Estimation of possible active layer depth Estimation of possible active layer depth changes in North-East of Russia using climate changes in North-East of Russia using climate projections and deterministic-stochastic projections and deterministic-stochastic approachapproach

Liudmila Lebedeva1,3, Olga Semenova2,3

1St.Petersburg State University2State Hydrological Institute, St. Petersburg, Russia

3Hydrograph Model Research Group www.hydrograph-model.ru

Goal and objectives

• Application and testing of the Deterministic-Stochastic Modelling system

• Assessment of possible changes of active layer depth properties on the base of IPCC climate change scenarios

Objectives

The goal of the research is to develop a tool for assessment of possible climate change impacts in permafrost environment of the North-East Russia using deterministic-stochastic modelling approach

Requirements • Process-oriented deterministic model with physically

observable parameters, minimum of calibration and ability to port calibrated parameters to similar environment

• Downscaled climate change projections in probabilistic mode

Research stages

• To simulate soil thawing and freezing processes at different landscapes using observed meteorological data as input

• To refine the deterministic model parameters on historical data

• To generate continuous series of daily meteorological data (30 years span) according the A1F1 and B1 IPCC climate change scenarios using the stochastic model

• To simulate water and energy fluxes in the permafrost sites with randomly generated series of meteorological elements as forcing data using the deterministic model with physical parameters

• To assess and compare possible changes in active layer properties within variable conditions and according different climate change scenarios

Hydrograph Model

• Deterministic distributed model of runoff formation processes

• Heat and water dynamics simulations in soil profile

• Use of observable physical properties of landscapes as model parameters

• Minimum of manual calibration

Forcing data: precipitation, temperature,

relative humidity

Output results: runoff, soil and snow

state variables, full water balance

Slope transformationof surface flow

Initial surfacelosses

Infiltration andsurface flow

Heat dynamicsin soil

Snow coverformation

Heat energy

Interception

Heat dynamicsin snow

Snow melt andwater yield

EvaporationWater dynamics in soil

Channel transformation

Runoff at basin outlet

Underground flow

Transformation of underground flow

PrecipitationRain Snow

R

Applications of the Hydrograph model in permafrost environments (runoff simulations)

simulated observed

01.200111.200009.200007.200005.2000

m3

/s

1. 1

1.1

1.0

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0

Granger watershed, 8 km2

(Yukon basin, Canada – zone of discontinuous permafrost) simulated observed

12.199910.199908.199906.199904.1999

m3

/s

1. 3

1.2

1.1

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

1999 2000

10000

5000

XIIXIXIXVIIIVIIVIVIVIIIIII

10000

8000

6000

4000

2000

XIIXIXIXVIIIVIIVIVIVIIIIII

10000

5000

XIIXIXIXVIIIVIIVIVIVIIIIII

15000

10000

5000

XIIXIXIXVIIIVIIVIVIVIIIIII

Q

T

1980 1981

1982 1983

1980 1981

1982 1983

Vitim at Bodaybo, 186000 km2 (Eastern Siberia, continuous permafrost)

simulated observed

10.198108.198106.1981

m3/s

0.10

0.08

0.06

0.04

0.02

0.00

simulated observed

10.198208.198206.1982

m3/s

0.10

0.08

0.06

0.04

0.02

0.00

Yuzhny creek, KWBS 0.27 km2

Stochastic Model “Weather” (SMW)

• Simulation of daily precipitation, temperature and

relative humidity

• Simulation of annual and intra-seasonal variations

• Spatial and temporal correlation of meteorological

elements

• Initial parameters are estimated from observed

series of meteorological data

• Parameters may be modified according to climate

change projections

Applications of SMW (observed and simulated series)

Чара 30372

рассч наблP, %

99 .2989795939085807565554535252015108654321

Го

до

ва

я с

ум

ма

ос

ад

ков

, м

м

5 00

450

400

350

300

250

200

• Calc • Obs

calc obsExceedance probabi li ty , %

50402515964210.40.10.01

No

rma

liz

ed

pre

cip

ita

tio

n

80

70

60

50

40

30

20

10

0

0

10

20

30

40

50

60

70

80

90

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Pre

cipi

tatio

n, m

m

obs calc

Monthly distribution of precipitation, Bodaybo station

-40

-30

-20

-10

0

10

20

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Tem

pera

ture

, deg

r C

obs calc

Monthly distribution of air temperature,

Vostochnaya station

Daily values of precipitation, Suntar-Hayata station

Annual sums of precipitation, Chara station

Research strategy

Stochastic Model of Weather

Deterministic hydrological model

Physically observable parameters

Parameters of observed

daily meteorologica

l series

Climate change

projections

Simulated ensembles

of meteorologic

al data according to IPCC climate

change projections

Deterministic simulation of processes

using stochastic

data Numerical evaluation of hydrological

changes

Sketch of the KWBS

Study area

Kolyma water-balance station (KWBS)Kolyma water-balance station (KWBS) – small research watershed (22 km2) in

the upper Kolyma river.

Watershed boundariesMeteorological StationRain gaugeRecording rain gaugePit gaugeSnow survey line

CryopedometerEvaporation plotPan evaporation plotSnow evaporation plotWater balance plot

• Mean annual temperature – -11,60C

• Precipitation – 314 mm/year

• Open wood, bare rocks

• Continuous permafrost

• High-mountain relief• Representative for

the North-East of Russia

Active layer depth in different landscapes

Site 1Upper part of the slope:

Site 2Lower part of the slope:

clay slate

•rock debrisrock debris•absence of vegetationabsence of vegetation

•peaty groundpeaty ground•swamp larch forestswamp larch forest

Soil physical properties

The main parameters for simulation soil thawing and freezing processes in the Hydrograph model are physical soil properties

Porosity, %

Field capacity,%

Heat capacity,

J/m3*K

Heat conductivity,

W/m*K

Peat 80 50 1920 0.8

Clay slate 50 40 750 2.3

Crushed

stone55 30 810 1.7

Crumbling

rock55 13 790 2

Deterministic modelling of active layer depth

Site 1:

•1100 m

•South-facing slope

•Absence of vegetation

•Rock debris

•Active layer depth up to 1.7 m

Calculated Observed

03.8412.8309.8306.8303.8312.8209.8206.8203.82

m

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

Calculated Observed

03.7912.7809.7806.7803.7812.7709.7706.7703.77

m

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

m

Site 2:•850 m•North-facing slope•Sphagnum, shrubs•Soil profile – peat, clay loam,

clay slate•Active layer depth up to 0.7 m

Observed and calculated active layer depth in two landscapes, KWBS

IPCC emission scenarios

Implications of emission scenarios for global Tº by 2100 relative to 1990

(chosen scenarios and the model marked as red)

Atmospheric-Ocean General Circulation ModelsAtmospheric-Ocean General Circulation Models

Scenario Global ΔT(0C)A1F1 4.5A1B 2.9A1T 2.5A2 3.8B1 2.0B2 2.7

Model Country ΔTglob

CCSR/NIES Japan 4.4CGCM2 Canada 3.5

CSIRO Mk2 Australia 3.4ECHAM4/OPYC3 Germany 3.3

GFDL R30 U.S.A. 3.1HadCM3 United Kingdom 3.2

NCAR DOE PCM U.S.A. 2.4

ECHAM4/OPYC3 model projection according to A1F1 and B1 scenarios for 2010-2039

Soil thawing – depth projections

Maximum active layer depth: projected according to B1 and A1F1 scenarios and historically observed in different landscape

Mean active layer depth: projected according to B1 and A1F1 scenarios and historically observed in different landscapes

Duration of thawing period (B1, A1F1 and historical)

Bare rocks

Swamp forest

Results

• Both mean and maximum annual active layer depths are projected to increase by 2039.

• Mean soil thawing is expected to be 40 and 50 cm deeper than historically observed reaching 195 and 205 cm in the bare rock site according to B1 and A1F1 scenario

• Mean soil thawing is expected to be 70 cm deeper than historically observed reaching 130 cm in the swamp forest site for both scenarios

• The starting date of soil thawing in bare rocks is projected to be almost one month earlier due to strong effect of south-facing slope and solar radiation income

• The starting dates of soil thawing in swamp forest landscape are projected to be only one week earlier in comparison with historical data

• Total duration of thawing period is projected to extend by 1 – 1.5 months for bare rocks and about 1 month for forest landscape

General conclusions

• The deterministic hydrological model Hydrograph is able to simulate adequately the processes of soil thawing and freezing in permafrost environment

• Observable physical properties of landscapes are used as the model parameters; the model requires minimum of calibration

• To assess the possible effect of climate change on active layer depth the processes-based deterministic models are required. The Hydrograph model may be considered to be one of those models

• The stochastic model of Weather was used here to downscale climate change projections for specific sites and generate numerous continuous series of meteorological data with assigned parameters. In general, it can be replaced with some more advanced models and was used here as an example of the approach

Acknowledgements

•This study was conducted within the research grant provided by the Russian-German Otto-Schmidt Laboratory for Polar and Marine research in 2010

•The conference attendance was made possible with the support of APECS which is highly appreciated

Thank you for attention!

More results on applications of the

Hydrograph model in assessment of climate

change impacts on runoff in permafrost

environment…

Poster 124Evaluation of climate

change impact on river runoff in Eastern Siberia

by Semenova et al.