Preferential elution of ionic solutes in melting snowpacks: Improving process understanding through field observations and modeling in the Rocky Mountains

Journal Pre-proof Preferential elution of ionic solutes in melting snowpacks: improving process understanding through field observations and modelling in the Rocky Mountains

Diogo Costa, Graham A Sexstone, John W Pomeroy, Donald H Campbell, David W Clow, Alisa Mast PII:

S0048-9697(19)36269-2

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136273

Reference:

STOTEN 136273

To appear in:

Science of the Total Environment

Received date:

12 July 2019

Revised date:

19 December 2019

Accepted date:

20 December 2019

Please cite this article as: D. Costa, G. A Sexstone, J. W Pomeroy, et al., Preferential elution of ionic solutes in melting snowpacks: improving process understanding through field observations and modelling in the Rocky Mountains, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136273

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Preferential elution of ionic solutes in melting snowpacks: improving process understanding through field observations and modelling in the Rocky Mountains

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Diogo Costaa,b,c , Graham A Sexstoned , John W Pomeroyb,c , Donald H Campbelld , David W Clowd , Alisa Mastd a

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Environment and Climate Change Canada, National Hydrology Research Centre, Saskatoon, SK, Canada [email protected] b Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada c Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada d U.S. Geological Survey, Colorado Water Science Center, Denver, CO, USA

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Abstract

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lem that has been linked to temporary acidification of water bodies. However,

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the understanding of these processes in snowpacks around the world, includ-

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ing the polar regions that are experiencing unprecedented warming and melt-

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ing, remain limited despite being instrumental in supporting climate change

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The preferential elution of ions from melting snowpacks is a complex prob-

adaptation.

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In this study, data collected from a snowmelt lysimeter and snowpits

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at meadow and forest-gap sites in a high elevation watershed in Colorado

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were combined with the PULSE multi-phase snowpack chemistry model to

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investigate the controls of meltwater chemistry and preferential elution. The

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snowdepth at the meadow site was 64% of that at the forest-gap site, and

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the snowmelt rate was greater there (meadow snowpit) due to higher solar

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irradiance. Cations such as Ca2+ and NH4 + were deposited mostly within

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Preprint submitted to Sience of the Total Environment

December 19, 2019

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the upper layers of both the meadow and forest-gap snowpacks, and acid

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anions such as NO3 – and SO4 2 – were more evenly distributed. The snow ion

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concentrations were generally greater at the forest-gap snowpit, except for

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NH4 + , which indicates that wind erosion of wet and dry deposited ions from

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the meadow may have reduced concentrations of residual snow. Furthermore,

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at the forest-gap site, snow interception and scavenging processes such as

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sublimation, ventilation, and throughfall led to particular ion enrichment of

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Ca2+ , Mg2+ , K+ , Cl – , SO4 2 – and NO3 – . Model simulations and observations

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highlight that preferential elution is enhanced by low snowmelt rates, with the

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model indicating that this is due to lower dilution rates and increased contact

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time and area between the percolating meltwater and the snow. Results

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suggest that low snowmelt rates can cause multiple early meltwater ionic

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pulses for ions subject to lower ion exclusion. Ion exclusion rates at the

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grain-size level have been estimated for the first time.

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Keywords: Meltwater chemistry, Snow chemistry, Preferential elution,

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Snow ion exclusion, Snowmelt, Cryosphere, Numerical modelling

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1. Introduction

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Much of the annual precipitation in mountainous and cold regions falls

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in the form of snow, accumulating in seasonal snowpacks that serve as

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large natural water reservoirs. The seasonal snowpack contains dust and

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solutes from anthropogenic and natural sources (Pomeroy et al., 2005); thus,

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the ionic composition of the snowpack around peak snow accumulation

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reflects the chemistry of much of the annual precipitation and atmospheric

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deposition that occurs in these regions (e.g., Clow et al., 2015; Ingersoll 2

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precipitation conditions around the snowmelt period. As a result, meltwater

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from seasonal snowpacks can be an important driver of both hydrological and

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biogeochemical processes in these environments (e.g., Lundquist et al., 2005;

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Williams et al., 2009; Sadro et al., 2018). Preferential elution of ions from

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melting snowpacks cause ionic pulses in early snowpack discharge (e.g., Bales

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et al., 2016), but it is also strongly affected by annual precipitation and

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et al., 1989; Tranter and Jones, 2001) and may result in rapid flush of snow-

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acid-neutralizing capacity (e.g., Williams and Melack, 1991). The preferen-

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pack chemical loads to aquatic ecosystems that can temporary affect their

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crystal metamorphism forcing the reallocation of ions within the snowpack

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(Colbeck, 1976; Brimblecombe et al., 1985; Lilbæk and Pomeroy, 2008).

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tial elution of both acid and alkaline solutes into meltwater occurs due to ice

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snowpack and snowmelt runoff chemistry is an important consideration for

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Therefore, an understanding of the effect of snowpack metamorphism on

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climate within hydrochemical models (Williams et al., 1996b).

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the accurate representation of solute fluxes and their response to changing

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(Colbeck, 1979), which cause fractionation of mass between snow grains and

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the accumulation of ionic solutes at the surface of growing crystals (Har-

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rington and Bales, 1998). This creates ion enriched ice-air interfaces within

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the micro-structures of the snow matrix (Fletcher, 1968). This process of ion

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exclusion depends on diffusion rates and solubility of ions in ice, which are re-

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lated to the ability of the ions to form hydrogen bonds and on their hydrated

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radii (Davies et al., 1987). During freeze-thaw cycles, snow metamorphism is

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accelerated, and grain clusters form by the action of capillary pressure (Col-

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Snow metamorphism is induced by temperature and pressure gradients

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beck, 1979), which can further enhance the redistribution of ions onto the

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surface of the ice crystals (Lilbæk and Pomeroy, 2008). In addition, liquid

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water in intercluster veins and on films around the snow grains (Harrington

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and Bales, 1998) enhances ion mobility in early snowmelt. Overwinter accumulation of ions in seasonal snowpacks is influenced by

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a variety of processes that are controlled by site exposure and local vegeta-

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tion. In windswept open, sparsely vegetated areas, accumulated snowfall can

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be eroded by blowing snow and transported to topographic depressions or

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taller vegetation where snowdrifts form (Pomeroy and Gray, 1995). Whilst

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the resulting snowdrifts will have enhanced concentrations of some ions due

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to sublimation of ice from blowing snow particles, and electrophoresis of soil

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and salt aerosols to blowing snow particles (Pomeroy et al., 1991), NO3 – con-

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centrations in windblown snow do not increase due to volitilization during

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sublimation process (Pomeroy et al., 2005). Simultaneously, dry deposition

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of ions may be removed from snowpacks by wind erosion. Snow accumulat-

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ing in forest canopies is well exposed to the atmosphere, permitting enhanced

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dry deposition and throughfall, and also concentration of ions as ice subli-

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mates from the intercepted snow load (Pomeroy et al., 1991). Again, NO3 –

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volatizes during intercepted snow sublimation, only partially compensating

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for its enhanced dry deposition to canopy snow over the winter. Despite

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evidence for late winter sublimation of surface snow in open areas, there is

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little experimental evidence to support in situ volatilization of N species

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(Jones et al., 1993). Mountain snowmelt rates are highest in open areas

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(Ellis et al., 2011). In contrast, rates in forest gaps can be very low due

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to low solar and longwave irradiance and low turbulent transfer of sensible

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and latent energy. Longwave irradiance is higher under forest canopies, and

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solar irradiance and turbulent transfer are higher in open areas (Reba et al.,

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2012).

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pulses, including hydrological and chemical processes as well as scale effects

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of sampling (Marsh and Pomeroy, 1993). Hydrological processes include

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Several factors are known to affect the timing and magnitude of ionic

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snowmelt rate (Colbeck, 1981; Marsh and Pomeroy, 1993), snowpack energy

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1991; Hewitt et al., 1991), melt-freeze cycles (Tsiouris et al., 1985; Bales

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fluxes (Suzuki, 1991; Williams et al., 1996a), metamorphic history (Davis,

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Marsh and Pomeroy, 1993; Harrington and Bales, 1998; Webb et al., 2018).

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Chemical factors include the pre-melt average snowpack solute concentration

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et al., 1993) and heterogeneous flowpaths (Jones, 1985; Bales et al., 1990;

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distribution of solutes along the vertical snow profile (Colbeck, 1981; Bales

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(Brimblecombe et al., 1987; Domine and Thibert, 1995) and variations in the

et al., 1989; Marsh and Pomeroy, 1999).

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Snow ions are eluted from snowpacks at different rates during snowmelt

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tion. This process is typically investigated by comparing the maximum

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concentration factors (CF, ratio between peak meltwater and depth-average

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pre-melt snow concentrations) for different ionic species. Preferential elution

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sequences have been derived in previous studies (Johannessen et al., 1977;

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Johannessen and Henriksen, 1978; Tranter et al., 1992; Eichler et al., 2001;

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Iizuka et al., 2002; Li et al., 2006) and have shown that SO4 2 – (sulfate)

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generally elutes quicker than Na+ (sodium ion) and Cl – (chloride ion)

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(Brimblecombe et al., 1985). Although some differences in the sequences

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(Brimblecombe et al., 1985) in a process referred to as preferential elu-

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have been observed in these studies, Brimblecombe et al. (1985) performed

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a rank order analysis and obtained the following most common elution order:

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´ ` ` 2` SO2´ ą Mg2` ą H` ą Na` ą Cl´ 4 ą NO3 ą NH4 ą K ą Ca

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where NO3 – is nitrate, NH4 + is ammonium, and K+ , Ca2+ , Mg2+ , and H+ are

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the potassium, calcium, magnesium, and hydrogen ions, respectively. The

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positioning of NH4 + and H+ can vary because NH4 + can dissociate (NH4 +

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= NO3 – + H+ ), where NH3 – is ammonia.

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Various field and laboratory studies have observed that ion elution during

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snowmelt yields the highest concentrations within the initial meltwaters from

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a snowpack (e.g., Tranter, 1991; Marsh and Pomeroy, 1999). However, the

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reported rates and sequences of the release of ionic solutes in snowmelt are

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highly variable as these processes are the complex result of snow metamor-

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phism, freeze-thaw cycles, snow chemistry concentration distribution, and

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snowmelt dynamics (e.g., Davis, 1991, refer to Materials and Methods sec-

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tion). Despite these variabilities, it has been suggested that snow ion exclu-

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sion causes the transfer of 50-80% of all snow ions in the peak snowpack into

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the initial 1/3 of the meltwater (Maul´e and Stein, 1990), with CF values typ-

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ically ranging between 2 (Hodson, 2006) and 6 (Tranter, 1991). The study

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of these processes has been largely based on field and laboratory research,

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whereas advances on diagnostic and predictive modelling are less common.

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An empirical model for the estimation of CF values in meltwater, from pre-

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melt average snow concentrations and SWE (snow water equivalent) dynam-

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ics, was first proposed by Stein et al. (1986), and it has since been success-

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2017). However, this is an empirical model where the vertical redistribution

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of snowpack ions is not resolved. More detailed, complex physically based

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models have been proposed (e.g.

Hibberd, 1984; Bales, 1991; Harrington

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and Bales, 1998; Costa et al., 2018). Harrington and Bales (1998) simulated

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ionic pulses in meltwater by modelling solute transport in melting snowpacks

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fully applied in several studies (e.g. Lilbaek and Pomeroy, 2007; Costa et al.,

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using medium flow theory. Costa et al. (2018) extended this model, naming

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including solute exchanges between the core and the surface of snow grains.

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it as PULSE, and improved the representation of the ion exclusion process by

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merical model simulations based on PULSE were used to evaluate snowpack

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and snowmelt chemistry dynamics in a subalpine watershed of the Colorado

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In this study, the results from a field-based experiment along with nu-

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to improve understanding of the role of snow accumulation and snowmelt

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Front Range, United States (U.S.). The overall objective of this study was

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elling component specifically, the objectives were to (1) evaluate the ability

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dynamics in the resulting preferential elution processes. For for the mod-

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due to snow metamorphism and ion exclusion, as well as the resulting (1b)

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meltwater chemistry and ionic pulses through comparison with field-based

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observations; (2) assess the feasibility of using lysimeter data for model cal-

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ibration and subsequent application to different snowpits within the same

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watershed; (3) provide estimates of ion exclusion rates at snow-grain size

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level for the first time; and (4) test the ability of PULSE to predict prefer-

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ential ion elution sequences.

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of PULSE to predict the (1a) vertical redistribution of ions in the snowpack

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2. Materials and Methods

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2.1. The Loch Vale watershed experiment The study was conducted in Loch Vale, which is a 6.9 km2 alpine and

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subalpine watershed located in Rocky Mountain National Park, Colorado,

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U.S. (Fig. 1; 40˝ 17’35”, -105˝ 39’16”) that ranges in elevation from 3097 m

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to 4009 m. The land cover across the watershed consists of 83% bare rock,

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boulder fields, and permanent snow and ice; 11% alpine tundra; and 6%

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subalpine forest and meadow (Baron and Mast, 1992). Average annual pre-

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cipitation (1984 -2012) in Loch Vale is 1050 mm with 65-85% of precipitation

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falling in the form of snow (Baron and Denning, 1993; Mast et al., 2014).

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This region is characterized by a substantial seasonal variation in accumu-

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lation and melting of the snowpack between years (Balk and Elder, 2000).

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Snowmelt is the primary hydrologic input into the Loch Vale watershed, pro-

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viding an important source of water contributing directly to streamflow as

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well as recharging talus groundwater, which can be an important source of

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streamflow during winter (Clow et al., 2003; Foks et al., 2018). The U.S. Ge-

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ological Survey monitors snowpack chemistry ăhttps://co.water.usgs.

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gov/projects/RM_snowpack/ą as well as streamflow chemistry and meteo-

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rology ăhttps://co.water.usgs.gov/lochvale/ą in Loch Vale, and has

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conducted research in the watershed since 1981 (Baron, 1992; Clow et al.,

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2000).

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Prior to the 1993-1994 snow season, a snowmelt lysimeter was installed

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in Loch Vale for measuring continuous snowmelt flow and chemistry. A com-

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mon problem with field studies of snowmelt chemistry is the contamination

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of meltwater samples by waters that have contacted organic or mineral soil 8

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cally to minimize such contamination, and additional design considerations

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minimized effects to natural systems and facilitated construction and opera-

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tion in a remote environment. The snowmelt lysimeter was located in a forest

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gap on a south-east facing slope of 15 degrees and installed on top of a large

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flat rock that protruded 1-2 m in height above the surrounding soil. The

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horizons. For this experiment, the snowmelt lysimeter was designed specifi-

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snowmelt lysimeter had a surface area of 6 m2 and was constructed of heavy-

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such as rocks and logs. The snowmelt lysimeter drained to a tipping-bucket

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duty plastic with a polyethylene liner and was anchored to native materials

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total flow from the tipping bucket was recorded on a datalogger. An autosam-

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pler was used to collect samples at a daily to sub-daily interval depending

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gauge installed inside a 12V thermoelectric cooler/warmer (Fig. 1). Daily

on snowmelt flow.

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During the 1993-1994 and 1994-1995 snow seasons, snowpack chemistry

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with the methods outlined in Ingersoll et al. (2002). During both years, a

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was monitored at three snowpit locations in Loch Vale (Fig. 1) in accordance

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the lysimeter location. In 1993-1994, the snowpack chemistry monitoring

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at the forest-gap snowpit and meadow snowpit (Fig. 1) was sampled for

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chemistry every «30 cm from the snow surface down to the soil interface.

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Additionally, the snowmelt outflow and chemistry were monitored at the

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snowmelt lysimeter during this time. All snowpack and snowmelt samples

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were analyzed for major ions using the analytical instruments listed in Table

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1. USGS data generated in this study are available in the National Water

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Information System at ăhttps://doi.org/10.5066/F7P55KJNą.

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depth-integrated snowpack chemistry sample was collected in early April at

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[Figure 1 about here.]

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[Table 1 about here.]

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2.2. The PULSE model The PULSE model was developed to predict ionic pulses in snowmelt

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runoff through the physically based simulation of snowpack internal phys-

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ical and chemical processes (Costa et al., 2018). The model simulates the

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release of solutes from melting snow grains to percolating meltwater, and

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its subsequent transport in aqueous solution through the snowpack. It con-

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sists of a multilayer snowpack numerical model with three interacting water

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phases: solid (snow grain core), quasi-liquid (snow grain surface), and liq-

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uid. The phases of water change from solid to quasi-liquid due to snowmelt

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metamorphism, and ions are exchanged due to snow ion exclusion based on

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a first-order exchange rate (α, ion exclusion coefficient). Subsequently, wa-

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ter and solutes are moved from the quasi-liquid layer to the liquid phase

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(meltwater) via snowmelt, and the transport of solute with the percolating

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meltwater is simulated by solving the advection-dispersion equation (ADE)

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using the implicit Crank-Nicolson method with variable timesteps to control

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the quality of the model results (Costa et al., 2018). This equation is mod-

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ified to account for flow through porous media with time-varying porosity.

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The snowmelt rate is provided as a model input (timeseries), and the chang-

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ing snowpack porosity is approximated by a linear function that depends on

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the snowmelt rate. This proved to be a valuable simplification as it avoids

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the need for more complex snow-physics models (Costa et al., 2018). Model

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applications of PULSE show that the model is very sensitive to the snowmelt

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Presently, PULSE does not account for any water or solute inputs during

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snowmelt, nor does PULSE account for preferential flow or rain on snow.

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The refreezing of meltwater is also not currently represented explicitely in the

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sense that water and ions in the liquid phase are not transferred back to the

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surficial quasi-liquid phase. However, the movement of the wetting front and

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rate and the ion exclusion coefficient, but less so to dispersivity in the ADE.

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the associated ion load front is suspended during refreezing. The model was

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the simulation of particulates and addressing the limitations listed above.

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developed for dissolved solutes, but continuous improvements could consider

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of PULSE to adequately capture the concentration pattern of snowpack dis-

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charge, including both the timing and magnitude of ionic pulses. The reader

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Despite these limitations, model applications have demonstrated the ability

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2.3. Model application

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is referred to Costa et al. (2018) for more information about the model.

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The model was applied to the Loch Vale watershed using two sets of

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meltwater chemistry) collected in 1994 and 1995 and (2) vertical snowpack

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data: (1) lysimeter data (bulk snowpack chemistry, snowpack discharge, and

profile chemistry data collected in 1994 in forest-gap and meadow areas lo-

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cated in proximity to the lysimeter (Fig. 1). Each of these datasets were

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used in independent model runs. PULSE uses pre-melt SWE and concen-

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trations, as well as dynamic snowmelt rates, as the forcing data. Lacking

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the observations to run an energy-budget snowmelt model, local air tem-

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perature data (Fig. 2a) was used in the lysimeter simulations to extend

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the recorded snowmelt rate to the ripening phase using a temperature-index

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(T-index) method (Dingman, 2015). This was important because snow ion

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exclusion-release-transport processes begin in the ripening phase before any

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snowpack discharge is recorded. In the case of the vertical snowpack pro-

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file chemistry simulations, the T-index method was needed to estimate the

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entire snowmelt timeseries. The model was run for Ca2+ , Mg2+ , Na+ , K+ ,

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NH4 + , Cl – , SO4 2 – , NO3 – and H+ . A previous model application to differ-

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ent sites showed little model sensitivity to the dispersivity parameter in the

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ADE solution, suggesting that preferential ion elution in melting snowpacks is

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likely an advection-dominated process (Costa et al., 2018). For this reason,

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only the model parameter accounting for ion exclusion (α) was calibrated

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through a Monte-Carlo analysis (Shreider and Buslenko, 1966). The calibra-

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tion and validation of the model was performed using independent datasets

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from the lysimeter corresponding to different hydrological years, each with

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distinct snow accumulation and spring climate conditions. The data included

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pre-melt snowdepths and average snow concentrations as well as continuous

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snowmelt rates and meltwater chemisty, which allowed for a robust evaluation

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of the model performance across different climate scenarios. The simulations

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using the vertical snowpack profile chemistry data (i.e., meadow and forest-

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gap snowpits), which were composed of only snow chemistry data but at a

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greater vertical resolution than in the lysimeter experiments, were performed

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using the same ion exclusion coefficients calibrated with the lysimeter data.

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It was assumed that the proximity of the lysimeters and snowpits resulted in

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similar climatic exposure that had similar effect on snow metamorphosis and

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ion exclusion rates. Because a main objective of this study was to examine

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ion elution sequences, the model was calibrated manually to prioritize the fit

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of the magnitude of the ionic pulse observed during the melt period. The

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performance of PULSE was evaluated using three objective functions: the

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Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE)

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and model bias (MB) computed using observations and model outputs of

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concentrations at hourly or sub-hourly resolution depending on the available

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observation data frequency.

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[Figure 2 about here.]

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3. Results 3.1. Lysimeter experiment

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equivalent to 30 cm in 1994 and 35 cm in 1995. In 1994, the onset of snowmelt

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occurred earlier in the year, and the snowmelt period was spread through a

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longer period, resulting in a slower snowmelt. In 1995, initial slow snowmelt

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pulses occurred in May, while the majority of snowmelt occurred a month

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later, with a faster overall snowmelt rate as compared to 1994 (Fig. 2b).

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The total snowmelt outflow recorded by the lysimeter (or SWE) was

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May temperatures being higher and above zero in 1994 and to a colder and

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The differences between these contrasting snowmelt seasons were related to

snowy winter in 1995 (Fig. 2a). Daily snowmelt rates were higher in 1995

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than in 1994 because of the warmer temperatures that occurred later in the

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spring when solar irradiance is greater (see the steeper ascending limb of the

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snowpack discharge hydrograph in 1995 when compared to 1994 in Fig. 2b).

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The elevated early meltwater ionic concentrations and CF max values

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during both years clearly highlight the typical effect of snow ion exclusion,

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which is characterized by an early ionic pulse in meltwater with CF values

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substantially exceeding one (Fig. 3). The initial meltwater concentrations

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were consistently elevated above the average pre-melt snow concentrations

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(calculated from multiple concentrations measured along the snowpack ver-

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tical profile; see Section 3.2 on Snowpack chemistry) but quickly decreased

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to concentrations similar to (and gradually becoming even lower than) pre-

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melt average snowpack concentrations (Fig. 3). The magnitude of the ionic

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pulses differed depending on the chemical species, with K+ , SO4 2 – , Na+ ,

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and Mg2+ showing the highest CFs, and NH4 + and H displaying the lowest

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(see the text inset above each panel in Fig. 3). K+ shows a particularly

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high concentration peak in 1995, which might have been caused by greater

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leaching of forest needles in that year. Differences in the peak CF values

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as well as the overall concentration distribution and rate of concentration

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decrease were also observed between years for the same chemical species. In

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1994, the melt of the first 1/3 of snowpack water equivalent exported about

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46%, 45%, and 42% of the total NH4 + , SO4 2 – , and NO3 – snowpack mass,

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respectively (lower panel, Fig. 4), whereas in 1995, only about 19%, 26%,

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and 21% of the snowpack NH4 + , SO4 2 – , and NO3 – mass totals were released,

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respectively, during that initial period. The average snowmelt rates in 1995

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were greater than the rates observed in 1994 resulting in more dilution and

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more homogenous elution of snow ions.

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[Figure 3 about here.]

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[Figure 4 about here.]

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[Figure 5 about here.]

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3.2. Snowpack Chemistry

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1993-1994 snow season, including the accumulation and snowmelt periods,

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at two locations in Loch Vale (Fig. 5). The Loch Vale meadow snowpit

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was located within a wind exposed subalpine meadow, and the Loch Vale

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forest-gap snowpit was located in a sheltered forest opening; both snowpits

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were in proximity to the snowmelt lysimeter (Fig. 1). Vertical ion concentra-

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Vertical snowpack profile chemistry data were collected throughout the

351

spatial variations in atmospheric deposition throughout the snow accumula-

352

tion season (i.e., until maximum snowdepth is reached in April) and similar

353

re

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tion profiles measured at the two different snowpits highlight temporal and

354

wards until the snow has completely melted) (Fig. 5). The effect of snow

355

ion exclusion can be seen by observing how the profile concentrations tend

356

to decrease during the snowmelt period. The meadow snowpit generally had

357

lower snow accumulation and lower ion concentrations than the forest-gap

358

snowpit. The distribution pattern of ions within the snowpack was quite vari-

359

able between chemical species. At the time of maximum snow accumulation,

360

high concentrations of acid anions such as NO3 – and SO4 2 – were distributed

361

vertically throughout the snowpack, whereas high concentrations of cations

362

such as Ca2+ and NH4 + were deposited mostly within the upper layers of the

363

snowpack (Fig. 5), probably due to dust deposition to snowpack surface in

364

spring (Clow et al., 2016).

365

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effects of snow ion exclusion during the snowmelt period (i.e, from April on-

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366

3.3. Model Results

367

3.3.1. Lysimeter experiment: meltwater chemistry The simulated concentrations during model calibration and validation

369

were compared to observations (Fig. 6). Observations were only available

370

for all ions in the 1995 dataset, with meltwater H+ concentrations missing in

371

the 1994. Thus, the model was run in calibration mode for 1995 and validated

372

for the available ions for 1994. The model was able to capture the overall con-

373

centration dynamics for most ions, but its performance varies between ionic

374

species. Table 2 shows the performance of the model based on the NSE,

375

RMSE and MB statistical metrics. Two distinct patterns can be observed in

376

both the observations and the simulations: (1) quasi-monotonic decreasing

377

concentrations of Ca2+ , Mg2+ , Na+ , K+ , SO4 2 – , and NO3 – , and (2) variable

378

(nonmonotonic) concentrations of NH4 + , Cl – , and H+ . The model simula-

379

tions showed that the main factor controlling these two distinct patterns is

380

the ion exclusion coefficient (α), with quasi-monotonic concentration dynam-

381

ics arising at high parameter values and nonmonotonic behavior emerging at

382

low parameter values (Table 2). When α is high, the snow is depleted of

383

ions very rapidly, producing only one high initial ionic pulse. However, when

384

α is small, the snow supplies ions to meltwater for a longer period of time;

385

therefore, snowmelt dynamics have the opportunity to influence the tem-

386

poral concentration distribution by affecting the dilution rates beyond the

387

early snowmelt period. These model results also suggest that higher early

388

meltwater concentration enhancements are more likely to occur at low early

389

snowmelt rates (see Fig. 2b), and that this process may occur multiple times

390

throughout the melt period, particularly for ionic species with lower ion ex-

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clusion rates that can sustain higher snowpack concentrations (solid phase)

391

through a longer period of time (e.g., NH4 + , H+ and Cl – ).

392

393

[Table 2 about here.]

394

of

[Figure 6 about here.]

3.3.2. Snowpit chemistry dynamics

395

396

for the forest-gap and meadow snowpits throughout the snowmelt period in

397

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Fig. 7 compares observed and simulated snowpack ion concentrations

398

idation mode. Results show that the model can capture the overall ionic

399

concentration profiles for most ions. Concentrations tend to decrease and

400

homogenize throughout the melting period due to snow ion exclusion and

401

release to meltwater, which transports the load vertically as it percolates the

402

snowpack. This is similar to the migration of light isotopes (analogous to

403

excluded ions) to the bottom of the snowpack during initial snowmelt ob-

404

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1994. The model calibrated for the lysimeter data was used here in val-

405

measurements likely arise from local contamination of the snow (e.g., forest-

406

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served by Taylor et al. (2001). Some unexpected high snow concentration

gap needles and animal feces), or the collected samples are observed in the

407

melting snowpack for some ions. Some examples are the concentration of

408

Mg2+ at the forest-gap snowpit at 0-50cm depth on May 18th of 1994 and

409

the concentrations of Na+ and K+ at the meadow snowpit at 50cm depth

410

on May 18th of 1994. However, the neighboring measurements of these un-

411

usually high concentration points, i.e. time (x-axis) and space (y-axis), show

412

much lower concentrations that corroborate better with the overall observed

413

concentration dynamics.

414

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[Figure 7 about here.]

415

416

4. Discussion

417

4.1. Interannual meltwater chemistry patterns

418

Results from the lysimeter experiment (Fig.

3) indicate that early

snowmelt ionic pulses may be less pronounced in Rocky Mountain snow-

420

packs than in other mountainous environments, such as the Sierra Nevada of

421

California (Williams and Melack, 1991), where melt-freeze cycles are more

422

prevalent and enhance the ionic pulse effect (Lilbæk and Pomeroy, 2008).

423

In the high elevations of the Rocky Mountains with deep snowpacks, lower

424

winter temperatures reduce the likelihood of mid-winter melt-freeze cycles.

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419

The results from two different snow seasons suggest that climatic vari-

426

ability affects the magnitude of the ionic pulse (peak concentrations often

427

differed by a factor of 2 between the 1994 and 1995 snowmelt events). Initial

428

meltwater concentrations were greater during 1995, the year with a deeper

429

snowpack and very slow initial snowmelt pulses followed by a relatively fast

430

snowmelt rate a month later (Fig. 2b). However, a greater percentage of

431

the total solute flux was eluted during the first 1/3 of meltwater flux in

432

1994, the lower snow year in which the onset of snowmelt occurred earlier,

433

and the overall snowmelt rate during the melt period was slower (Fig. 2b;

434

Fig. 4). These results have important implications in the context of chang-

435

ing snowmelt conditions that have been reported in this region (Clow, 2010;

436

Musselman et al., 2017; Fassnacht et al., 2018). These studies have high-

437

lighted an earlier snowmelt season timing from warming climate conditions

438

that may result in slower snowmelt rates due to snowmelt occurring during

Jo

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a time of lower available energy. Accordingly, the results from this study

439

suggest that this may lead to an overall enhancement of the magnitude of

440

the early snowmelt ionic pulse.

441

442

years is the additional atmospheric deposition that late season rain and

443

snow events contributed that is not considered in the total solute mass of

444

of

Another important consideration for the differences between the two

ro

snowpack prior to the initiation of snowmelt. An evaluation of data col-

445

446

http://nadp.slh.wisc.edu/) site (NTN-CO98; Fig. 1) showed that spring

447

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lected at the Loch Vale National Atmospheric Deposition Program (NADP;

448

compared to 1994. These differences in spring snowfall could have con-

449

tributed to differences in the lower 1995 fraction of total eluted ion mass

450

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re

precipitation during the snowmelt period was 2.5 times greater in 1995 as

with fraction of total snowmelt highlighted in Fig. 4.

na

Although the direct acidification of surface waters from snowmelt elution

451

452

453

Pomeroy, 2006), initial snowmelt runoff is more likely to infiltrate unsatu-

454

ur

has been observed in regions with saturated frozen ground (e.g., Quinton and

455

surface waters (e.g., Williams et al., 2009; Foks et al., 2018). Williams et al.

456

(2009) suggested that flushes of high concentration soil waters coincident with

457

peak snowmelt runoff were a result of the majority of the snowmelt water

458

moving through the soil into the stream, whereas, Foks et al. (2018) high-

459

lighted distinct signatures in the soil water as compared to snowmelt inputs

460

to streamflow. These results highlight the importance of representing the

461

physical and chemical processes of snowmelt elution within hydrochemical

462

modeling to more realistically represent basin biogeochemical fluxes.

463

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rated soils, frozen or not, and mobilize with previously stored soil water to

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464

465

466

4.2. The effect of the landscape on snow accumulation, chemistry and concentration profiles 4.2.1. Snow accumulation Although the vertical profile of the snow chemistry observations collected

468

in Loch Vale were at sites in proximity («100 m; Fig. 1) that receive

469

similar snowfall amounts, the sites display varying snow accumulation and

470

melt dynamics as a result of differing wind redistribution, snow intercep-

471

tion, and sublimation processes due to their exposure to wind and solar

472

radiation (Balk and Elder, 2000). The forest-gap snowpit was in a small

473

forest opening generally sheltered from wind. Snow sublimation losses from

474

this land cover type are dominated by fluxes from the snow surface and

475

are generally much smaller than from surrounding forested or wind-exposed

476

areas where sublimation also occurs from canopy-intercepted or blow-

477

ing snow (Ellis et al., 2011; Reba et al., 2012; Sexstone et al., 2016, 2018).

478

Thus, snow accumulation at the forest-gap snowpit site is often similar to the

479

total seasonal snowfall amount. The meadow snowpit was in a wind-exposed

480

meadow that has periodic blowing snow events and higher overall rates of

481

snow sublimation (Sexstone et al., 2018). Pomeroy et al. (1991) found that

482

sublimation of blowing snow can greatly reduce alpine snowpacks and remove

483

layers containing much of the winters wet and dry deposition. As a result,

484

peak snowdepth at the meadow snowpit was 64% of the snow accumulation

485

at the forest-gap snowpit, which is consistent with studies throughout the

486

Rocky Mountains (Pomeroy and Gray, 1995). Additionally, the snowmelt

487

rate at the meadow snowpit was greater than the forest-gap snowpack, likely

488

as a result of the greater solar irradiance and turbulent transfer in the ex-

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posed meadow location (Reba et al., 2012; Ellis et al., 2011) as is evident by

489

comparing the slope of the snowdepths curve between the forested-gap and

490

meadow snowpits in Fig. 5.

491

4.2.2. Snowpack chemistry: accumulation period

492

of

The varying snow accumulation and melt dynamics between the two sites

493

494

variability of ion concentrations within the snowpack. Fig. 8 compares the

495

depth-integrated concentrations throughout the accumulation and snowmelt

496

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ro

likely contributed to the observed differences in the spatial and temporal

497

Fig. 5 and simulation results presented in Fig. 7. Snowpack ion con-

498

re

periods calculated from both the in-snowpack measurements presented in

499

the meadow snowpit with the exception of NH4 + concentrations. Overall,

500

this result suggests that enhancement of ion concentrations during sublima-

501

tion of intercepted snow that was then deposited into the forest-gap snow-

502

pack and possibly erosion of over-winter dry deposition from the meadow

503

snow may have been responsible for the difference in observed concentra-

504

tions (Pomeroy et al., 1991, 1999). Canopy storage unloaded by wind and

505

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centrations at the forest-gap snowpit were generally greater than those at

warming of intercepted snow with high ion concentrations (because of canopy

506

sublimation, translocation of ions from the root zone during transpiration or

507

contact with vegetation) into the forest opening could also have contributed

508

to higher observed concentrations at the forest-gap snowpit (Pomeroy et al.,

509

1999). Forest canopy is a much more efficient scavenger of dry deposition

510

than the smooth snow surfaces in the meadow due to a much greater snow

511

surface area and higher ventilation rates (Pomeroy and Schmidt, 1993). Ad-

512

ditionally, both NH4 + and K+ are potentially biologically mediated, both in

513

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514

the canopy and in the needles that accumulate in the snowpack, which could

515

further explain the potential differences between the forest-gap and meadow

516

snowpit concentrations. [Figure 8 about here.]

517

4.2.3. Snowpack chemistry: snowmelt period

519

Snowpit observations

520

Differences in the snowmelt rate and duration between the two sites also

521

likely contributed to varying changes in snowpack ion concentrations during

522

the snowmelt period (red-shaded area in all panels of Fig. 8). Results show

523

that the snowpack concentrations at the forest-gap snowpit decrease mono-

524

tonically during the snowmelt period for Ca2+ , Mg2+ , SO4 2 – and NO3 – ;

525

whereas other chemical species such as Na+ , NH4 + and Cl – also show an

526

overall decrease in the average concentration, but the signal is more dynamic

527

and nonmonotonic (Fig. 8). In contrast, average snowpack concentrations

528

of Mg2+ , Na+ , K+ , SO4 2 – , and NO3 – , at the meadow snowpit generally

529

decreased at the beginning of the snowmelt period but exhibited a small

530

increase towards the end of the snowmelt period (Fig. 8). This difference

531

may have been attributed to a faster snowmelt rate at the meadow snowpit,

532

which is a factor known to weaken the ion exclusion process (Davis et al.,

533

1995), causing a smaller fraction of the mass load to be eluted in the initial

534

snowmelt outflow. However, the overall decrease observed in snowpack con-

535

centrations during the melt period is in accordance with the mechanism of

536

snow ion exclusion responsible for the reallocation of snow ions to the sur-

537

face of snow grains during winter and melt metamorphism, which promote

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518

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538

(Colbeck, 1976; Pomeroy et al., 2005).

539

Snowpack modelling

540

The model (dashed line in Fig. 8) captures reasonably well the depth-

541

averaged snowpack concentrations throughout the snowmelt period for most

542

ions in the forest-gap snowpit. However, there are larger discrepancies be-

543

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of

their rapid depletion from the snowcover and mobilization in early snowmelt

544

the model tends to predict a faster snow concentration depletion than ob-

545

served, which may suggest that ion exclusion and meltwater transport were

546

not the only processes involved.

547

re

-p

tween observed and simulated concentrations for the meadow snowpit. Here,

lP

The differences in the magnitude of snowpack concentrations observed be-

548

549

by the PULSE model (Fig. 7). The model agrees with the observations in

550

na

tween the meadow and forest-gap sites during snowmelt were also captured

551

shallower snowpack and faster snowmelt rate. These results demonstrate the

552

ur

that a quicker depletion of snow ions occurred for the meadow site with a

553

ical scenarios from physically based, multi-phase mass balances of snowpack

554

chemistry.

555

4.3. Observation and simulation of the ion exclusion process

556

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ability of PULSE to predict meltwater concentrations for different hydrolog-

Fig. 9 compares the maximum concentration factors (CFs) estimated

557

from the lysimeter data (Fig. 3) with the model ion exclusion coefficients

558

(α), which are shown in Table 2. Note that the concept of CF is typically

559

used for observations only, and in Fig. 9, it was calculated based on the 1994

560

dataset because it was more complete than that of 1995. The model ion

561

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exclusion coefficients (α) are the same for each ion in both calibration and

563

validation models because they were fixed during calibration using the 1995

564

dataset and reused in validation mode based on the 1994 dataset (calibrated

565

parameters are shown in Table 2). Results show that the patterns of the

566

model parameter α (right y-axis) generally align well with those observed

567

for CFs (left y-axis). However, the model α parameter and the observed

568

CFs cannot be linearly related: although the CF values are simply the ratio

569

between the maximum meltwater concentration peak and the average pre-

570

melt snowpack concentration, the model parameter α represents directly the

571

rate at which ions are excluded from the ice lattice to the surface of the ice

572

crystals. This, unlike the CF method, allows to separate the ion exclusion

573

process from the effect of dilution and diffusion during percolation through

574

the snow matrix. The model predicts the lowest α (ion exclusion) rates

575

for NH4 + and Cl – (between 10´3 and 10´2 1/hour), and the highest for

576

Ca2+ , Mg2+ , K+ , SO4 2 – and NO3 – (between 10´2 and 10´1 1/hour). While

577

this generally agrees with the observation-based CF dynamics calculated for

578

most ions, the relative difference between ions in the two methods seems to

579

increase for the ions with higher CF values (compare the distance between

580

the CF and α lines in Fig. 9). Despite the units and scales being different

581

between the observation- and model-based metrics used to quantify snow ion

582

exclusion, this difference may suggest that the ion exclusion process at the

583

snow grain level may be even more pronounced than what is suggested by the

584

CFs, which is effected by peak attenuation through dilution and dispersion

585

during transport with meltwater before the water exits the snowpack. This is

586

because the model computes the snow-to-meltwater ion exchange due to ion

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587

captures the effect more locally as opposed to the data-driven CF values that

588

are calculated from snowpack discharge when exiting the snowpack. Similar

589

evidence that the ion exclusion process may be even more pronounced than

590

previously thought has been shown by Costa and Pomeroy (2019). They

591

used exceptionally high resolution (time and space) measurements of early

592

of

exclusion at the different vertical layers of the model, which means that it

ro

snowmelt chemistry to look at the snow ion exclusion process in greater

593

594

on snow on the preferential elution of chemicals from melting snowpacks.

595

-p

detail and investigate the effect of preferential meltwater flowpaths and rain

re

[Figure 9 about here.]

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4.4. Preferential Elution Sequences

596

597

598

data with those predicted by the model. The average values reported by

599

Brimblecombe et al. (1985) are also shown. In this study, the authors ex-

600

amined previous studies and performed a rank analysis to identify the most

601

common ion elution sequence, which similar to the sequence determined in

602

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Fig. 10 compares the elution sequences calculated from the lysimeter

our experiment based on the lysimeter data, was established based on CFs.

603

The overall pattern of observed and simulated sequences generally agrees well

604

with the literature (compared the lines with the gray bars in Fig. 10). How-

605

ever, both the observations and simulated results reported in this study for

606

the Loch Vale watershed agree in that K+ and Na+ have been eluted faster

607

than typically reported in the literature. Such differences are not uncommon

608

and have been observed to vary from site to site (e.g., Johannessen et al.,

609

1977; Johannessen and Henriksen, 1978). The differences may be caused by

610

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heterogeneities in the snowpack concentration profile and how that inter-

612

plays with diffusion and dilution processes during transport in matrix and

613

preferential flow (e.g., Costa and Pomeroy, 2019), as well as leaching of for-

614

est needles, which may contribute particularly with K+ . Forest litter can be

615

an important source of K+ via the forest needles themselves and throughfall

616

from needle contact with snow. Therefore, the differences in elution patterns

617

of K+ observed between the two years of the experiement provides less con-

618

fidence in understanding of its key control factors and position within the

619

elution sequence as compared to the other ions. Despite these challenges, Ta-

620

ble 2 shows reasonable validation performance of K+ by the PULSE model

621

highlighting some determination of the main ionic patterns by this study.

622

The observed and simulated results disagree particularly in the positioning

623

of NO3 – and Ca2+ in the elution sequence, with the observation results gen-

624

erally agreeing better with the literature values likely due to using the same

625

metrics based on CFs. Depending on the year, this could be caused by dust

626

deposition in spring that contributes Ca-rich particulate matter to snowpack

627

that would be affected by ion exclusion processes from snow (Clow et al.,

628

2016).

630

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629

of

611

[Figure 10 about here.]

5. Conclusions

631

The process of preferential elution from melting snowpacks has been in-

632

vestigated through a combined field-modelling study. The field data were

633

collected from the Loch Vale watershed between 1994 and 1995 and con-

634

sisted of lysimeter and snowpit measurements. Two snowpits in proximity to 26

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the lysimeter were examined, one within an exposed subalpine meadow and

635

the other in a sheltered forest gap.

636

637

both the quantity and ionic strength of the snow resulting in substantial dif-

638

ferences in meltwater chemistry, with peak chemical concentrations doubling

639

in 1995 when a deeper snowpack and very slow initial snowmelt pulses were

640

of

Results show that climatic variability between 1994 and 1995 affected

ro

observed. The concentration factors were lower in 1995 like due to spring

641

642

highly heterogeneous snowpack concentrations, with cations such as Ca2+

643

-p

rain and snow with high ionic strength. The snowpit measurements revealed

644

(meadow and forest-gap snowpits) and acid anions such as NO3 – and SO4 2 –

645

being more evenly distributed. The snow ion concentrations were generally

646

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re

and NH4 + being deposited mostly within the upper layers of both snowpacks

647

erosion of wet and dry deposited ions from the meadow may have reduced

648

na

greater at the forest-gap snowpit, except for NH4 + , which indicates that wind

649

ing by wind and warming of intercepted snow with high ion concentrations

650

ur

concentrations of residual snow. At the forest-gap site, the canopy unload-

651

gap opening likely contributed to the higher observed concentrations at the

652

forest-gap snowpit.

653

Jo

(because of canopy sublimation and contact with the canopy) into the forest-

Model simulations and observations highlighted that preferential elution

654

is enhanced at low snowmelt rates, with the model indicating that this is

655

caused by an increase in the contact time and area between the percolating

656

meltwater and the snow. The model results support the conclusion that the

657

ion exclusion process at the grain-size level may be even more pronounced

658

than previously thought, but its effect is attenuated by diffusion and dilution

659

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processes before the percolating meltwater exits the snowpack and can be

661

measured for calculation of concentration factors (CF). The ion exclusion

662

rates were estimated between 10´3 and 10´2 1/hour for NH4 + and Cl – , and

663

between 10´2 and 10´1 1/hour for Ca2+ , Mg2+ , K+ , SO4 2 – and NO3 – .

664

6. Acknowledgements

of

660

The authors would like to thank the Canada Excellence Research Chair in

666

Water Security, the Canada Research Chair in Water Resources and Climate

667

Change, the Canadian Water Network and the Natural Sciences and Engi-

668

neering Research Council (NSERC) through its CREATE in Water Security

669

and Discovery grants (463960-2015) for financial support. Data and addi-

670

tional funding were provided by the U.S. Geological Survey (USGS) through

671

the Water, Energy, and Biogeochemical Budgets Program and the U.S. Geo-

672

logical Survey Rocky Mountain Snowpack Chemistry Project in cooperation

673

with the National Park Service, U.S. Forest Service, Colorado Department

674

of Public Health and Environment, and Teton Conservation District. U.S.

675

Geological Survey data generated in this study are available in the National

676

Water Information System at ăhttps://doi.org/10.5066/F7P55KJNą. Gra-

677

ham A. Sexstone, Donald H. Campbell, David W. Clow, or M. Alisa Mast

678

did not materially contribute to the model application described in this pub-

679

lication. Any use of trade, firm, or product names is for descriptive purposes

680

only and does not imply endorsement by the U.S. Government.

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Bales, R. C., Davis, R. E., Stanley, D. A., 1989. Ion elution through shallow

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Loch Vale watershed experiment: (a) site map and (b) lysimeter. Land cover data are available at https://www2.nrel.colostate.edu/projects/lvws/data.html. . . (a) Air temperature from the Loch Vale Main Weather Station (USGS station number 401719105394311) and (b) Lysimeter meltwater flow recorded in 1994 and 1995 . . . . . . . . . . . . Ion concentrations and concentration factors in meltwater and average pre-melt snow concentrations measured in the lysimeter (USGS station number 401719105395502) in 1994 and 1995 Fraction of the total snowmelt volume versus the fraction of total eluted ion mass (upper panel), and fraction of the mass load eluted in the first 1/3 of snowmelt for each ion . . . . . . Vertical profile of snow chemistry during the 1994 accumulation and snowmelt periods at the (a) forest-gap snowpit (USGS station number 401722105400300) and (b) meadow snowpit (USGS station number 401725105400001). The solid points are the measurement locations, which were used to generate the coloured surface underneath via 2D linear interpolation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Observed and simulated meltwater ionic concentrations (”conc”) during calibration (1995) and validation (1994) . . . Observed (dots) and simulated (shaded area) snowpack ion concentrations at the (a) forest-gap snowpit and (b) meadow snowpit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average snowpack concentrations (vertically integrated) throughout the accumulation and snowmelt periods the (a) forest-gap snowpit and (b) meadow snowpit . . . . . . . . . . Comparison between concentrations factors (CFs) simulated from the data and the ion exclusion rates calculated by the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elution sequences derived from the models results, observation (”obs.”) and comparison with the literature . . . . . . . . . .

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Figure 2: (a) Air temperature from the Loch Vale Main Weather Station (USGS station number 401719105394311) and (b) Lysimeter meltwater flow recorded in 1994 and 1995

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Figure 3: Ion concentrations and concentration factors in meltwater and average pre-melt snow concentrations measured in the lysimeter (USGS station number 401719105395502) in 1994 and 1995

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Figure 4: Fraction of the total snowmelt volume versus the fraction of total eluted ion mass (upper panel), and fraction of the mass load eluted in the first 1/3 of snowmelt for each ion

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(a) Forest-gap Snowpit

(b) Meadow Snowpit Figure 5: Vertical profile of snow chemistry during the 1994 accumulation and snowmelt periods at the (a) forest-gap snowpit (USGS 45 station number 401722105400300) and (b) meadow snowpit (USGS station number 401725105400001). The solid points are the measurement locations, which were used to generate the coloured surface underneath via 2D linear interpolation.

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Simulated Observed

Mg

20 40

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0 0

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Calibration 1995

80

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conc [

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Validation 1994

4 2 0 0

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0

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Time [days] H 20 Calibration 1995

0 0

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Validation No pre-melt snowpack concentrations available to run the model

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Time [days]

Figure 6: Observed and simulated meltwater ionic concentrations (”conc”) during calibration (1995) and validation (1994)

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30

Cl Calibration 1995

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Time [days]

20

Time [days]

Time [days]

Calibration 1995

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NO 3

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eq L -1 ]

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SO 4

conc [

Calibration 1995

20

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Validation 1994

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NH 4 eq L -1 ]

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Validation 1994

Time [days]

eq L -1 ]

eq L -1 ] conc [

Calibration 1995

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eq L -1 ]

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Calibration 1995

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conc [

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K

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eq L -1 ]

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Time [days]

0 0

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conc [

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conc [

conc [

40

Calibration 1995

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eq L -1 ]

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Calibration 1995

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eq L -1 ]

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(a) Forest-gap Snowpit

(b) Meadow Snowpit Figure 7: Observed (dots) and simulated (shaded area) snowpack ion concentrations at the (a) forest-gap snowpit and (b) meadow 47 snowpit

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(a) Forest-gap Snowpit

(b) Meadow Snowpit Figure 8: Average snowpack concentrations (vertically integrated) throughout the accumulation and snowmelt periods the (a) forest-gap snowpit and (b) meadow snowpit

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Maximum Concentration factors (left y-axis) [-] Calibrated ion exclusion rates (right y-axis) [1/h]

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NO 3

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Figure 9: Comparison between concentrations factors (CFs) simulated from the data and the ion exclusion rates calculated by the model

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Most common in the literature (Brimblecombe et al. (1985) Determined from CF factors (obs.) Determined from simulated ion exclusion rates (model)

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Figure 10: Elution sequences derived from the models results, observation (”obs.”) and comparison with the literature

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List of Tables

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2

Analytical instruments included Ion Chromatography (IC), Inductively Coupled Plasma - Atomic Emission Spectroscopy (ICP-AES), and Red-Rod pH Electrode. The samples were analyzed at the USGS Colorado Water Science Center WaterQuality Research Lab, Denver (Fishman and Friedman, 1989) 52 Model performances for all ions during calibration and validation. Positive and negative MB values implies model overprediction and under prediction, respectively. NSE values equal to one indicate a perfect match between observations and model results, and NSE values equal to zero indicate model predictions as accurate as the mean of all observations. RMSE values equal to zero indicate perfect match between observations and the model, but the higher the RMSE value, the worse the model performance. ”N.A.” (Not Applicable) identifies the simulations that could not be performed due to the absence of pre-melt snow ionic concentrations) . . . . . . . . 53

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949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964

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Table 1: Analytical instruments included Ion Chromatography (IC), Inductively Coupled Plasma - Atomic Emission Spectroscopy (ICP-AES), and Red-Rod pH Electrode. The samples were analyzed at the USGS Colorado Water Science Center Water-Quality Research Lab, Denver (Fishman and Friedman, 1989)

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Major Ion Analytical Instrument K+ (µeqL´1 ) IC Mg2+ (µeqL´1 ) ICP-AES 2+ ´1 SO4 (µeqL ) IC Ca2+ (µeqL´1 ) ICP-AES 3– ´1 NO (µeqL ) IC NH4+ (µeqL´1 ) IC Na+ (µeqL´1 ) IC – ´1 Cl (µeqL ) IC pH pH Electrode

52

Analytical Precision Detection Limit 0.001 0.016 0.001 0.002 0.01 0.02 0.001 0.008 0.001 0.007 0.001 0.014 0.001 0.011 0.01 0.02 0.01 NA

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Calibration (1995) NSE RMSE -0.1706 7.56 µeqL´1 0.1635 5.01 µeqL´1 0.3119 16.98 µeqL´1 0.3770 13.80 µeqL´1 0.4572 8.42 µeqL´1 -1.5215 6.09 µeqL´1 0.3244 4.34 µeqL´1 0.2048 3.40 µeqL´1 -0.2272 1.81 µeqL´1

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α (ordered) 0.0257 0.0254 0.0254 0.0252 0.0245 0.0012 0.0011 0.0100 0.0010

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Chemical Species K+ Mg2+ SO4 2 – Ca2+ NO3 – NH4 + H+ Na+ Cl –

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Table 2: Model performances for all ions during calibration and validation. Positive and negative MB values implies model overprediction and under prediction, respectively. NSE values equal to one indicate a perfect match between observations and model results, and NSE values equal to zero indicate model predictions as accurate as the mean of all observations. RMSE values equal to zero indicate perfect match between observations and the model, but the higher the RMSE value, the worse the model performance. ”N.A.” (Not Applicable) identifies the simulations that could not be performed due to the absence of pre-melt snow ionic concentrations)

53

MB 6.57 1.60 1.00 0.77 0.63 -0.08 0.54 0.23 0.35

Validation (1994) NSE RMSE 0.1114 1.56 µeqL´1 0.2649 1.35 µeqL´1 0.3461 5.51 µeqL´1 0.4506 5.51 µeqL´1 0.2252 6.32 µeqL´1 -0.6526 4.82 µeqL´1 N.A. N.A. -0.0066 1.67 µeqL´1 -1.2586 0.98 µeqL´1

MB 1.89 0.54 0.43 0.61 0.32 1.06 N.A. 1.10 1.87

Journal Pre-proof

2) Snowpack Modelling 1) Snowpack Monitoring

Modelling of snowpack discharge and redistribution of ions

Snowpit and lysismeter chemistry monitoring

3) Model validation Comparing observed and simulated snow and meltwater concentrations

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meltwater snowpit

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4) Discussion and hypotheses

Modelling helps interpret field data and improve theory

Journal Pre-proof Highlights

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Ion exclusion rates have been estimated directly from internal snowpack processes for the first time. Combined low snowmelt and low ion exclusion rates can cause multiple early meltwater ionic pulses. Snow layer containing much of winter wet and dry deposition can be removed by blowing snow in meadow snowpits. Wind-unloading of intercepted ion concentration sublimation- and vegetation-enhanced snow can increase concentrations in forest-gap snowpacks The model validations presented are a key milestone for improved assessment of climate change impacts on water quality and the cryosphere

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