1/30 modelling deep ventilation of lake baikal swiss federal institute of aquatic science and...

1/30Modelling deep ventilation of Lake Baikal

Swiss Federal Institute of Aquatic Science and Technology

Kastanienbaum - Switzerland

Modelling deep ventilation of Lake Baikal:a plunge into the abyss of the world's deepest lake

Department of Civil and Environmental Engineering University of Trento - Italy

Kastanienbaum, Switzerland, October 17th 2011

Group of Environmental Hydraulics and Morphodynamics, Trento

PhD Candidate:Sebastiano Piccolroaz

Supervisor:Dr. Marco Toffolon


The lake of records

Lake Baikal - Siberia (Озеро Байкал - Сибирь)

The oldest, deepest and most voluminous lake in the world

Lake Baikal - introduction


Main characteristics:Volume: 23 600 km3

Surface area: 31 700 km2

Length: 636 kmMax. width: 79 kmMax .depth: 1 642 mAve. Depth: 744 mShore Length: 2 100 kmSurf. Elevation: 455.5 mAge: 25 million yearsInflow rivers: 300Outflow rivers: 1 (Angara River)World Heritage Site in 1996

Lake Baikal in numbers

Divided into 3 sub-basins:South BasinCentral BasinNorth Basin

1461 m

Lake Baikal formed in an ancient rift valley tectonic origin



Some curiosities

The largest freshwater basin in the world: 20% of the world’s total unfrozen fresh water reserve

Great Lakes are almost 8 times more extended than Lake Baikal

but

taken all together, have the same volume of water!




Freezes annually:Jan - May in the South Basin Dec - June in the North Basin


Baikal oilfish (Golomyanka): a translucent abyssal fish famous for decomposing almost instantly to fat and

bones when exposed to the sun

The Pearl of Siberia 1/2

Singular, sometimes extreme, environmental conditions: – enormous depth– several months of ice cover– high oxygen concentration– low nutrient concentration

gave rise to a unique ecosystem: more than 1000 endemic species(diatoms, sponges, salmonid fish and the Baikal freshwater seal)

Baikal seal or Nerpa (Pusa sibirica): the only exclusively freshwater pinniped species

An amphipod regards a diver from a sponge forest in Lake Baikal. It has been estimated that the biomass of crustaceans in the lake exceeds 1,800,000 tons



The bathymetry


An impressive bathymetry: average depth at 744 m

flat bottom steep sides


Strong external forcing

Deep ventilation

The physical mechanism

Deep ventilation

Phenomenon triggered by thermobaric instability (Weiss et al., 1991):

− density depends on T and P (equation of state: Chen and Millero, 1976)

− T of maximum density decreases with the depth (P=Patm Tρmax ≈ 4°C)

Temperature [°C]

De

pth

[m

]

1 2 3 40

500

1000

1500

Tρmax

Temperature profile

Reference parcelρparcel< ρlocal

Weak external forcing

ρparcel = ρlocal

ρparcel > ρlocal Compensation depth - hc


Deep ventilation

wind

sinking volume of water

A simplified sketch

The main effects:

− deep water renewal;

− a permanent, even if weak, stratified temperature profile.

− high oxygen concentration up to the bottom;

Presence of aquatic life down to huge depths!

deep ventilation at the shore


Deep ventilation

The state of the art− Observations and data analysis:

Weiss et al., 1991; Killworth et al., 1996; Peeters et al., 1997, 2000; Wüest et al., 2005; Schmid et al., 2008

− Downwelling periods (May – June and December – January)

− Downwelling temperature (3 ÷ 3.3 °C)

− Downwelling volumes estimations (10 ÷ 100 per year)

− Numerical simulations:

Akitomo, 1995; Walker and Watts, 1995; Tsvetova, 1999; Botte and Kay, 2002; Lawrence et al., 2002

− 2D or 3D numerical models

− Simplified geometries or partial domains

− Main aim: understand the phenomenon (triggering factors/conditions)

Wal

ker a

nd W

atts,

199

5


A simplified 1D numerical model

A simplified 1D model

The aims− simple way to represent the phenomenon (at the basin scale)

− just a few input data required (according to the available measurements)

− suitable to predict long-term dynamics (i.e. climate change scenarios)

The model in three parts

− simplified downwelling mechanism (wind energy input vs energy required to reach hc)

− lagrangian vertical stabilization algorithm (looking for unstable regions)

− vertical diffusion equation solver with source terms (for temperature, oxygen and other solutes)


Volume [km3]

Dep

th [m

]

Hypsometric curve

The downwelling mechanism

Vi = 5 km3

1272 sub-volumes

Constant-volume discretization scheme

Temperature [°C]

Dep

th [m

]

3 3.50

200

1500

4

1

Vi

Vi+1

Vi-1

Tρmax

Procedural steps:– assign Vd and ew

– compute Td

– calculate hc – compute the energy required to move Vd

– move the Vd until zd

Two cases:1.Shallow downwelling2.Deep downwelling

Td

dT/dz|ad

hc



The lagrangian vertical stabilization algorithm

The profile is unstable: the sinking Vd is heavier (lighter) than the

surrounding water

We need to stabilize the temperature profile

mixing factor included to take into account the exchanges occurring during the sinking of Vd

Remark: the stabilization is computed on the temperature profile, but all the other tracers

follow the same re-arrangement!

Temperature [°C]

Dep

th [m

]

3 3.50

200

1500

Tρmax

dT/dz|ad

hc



The vertical diffusion equation solver

The diffusion equation is solved for any tracer C

given the boundary conditions at the surface:– surface water temperature– oxygen saturation concentration– evolution of the CFC concentration

and along the shores:– geothermal heat flux– oxygen consumption rate

geothermal heat flux

coolingTemperature [°C]

Dep

th [m

]

3 3.50

200

1500

Tρmax


geot

herm

al h

eat fl

ux


The input data

The main input data of the model− seasonal cycle of surface water temperature - Tsurf

− energy input from the external forcing - ew

− sinking water volume - Vd

wind parameters unknowns equationswind speed W specific energy input ew ew=ξCD

0.5W

wind duration Δtw downwelling volume Vd Vd=ηCDW2Δtw

CD is the drag coefficientξ and η are the calibration parameters (mainly dependent on the geometry)

The wind forcing

The input data

Thanks to:Chrysanthi Tsimitriprof. Alfred Wüestdr. Martin Schmid


0.6620.645

Poor wind data seasonal probabilistic curves of W and Δtw

from: Rzheplinsky and Sorokina, 1997 Atlas of wave and wind action in Lake Baikal (in Russian)

(Атлас волнения и ветра озера Байкал)

Stochastic reconstruction of wind forcing

Wsummer IV-IX

Δtw

winter X-IIIΔtw

0.1540.7850.5860.9450.4540.5710.6460.231

7

0.571

0.1540.7850.5860.9450.4540.829

0.829

8-12

h

1st random extraction 2nd random extraction

The input data


Parameters to be calibrated:

− ξ (for the energy input)

− η (for the downwelling volume)

− vertical profile of the “effective” diffusivity

− mixing factor

Calibration of the model

Calibration parameters and procedure


Calibration procedure:

Medium term simulations in the period 1945-2000:

− formation of the CFC profile (1988-1996) no reactive, no decay rate

− comparison of simulated temperature and oxygen profiles with measured data



Numerical solution and the probabilistic approach

The numerical solution depends on the random seed used for the stochastic reconstruction of winds and the definition of surface temperature

Problems for the medium term simulations: we want to numerically reproduce a specific condition of the lake during a particular historical period (1980s- 1990s).

Possible solutions:

− Set of simulations having different random seeds average the solution over all the runs

− Use re-analysis data for wind and surface temperature.

Past observations of the main meteorological variables are analyzed and interpolated onto a system of grids, giving the meteorological conditions really occurred.

Thanks to Samuel Somot (Meteo France)


Re-analysis data 1/2

ERA-40 re-analysis dataset (53.375°N, 108.125°E) wind speed and air temperature every 6

hours from 1958 to 2002

Large interpolation grid:– the data are not representative of the real conditions at the lake surface– the data can give a good description of the historical sequence of events


to be suitably rescaled to the observed values



Re-analysis data 2/2

1. at every time step the wind speed value (occourred) is extracted from the series

2. The probability associated to that wind speed is extracted from the reanalysis cdf

3. The adjusted wind speed is calculated through the observational-based cdf

Dec 1974

0.91 0.91

6 14


Some results

Some results

15th of February: average over the last 15 years simulation


Some results

Validation of the model 1/2

Long term simulation (1000 years) same boundary conditions (wind, surface temperature, geothermal heat flux etc.) different initial condition of the temperature: T = const = 4°C

The aim:– validate the model comparing numerical solution with observations;– investigate the general behavior of Lake Baikal;– characterize deep ventilation (i.e.statistically estimate the typical downwelling volume and temperature).

Mean temperature after 150 years3.36 °C


Some results

Validation of the model 2/2

15th of February 15th of September

features of downwelling eventsVdM [km3] TdM [°C]

Present model 60 ± 43 3.22 ± 0.08

Peeters et al. [2000] 110 -

Wüest et al., [2005] 10÷30 3.15÷3.27

Schmid et al., [2008] 50÷100 (winter season) 3.03÷3.28

VdM = mean annual sinking volume

TdM = typical downwelling temperature range


15 August

Current condition analysis

Temporal distribution of downwelling events

Late spring Betweenfall and winter

15 April 15 June 15 December


Current condition analysis

Energy demand

Energy demand is higher in winter

Annual probability of downwelling

Period Prob. (>1300 m)

Winter 0.82

Summer 0.55


Climate change scenarios

The scenarios

Climate change

Simplified scenarios changing the main external forcing

Spring Winter

Wind: increasing/decreasing of the winds Temperature trend: global warming +4°C in summer, +2°C in autumn (Hampton et al., 2008)reduction of ice-covered period (Magnuson et al., 2000)

iceice

iceice

+4°C

+2°C 5 days 11 days


Calm wind

Current condition vs climate change 1/2

VdM = 24 ± 22 km3

TdM = 3.34 ± 0.12 °C

VdM = 60 ± 43 km3

TdM = 3.22 ± 0.08 °C

Strong wind

VdM = 83 ± 72 km3

TdM = 3.02 ± 0.06 °C

Warming+4°C; +2°C

VdM = 59 ± 46 km3

TdM = 3.20 ± 0.09 °C

Warming and strong wind+4°C; +2°C

VdM = 83 ± 76 km3

TdM = 3.02 ± 0.06 °C


Current condition:


iceice

iceice

+4°C

+2°C

5 days 11 days

Warming+4°C; +2°C

Warming and strong windy+4°C; +2°C

Current condition vs climate change 2/2


The favorable periods are only shifted in time, not significantly modified in duration.


ConclusionsModelling results:

−simplified model suitable to simulate deep ventilation

−analyse downwelling dynamics statistically

Physical results:

−downwelling volume is estimated as 60 ± 43 km3/year

−wind forcing and the duration of the favourable downwelling periods are the most important factors

−surface temperature warming in summer does not strongly influence the downwelling mechanism

Conclusions

Further activities

−construct more realistic/robust scenarios

−use a 3D model to better investigate the initiation of thermobaric instability

−investigate the periodical turnover of Lake Garda


− M. Toffolon, C. Carlin, S. Piccolroaz, G. Rizzi, Can turbulence anisotropy suppress horizontal circulation in lakes?, 7th International Symposium on Stratified Flows (ISSF), Roma (Italy), 22-26 August 2011

− A.Zorzin, S. Piccolroaz, M. Toffolon, M. Righetti, On the reduction of thermal destratification by a horizontal ciliate jet, 7th International Symposium on Stratified Flows (ISSF), Roma (Italy), 22-26 August 2011

Parallel works

Conclusions

Altered colors


Conclusions

Thank [email protected]

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1/30 modelling deep ventilation of lake baikal swiss federal institute of aquatic science and...

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