Download Слайд 1 - Hydrograph Model

Liudmila Lebedeva1,3, Olga Semenova2,3
1St.Petersburg
State University
2State Hydrological Institute, St. Petersburg, Russia
3Hydrograph Model Research Group www.hydrograph-model.ru
Estimation of possible active layer depth changes
in North-East of Russia using climate projections
and deterministic-stochastic approach
Goal and 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
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
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
Precipitation
Rain Snow
R
Heat energy
Snow cover
formation
Interception
Heat dynamics
in soil
Heat dynamics
in snow
Snow melt and
water yield
Initial surface
losses
Infiltration and
surface flow
Water dynamics in soil
Slope transformation
of surface flow
Evaporation
Underground flow
Transformation of underground flow
Channel transformation
Runoff at basin outlet
Forcing data: precipitation, temperature,
relative humidity
Output results: runoff, soil and snow state
variables, full water balance
Applications of the Hydrograph model
in permafrost environments (runoff simulations)
0.10
Q
1980
1981
0.08
10000
m3/s
10000
8000
5000
0.06
0.04
6000
0.02
4000
0.00
06.1981
08.1981
2000
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
XII
simulated
I
II
III
IV
V
VI
1982
VII
VIII
IX
X
XI
XII
10.1981
observed
0.10
1983
0.08
m3/s
15000
0.06
0.04
10000
10000
0.02
0.00
5000
06.1982
5000
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
XII
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
XII
T
10.1982
observed
Yuzhny creek, KWBS 0.27 km2
Vitim at Bodaybo, 186000 km2 (Eastern Siberia, continuous permafrost)
1. 1
1. 3
1999
1. 2
1. 1
2000
1. 1
1. 0
0. 9
0. 9
0. 8
1. 0
0. 8
0. 9
0. 7
0. 7
0. 8
0. 6
0. 7
m 3/ s
m 3/ s
Granger
watershed,
8 km2
(Yukon basin,
Canada –
zone of
discontinuous
permafrost)
08.1982
simulated
0. 6
0. 6
0. 5
0. 5
0. 4
0. 5
0. 4
0. 3
0. 4
0. 3
0. 3
0. 2
0. 2
0. 2
0. 1
0. 1
0. 1
0. 0
0. 0
0. 0
04.1999
06.1999
08.1999
s imulated
10.1999
obs erved
12.1999
05.2000
07.2000
09.2000
s imulated
11.2000
obs erved
01.2001
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)
Monthly distribution of precipitation, Bodaybo station
Чара 30372
500
• Calc
• Obs
90
450
Precipitation, mm
80
400
350
300
250
70
60
50
40
30
20
10
0
Jan
200
1
2
3 4 5 6
8 10
15
20 25
35
45
55
P, %
р ас с ч
65
75 80
85
90
93 95
97 98
Feb
Mar
Apr
May
Jun
Jul
obs
9 9 .2
Aug
Sep
Oct
Nov
Dec
calc
наб л
Annual sums of precipitation, Chara station
Monthly distribution of air temperature,
Vostochnaya station
80
70
20
60
10
Temperature, degr C
50
40
30
20
0
-10
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
-20
-30
10
-40
0
obs
0.01
0.1
0.4
1
2
4
6
9
Exc e e da nc e pro ba bi li t y , %
c alc
15
25
40
obs
Daily values of precipitation, Suntar-Hayata station
50
calc
Sep
Oct
Nov
Dec
Research strategy
Parameters of
observed daily
meteorological
series
Stochastic Model of
Weather
Climate change
projections
Simulated
ensembles of
meteorological
data according
to IPCC
climate change
projections
Physically
observable
parameters
Deterministic
hydrological model
Deterministic
simulation of
processes
using
stochastic data
Numerical
evaluation of
hydrological
changes
Study area
Kolyma water-balance station (KWBS) – small
research watershed (22 km2) in the upper
Kolyma river.
Watershed boundaries
Meteorological Station
Rain gauge
Recording rain gauge
Pit gauge
Snow survey line
Cryopedometer
Evaporation plot
Pan evaporation plot
Snow evaporation plot
Water balance plot
Sketch of the KWBS
• 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 1
Upper part of
the slope:
•
•
rock debris
absence of vegetation
Site 2
Lower part of
the slope:
•
•
clay slate
peaty ground
swamp 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
55
30
810
1.7
55
13
790
2
Crushed
stone
Crumbling
rock
m
Deterministic modelling of active layer depth
Site 1:
Site 2:
• 850 m
• North-facing slope
• Sphagnum, shrubs
• Soil profile – peat, clay loam, clay
slate
• Active layer depth up to 0.7 m
• 1100 m
• South-facing slope
• Absence of vegetation
• Rock debris
• Active layer depth up to 1.7 m
0
0
0.2
0.2
0.4
0.4
0.6
0.6
0.8
0.8
m
1
1
1.2
1.2
1.4
1.4
1.6
1.6
1.8
03.77
1.8
06.77
09.77
12.77
03.78
Calculated
06.78
09.78
Observed
12.78
03.79 03.82
m
06.82
09.82
12.82
03.83
Calculated
Observed and calculated active layer depth in two landscapes, KWBS
06.83
09.83
Observed
12.83
03.84
IPCC emission scenarios
Implications of emission
scenarios for global Tº by 2100
relative to 1990
(chosen scenarios and the
model marked as red)
Scenario
A1F1
A1B
A1T
A2
B1
B2
Global ΔT(0C)
4.5
2.9
2.5
3.8
2.0
2.7
Atmospheric-Ocean General Circulation Models
Model
CCSR/NIES
CGCM2
CSIRO Mk2
ECHAM4/OPYC3
GFDL R30
HadCM3
NCAR DOE PCM
Country
Japan
Canada
Australia
Germany
U.S.A.
United Kingdom
U.S.A.
ΔTglob
4.4
3.5
3.4
3.3
3.1
3.2
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 124
Evaluation of climate
change impact on river
runoff in Eastern Siberia
by Semenova et al.