Marion Garçon
email: garconm@dtm.ciw.edu

Catherine Chauvel

Christian France-Lanord

Mara Limonta

Eduardo Garzanti

Which minerals control the Nd-Hf-Sr-Pb isotopic compositions of river sediments?

Keywords: isotopic fractionation, bedload, suspended load, continental crust, Himalayas, minerals

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Because they integrate large portions of continental crust exposed to weathering, river sediments from worldwide fluvial systems have been widely used to trace sediment provenance, estimate the average isotopic composition of the upper continental crust, and constrain its evolution through time [START_REF] Goldstein | A Sm-Nd isotopic study of atmospheric dusts and particulates from major river systems[END_REF][START_REF] Goldstein | Nd and Sr isotopic systematics of river water suspended material: implications for crustal evolution[END_REF][START_REF] Asmerom | The Pb isotopic evolution of the Earth: inferences from river water suspended loads[END_REF][START_REF] Allègre | Sr-Nd-Pb isotope systematics in Amazon and Congo River systems: constraints about erosion processes[END_REF][START_REF] Clift | Nd and Pb isotope variability in the Indus River System: implications for sediment provenance and crustal heterogeneity in the Western Himalaya[END_REF][START_REF] Singh | Tracing the distribution of erosion in the Brahmaputra watershed from isotopic compositions of stream sediments[END_REF][START_REF] Millot | Lead isotopic systematics of major river sediments: a new estimate of the Pb isotopic composition of the Upper Continental Crust[END_REF][START_REF] Kamber | A new estimate for the composition of weathered young upper continental crust from alluvial sediments, Queensland, Australia[END_REF][START_REF] Richards | Himalayan architecture constrained by isotopic tracers from clastic sediments[END_REF][START_REF] Roddaz | Sediment provenances and drainage evolution of the Neogene Amazonian foreland basin[END_REF][START_REF] Singh | Sr and Nd isotopes in river sediments from the Ganga Basin: Sediment provenance and spatial variability in physical erosion[END_REF][START_REF] Cina | Gangdese arc detritus within the eastern Himalayan Neogene foreland basin: Implications for the Neogene evolution of the Yalu-Brahmaputra River system[END_REF][START_REF] Belousova | The growth of the continental crust: Constraints from zircon Hf-isotope data[END_REF][START_REF] Hawkesworth | The generation and evolution of the continental crust[END_REF][START_REF] Wu | Isotopic characteristics of river sediments on the Tibetan Plateau[END_REF][START_REF] Dhuime | From sediments to their source rocks: Hf and Nd isotopes in recent river sediments[END_REF][START_REF] Padoan | Tracing Nile sediment sources by Sr and Nd isotope signatures (Uganda, Ethiopia, Sudan)[END_REF]. Most studies assume that the isotopic compositions of river sediments reflect that of the drained continental area.

However, depending on its size, shape, and density, detrital material transported by rivers is segregated within the water column by hydrodynamic processes: fast-settling coarse and heavy minerals concentrate in bedloads while fine, platy and light minerals are preferentially transported in suspension, and at different depths in the water column depending on their settling velocities [START_REF] Singh | Tracing the distribution of erosion in the Brahmaputra watershed from isotopic compositions of stream sediments[END_REF][START_REF] Komar | The Entrainment, Transport and Sorting of Heavy Minerals by Waves and Currents, in: Heavy Minerals in Use[END_REF][START_REF] Galy | Loading and fate of particulate organic carbon from the Himalaya to the Ganga-Brahmaputra delta[END_REF][START_REF] Garzanti | Settling equivalence of detrital minerals and grainsize dependence of sediment composition[END_REF]. Because chemical elements are carried in different amounts by the various minerals, it is now well described that mineral sorting processes lead to large chemical variability between bedload and suspended load sediments [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Bouchez | Grain size control of river suspended sediment geochemistry: Clues from Amazon River depth profiles[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF][START_REF] Garçon | Variabilité chimique et isotopique causée par les processus sédimentaires dans les sédiments de rivières Himalayennes[END_REF].

In contrast, our knowledge of how such processes affect the Nd, Hf, Sr and Pb isotopic compositions of sediments is relatively limited. While several studies have pointed out Nd, Hf, Sr and Pb isotopic fractionations between fine and coarse-grained sediments due to mineral sorting processes [START_REF] Patchett | Hafnium/rare earth element fractionation in the sedimentary system and crustal recycling into the Earth's mantle[END_REF][START_REF] Mclennan | Effects of sedimentary sorting on neodymium isotopes in deep-sea turbidites[END_REF][START_REF] Revel | Grain-size and Sr-Nd isotopes as tracer of paleo-bottom current strength[END_REF][START_REF] Eisenhauer | Grain size separation and sediment mixing in Arctic Ocean sediments: evidence from the strontium isotope systematic[END_REF][START_REF] Singh | Tracing the distribution of erosion in the Brahmaputra watershed from isotopic compositions of stream sediments[END_REF][START_REF] Chauvel | Role of recycled oceanic basalt and sediment in generating the Hf-Nd mantle array[END_REF][START_REF] Bayon | Hf and Nd isotopes in marine sediments: Constraints on global silicate weathering[END_REF][START_REF] Carpentier | The "zircon effect" as recorded by the chemical and Hf isotopic compositions of Lesser Antilles forearc sediments[END_REF][START_REF] Chauvel | Hf-Nd input flux in the Izu-Mariana subduction zone and recycling of subducted material in the mantle[END_REF][START_REF] Vervoort | The Hf-Nd isotopic composition of marine sediments[END_REF]Garçon et al., 2013aGarçon et al., , 2013b)), the influence of each mineral species on the isotopic composition of sediments has never been thoroughly quantified.

In this paper, we report trace element contents and Nd, Hf, Sr and Pb isotopic compositions for a large number of pure mineral fractions separated from a bedload and a bank sediment sampled at the outflow of the Ganga fluvial system in the Bangladesh delta. Using Monte-Carlo simulations, we combine these data with mineral proportions reported by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) and [START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF] in river sediments sampled at the same location to (1) evaluate the individual contribution of each mineral species to the Nd, Hf, Sr and Pb isotopic budgets of bedloads and suspended loads and (2) determine if the known mineralogy of bedloads and suspended loads accounts for the observed difference in isotopic compositions. Finally, we discuss the implications of our results for sediment provenance studies based on isotope data and for large-scale isotopic partitioning between continental and oceanic sediments.

Studied Area and Samples

The studied mineral fractions were separated from two samples (bank sediment BGP 6 and bedload BR 717) collected at the outflow of the Ganga River, before the confluence with the Brahmaputra River (Figure 1). The sampling site was selected because it is located downstream, far away from the mountain range. As a consequence, the sediments integrate all materials transported by the Ganga River and its tributaries.

These rivers belong to one of the largest fluvial systems on Earth [START_REF] Milliman | World-wide delivery of river sediment to the oceans[END_REF], stretch over more than 2500 km and deliver about four hundred million tons of sediments to the ocean each year [START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF]. Such sediments are dominantly (>95%) derived from the Himalayan mountain range with minor contributions from the Deccan Trapps and Indian craton supplied by the southern tributaries of the Ganga [START_REF] Galy | Higher erosion rates in the Himalaya: Geochemical constraints on riverine fluxes[END_REF][START_REF] Singh | Sr and Nd isotopes in river sediments from the Ganga Basin: Sediment provenance and spatial variability in physical erosion[END_REF][START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF]. Himalayan materials mainly consist of sedimentary series and crystalline metamorphic rocks (e.g., [START_REF] Fort | Himalayas: the collided range. Present knowledge of the continental arc[END_REF]. Because of the large lithological and chemical diversity of Himalayan rocks, the Ganga basin can be considered as a representative portion of the upper continental crust.

All minerals analyzed in our study are thus ubiquitous in the erosion products of any upper continental crust exposed to weathering [START_REF] Mclennan | Rare earth elements in sedimentary rocks; influence of provenance and sedimentary processes[END_REF][START_REF] Taylor | The Geochemical evolution of the Continental Crust[END_REF][START_REF] Mclennan | Relationships between the trace element composition of sedimentary rocks and upper continental crust[END_REF] and our results should be relevant for river sediments sampled in other worldwide fluvial systems draining large continental areas.

The second major advantage of the selected site is that these sediments were extensively studied for their mineralogy by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) and [START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF] (Supplementary Table 1). These authors showed that bedloads and suspended loads share quite similar types of minerals but in different proportions. Surface suspended loads are generally finer and relatively rich in micas and clays while bedloads tend to be coarser and richer in quartz and heavy minerals. Among the heavy minerals that are commonly found in the Ganga sediments, amphibole, epidote and garnet are the most abundant but many others are also present: titanite, zircon, rutile, apatite, monazite, tourmaline and allanite for example [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011). Detailed mineral proportions in the Ganga bedloads and suspended loads are listed in Supplementary Table 1.

Finally, several studies have reported major and trace element contents, as well as Nd, Hf, and Pb isotopic compositions, of bulk suspended loads and bedloads from this sampling site [START_REF] Galy | Higher erosion rates in the Himalaya: Geochemical constraints on riverine fluxes[END_REF][START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF][START_REF] Garçon | Variabilité chimique et isotopique causée par les processus sédimentaires dans les sédiments de rivières Himalayennes[END_REF][START_REF] Garçon | Variabilité chimique et isotopique causée par les processus sédimentaires dans les sédiments de rivières Himalayennes[END_REF]Garçon et al. , 2013a;;2013b; see Supplementary Table 2). We used these data to evaluate the role of individual minerals in the bulk isotopic budget of river sediments. Because no Sr isotopic data exist in the literature for unleached bedloads and suspended loads, we analyzed the bulk Sr isotopic compositions of two bedloads (BR 717 and BR 418), one bank sediment (BGP 6) and 2 suspended loads (BR 414 and BR 412) sampled in the Bangladesh delta (Figure 1). The trace element data and the Nd, Hf, and Pb isotopic compositions of these five samples are reported in Supplementary Table 2. The detailed sampling methods and the major element data can be found in [START_REF] Galy | Higher erosion rates in the Himalaya: Geochemical constraints on riverine fluxes[END_REF], [START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF][START_REF] Garçon | Variabilité chimique et isotopique causée par les processus sédimentaires dans les sédiments de rivières Himalayennes[END_REF] and Garçon et al. (2013a;2013b). We also analyzed the Sr isotopic compositions of several grain-size fractions separated from a suspended load from the Ganga River (sample BR 522, Figure 1) to help us interpreting the Sr results of the Monte-Carlo simulations.

Methods

Mineral separation

The starting material consists of several kilos of sediment collected during the monsoon season. With the exception of the clay fraction separated from bank sediment BGP 6, all mineral fractions were separated from bedload BR 717 dredged in the main channel of the Ganga River. This sample was first washed and dried, then divided into 3 grain-size fractions by sieving: 63-100 μm, 125-250 μm, and >250 μm. For each fraction, we used successive centrifugations in heavy liquids (sodium metatungstate and methylene iodide) to separate minerals and partial freezing in liquid nitrogen to recover them. After this step, some of the minerals were isolated using a hand magnet or the Frantz isodynamic magnetic separator. Biotite, muscovite, plagioclase, quartz, and Kfeldspar were isolated from the >250 μm fraction; magnetite, amphibole, apatite, garnet, epidote, titanite, monazite, allanite, and zircon from the 63-100 μm and/or the 125-250 μm fractions. Each mineral fraction was finally carefully purified under the binocular and polarizing microscopes to remove impurities.

Because monazite and allanite were very small and difficult to distinguish from each other, we analyzed an impure fraction containing high amounts of both minerals. In the rest of the paper, this fraction is called the "monazite/allanite fraction". Similarly, we could not remove all impurities in the apatite fraction; hence we analyzed an impure fraction highly enriched in apatite. Following [START_REF] Galy | The strontium isotopic budget of Himalayan Rivers in Nepal and Bangladesh[END_REF], the composition of the carbonate fraction was obtained using a 1N acetic acid leaching procedure for one hour in an ultrasonic bath. To ensure a pure carbonate fraction and avoid dissolution of other minerals or contamination by clay minerals, we leached a coarse fraction containing only carbonate, quartz, K-feldspar, plagioclase, and few phyllosilicates (non-magnetic 125-250 μm fraction with a density < 2.90 g.cm -3 ). The carbonate-rich leachate was then isolated by centrifugation and evaporated on a hot plate. Finally, a clay fraction, selected as the <2 μm fraction, was separated by sieving and settling from bank sediment BGP 6 because we lacked sufficient material to recover it from bedload BR 717.

For each mineral species, only one fraction was separated and analyzed; the only exception is the K-feldspar fraction (Table 1). We recognized that multiple analyzes of the same mineral species could have been interesting to assess the compositional heterogeneity of the Himalayan minerals. However, due to the limited quantity of starting material and the time-consuming work involved in the handpicking procedure we could not perform other duplicate analyses. We are however confident that the measured fractions should be representative of the mineral populations because for each mineral separate, the number of grains present in the analyzed fraction was always higher than a thousand. For example, the 5mg fraction of zircons analyzed in this study most probably contains between 1,000 to 2,000 zircon grains assuming that grains are spherical with a mean diameter of 50-60 μm, as suggested by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) and Garçon et al. (2013b).

Chemistry

For each mineral separate, we performed a single dissolution used to analyze both trace element concentrations and Nd, Hf and Sr isotopes. Note that Pb isotopic compositions of these sediments were previously published by Garçon et al. (2013b).

Methods used to obtain Pb data are described in this latter paper.

After washing with ultrapure water, 5 to 100 mg of mineral grains or powder were first attacked for 2 days in 14N HNO3 on a hot plate at 130°C, then evaporated and dissolved in a mixture of HF and HClO4 in teflon containers placed in steel PARR bombs for 10 days at 200°C to ensure complete dissolution. For bulk bedloads and suspended loads, 50 to 100 mg of fine powder were dissolved using the same procedure as for the mineral fractions. Finally, the bulk suspended load BR 522 and its grain-size fractions, separated using settling and sieving techniques, were leached in 1N acetic acid for one hour in an ultrasonic bath to remove the carbonates before being digested following the same dissolution procedure as the one described above.

We measured trace element concentrations by ICP-MS with a dilution factor of 5000 and the equivalent of 2mg of sample. Measurements were performed using an Agilent 7500ce ICP-MS in Grenoble (France). Oxide interference and analytical drift were corrected following the procedure of [START_REF] Chauvel | Comprehensive chemical and isotopic analyses of basalt and sediment reference materials[END_REF]. BR-24 rock standards were measured every four samples to calibrate and convert peak signals into concentrations offline. To ensure the validity of our results, international rock standards were run as unknown samples, including sedimentary materials such as JSd-2 (see Supplementary Table 3).

Hafnium, Nd and Sr were isolated from the same aliquot using the ion resin techniques and following the procedure described by [START_REF] Chauvel | Comprehensive chemical and isotopic analyses of basalt and sediment reference materials[END_REF]. Hafnium was recovered in three steps using a first column filled with cationic resin (AG50W-X8) to isolate the High Field Strength Elements (HFSE) followed by a second anion resin column (AG1-X8) and a third cation resin column (AG50W-X8) to isolate Hf from the other HFSE.

Nd was recovered in two steps: a first cation resin column (AG50W-X8) to isolate the Rare Earth Elements (REE) and a second column filled with Ln-Spec resin to separate Nd from the other REE. Finally, Sr was isolated using two successive columns filled with cationic resin (AG50W-X8) and Sr-Spec resin. Total procedural blanks were < 80 pg for Nd (n=1), < 35 pg for Hf (n=2) and < 140 pg for Sr (n=2). These small amounts can be considered negligible compared to the amount of Nd, Hf and Sr isolated in all samples. Nd and Hf isotopes were measured on a Nu Plasma MC-ICP-MS at the ENS Lyon (France). Nd isotopic compositions were normalized to 146 Nd/ 144 Nd=0.7219 and Hf isotopic compositions to 179 Hf/ 177 Hf=0.7325. The Ames-Rennes Nd and Ames-Grenoble Hf reference standards run every two or three samples yielded average 143 Nd/ 144 Nd = 0.511953 ± 8 (2σ, n = 15) and 176 Hf/ 177 Hf = 0.282157 ± 18 (2σ, n = 14), respectively. We used the recommended values published by [START_REF] Chauvel | A hafnium isotope and trace element perspective on melting of the depleted mantle[END_REF] and [START_REF] Chauvel | Comprehensive chemical and isotopic analyses of basalt and sediment reference materials[END_REF] for these two standards to correct the analytical drift all through the measurement sequence. Sr isotopic compositions were measured using a solid source ThermoScientific Triton mass spectrometer in Brest (France) and normalized to 86 Sr/ 88 Sr=0.1194. The NBS 987 reference material yielded average 87 Sr/ 86 Sr = 0.710270 ± 7 (2σ, n = 10) and values measured for the samples were not normalized.

Results

Trace element concentrations

Trace element concentrations measured in the mineral fractions are reported in Table 1 and shown in Figure 2 as spidergrams normalized to the average composition of upper continental crust [START_REF] Rudnick | Composition of the Continental Crust[END_REF]. Except for mobile elements such as Cs, Rb, Ba, Sr, Li, Co, or Ni, heavy minerals (Figure 2a) generally display higher trace element contents than light minerals (Figure 2b). The trace element patterns of the various minerals are generally consistent with published values (e.g., [START_REF] Götze | Distribution of REE and trace elements in size and mineral fractions of high-purity quartz sands[END_REF][START_REF] Bea | Residence of REE, Y, Th and U in granites and crustal protoliths; implications for the chemistry of crustal melts[END_REF][START_REF] Garçon | Beach placer, a proxy for the average Nd and Hf isotopic composition of a continental area[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF]. Zircon and garnet show typical depletions in light REE while monazite/allanite show depletion in heavy REE. The REE distributions of other minerals, including light minerals, are rather flat. The monazite/allanite and zircon separates clearly exhibit the highest concentrations in LREE (including Nd) and Hf, respectively, with enrichment factors > 1000 (see Figure 2). The highest Pb contents are found in clay (95 ppm) and K-feldspar (75 ppm). The highest Sr concentrations are found in epidote (1509 ppm), carbonate (526 ppm), K-feldspar (243 ppm) and plagioclase (195 ppm). Both apatite and monazite/allanite fractions display lower REE and higher Zr, Hf concentrations compared to what could be expected in pure apatite and monazite/allanite separates [START_REF] Ayres | REE fractionation and Nd-isotope disequilibrium during crustal anatexis: constraints from Himalayan leucogranites[END_REF][START_REF] Garçon | Beach placer, a proxy for the average Nd and Hf isotopic composition of a continental area[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF], a feature probably due to the presence of impurities such as light minerals and zircon grains. This can easily be explained by the fact that we were unable to purify the apatite and monazite/allanite fractions under the binocular microscope due to the very small size of the minerals. Similarly, the quartz fraction contains anomalously high REE and Sr concentrations compared to what is usually obtained in pure quartz (see for example [START_REF] Monecke | Determination of trace elements in the quartz reference material UNS-SpS and in natural quartz samples by ICP-MS[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]. It is thus likely that this fraction also contains impurities.

Isotopic compositions

The Nd, Hf and Sr isotopic compositions for all mineral separates are reported in Table 1. No Hf isotopic composition could be measured for the carbonate fraction because of its very low Hf concentration. We also did not measure the isotopic compositions of the quartz fraction and the Hf and Sr isotopes of the monazite/allanite-rich and the apatiterich separate because of the presence of other minerals in these separates that could strongly influence the measured isotopic compositions.

Nd, Hf and Sr isotopic compositions span large ranges of values between 0.511732 and 0.512235 for 143 Nd/ 144 Nd, 0.281970 and 0.282803 for 176 Hf/ 177 Hf and 0.7125 and 0.8313 for 87 Sr/ 86 Sr. The monazite/allanite fraction has the least radiogenic Nd isotopic compositions of all minerals while the zircon separate has the least radiogenic Hf isotopic compositions. Biotite and muscovite, known for their very high Rb/Sr ratios, have accordingly highly radiogenic Sr isotopic compositions (0.8123 and 0.8313, respectively). By contrast, epidote and carbonate have the least radiogenic Sr isotopic compositions (0.7125 and 0.7139) of all minerals.

Sr isotopic compositions measured in bulk bedloads, banks and suspended loads from the Ganga River are listed in Table 2. They are particularly radiogenic with 87 Sr/ 86 Sr varying between 0.7546 and 0.7737 and bedloads tending to have less radiogenic Sr isotopes than suspended loads. These results fall within the large range of Sr isotopes usually measured in the silicate and/or carbonate fractions isolated from Himalayan derived sediments [START_REF] Harris | The significance of Himalayan rivers for silicate weathering rates: Evidence from the Bhote Kosi tributary[END_REF][START_REF] Galy | The strontium isotopic budget of Himalayan Rivers in Nepal and Bangladesh[END_REF][START_REF] Galy | Higher erosion rates in the Himalaya: Geochemical constraints on riverine fluxes[END_REF][START_REF] Pierson-Wickmann | Os-Sr-Nd results from sediments in the Bay of Bengal: Implications for sediment transport[END_REF][START_REF] Singh | Sr and Nd isotopes in river sediments from the Ganga Basin: Sediment provenance and spatial variability in physical erosion[END_REF]. Such radiogenic isotopic compositions are due to the presence of extremely radiogenic sources in the Himalayan mountain range, in particular in the Lesser Himalayan units ( 87 Sr/ 86 Sr > 0.8; [START_REF] Derry | Neogene Himalayan weathering history and river 87Sr86Sr: impact on the marine Sr record[END_REF][START_REF] Singh | Sr and Nd isotopes in river sediments from the Ganga Basin: Sediment provenance and spatial variability in physical erosion[END_REF].

Sr isotopic compositions measured in the silicate fractions of the suspended load BR 522 and its grain-size fractions are provided in Table 2. The bulk leached suspended load BR 522 shares comparable 87 Sr/ 86 Sr ratios with the other unleached suspended loads from the Ganga River (Table 2). Significant Sr isotopic differences are however observed between the bulk sample and its grain-size fractions with the 2-20 μm fraction being much more radiogenic than the others.

Discussion

River sediments are basically mixtures of mineral species. Some minerals, such as quartz, contain virtually no trace elements and their proportion in the sedimentary material influences only the silica content of the sediment but has no impact on most isotopic ratios. In contrast, other minerals very rich in certain trace elements or with very unusual isotopic compositions have the potential to significantly contribute or can even overwhelm the trace element and isotopic budget of river sediments even if they are present in very low proportions. A proper understanding of the role of individual mineral species in the isotopic budget of river sediments thus requires the consideration of several parameters. These include the nature and the proportion of the different mineral species as well as their trace element concentration and isotopic composition.

Here, we present Monte-Carlo simulations that quantitatively evaluate the role of individual minerals in the final Nd, Hf, Sr and Pb isotopic budget of river sediments. We simulate the composition of a hundred thousand mixtures between common mineral species that are present in the sedimentary material and that have the potential to significantly influence its isotopic compositions. Then, we compare our results to the data published on bulk sediments.

Using mineral proportions estimated by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) and calcite contents measured by [START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF] in suspended loads and bedloads from the Ganga River in the Bangladesh delta, we first defined a range of possible proportions for each type of mineral (see Supplementary File). For each mineral species, we then randomly sample proportions within this range and associate them with the Nd, Hf, Sr or Pb concentrations and isotopic compositions measured in our mineral separates. Finally, we calculate the isotopic compositions of the resulting mixtures using the following equation, written here for the Nd isotopic system:

! Nd 143 Nd 144 # $ mixture = % & x i . C Nd, i C Nd, mixture . ! Nd 143 Nd 144 # $ i ' n i=1
where n is the number of minerals involved in the mixture; xi the weight proportion of mineral i sampled randomly in a defined range; CNd,i and ! Nd and the Nd isotopic composition of mineral i (see Table 1); and

C Nd, mixture = ∑ )x i . C Nd, i * n i=1
, the Nd concentration of the mixture.

In the following sections, we compare the modeled Nd, Hf, Sr and Pb isotopic compositions to those measured in both suspended load and bedload sediments as sampled in the Ganga River in the Bangladesh delta (data from the literature and our Sr isotope measurements). Using such approach, we can evaluate the role of each mineral in the isotopic budget of all river sediments. We illustrate the results of our Monte-Carlo simulations for each isotopic system in diagrams that show the contribution of the various mineral species as a function of the modeled isotopic composition for all mixtures. The contribution of each mineral i in the 143 Nd/ 144 Nd budget, for example, is determined in percentage using the following equation:

Mineral contribution = 100 . x i . C Nd, i C Nd, mixture . ! Nd 143 Nd 144 # $ i ! Nd 143 Nd 144 # $ mixture #
All details about the input parameters, the way simulations were performed and the robustness of the Monte-Carlo results are provided in the Supplementary File. We performed several simulations to assess the sensitivity of our models to the proportions, concentrations and isotopic compositions of the minerals (see Supplementary File). Here, we just emphasize that the simulation results are very well constrained for the four isotopic systems. The contribution of each mineral species in the whole-rock isotopic budgets remains quite constant even if we introduce large errors on the input parameters, such as the mineral proportions or the concentrations and isotopic compositions of mineral species. An important implication is that the results presented in this study may be valid not only for the Ganga sediments, but also for any sediment sampled in other large river systems all over the world.

Which minerals control Nd isotopes of river sediments?

To perform the Monte-Carlo simulation modeling Nd isotopic compositions of sediments, we considered the 14 analyzed minerals (K-feldspar, plagioclase, muscovite, biotite, magnetite, zircon, titanite, apatite, monazite/allanite, amphibole, epidote, garnet, carbonate, and clay) and we set the mineral proportions within the range known for river sediments sampled in the Bangladesh delta (see Supplementary File and inset in Figure 3). Results are shown in Figure 3 while 143 Nd/ 144 Nd for the most important mineral phases are shown along the horizontal axis.

The first important observation is that minerals that significantly contribute to the Nd isotopic budget are very few: monazite/allanite, clay, titanite and biotite. All other minerals (K-feldspars, plagioclase, muscovite, magnetite, zircon, apatite, amphibole, epidote, garnet, and carbonate) always contribute to less than 10% of the isotopic budget, over the entire range of modeled Nd isotopic compositions; this is why they are not shown in Figure 3. Because of its extreme enrichment in Nd (Figure 2), the monazite/allanite fraction entirely dominates the Nd isotopic budget of modeled mixtures and it can even control 100% of the isotopic budget (Figure 3) while its proportion in the mineral assemblage never exceeds 0.5 wt.% (see Supplementary File and inset of Figure 3). It is only when the modeled Nd isotopic composition is greater than 0.51185 that the contribution of clay, titanite and biotite starts to surpass that of monazite and allanite (Figure 3).

As shown by the grey band in Figure 3, Nd isotopic ratios reported for bedloads and suspended loads sampled at the outflow of the Ganga span a limited range of values between 0.51172 and 0.51179 (Garçon et al., 2013a), values similar to those reported by [START_REF] Galy | Higher erosion rates in the Himalaya: Geochemical constraints on riverine fluxes[END_REF] for sediments sampled at the same location. According to our simulation (Figure 3), such compositions correspond to mixtures highly dominated by monazite and allanite (60-100%) with a small but significant contribution from clays (0-30%). We can therefore suggest that the few monazite and allanite grains present in the sediments (<0.5 wt%, see Supplementary File) totally overwhelm the Nd isotopic compositions of river sediments in the Bangladesh delta.

In a more classical diagram showing 143 Nd/ 144 Nd ratios as a function of 147 Sm/ 144 Nd ratios (Figure 4), it becomes even more obvious that the unradiogenic Nd isotopic signature of the monazite/allanite fraction is the controlling factor of the composition of the Ganga sediments since no other mineral has a Nd isotopic composition similar to or lower than that of the bulk sediments. As suggested by our Monte-carlo simulation, both bedloads and suspended loads are well reproduced by a mixture of monazite, allanite, clay ± biotite, titanite (Figure 4).

Which minerals control Hf isotopes of river sediments?

The results of the Monte-Carlo simulation for Hf isotopes are shown in Figure 5 together with the results obtained on the various mineral separates. Eleven minerals were introduced in the simulation: K-feldspar, plagioclase, muscovite, biotite, magnetite, zircon, titanite, amphibole, epidote, garnet, and clay. Quartz and carbonates were ignored because they do not contain any Hf and we did not measure their Hf isotopic compositions. The monazite/allanite fraction and the apatite fraction were also ignored because they contain a lot of small zircons, a feature that explains their unusually high Zr-Hf concentrations compared to what is commonly measured in such minerals [START_REF] Ayres | REE fractionation and Nd-isotope disequilibrium during crustal anatexis: constraints from Himalayan leucogranites[END_REF]. We suggest, therefore, that if we had measured their Hf isotopic compositions, the results would not have been representative of the composition of the monazite, allanite or the apatite but would have reflected that of the zircons present in the fractions.

As for Nd isotopes, only few minerals significantly contribute to the Hf isotopic budget of the modeled mixtures: zircon, clay, muscovite, biotite and titanite. By contrast, garnet, amphibole, epidote, magnetite, plagioclase, and K-feldspar contribute to less than 10% of the Hf isotopic budget and are not shown in Figure 5. Modeled Hf isotopic compositions span a relatively large range of values between 0.28197 and 0.28235. One of the most remarkable features is that unradiogenic Hf-rich zircon totally buffers the Hf isotopic budget over a large range of isotopic compositions (Figure 5) while its proportion in the mineral assemblage is always less than 0.5 wt% (see Supplementary File and inset of Figure 5). Clay is the second important mineral but its contribution never exceeds 50%.

Together with muscovite, biotite and titanite, the clay fraction accounts for the most radiogenic Hf isotopic compositions in the simulation.

In contrast to Nd, there is a difference between the 176 Hf/ 177 Hf ratio of bedloads and suspended loads sampled at the outflow of the Ganga River [START_REF] Garçon | Variabilité chimique et isotopique causée par les processus sédimentaires dans les sédiments de rivières Himalayennes[END_REF]2013a).

As shown by grey bands in Figure 5, bedloads are less radiogenic than suspended loads.

The results of our Monte-carlo simulation show that the difference is easily explained by different contributions of unradiogenic zircon. The range of 176 Hf/ 177 Hf measured in bedloads results from a very high contribution of zircon (i.e. 70-100% of their Hf isotopic budget) and the relative contribution of all other minerals remains always below 15%.

The higher 176 Hf/ 177 Hf measured in suspended loads results from a higher contribution of more radiogenic minerals such as clay, muscovite, biotite and titanite and a lower contribution of zircon, even if it can still contribute to up to 50% of the isotopic budget (Figure 5).

In Figure 6, we show the relationship between 176 Hf/ 177 Hf and 1/Hf for minerals, bedloads and suspended loads. In contrast to what we did for the Nd case, we chose to not represent Hf isotopic compositions as a function of their parent/daughter ratios (i.e.

176 Lu/ 177 Hf ratios) because Lu and Hf are not carried by the same minerals and it would introduce confusion when evaluating the role of individual minerals in the Hf isotopic compositions of sediments. We also normalized the 1/Hf ratio of all sediments to the SiO2 content of the clay fraction (see footnote of Figure 6 for more details), because dilution by variable amounts of quartz has a drastic effect on the Hf concentration of river sediments.

As highlighted by our Monte-Carlo simulations, Figure 6 shows clearly that all bedloads plot close to zircon, confirming that this mineral buffers the Hf isotopic budget of bedloads. The composition of suspended loads requires a mixture of more radiogenic minerals such as biotite, muscovite and clay together with less radiogenic titanite and zircon for some of them (Figure 6).

Which minerals control Sr isotopes of river sediments?

In Figure 7a 8). It seems thus unlikely that these phases could have the elevated 87 Sr/ 86 Sr that is required to explain the observed mismatch between model and measurements. Adding these mineral phases in the simulations would probably even make the modeled Sr isotopic compositions less radiogenic.

A second possibility is that we underestimated the proportions of very radiogenic minerals, such as muscovite or biotite (Figure 8), in the fine suspended loads. However, as shown in details in the Supplementary File, the results of the Monte-Carlo simulations are robust and the range of modeled Sr isotopic compositions remains incompatible with the measured values even with very different mineral proportions.

The last possibility is that one or several mineral phases that contribute significantly to the Sr isotopic budget have 87 Sr/ 86 Sr ratios higher than the value that we measured.

We suspect that it could be the case for K-feldspar and muscovite for the following reason:

for convenience, we separated these two minerals from the coarsest fraction (>250 μm) of bedload BR 717 (see section 3.1) and we suspect that smaller minerals might have different isotopic compositions. Indeed, large ranges of 87 Sr/ 86 Sr are reported for Kfeldspar (0.737 to 0.801) and muscovite (0.765 to 8.353) in the Himalayan rocks [START_REF] Mehta | Rb-Sr geochronology of the Kulu-Mandi Belt: Its implications for the Himalayan Tectogenesis[END_REF][START_REF] Kai | Rb-Sr Ages of the Biotite and Muscovite of the Himalayas, Eastern Nepal -Its Implication in the Uplift History[END_REF][START_REF] Ferrara | Rb/Sr geochronology of granites and gneisses from the Mount Everest region, Nepal Himalaya[END_REF][START_REF] Deniel | Isotopic study of the Manaslu granite (Himalaya, Nepal): inferences on the age and source of Himalayan leucogranites[END_REF][START_REF] Johnson | Rb-Sr ages of micas from the Kathmandu complex, Central Nepalese Himalaya: Implications for the evolution of the Main Central Thrust[END_REF] and we believe that the coarse feldspar and muscovite that we isolated ( 87 Sr/ 86 Sr of 0.7587 and 0.8313, respectively) might have lower 87 Sr/ 86 Sr than grains present in fine suspended loads.

Analyses of finer K-feldspar and muscovite fractions would help support this hypothesis but the separation and purification of a significant amount of fine minerals constitute a difficult technical challenge. As an alternative, we performed additional Sr isotopic analyses on several grain-size fractions separated from a Ganga suspended load sampled in the Bangladesh delta (sample BR 522; Figure 1, Table 2). These analyses were carried out on the silicate part of the samples to establish whether the Sr isotopic compositions of the silicate minerals could vary as a function of mineral grain-size.

Results clearly indicate that the grain-size fractions do not have the same Sr isotopic composition as the bulk sample and span a relatively large range of values from 0.7575 to 0.7824 (Table 2). Interestingly, the 2-20 μm fraction, representing almost half of the suspended load sample (see Table 2), has the most radiogenic Sr isotopic composition, supporting the idea that finer K-feldspars and muscovites may be more radiogenic than the coarse minerals that we analyzed. This could occur if fine K-feldspars and muscovites were older than coarse ones. Calculations show that an age difference of only 50-70 Ma is sufficient to increase the 87 Sr/ 86 Sr ratio of muscovite from 0.8 to 1.0 assuming a 87 Rb/ 86 Sr ratio of 200-300, values commonly measured in Himalayan muscovite (e.g. [START_REF] Mehta | Rb-Sr geochronology of the Kulu-Mandi Belt: Its implications for the Himalayan Tectogenesis[END_REF][START_REF] Kai | Rb-Sr Ages of the Biotite and Muscovite of the Himalayas, Eastern Nepal -Its Implication in the Uplift History[END_REF][START_REF] Ferrara | Rb/Sr geochronology of granites and gneisses from the Mount Everest region, Nepal Himalaya[END_REF][START_REF] Deniel | Isotopic study of the Manaslu granite (Himalaya, Nepal): inferences on the age and source of Himalayan leucogranites[END_REF][START_REF] Johnson | Rb-Sr ages of micas from the Kathmandu complex, Central Nepalese Himalaya: Implications for the evolution of the Main Central Thrust[END_REF]. The age difference required to increase the 87 Sr/ 86 Sr ratio of K-feldspar from 0.76 to 0.80 is of course higher (about 150 Ma) because 87 Rb/ 86 Sr is usually around 20 (e.g. [START_REF] Ferrara | Rb/Sr geochronology of granites and gneisses from the Mount Everest region, Nepal Himalaya[END_REF][START_REF] Deniel | Isotopic study of the Manaslu granite (Himalaya, Nepal): inferences on the age and source of Himalayan leucogranites[END_REF][START_REF] Johnson | Rb-Sr ages of micas from the Kathmandu complex, Central Nepalese Himalaya: Implications for the evolution of the Main Central Thrust[END_REF]. Another possibility is that the fine K-feldspars and muscovites had initially crystallized with high Rb but similar Sr contents, leading to higher Rb/Sr ratios than the ratio measured in the coarse minerals, and leading in turn, to more radiogenic Sr isotopic compositions over the same amount of time.

Unfortunately, available data are not sufficient to constrain the origin of the observed isotopic variability as a function of grain-size and further work is needed to understand this feature.

In order to reproduce the isotopic compositions measured on whole-rock sediments, we revised the Monte-Carlo simulation assuming that K-feldspars and muscovites had more radiogenic Sr isotopic compositions but similar Sr concentrations.

The results of the simulation using 87 Sr/ 86 Sr ratios of 0.800 for K-feldspar (instead of 0.7587) and 1.0 for muscovite (instead of 0.8313) are shown in Figure 7b. In this case, the simulated mixtures overlap with the fields known for bedloads and suspended loads. As in the first simulation (Figure 7a), K-feldspar, carbonate, clay, plagioclase, epidote and muscovite control the Sr isotopic budget of sediments (Figure 7b). Assuming that the revised simulation is valid, isotopic differences between bedloads and suspended loads could be explained by higher relative contributions of epidote and carbonate in bedloads and muscovite/K-feldspar in suspended loads.

Which minerals control Pb isotopes of river sediments?

To perform the Monte-Carlo simulation for Pb isotopes, we used the data already published by Garçon et al. (2013b) implies that the Pb isotopic budget of both bedloads and suspended loads are influenced by a "heavy mineral effect", with a stronger impact on bedloads than on suspended loads (see Figure 9 and Garçon et al. (2013b) for further discussion).

Geological implications

We showed above that the difference in Hf, Sr and Pb isotopic compositions between bedloads and suspended loads sampled at the same location are due to different mineral contributions that are ultimately controlled by mineral sorting processes within the water column. We now evaluate the implications of these results for (1) sediment provenance studies based on isotope data and (2) large-scale isotopic partitioning between continental and oceanic sediments.

Sediment provenance studies

Isotopic data are often used to trace erosion distribution in watersheds (e.g., [START_REF] Galy | Higher erosion rates in the Himalaya: Geochemical constraints on riverine fluxes[END_REF][START_REF] Clift | Nd and Pb isotope variability in the Indus River System: implications for sediment provenance and crustal heterogeneity in the Western Himalaya[END_REF][START_REF] Singh | Tracing the distribution of erosion in the Brahmaputra watershed from isotopic compositions of stream sediments[END_REF][START_REF] Singh | Sr and Nd isotopes in river sediments from the Ganga Basin: Sediment provenance and spatial variability in physical erosion[END_REF] as well as changes through time of the source of sedimentary material in relation to changes of tectonic regimes and/or climate (e.g. [START_REF] Walter | Provenance and transport of terrigenous sediment in the South Atlantic Ocean and their relations to glacial and interglacial cycles: Nd and Sr isotopic evidence[END_REF][START_REF] Gingele | Late Pleistocene and Holocene climate of SE Australia reconstructed from dust and river loads deposited offshore the River Murray Mouth[END_REF][START_REF] Yan | Geochemical and Nd isotope composition of detrital sediments on the north margin of the South China Sea: provenance and tectonic implications[END_REF][START_REF] Haley | Radiogenic isotope record of Arctic Ocean circulation and weathering inputs of the past 15 million years[END_REF][START_REF] Singh | Sr and Nd isotopes in river sediments from the Ganga Basin: Sediment provenance and spatial variability in physical erosion[END_REF][START_REF] Cina | Gangdese arc detritus within the eastern Himalayan Neogene foreland basin: Implications for the Neogene evolution of the Yalu-Brahmaputra River system[END_REF][START_REF] Rahaman | Climate control on erosion distribution over the Himalaya during the past 100 ka[END_REF][START_REF] Stumpf | Climatically driven changes in sediment supply on the SW Iberian shelf since the Last Glacial Maximum[END_REF][START_REF] Hu | Provenance and climate change inferred from Sr-Nd-Pb isotopes of late Quaternary sediments in the Huanghe (Yellow River) Delta, China[END_REF]. They are also largely used in studies focusing on the changes of ocean circulation through time [START_REF] Revel | Grain-size and Sr-Nd isotopes as tracer of paleo-bottom current strength[END_REF][START_REF] Innocent | Sm-Nd signature of modern and late Quaternary sediments from the northwest North Atlantic: Implications for deep current changes since the Last Glacial Maximum[END_REF][START_REF] Stille | Nd isotope evidence for the evolution of the paleocurrents in the Atlantic and Tethys oceans during the past 180 Ma[END_REF][START_REF] Bayon | An improved method for extracting marine sediment fractions and its application to Sr and Nd isotopic analysis[END_REF][START_REF] Frank | Radiogenic isotopes: Tracers of past ocean circulation and erosional input[END_REF][START_REF] Goldstein | Long-lived isotopic tracers in oceanography, paleoceanography, and ice-sheet dynamics[END_REF][START_REF] Piotrowski | Intensification and variability of ocean thermohaline circulation through the last deglaciation[END_REF][START_REF] Gourlan | Tectonically driven changes in the Indian Ocean circulation over the last 25 Ma: Neodymium isotope evidence[END_REF][START_REF] Haley | Radiogenic isotope record of Arctic Ocean circulation and weathering inputs of the past 15 million years[END_REF][START_REF] Ehlert | Current transport versus continental inputs in the eastern Indian Ocean: Radiogenic isotope signatures of clay size sediments[END_REF] or in studies that attempt to establish average isotopic compositions or ages for large areas of continental crust [START_REF] Goldstein | A Sm-Nd isotopic study of atmospheric dusts and particulates from major river systems[END_REF][START_REF] Goldstein | Nd and Sr isotopic systematics of river water suspended material: implications for crustal evolution[END_REF][START_REF] Asmerom | The Pb isotopic evolution of the Earth: inferences from river water suspended loads[END_REF][START_REF] Allègre | Sr-Nd-Pb isotope systematics in Amazon and Congo River systems: constraints about erosion processes[END_REF][START_REF] Millot | Lead isotopic systematics of major river sediments: a new estimate of the Pb isotopic composition of the Upper Continental Crust[END_REF][START_REF] Kamber | A new estimate for the composition of weathered young upper continental crust from alluvial sediments, Queensland, Australia[END_REF][START_REF] Dhuime | From sediments to their source rocks: Hf and Nd isotopes in recent river sediments[END_REF]. It is therefore important to know which isotopic systems are robust tracers of source compositions and in which conditions.

Nd isotopes are certainly an important source tracer. Our study of bedloads, suspended loads and individual minerals confirms that it is robust. More specifically, we demonstrate that the isotopic budget of both suspended loads and bedloads is buffered by monazite and allanite even though these minerals are present in extremely small proportions (< 0.5 wt.%) in the sediments. Both types of sediments can be used for provenance studies. We also confirm the conclusions presented by [START_REF] Garçon | Beach placer, a proxy for the average Nd and Hf isotopic composition of a continental area[END_REF] who demonstrated that, in beach placers, monazite by itself was a good proxy to estimate the Nd isotopic composition of large continental areas. Analyses of populations of monazite grains or analyses of single grains could be an alternative to whole rock measurements.

Hf isotopic analysis of zircon grains is another widely used approach to trace source compositions [START_REF] Bodet | Evolution of the SE-Asian continent from U-Pb and Hf isotopes in single grains of zircon and baddeleyite from large rivers[END_REF][START_REF] Davis | Hf isotopes in zircon from the western Superior province, Canada: Implications for Archean crustal development and evolution of the depleted mantle reservoir[END_REF][START_REF] Harrison | Heterogeneous Hadean Hafnium: Evidence of Continental Crust at 4.4 to 4.5 Ga[END_REF][START_REF] Veevers | U-Pb ages and source composition by Hf-isotope and trace-element analysis of detrital zircons in Permian sandstone and modern sand from southwestern Australia and a review of the paleogeographical and denudational history of the Yilgam Craton[END_REF][START_REF] Flowerdew | The source of granitic gneisses and migmatites in the Antarctic Peninsula: a combined U-Pb SHRIMP and laser ablation Hf isotope study of complex zircons[END_REF][START_REF] Hawkesworth | The differentiation and rates of generation of the continental crust[END_REF][START_REF] Kemp | Magmatic and Crustal Differentiation History of Granitic Rocks from Hf-O Isotopes in Zircon[END_REF][START_REF] Zeh | Archaean to Proterozoic Crustal Evolution in the Central Zone of the Limpopo Belt (South Africa-Botswana): Constraints from Combined U-Pb and Lu-Hf Isotope Analyses of Zircon[END_REF][START_REF] Belousova | The growth of the continental crust: Constraints from zircon Hf-isotope data[END_REF]. Using the 176 Hf/ 177 Hf of bulk sediments as source tracer is definitively trickier in view of the "zircon effect" highlighted by previous studies [START_REF] Patchett | Hafnium/rare earth element fractionation in the sedimentary system and crustal recycling into the Earth's mantle[END_REF][START_REF] Vervoort | Relationships between Lu-Hf and Sm-Nd isotopic systems in the global sedimentary system[END_REF][START_REF] Chauvel | Role of recycled oceanic basalt and sediment in generating the Hf-Nd mantle array[END_REF][START_REF] Bayon | Hf and Nd isotopes in marine sediments: Constraints on global silicate weathering[END_REF][START_REF] Carpentier | The "zircon effect" as recorded by the chemical and Hf isotopic compositions of Lesser Antilles forearc sediments[END_REF][START_REF] Vervoort | The Hf-Nd isotopic composition of marine sediments[END_REF]. However, our study demonstrates and quantifies the fact that since zircon almost entirely controls the 176 Hf/ 177 Hf ratio of bedloads, these sediments can be used for source provenance studies. In contrast, suspended loads that are depleted in zircon and have their Hf isotopic budget controlled by radiogenic minerals such as biotite, muscovite, clay, or amphibole, have isotopic compositions that differ from that of their source and cannot be used to trace sediment provenance.

A surprising feature is that heavy minerals not only affect the Hf isotopic compositions of river sediments but also have dramatic effects on their Pb isotopic compositions. Indeed, our calculations indicate that, because of their extremely radiogenic Pb isotopes, zircon, monazite and allanite have the potential to significantly increase the Pb isotopic ratios of both bedloads and suspended loads (e.g., as high as 60

for 206 Pb/ 204 Pb; Figure 9). Garçon et al. (2013) have shown that mineralogical sorting processes during fluvial transport lead to an excess concentration of heavy minerals in bedloads and, to lesser extent, in suspended loads. This "heavy mineral effect" shifts the Pb isotopic compositions of most river sediments, including some suspended loads, towards more radiogenic values than their source areas. The best approach to constrain the Pb isotopic composition of the source areas is thus to concentrate on K-feldspars or clays separated from sediments because these fractions contain most of the lead initially present in crustal rocks and are almost not affected by the "heavy mineral effect" (see Garçon et al., 2013b for further discussion).

Finally, using Sr isotopes in river sediments to trace the composition of source areas appears very difficult without taking into account potential bias due to mineralogical sorting processes during sediment transport. Bedloads and suspended loads have different isotopic compositions and, according to our calculations, the more radiogenic Sr signature of suspended loads compared to bedloads probably comes from higher contributions of radiogenic muscovite and/or K-feldspar in suspended loads (Figure 7) together with higher contributions of less radiogenic epidote and carbonate in bedloads.

It remains unclear whether either bedloads or suspended loads have compositions similar to that of the source areas. As a consequence, Sr isotopes can only be used as a source proxy if the sources are isotopically extremely different (i.e. range larger than that created by mineralogical sorting processes), as is rarely the case but occurs in the Himalayan system.

Difference between what goes into the deep ocean and what remains on continents

At a global scale, mineral sorting processes are responsible for significant isotopic fractionation between continental sediments and terrigeneous oceanic clays. [START_REF] Patchett | Hafnium/rare earth element fractionation in the sedimentary system and crustal recycling into the Earth's mantle[END_REF] were the first to discuss such effects and showed their impact on the Hf isotopic systematics of sediments. They argued that the "zircon effect", i.e. retention of unradiogenic Hf-rich zircons on continents, led to the delivery into the oceans of finer detrital material, poor in zircons and with radiogenic Hf isotopes. Numerous other studies confirmed the observation since 1984 [START_REF] Chauvel | Role of recycled oceanic basalt and sediment in generating the Hf-Nd mantle array[END_REF][START_REF] Bayon | Hf and Nd isotopes in marine sediments: Constraints on global silicate weathering[END_REF][START_REF] Carpentier | The "zircon effect" as recorded by the chemical and Hf isotopic compositions of Lesser Antilles forearc sediments[END_REF][START_REF] Chauvel | Hf-Nd input flux in the Izu-Mariana subduction zone and recycling of subducted material in the mantle[END_REF][START_REF] Vervoort | The Hf-Nd isotopic composition of marine sediments[END_REF]. Recently, [START_REF] Garçon | Variabilité chimique et isotopique causée par les processus sédimentaires dans les sédiments de rivières Himalayennes[END_REF]2013a) showed that almost half of the bias towards radiogenic Hf isotopic seen today in terrigeneous oceanic clays is observed in suspended river sediments on continents. The Hf Monte-Carlo simulation presented in Figure 5 confirms that suspended loads delivered at the mouth of a river as large as the Ganga still contain zircons. In particular, we show that, even if radiogenic minerals such as phyllosilicates contribute to a non-negligible part of the Hf isotopic composition of the Ganga suspended loads, zircon can still control up to 50% of their Hf isotopic budget (Figure 5). Thus, further sorting must occur in the oceanic environment to reach the maturity of terrigeneous oceanic clay and their characteristic highly radiogenic zircon-free Hf signature. This sorting could occur in the ocean, next to the continental margins due to a rapid change of the hydrodynamic conditions [START_REF] Lisitzin | Oceanic Sedimentation: Lithology and Geochemistry[END_REF][START_REF] Stummeyer | The composition of suspended matter from Ganges-Brahmaputra sediment dispersal system during low sediment transport season[END_REF].

Deposition of heavy minerals in the coastal zone is probably a key process that not only affects the Hf isotopic composition of sedimentary material delivered to the ocean, but also affects their Pb and Sr isotopes in a similar way. Our simulations demonstrate that the Pb isotopic composition of bedloads and even that of suspended loads are biased towards radiogenic values by a "heavy mineral effect". Thus, we expect terrigeneous oceanic clays to be characterized by less radiogenic Pb isotopic ratios than river suspended loads. Similarly, the deposition of dense epidote and/or coarse particles in coastal zones should lead to the delivery into the deep oceans of materials with more radiogenic Sr isotopic compositions than river suspended loads. Such a potential Sr isotopic fractionation at the land/ocean interface has already been pointed out by [START_REF] Eisenhauer | Grain size separation and sediment mixing in Arctic Ocean sediments: evidence from the strontium isotope systematic[END_REF] and our calculations confirm their suggestions. Finally, deposition of heavy monazite and allanite in the coastal zone should not modify the Nd isotopic composition of sediments delivered in the deep ocean, since these two minerals have essentially the same 147 Sm/ 144 Nd as both bulk sediments and continental sources [START_REF] Patchett | Hafnium/rare earth element fractionation in the sedimentary system and crustal recycling into the Earth's mantle[END_REF][START_REF] Mclennan | Rare earth elements in sedimentary rocks; influence of provenance and sedimentary processes[END_REF][START_REF] Garçon | Beach placer, a proxy for the average Nd and Hf isotopic composition of a continental area[END_REF][START_REF] Vervoort | The Hf-Nd isotopic composition of marine sediments[END_REF].

Summary and Conclusions

The Monte-Carlo simulations performed in this study provide a framework to decipher the contribution of individual mineral species to the Nd, Hf, Sr and Pb isotopic budget of river sediments. They also provide clues to understand the isotopic differences observed between bedloads and suspended loads that are related to mineral sorting processes during the fluvial transport of sediments. The results of the Monte-Carlo simulations are well constrained and could be used to understand the isotopic systematics of other sediments from large river systems all over the world.

An important result is that, despite the diversity of mineral species present in river sediments, only a few minerals really participate in the Nd, Hf, Sr and Pb isotopic budgets.

Nd isotopes of both bedloads and suspended loads are mainly controlled by monazite and allanite and they are not modified by mineral sorting processes. Hence, they can be used to trace the continental sources of sediments. By contrast, mineral sorting processes lead to significant Hf, Sr and Pb isotopic diversity. We demonstrate that the Hf isotopic budget of bedloads is totally dominated by the contribution of unradiogenic Hf-rich zircons. The more radiogenic Hf signature of suspended loads results from lower but still significant contribution of zircons combined to higher contributions of more radiogenic minerals such as phyllosilicates. The Pb isotopic budget of all river sediments is dominated by Kfeldspar and clay contributions but it is also significantly affected by a "heavy mineral effect" that deviates the Pb isotopic compositions towards very radiogenic values. Sr isotopes in river sediments are more difficult to model but they are mainly controlled by K-feldspar, plagioclase, muscovite, carbonate, clay and epidote with a systematic difference between bedloads and suspended loads.

Because isotopic fractionations caused by mineral sorting processes during sediment transport can be very large, we stress the need for caution when using the isotopic compositions of river sediments as provenance proxies. Of the four isotopic systems studied here, Nd is this only one that is not affected by the mineralogical effects.

We anticipate that hydrodynamic sorting in the coastal zone should further fractionate the Hf, Sr and Pb isotopic systems in terrigeneous oceanic sediments. In particular, the deposition of heavy minerals at the river/ocean interface could lead to the delivery into the deep ocean of sediments with even higher 176 Hf/ 177 Hf and 87 Sr/ 86 Sr and lower Pb isotopic ratios than river suspended loads. Further studies on the isotopic variability of terrigeneous oceanic sediments are needed to better constrain the impact of mineral sorting in the oceanic environment and its role on the decoupling of isotopic systems at a large scale.

Tables Table 1: Trace element concentrations (ppm) and Nd-Hf-Sr-Pb isotopes of the mineral fractions separated from the Ganga sediments.

Footnotes: * Data already published by Garçon et al. (2013b). ±2σ are in-run errors. Footnotes: * These Sr isotopic compositions were analyzed on the silicate fractions of the sediments (i.e. after a leaching in 1N acetic acid) in Nancy, France, following the methods described by [START_REF] Galy | The strontium isotopic budget of Himalayan Rivers in Nepal and Bangladesh[END_REF]. DL stands for detection limit. ±2σ are in-run errors. Data for bedloads and suspended loads sampled at the outflow of the Ganga River are from Garçon et al. (2013a). 1/Hf ratios of river sediments have been recalculated on a quartz-free basis by normalizing their SiO2 contents, as published by [START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF], to that of our clay fraction (i.e. 43 wt%). Normalized 1/Hf ratios were thus calculated following this equation: (1/Hf)N = (1/Hf)sample * (SiO2, clay /SiO2, sample) • Concentrations and isotopic compositions of minerals We performed the Monte-Carlo simulations using the Nd, Hf, Sr, Pb concentrations and isotopic compositions of mineral separates as listed in Table 1. The only exceptions are the Nd concentrations of monazite and apatite that we took from [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF] (94, 000 ppm and 635 ppm, respectively) because the fractions that we analyzed were not pure enough. In the fraction that we measured, the REE are certainly diluted by the presence of other minerals but we are still confident that the Nd isotopic compositions are representative of the pure minerals. As explained in the main text of the manuscript, only one analysis was performed for each mineral species because of the complexity of the mineral separation procedure and the limited amount of samples available for the study. However, a large number of grains were analyzed in each mineral fraction, meaning that our measurements should be representative of the average compositions of the mineral populations. Errors in the simulations associated to such uncertainties are evaluated below.

Monte-Carlo simulations

For each mineral species, proportions were randomly sampled (n=100,000) in uniform distributions within the range of proportions indicated in the Table A (first column). The simulations were designed to allow the sum of the mineral proportions to add up to more or less than 100% because the calculations done in the simulations use relative mineral proportions only, hence this does not influence the final results. We have tested other simulations in which the mineral proportions were rejected and re-sampled if their sums were >105% and/or <95% but this does not change the results of the simulations. Then, using these proportions together with concentrations and isotopic compositions of individual minerals, we calculated the isotopic compositions of the resulting mixtures. Note that Nd, Hf, Pb and Sr concentrations modeled by this set of Monte-Carlo simulations are not identical to concentrations measured in sediments because (a) the percentages of important minerals are relative and not absolute, and (b) we did not take into account quartz that significantly dilutes all concentrations. This is why we performed other simulations not shown here. They were designed to model only the concentrations in the sediments but not the isotopic compositions. Results showed that the range of Nd, Hf, Sr and Pb concentrations in the Ganga sediments are perfectly reproduced when quartz is added to the minerals listed in Table A.

Robustness of the Monte-Carlo simulations

To assess the robustness and uncertainties of the Monte-Carlo simulations we performed several tests. We first increased the ranges of possible mineral proportions by 100% (second column in Table A) to evaluate the sensitivity of the simulations to this parameter. Results are shown below in Figure A for the four isotopic systems. The important point is that the contribution of the different mineral species to the isotopic budgets of the Himalayan sediments remain remarkably similar, showing that the simulation results are well constrained. The mineralogy would need to be completely different for the results to be modified. In turn, this implies that the results presented in this study should be valid for most sediment sampled in large river systems all over the world. We also tested the sensitivity of the simulations to changes of both concentrations and isotopic compositions of the minerals. To do that, we added an initial step that randomly samples the concentrations and isotopic compositions of each mineral species in given Gaussian distributions centered on the values that we measured in the mineral separates. We deliberately assumed very large errors on the values, with standard deviations (2σ) of ±20% for the concentrations, ±0.0001 for Nd and Hf isotopic compositions, ±0.01 for Sr isotopic compositions and ±1% for Pb isotopic compositions. The rest of the calculation is similar to those shown in Figures 3,5, 7 and 8. The results of these simulations (not shown here) indicate that the contributions of the different mineral species to the isotopic budget of the Ganga sediments still remain the same as in Figures 3,5, 7 and 8. The uncertainties entailed by our data set are thus limited and we are confident that the results presented in this study would not significantly change even if our data set did not catch all the compositional heterogeneity of the Himalayan minerals. A). Minerals that contribute to less than 10% of the isotopic budgets are not shown in the figures. Note that the ranges of modeled Sr isotopic compositions simulations still remain incompatible with the ranges measured for suspended loads from the Ganga River.

Figure 2 :Figure 3 :

 23 Figure 2

Figure 4 :

 4 Figure 4: Variations of 143 Nd/ 144 Nd ratios against 147 Sm/ 144 Nd ratios for the different

Figure 5 :Figure 6 Figure 6 :

 566 Figure 4

Figure 7 :

 7 Figure 7: Results of the Monte-Carlo simulation for Sr isotopes.

Figure 9 :

 9 Figure 9: Results of the Monte-Carlo simulation for Pb isotopes.

Figure A :

 A Figure A: Results of the Monte-Carlo simulations when increasing the input ranges of mineral proportions. a) Nd; b) Hf; c) Sr and d) Pb. These simulations were performed using the same minerals as those considered in Figures 3, 5, 7 and 9 but using proportion ranges much larger (values provided in the second column of TableA). Minerals that contribute to less than 10% of the isotopic budgets are not

  , we show 87 Sr/ 86 Sr measured on the various minerals and the modeled 87 Sr/ 86 Sr for mixing simulations between K-feldspar, plagioclase, muscovite, biotite, ions and very fine particles cannot participate to the Sr isotopic budget of suspended loads. Even if we assume that the filtration was not complete, the Sr isotopic composition of these fractions (clay < 2 μ m at 0.747 and river water with 87 Sr/ 86 Sr < 0.73, see Table1and[START_REF] Galy | The strontium isotopic budget of Himalayan Rivers in Nepal and Bangladesh[END_REF] are less radiogenic than the sediments, hence they cannot constitute the missing components with high 87 Sr/ 86 Sr ratios. Given their high Sr contents (Table1), potential other candidates are apatite and monazite/allanite. However, these minerals have low 87 Rb/ 86 Sr ratios, just like carbonate and epidote that have the lowest 87 Sr/ 86 Sr ratios (Figure

magnetite, zircon, titanite, amphibole, epidote, garnet, carbonate, and clay. Six minerals (garnet, amphibole, titanite, zircon, magnetite, and biotite) are not shown in the figure because they all are minor contributors (<10%) to the Sr isotopic budget. In contrast, because of their high Sr contents (Table

1

), epidote, K-feldspar, plagioclase, carbonate and, to a lesser extent, clay, and muscovite exert a major role on the 87 Sr/ 86 Sr of the modeled mixtures.

The range of Sr isotopic compositions measured in bedloads and suspended loads from the Ganga River is shown by grey bands in Figure

7a

. Variations of 87 Sr/ 86 Sr ratios as a function of 1/Sr are reported in Figure

8

. Figure

7a

demonstrates clearly that our Monte-Carlo simulation fails in reproducing the high 87 Sr/ 86 Sr of all sediments, in particular those of suspended loads. We can think of several possible reasons to explain the mismatch between modeled and measured Sr isotope ratios.

The first possibility is that we have missed a component/mineral with high 87 Sr/ 86 Sr and high Sr content in our Monte-Carlo procedure. The missing component cannot be dissolved Sr, or Sr carried by small particles (< 0.2 μm) because all suspended loads were filtered under pressure at 0.2 μ m

[START_REF] Lupker | A Rouse-based method to integrate the chemical composition of river sediments: Application to the Ganga basin[END_REF]

and the filtered fractions containing particles smaller than 0.2 μ m were not analyzed in this study. As a consequence, dissolved

  but we list them for convenience in Table1. Because we did not measure the Pb isotopic composition of all the mineral fractions, we used an estimated value for the "heavy mineral" fraction, containing zircon, monazite and allanite, to perform our simulation (i.e.206 Pb/ 204 Pb ratio of about 800-900 afterGarçon et al., 2013b). As we obtained similar results for 208 Pb/ 204 Pb, 207 Pb/ 204 Pb and 206 Pb/ 204 Pb ratios, only the results obtained for 206 Pb/ 204 Pb ratios are shown in Figure9. Seven minerals (biotite, magnetite, titanite, amphibole, epidote, garnet and carbonate) always contribute to less than 10% to the Pb isotopic budget and are not shown in Figure9. In contrast, clay, K-feldspar, muscovite, plagioclase and zircon/monazite/allanite contribute significantly to the final isotopic composition. The modeled 206 Pb/ 204 Pb cover a very large range of

	values between 19 and 55, the highest values being associated with high contributions of
	zircon, monazite and allanite in the mixture (Figure 9). Indeed, if we perform a simulation
	without these heavy minerals, the modeled 206 Pb/ 204 Pb ratios only range between 19.0
	and 19.8. This is because zircon, monazite and allanite are extremely radiogenic
	(estimated 206 Pb/

204

Pb ratio of 800-900;

Garçon et al., 2013b)

, and even a minute amount of this mineral mixture in the sediment (≤1wt%) significantly shifts the modeled Pb isotopic compositions towards very radiogenic values (Figure

9

). The ranges of 206 Pb/ 204 Pb measured for bedloads (20.6-22.2) and suspended loads (19.8-20.1) are shown using grey bands in Figure

9

. Our results show that for both types of sediments, the Pb-rich clay and K-feldspar control up to 80% of the Pb isotopic budget while muscovite and plagioclase are other significant contributors. Our simulations also show that zircon, monazite and allanite play a key role in increasing the Pb isotopic compositions of both bedloads and suspended loads. Indeed, if these heavy minerals were not present, the 206 Pb/ 204 Pb ratio of any mixture could not exceed 19.8, a value significantly lower than what is measured on both bedloads and suspended loads. This

Table 1 : Trace element concentrations (ppm) and Nd-Hf-Sr-Pb isotopic compositions of the mineral fractions separated from the Ganga sediments.

 1

	Sample Name	BR 717 Msc	BR 717 Bio	BR 717 Fd-K BR 717 Fd-K Dup BR 717 Plg	BR 717 Qtz	BR 717Mag	BR 717 Ep	BR 717 Grt	BR 717 Ttn	BR 717 Amp BR 717 Carb BR 717 Zrn	BR 717 Mnz	BR 717 Ap	BGP 6 A
	Type of mineral Muscovite	Biotite	K-Feldspar	K-Feldspar (Duplicate)	Plagioclase	Quartz	Magnetite	Epidote	Garnet	Titanite	Amphibole	Carbonate	Zircon	Rich in Allanite Monazite and	Rich in Apatite Clay
	Cs	15.4	69.9	8.94		0.838	0.209	0.579	0.267	0.153	0.557	1.80	0.00913	0.214	0.935	0.504	17.0
	Rb	311	425	313		31.1	3.34	7.37	2.54	1.50	9.73	23.3	0.846	3.03	12.0	9.16	167
	Ba	967	478	1393		146	19.8	33.0	14.9	3.49	39.9	60.2	16.6	7.01	71.3	427	373
	Th	4.58	24.9	4.10		2.37	2.91	13.7	17.8	15.6	151	24.0	1.17	219	2374	28.8	62.0
	U	1.54	2.36	0.663		0.692	0.83	3.61	6.49	3.49	50.1	7.40	0.522	612	400	28.9	8.55
	Nb	36.1	87.3	0.992		1.39	0.581	20.8	3.69	3.25	229	403	< DL	171	1068	119	16.7
	Ta	2.78	9.58	0.112		0.106	0.0304	1.66	0.267	0.42	31.0	43.5	0.013	17.9	162	8.24	1.49
	La	9.99	60.9	4.41		3.74	5.23	19.8	62.2	40.4	172	32.6	6.89	32.8	3763	71.1	103
	Ce	27.5	110	14.3		14.4	11.3	41.1	134	91.0	527	71.6	14.7	113	7785	206	206
	Pr	2.34	10.2	1.07		0.933	1.29	4.58	17.2	9.79	93.1	8.76	1.95	16.3	883	30.3	24.3
	Pb	11.1	4.96	75.1		12.6	3.89	19.1	46.9	0.916	5.52	24.1	7.28	19.3	81.9	7.98	95.2
	Nd	8.29	32.0	3.77		3.57	4.59	16.6	71.8	34.5	414	34.1	8.25	79.6	3180	146	90.5
	Sr	32.3	7.01	243		195	70.6	11.8	1509	3.31	52.9	38.4	526	16.9	274	197	47.9
	Sm	1.57	4.77	0.837		0.684	1.04	3.17	17.6	7.83	105	7.88	2.07	32.7	670	54.0	17.3
	Zr	210	264	21.9		94.4	22.1	265	183	251	2128	539	< DL	416415	20849	4240	447
	Hf	5.70	7.30	0.658		2.56	0.749	7.01	4.76	6.95	72.8	15.5	< DL	10171	639	119	12.2
	Ti	4898	16573	185		273	71.4	17180	2706	770	32279	168099	2.29	41321	107327	18799	6416
	Eu	0.253	0.416	0.744		0.30	0.176	0.54	5.75	1.01	19.4	1.73	0.523	7.19	59.8	6.20	2.70
	Gd	1.25	3.41	0.733		0.585	0.921	2.74	18.2	16.4	100	8.96	2.37	65.1	494	74.3	14.3
	Tb	0.185	0.462	0.118		0.0907	0.155	0.46	2.94	5.95	17.4	1.78	0.414	17.8	61.5	14.1	2.02
	Dy	1.14	2.59	0.658		0.537	1.01	2.86	18.6	60.9	113	13.3	2.59	178	310	88.5	11.4
	Ho	0.226	0.474	0.127		0.108	0.214	0.612	3.78	15.7	22.0	2.83	0.577	55.7	52.5	16.2	2.12
	Y	6.49	13.6	3.77		3.39	6.65	17.0	110	379	536	86.1	19.4	1463	1148	432	58.0
	Er	0.677	1.32	0.345		0.335	0.645	1.83	10.6	52.0	62.7	8.42	1.76	243	140	42.3	5.84
	Li	24.6	334	11.8		0.461	5.96	3.69	3.42	14.3	8.78	26.2	0.889	15.7	19.8	13.3	40.8
	Yb	0.685	1.22	0.322		0.381	0.587	1.92	9.61	58.0	58.9	8.50	1.82	424	140	33.3	5.21
	Lu	0.108	0.192	0.0478		0.062	0.0861	0.296	1.39	8.54	7.91	1.22	0.283	83.5	21.3	4.47	0.736
	Sc	13.3	27.3	0.655		0.917	0.299	8.14	90.1	102	33.8	44.8	1.95	3225	211	32.9	19.7
	V	171	243	1.89		6.86	8.08	1281	414	54.4	139	943	< DL	99.2	408	128	220
	Cr	91.3	144	2.74		5.13	3.34	1950	131	76.1	44.8	197	2.00	70.7	236	81.1	143
	Co	2.76	47.9	0.691		0.907	0.269	51.0	2.69	23.8	0.683	44.6	0.956	0.443	2.83	1.25	27.3
	Ni	6.39	79.7	2.77		3.62	0.464	170	6.34	1.55	3.28	35.6	1.40	3.86	4.74	2.50	99.0
	143 Nd/ 144 Nd ± 2σ	0.511844 ± 15 0.511834 ± 11 0.511834 ± 19		0.511901 ± 33		0.512089 ± 9	0.512235 ± 10 0.511906 ± 11 0.512182 ± 9	0.511929 ± 24 0.511843 ± 10 0.512062 ± 10 0.511732 ± 8	0.511921 ± 7	0.511827 ± 10
	Re-run		0.511820 ± 9												0.511741 ± 6
	176 Hf/ 177 Hf ± 2σ	0.282218 ± 9	0.282162 ± 11 0.282400 ± 15 0.282313 ± 15	0.282325 ± 9		0.282602 ± 8	0.282803 ± 12 0.282698 ± 5	0.282055 ± 16 0.282236 ± 8		0.281970 ± 5			0.282234 ± 8
	Re-run										0.282077 ± 8			0.281979 ± 4
	87 Sr/ 86 Sr ± 2σ	0.831306 ± 10 0.812332 ± 8	0.758656 ± 8	0.755512 ± 7	0.722744 ± 8		0.728827 ± 7	0.712509 ± 7	0.746224 ± 28 0.722704 ± 7	0.725748 ± 8	0.713942 ± 8	0.734440 ± 20			0.746642 ± 8
	206 Pb/ 204 Pb ± 2σ * 20.2008 ± 16	20.5508 ± 26	19.3257 ± 16	19.3318 ± 12	18.9724 ± 18		19.5343 ± 14	18.9028 ± 14		31.4562 ± 22	19.9482 ± 12	18.9809 ± 8				18.8658 ± 12

Table 2 :

 2 Sr isotopic compositions of bedloads and suspended loads sampled at the outflow of the Ganga River.

Table 2 :

 2 Sr isotopes in bedloads and suspended loads sampled at the outflow of the Ganga River Map of the Bangladesh delta showing sampling sites for river sediments. Nb Ta La Ce Pr Pb Nd Sr Sm Zr Hf Ti Eu Gd Tb Dy Ho Y Er Li Yb Lu Sc V Cr Co Ni

	Sample Name	Latitude	Longitude Sampling date Type of sediment	Sampling depth (m)	86 Sr/ 87 Sr	± 2σ	Percentage of the bulk sample (%)	Sr (ppm)
	BGP 6	24.35833 88.60833 2-Aug-93	Bedload		0.758885		115
	BR 414	24.05290 89.02465 13-Jul-04	Suspended Load 2	0.773743		91.0
	BR 412	24.05290 89.02465 13-Jul-04	Suspended Load 6.5	0.768185		101
	BR 418	24.05290 89.02465 13-Jul-04	Bedload	10	0.754593		103
	BR 717	24.05290 89.02465 17-Aug-07	Bedload	11	0.757160		98.3
	BR 522*	24.04187 89.0362	23-Jul-05	Suspended load	0	0.771110		95.8
	BR 522 50-200 µm*								4.6	98.3
	BR 522 20-50 µm*						0.755340		42.8	99.3
	BR 522 2-20 µm*						0.782400		42.2	83.0
	BR 522 0.2-2 µm*						0.757570		10.3	48.4

Table 1 :

 1 Weight mineral proportions in Ganga sediments modified after[START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF] Minerals taken into account in our Monte-Carlo simulations are highlighted in gray.ND: not determined or under detection limit *not determined by[START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF] 2011). Values from[START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF]

	Minimum Maximum Surface suspended loads (<3m) Supplementary Average 2σ		Average 2σ Deep suspended loads (>8m) Minimum Maximum		Average 2σ Bedloads Minimum Maximum
	Quartz	34.3	22.8	26.2	42.4	Quartz	48.7	10.6	43.2	54.1	Quartz	55.4	14.9 39.8	66.0
	K-feldspar	2.6	1.6	2.1	3.2	K-feldspar	5.7	2.3	4.3	6.9	K-Feldspar	6.6	3.1	3.9	8.5
	Albite	4.6	2.4	3.8	5.4	Albite	5.9	4.0	3.9	8.2	Plagioclase	6.0	2.9	2.9	7.4
	Ca-plagioclase 2.4	2.3	1.6	3.2	Ca-plagioclase 4.2	2.8	3.5	6.4
	Lithic grain	ND				Lithic grain	ND				Lithic grain	14.5	5.2	10.9	18.6
	Calcite	1.2	0.3	1.1	1.3	Calcite	2.9	1.6	1.9	3.7	Calcite*	3.4	2.5	0.5	4.7
	Dolomite	1.8	0.2	1.7	1.9	Dolomite	2.3	2.1	1.1	3.5	Dolomite	ND
	Muscovite	17.5	9.2	14.2	20.7	Muscovite	8.9	5.4	5.0	11.0	Micas	9.8	21.9 2.2	36.1
	Biotite	10.4	4.3	8.8	11.9	Biotite	5.5	3.0	3.8	7.3
	Chlorite	2.8	1.4	2.4	3.3	Chlorite	1.7	1.1	1.1	2.3	Chlorite	ND
	Clay	19.5	18.6	12.9	26.0	Clay	9.8	3.6	8.2	12.0	Clay	ND
	Fe-oxides	0.2	0.2	0.2	0.3	Fe-oxides	0.1	0.1	0.02	0.1	Fe-oxides	ND
	Fibrolite	ND				Fibrolite	ND				Fibrolite	0.1	0.2	0	0.3
	Opaques	1.3	0.1	1.2	1.3	Opaques	1.0	0.7	0.8	1.6	Opaques	1.1	2.8	0.1	3.7
	Zircon	0.1	0.02	0.1	0.1	Zircon	0.1	0.3	0.03	0.4	Zircon	0.1	0.4	0	0.5
	Tourmaline	0.2	0.1	0.2	0.3	Tourmaline	0.2	0.1	0.2	0.3	Tourmaline	0.3	0.7	0.1	1.1
	Rutile	0.2	0.2	0.1	0.3	Rutile	0.2	0.2	0.1	0.3	Rutile	0.1	0.2	0	0.2
	Ti oxide	0.4	0.1	0.4	0.4	Ti oxide	0.2	0.2	0.1	0.3	Ti oxide	0.0	0.1	0	0.2
	Titanite	0.1	0.1	0.1	0.2	Titanite	0.2	0.3	0.1	0.5	Titanite	0.3	0.5	0.05	0.7
	Apatite	0.1	0.04	0.1	0.1	Apatite	0.1	0.1	0.03	0.1	Apatite	0.1	0.4	0	0.5
	Monazite	ND				Monazite	ND				Monazite	0.1	0.3	0	0.4
	Amphibole	0.5	0.6	0.3	0.7	Amphibole	1.1	0.6	0.9	1.6	Amphibole	1.4	2.0	0.4	3.0
	Pyroxene	0.2	0.1	0.2	0.3	Pyroxene	0.3	0.2	0.2	0.4	Pyroxene	0.3	0.5	0.1	0.8
	Spinel	ND				Spinel	ND				Cr-spinel	ND		0	0.0
	Epidote	1.4	0.8	1.1	1.7	Epidote	1.0	0.3	0.8	1.1	Epidote	1.0	1.5	0.2	2.2
	Allanite	ND				Allanite	0.02	0.1	0	0.1	Allanite	ND		0	0.0
	Chloritoid	ND				Chloritoid	ND				Chloritoid	ND		0	0.1
	Garnet	0.4	0.5	0.3	0.6	Garnet	1.2	1.9	0.5	2.6	Garnet	2.8	6.5	0.2	8.2
	Staurolite	ND				Staurolite	0.03	0.02	0.02	0.04	Staurolite	0.1	0.1	0	0.2
	Kyanite	0.04	0.1	0.0	0.1	Kyanite	0.1	0.1	0.07	0.19	Kyanite	0.2	0.5	0	0.7
	Sillimanite	ND				Sillimanite	0.1	0.1	0.03	0.2	Sillimanite	0.02	0.1	0	0.1
	Sum	102.2				Sum	101.4				Sum	103.7

Table 2 :

 2 Trace element concentrations and Nd-Hf-Pb isotopes in sediments sampled at the outflow of the Ganga River fromGarçon et al. (2013a; 2013b)

	Sample Name	BGP 6	BGP 6 Dup	BR 415	BR 414	BR 413	BR 412	BR 411	BR 418	BR 8252	BR 8253	BR 717
	Type of sediment	Bedload	Bedload	Suspended Load Suspended Load Suspended Load Suspended Load Suspended Load Bedload	Bedload	Suspended Load Bedload
	Sampling depth (m)			0	2	4	6.5	9	10	14	0	11
	Cs	3.83	4.02	15.4	13.6	11.8	11.7	8.08	2.64	3.05	8.92	3.87
	Rb	77.9	85.2	202	186	174	175	107	56.6	34.5	51.7	50.3
	Ba	294	307	627	562	531	527	372	212	203	240	244
	Th	30.2	31.3	20.0	19.0	19.3	18.0	15.1	29.7	24.3	16.1	74.2
	U	5.41	5.71	4.64	4.08	4.15	3.96	2.92	5.14	3.33	1.85	11.7
	Nb	12.9	12.0	17.4	17.9	16.5	15.7	13.6	18.4	13.6	15.8	20.3
	Ta	1.78	1.44	1.54	1.57	1.45	1.39	1.37	2.08	2.91	1.31	2.50
	La	67.1	70.2	42.0	41.3	42.2	40.0	32.8	71.0	41.6	30.8	141
	Ce	144	145	89.4	88.1	89.2	84.7	76.3	146	110	75.4	292
	Pr	15.5	16.4	9.75	9.77	9.87	9.29	7.84	16.7	10.6	8.12	33.1
	Pb	17.8	17.6	30.4	26.2	25.3	25.3	17.4	15.0	10.7	10.5	14.2
	Nd	56.5	60.8	35.0	35.7	35.8	34.0	29.0	60.4	39.4	31.2	121
	Sr	115	117	84.1	91.0	99.3	101	88.6	103	83.6	56.5	98.3
	Sm	10.9	11.4	6.93	6.98	7.01	6.77	5.60	12.0	7.63	6.27	22.3
	Zr	683	667	178	204	227	199	249	479	403	196	1550
	Hf	17.1	17.4	4.73	5.22	5.79	5.17	6.14	12.1	10.5	5.13	40.5
	Ti	3155	3166	4137	4198	3976	3833	3314	4355	2523	4323	5299
	Eu	1.39	1.45	1.27	1.26	1.23	1.20	1.04	1.48	0.928	1.19	2.24
	Gd	8.51	9.10	6.19	6.15	6.05	5.80	4.96	10.1	6.41	5.44	17.7
	Tb	1.25	1.29	0.984	0.945	0.93	0.872	0.782	1.64	0.977	0.854	2.52
	Dy	7.24	7.61	5.97	5.74	5.72	5.42	4.81	10.3	5.70	5.32	14.7
	Ho	1.39	1.48	1.17	1.15	1.13	1.06	0.95	2.12	1.16	1.06	2.83
	Y	41.1	43.5	32.7	32.8	32.5	30.9	27.1	62.0	34.5	31.1	85.6
	Er	3.99	4.24	3.33	3.30	3.21	3.07	2.65	6.35	3.33	3.04	8.11
	Li	14.7	16.3	51.8	48.2	43.5	41.9	32.2	15.8	12.6	45.3	15.7
	Yb	3.87	4.16	3.18	3.11	3.06	2.88	2.51	6.23	3.32	2.95	8.17
	Lu	0.587	0.614	0.461	0.45	0.448	0.417	0.366	0.905	0.50	0.433	1.25
	Sc	7.74	9.81	15.0	14.4	13.5	12.8	9.04	13.2	4.56	11.8	14.3
	V	32.7	34.8	93.4	90.7	81.1	79.1	61.8	46.9	34.5	100	53.5
	Cr	36.9	38.4	76.6	81.9	67.0	63.4	53.4	40.0	28.2	73.6	44.1
	Co	3.93	4.32	15.5	15.1	13.6	13.4	10.3	5.58	4.46	13.8	6.40
	Ni	11.0	11.3	38.6	40.5	32.4	31.5	26.4	11.7	9.39	39.4	12.4
	143 Nd/ 144 Nd ± 2σ	0.511758 ± 9	0.511749 ± 7	0.511752 ± 6	0.511736 ± 8	0.511748 ± 6	0.511745 ± 7	0.511755 ± 6	0.511719 ± 5	0.511788 ± 11	0.511767 ± 8	0.511769 ±
	176 Hf/ 177 Hf ± 2σ	0.282027 ± 11	0.282009 ± 7	0.282241 ± 9	0.282226 ± 6	0.282205 ± 6	0.282209 ± 6	0.282133 ± 7	0.281992 ± 11		0.282268 ± 7	0.281930 ±
	208 Pb/ 204 Pb ± 2σ	40.6213 ± 43	40.579 ± 39	40.3003 ± 18	40.3019 ± 25	40.2931 ± 34	40.2725 ± 16	40.1312 ± 28	40.8292 ± 23	40.8030 ± 26	39.9963 ± 28	42.4525 ±
	207 Pb/ 204 Pb ± 2σ	15.9733 ± 14	15.9644 ± 13	15.8991 ± 6	15.9013 ± 5	15.904 ± 7	15.9040 ± 6	15.8984 ± 10	15.9684 ± 9	15.9548 ± 9	15.873 ± 13	16.1109 ± 9
	206 Pb/ 204 Pb ± 2σ	20.7277 ± 14	20.6899 ± 18	20.0301 ± 7	20.0574 ± 9	20.0912 ± 8	20.0756 ± 7	20.0387 ± 11	20.6767 ± 9	20.5845 ± 10	19.7765 ± 10	22.1618 ±

Acknowledgments

We would like to thank A. Galy, V. Galy and M. Lupker for sample collection, S. Andò for his help during mineral separation, S. Bureau and C. Poggi for their help in the clean lab, P. Telouk and P. Nonnotte for assistance during isotopic measurements in Lyon and Brest, as well as N.T. Arndt and E. Lewin for constructive discussions that helped to develop and interpret the results of the Monte-Carlo simulations. We also greatly thank the editor Laurie Reisberg and the two reviewers, Jeff Vervoort and Bill White, who made very constructive comments that certainly improve the content of this manuscript. This study was supported by fundings from CNRS and INSU programs.

 6, we normalized the 1/Sr ratios of bedloads and suspended loads using their SiO2 contents and that of the clay fraction to correct from quartz dilution effects.

Supplementary File

Input parameters for the Monte-Carlo simulations

• Mineral proportions Based on the mineral proportions estimated in suspended loads and bedloads by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF] and [START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF] (see Supplementary Table 1), we defined possible ranges of weight proportions for the most important mineral species in the Ganga river sediments (see Table A below). We combined the albite and Ca-plagioclase estimates from [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) to determine a range for plagioclase; similarly, we combined the calcite + dolomite estimates from [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) and [START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF] to estimate the carbonate proportion and we used the opaques + Fe-oxides estimates from [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF]2011) to establish the proportion of magnetite. The first column of Table A shows the range of proportions used for the simulations that are shown in Figures 3,5, 7 and 9. We deliberately choose to use ranges that are larger than those determined by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF] and [START_REF] Lupker | Predominant floodplain over mountain weathering of Himalayan sediments (Ganga basin)[END_REF] to insure that our simulations maximize the errors and include extreme scenarios. The second column in Table A provides the ranges used to assess the robustness of the method (see below). Some of the minerals identified by [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF] (Supplementary Table 1) are not included in our simulations: quartz, chlorite, tourmaline, rutile, Ti oxide, pyroxene, spinel, chloritoid, staurolite, kyanite, sillimanite, fibrolite, olivine and xenotime. [START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 1. Bedload sand (Ganga-Brahmaputra, Bangladesh)[END_REF][START_REF] Garzanti | Mineralogical and chemical variability of fluvial sediments 2. Suspended-load silt (Ganga-Brahmaputra, Bangladesh)[END_REF] showed that these minerals contribute to less than 2% in the Nd, Hf, Sr and Pb elemental budgets of the sediments. We therefore chose to minimize the analytical work and did not analyze their trace element contents and isotopic compositions.

Range of proportions used for the simulations