Isotopic Evolution of Snowmelt by k135E9

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									Isotopic Evolution of
     Snowmelt


                   Vicky Roberts
                   Paul Abood
                   Watershed
                   Biogeochemistry
                   2/20/06
Isotopes in Hydrograph Separation

• Used to separate stream discharge into a
  small number of sources
• Oxygen and hydrogen isotopes are widely
  used because they are components of
  water and are conservative over short time
  scales
                 Problem
• For hydrograph separations involving
  snowmelt runoff
  – Some studies assume snowmelt to have a
    constant d18O value equal to the average d18O
    of the snowpack
  – d18O in snowmelt ≠ d18O snowpack
         Snowmelt Isotopes
• Snowmelt
  – Depleted in d18O early in melting season
  – Enriched in d18O later in melting season
• Why?
  – Isotopic exchange between liquid water and
    solid ice as water percolates down the snow
    column
          Physical Process
• At equilibrium, the d18O of water is less
  than the d18O of ice; initial snowmelt has
  lower d18O than the snowpack
• Snowpack becomes enriched in d18O ;
  melt from the enriched pack is itself
  enriched (d18O )
                   Papers
• Theory
  – Feng, X., Taylor, S., and Renshaw, C.E.
    2002.
• Lab
  – Taylor, S., Feng, X., and Renshaw, C.E.
    2002.
• Field
  – Taylor, S., Feng, X., Williams, M., and
    McNamara, J. 2002.
Feng: Theoretical model
quantitatively indicating
   isotope exchange
•Varied two parameters:
  –Effectiveness of isotopic exchange
  (Ψ)
  –Ice-liquid ratio (γ)
                  Isotopic exchange
• Rliq controlled by advection, dispersion and ice-water isotopic
  exchange
• Rice controlled by ice-water exchange
• Rate of isotopic exchange dependent on:
    Fraction of ice involved in exchange, f
    – Dependent on size and surface roughness of ice grains
    – Accessibility of ice surface to infiltrating water
    – Extent of diffusion within ice
    – Amount of melting and refreezing at ice surface
    Ice-liquid ratio quantified by: γ =        bf
                                                a + bf
    where         a = mass of water
                  b = mass of ice
    per unit volume of snow i.e. ratio of liquid to ice
    Effectiveness of exchange:
Ψ= krZ
       u*
• Kr is a constant
• Z = snow depth
• U* = flow velocity

     Ψ and γ dependent on melt rate and snow
      properties e.g. grain size, permeability
Results:
• Effect of varying ψ
  (effectiveness of isotope
  exchange)
• Relative to original bulk
  snow (d18O=0)
• Where Ψ is large = curved
  trend (a)
   – Base of snowpack is 18O
     depleted as substantial
     exchange occurs
   – Low melt rate so slower
     percolation velocity
• Where Ψ is small = linear
  trend (e)
   – Constant 3‰ difference
     between liquid and ice
• Effect of varying γ (and
  therefore f):
• Relative to original bulk
  snow (δ18O=0)
• Low γ = curved trend (e)
   – Slow melt rate
   – Lower liquid: ice ratio as
     lower water content
• High γ = linear trend (a)
   – Fast melt rate
   – Higher water content so
     more recrystallization
   Therefore constant difference
     in 18O of snowmelt and bulk
     snow
Conclusions:
• High melt rate = effective exchange and high liquid: ice
  ratio. Higher percolation velocity so constant difference
  in 18O. Increased water content triggers recrystallisation,
  a mechanism of isotope exchange.
   – linear trend
• Low melt rate = Large difference in 18O initially due to
  substantial exchange
   – Only a small proportion of ice is involved in isotopic exchange
     therefore insignificant change in 18O of bulk ice
   – 18O of liquid and ice reach steady state resulting in curved trend
     as equilibrium is reached
Assumptions:
• Snow melted from the surface at constant
  rate
• Dispersion is insignificant
• 18O exchange occurs between percolating
  water and ice
Implications:
• Variation in d18O between snowmelt and
  bulk snow causes errors in hydrograph
  separation if bulk snow values are used
Taylor: Laboratory experiment to
determine kr
• Determination of kr to allow
  implementation of model in the field
• Controlled melting experiments:
  – Melted 3 snow columns of different heights at
    different rates
  – 18O content of snowmelt relative to snow
    column substituted into model equation to
    obtain kr
     • Kr = Ψu*
            Z
                Kr = Ψu*
                     Z
• Range of ψ (effectiveness of isotopic
  exchange) values obtained by melting a
  short column rapidly (low ψ) and long
  column slowly (high ψ)
• Z = initial snow depth
• U* = percolation velocity
• Model used to calculate kr
  as d18O is used to infer Ψ
  (effectiveness of
  exchange) so equation
   Kr = Ψu*
        Z can be solved
                   Results
•   kr = 0.16  0.02 hr-1
•   Small range (0.14 – 0.17 hr-1)
•   Small standard deviation (15%)
•   Successful parameterization of kr indicates
    that the model captures the physical
    processes that control the isotopic
    composition of meltwater
                   Results
• Estimate of f is uncertain
  – Test 1:      0.9
    Tests 2-3: 0.2
  – Uncertainties
     • Snowpack heterogeneity
     • Recrystallization
    Snowpack Heterogeneity
• Real snowpacks are not homogeneous in
  terms of pore size
• If water content is low, water may only
  percolate in small pores
• Reduces surface area where isotopic
  exchange can occur
            Recrystallization
• Snow metamorphism due to wetting of
  snow
  – Small ice grains melt completely
    • No isotopic fractionation
  – Water refreezes onto larger ice crystals
    • 18O preferentially enters ice
    • Liquid becomes depleted
            Recrystallization
• Change to fraction of ice participating in
  isotope exchange (f) depends on two
  processes
  – Increase in f
     • High mass of snow involved in melt – freeze
  – Decrease in f
     • Larger mean particle size reduces surface area
       available for ice – liquid interaction
• Taylor, S., Feng, X., Williams, M., and
  McNamara, J. 2002.
• How isotopic fractionation of snowmelt
  affects hydrograph separation
                Locations
• Central Sierra Snow Laboratory (CA)
  – Warm, maritime snowpack
• Sleeper River Research Watershed (VT)
  – Temperate, continental snowpack
• Niwot Ridge (CO)
  – Cold, continental snowpack
• Imnavit Creek (AK)
  – Arctic snowpack
                  Methods
• Sample collection
  – Meltwater collected from a pipe draining a
    meltpan (CA, VT, CO)
  – Plastic tray inserted into the snowpack at the
    base of a snow pit (AK)
• Determination of d18O for meltwater
  samples
Results
                 Results
• At all locations, meltwater had lower d18O
  values at the beginning of the melt event
  and increasingly higher values throughout
  the event (3.5% to 5.6%)
• Trend holds despite widely different
  climate conditions
       Why is this important?
• Using the average d18O value of pre-melt
  snowpack leads to errors in the hydrograph
  separation
  Timing     early        late
  d18O       lower        higher
  New water overestimated underestimated
  estimation
               Error Equation
                           x
              x   18               d 18ONew
                    d ONew  d 18OOld

x = estimated error in x
x = fraction of new water
d18ONew - d18OOld = isotopic difference between new and old water
d18ONew = difference between d18O in average
                   snowpack and meltwater samples
             Error Equation
• Error is proportional to:
  – Fraction of new water in discharge (x)
  – Difference in d18O between snowpack and
    meltwater (d18ONew)
• Error is inversely proportional to:
  – Isotopic difference between new and old
    water (d18ONew - d18OOld)
                   Error
• Large error if meltwater dominates the
  hydrograph
• Expected in areas of low infiltration
  – Permafrost
  – Cities
• Underestimate new water
  – Assume more enriched water is a mixture of
    new and old water
                      Error
• Error magnitude depends on time frame of
  interest
  – Maximum error at a given instant in time
  – Error is lower if entire melt event is
    considered
    • d18OMelt ≈ d18OPack during middle of melt season
    • Negative error and positive error cancel out
               Other Factors
•   Additional precipitation events
•   Varying melt rates
•   Meltwater mixing
•   Spatial isotopic heterogeneity
      Additional Applications
• Incorporation into other models
  – Mass and energy snowmelt model
     • SNTHERM
• Glaciers
  – Climate studies involving ice cores
Questions

								
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