discharge and sediment load in the Ministry of Earth Sciences for Bnancial support Alaknanda and Bhagirathi Rivers in Himalaya, India; Asian through the project PACER – Cryosphere and Earth Sci. 35(6) 545–553, https://doi.org/10.1016/j.jseaes. Climate. Ajit Singh would like to thank A Prajith 2009.04.002. and Abhishek Tyagi for their assistance with grain Davies Colley R J and Smith D G 2001 Turbidity suspended sediment, and water clarity: A review; J. Am. Water size analysis. Authors also thank Suresh Sahi and Resour. Assoc. 37(5) 1085–1101, https://doi.org/10.1111/ other porters for their help during the Himalayan j.1752-1688.2001.tb03624.x. Beld expedition. The authors acknowledge Anupam Descloux S, Datry T and Marmonier P 2013 Benthic and Anand Gokhale, Mahalinganathan K and Rahul hyporheic invertebrate assemblages along a gradient of Dey for their valuable suggestions. This is NCPOR increasing streambed colmation by Bne sediment; Aquat. contribution J-22/2020-21. Sci. 75(4) 493–507, https:// doi.org/https://doi.org/10. 1007/s00027-013-0295-6. Dharmadhikary S 2008 Mountains of Concrete: Dam Building in the Himalayas; International Rivers, Berkeley. Author statement Dimri A P, Niyogi D, Barros A P, Ridley J, Mohanty U C, Yasunari T and Sikka D R 2015 Western disturbances: A review; Rev. Geophys. 53(2) 225–246, https://doi.org/10. ATS, PS, CS, CML, and MT deBned the objectives 1002/2014RG000460. of the study and the writing of the manuscript. Folk R L and Ward W C 1957 Brazos River bar: A study in ATS and CS have analysed samples for the SSC the significance of grain size parameters; J. Sedim. Res. and grain size at the NCPOR, Goa. LP contributed 27(1) 3–26, https://doi.org/10.1306/74D70646-2B21- with plotting the study area map using satellite 11D7-8648000102C1865D. ArcGIS version 10.4. ATS, LP, SO, and BP sup- Fort M 2000 Glaciers and mass wasting processes: Their inCuence on the shaping of the Kali Gandaki valley (higher ported collecting discharge and meteorological Himalaya of Nepal); Quat. Int. 65 101–119, https://doi. data from the Chandra Basin. All authors con- org/10.1016/S1040-6182(99)00039-7. tributed to the data interpretation and discussion Geological Survey of India (GSI) 2011 Geological and Mineral of the manuscript. Map of Himachal Pradesh; Source: www.gsi.gov.in. 206 Page 14 of 15 J. Earth Syst. Sci. (2020) 129:206

Hall D K and Riggs G A 2016 MODIS/Terra Snow Cover Owens P N, Batalla R J, Collins A J, Gomez B, Hicks D M, 8-Day L3 Global 500 m SIN Grid, Version 6. Boulder, Horowitz A J and Petticrew E L et al. 2005 Fine-grained Colorado USA, NASA National Snow and Ice Data Center, sediment in river systems: Environmental significance and Distributed Active Archive Center. management issues; River Res. Appl. 21(7) 693–717, Haritashya U K, Kumar A and Singh P 2010 Particle size https://doi.org/10.1002/rra.878. characteristics of suspended sediment transported in melt- Patel L K, Sharma P, Thamban M, Singh A and Ravindra R 2016 water from the Gangotri Glacier, central Himalaya – An Debris control on glacier thinning – a case study of the indicator of subglacial sediment evacuation; Geomorphology Batal glacier, Chandra Basin, Western Himalaya; Arab. J. 122(1–2) 140–152, https://doi.org/10.1016/j.geomorph. Geosci. 9(4) 309, https://doi.org/10.1007/s12517-016- 2010.06.006. 2362-5. Haritashya U K, Singh P, Kumar N and Gupta R P 2006 Rao S V N, Rao M V, Ramasastri K S and Singh R N P 1997 Suspended sediment from the Gangotri Glacier: QuantiB- A study of sedimentation in Chenab basin in Western cation, variability and associations with discharge and air Himalayas; Hydrol. Res. 28(3) 201–216, https://doi.org/ temperature; J. Hydrol. 321 (1) 116–130, https://doi.org/ 10.2166/nh.1997.0012. 10.1016/j.jhydrol.2005.07.037. Sharma P, Patel L K, Ravindra R, Singh A, Mahalinganathan Iida T, Kajihara A, Okubo H and Okajima K 2012 EAect of K and Thamban M 2016 Role of debris cover to control seasonal snow cover on suspended sediment runoA in a speciBc ablation of adjoining Batal and Sutri Dhaka glaciers mountainous catchment; J. Hydrol. 428 116–128, https:// in Chandra Basin (Himachal Pradesh) during peak ablation doi.org/10.1016/j.jhydrol.2012.01.029. season; J. Earth Syst. Sci. 125(3) 459–473, https://doi. Immerzeel W W, Van Beek L P H, Konz M, Shrestha A B and org/10.1007/s12040-016-0681-2. Bierkens M F P 2012 Hydrological response to climate change in Singh A T, Laluraj C M, Sharma P, Patel L K and a glacierized catchment in the Himalayas; Clim. Change Thamban M 2017 Export Cuxes of geochemical solutes 110(3–4) 721–736, https://doi.org/10.1007/s10584-011-0 in the meltwater stream of Sutri Dhaka Glacier, 143-4. Chandra Basin, Western Himalaya; Environ. Monit. Jha P K, Subramanian V and Sitasawad R 1988 Chemical and Assess. 189(11) 555, https://doi.org/10.1007/s10661- sediment mass transfer in the Yamuna River – a tributary 017-6268-9. of the Ganges system; J. Hydrol. 104(1–4) 237–246, Singh A T, Rahaman W, Sharma P, Laluraj C M, Patel L K, https://doi.org/10.1016/0022-1694(88)90167-9. Pratap B and Thamban M 2019 Moisture sources for Kumar A, Gokhale A A, Shukla T and Dobhal D P 2016 precipitation and hydrograph components of the Sutri Hydroclimatic inCuence on particle size distribution of Dhaka Glacier Basin, Western Himalayas; Water 11(11) suspended sediments evacuated from debris-covered Chor- 2242, https://doi.org/10.3390/w11112242. abari Glacier, upper Mandakini catchment, central Hima- Singh V B and Ramanathan A L 2018 Suspended sediment laya; Geomorphology 265 45–67, https://doi.org/10.1016/j. dynamics in the meltwater of Chhota Shigri glacier, geomorph.2016.04.019. Chandra basin, Lahaul-Spiti valley, India; J. Mt. Kumar A, Verma A, Gokhale A A, Bhambri R, Misra A, Sundriyal Sci. 15(1) 68–81, https://doi.org/10.1007/s11629-017- S, Dobhal D P and Kishore N 2018 Hydrometeorological 4554-1. assessments and suspended sediment delivery from a Singh V B, Ramanathan A L, Pottakkal J G and Kumar M central Himalayan glacier in the upper Ganga basin; Int. J. 2014 Seasonal variation of the solute and suspended Sedim. Res. 33(4) 493–509, https://doi.org/10.1016/j.ijsrc. sediment load in Gangotri glacier meltwater, central 2018.03.004. Himalaya, India; J Asian Earth Sci. 79 224–234, https:// Kumar S, Rai H, Purohit K K, Rawat B R S and Mundepi doi.org/10.1016/j.jseaes.2013.09.010. A K 1987 Multi-disciplinary glacier expedition to Chhota Srivastava D, Kumar A, Verma A and Swaroop S 2014 Shigri glacier; Department of Science and Technology, Characterization of suspended sediment in meltwater from Government of India (No. 1, pp. 1–29). New Delhi, Tech- glaciers of Garhwal Himalaya; Hydrol. Process. 28(3) nical Report. 969–979, https://doi.org/10.1002/hyp.9631. Łepkowska E and Stachnik Ł 2018 Which drivers control the Subramanian V 1993 Sediment load of Indian rivers; Curr. suspended sediment Cux in a high arctic glacierized basin Sci. 928–930. (Werenskioldbreen, Spitsbergen); Water 10(10) 1408, Subramanian V 1996 The sediment load of Indian rivers – an https://doi.org/10.3390/w10101408. update; IAHS Publications-Series of Proceedings and Mao L, Comiti F, Carrillo R and Penna D 2019 Geomorphol- Reports; Int. Assoc. Hydrol. Sci. 236 183–190. ogy of proglacial systems (eds) Tobias H and David M, Tananaev N I 2015 Hysteresis eAects of suspended sediment Springer International Publishing, pp. 199–217. transport in relation to geomorphic conditions and domi- Milner A M, Khamis K, Battin T J, Brittain J E, Barrand N E, nant sediment sources in medium and large rivers of the Furede€ L and Hodson A J 2017 Glacier shrinkage driving Russian Arctic; Hydrol. Res. 46(2) 232–243, https://doi. global changes in downstream systems; Proc. Natl. Acad. org/10.2166/nh.2013.199. Sci. 114(37) 9770–9778, https://doi.org/10.1073/pnas. Tawde S A, Kulkarni A V and Bala G 2017 An estimate of 1619807114. glacier mass balance for the Chandra Basin, Western Oinam S S, Rawat Y S, Khoiyangbam R S, Gajananda K, Himalaya, for the period 1984–2012; Ann. Glaciol. 58 Kuniyal J C and Vishvakarma S C R 2005 Land use and land 99–109, https://doi.org/10.1017/aog.2017.18. cover changes in Jahlma watershed of the Lahaul valley, cold Udden J A 1914 Mechanical composition of clastic sediments; desert region of the northWestern Himalaya, India; J. Mount. Bull. Geol. Soc. Am. 25(1) 655–744, https://doi.org/10. Sci. 2(2) 129–136, https://doi.org/10.1007/BF02918328. 1130/GSAB-25-655. J. Earth Syst. Sci. (2020) 129:206 Page 15 of 15 206

Walling D E and Moorehead P W 1989 The particle size Wulf H, Bookhagen B and Scherler D 2012 Climatic and geologic characteristics of Cuvial suspended sediment: An overview; controls on suspended sediment Cux in the Sutlej River In: Sediment/Water Interactions. Developments in Hydro- Valley, western Himalaya; Hydrol. Earth Syst. Sci. biology (eds) Sly P G and Hart B T, vol 50, Springer, 16(7) 2193–2217, https://doi.org/10.5194/hess-16-2193- Dordrecht, https://doi.org/10.1007/978-94-009-2376-8˙12. 2012.

Corresponding editor: C GNANASEELAN