Evaluation of climate change impact on soil and snow Evaluation of climate change impact on soil and snow processes in small watersheds of European part of processes in small watersheds of European part of

Russia using various scenarios of climateRussia using various scenarios of climate

Lebedeva L.1 Semenova O.2

1St.Petersburg State University 2State Hydrological Institute

St. Petersburg, Russia

Key objectiveKey objective

Necessity of understanding the effect of climate change in the hydrological processes

The appropriate instrument for its

quantitative estimation

• Application and testing of the Deterministic-Stochastic Modelling system

• Assessment the possible change in soil and snow processes according to IPCC climate change scenarios

TasksTasks

requires

• Model input – air temperature, relative humidity, precipitation

• Time step – day• Model output – water balance

elements, runoff hydrograph, state variables

• Can be applied in any landscape and climate zone

• Basins of any size• Distributed

parameters

Deterministic hydrological model “Hydrograph”Deterministic hydrological model “Hydrograph”

Stochastic Model “Weather”Stochastic Model “Weather”

• Simulation of daily precipitation, temperature and relative

humidity

• Simulation of annual and intra-seasonal variations

• Simulation for hexagonal system of representative points

• Spatial and temporal correlation of meteorological elements

Parameters may be modified according to

climate change projections

Parameters are estimated from observed series of

meteorological data

Stochastic Model of Weather

Research strategyResearch strategy

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

Runoff generation processes

simulations Numerical evaluation of hydrological changes in

probabilistic mode

3. Valday station• Upper Volga• 820 mm per year• Taiga

2.Podmoskovnaya station

• Volga middle course• 650 mm per year• Mixed forest

Nizhnedevickaya

Valday

Podmoskovnaya1. Nizhnedevickaya station

• Don river tributary – river Devica

• 550 mm per year• Forest-steppe

Objects of researchObjects of research

Podmoskovnaya station, 1979–1981

Niznedevickaya station, 1980–1983

Modelling results: snowModelling results: snowusing historical meteorological datausing historical meteorological data

Snow water equivalent (mm)

Snow depth (m)

Podmoskovnaya station,1979–1981

Nizhnedevickaya station, 1979–1983

Valday station, 1978–1983

Modelling results: soil moisture in 1 m layerModelling results: soil moisture in 1 m layerusing historical meteorological datausing historical meteorological data

Modelling results: soil temperature at 0,4 m depthModelling results: soil temperature at 0,4 m depthusing historical meteorological datausing historical meteorological data

Podmoskovnaya station,

1980–1983

Nizhnedevickaya station, 1974–1977

Valday station, 1980–1983

IPCC emission scenariosIPCC 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 4.5

A1B 2.9

A1T 2.5

A2 3.8B1 2.0B2 2.7

Model Country ΔTglob

CCSR/NIES Japan 4.4

CGCM2 Canada 3.5

CSIRO Mk2 Australia 3.4

ECHAM4/OPYC3 Germany 3.3GFDL R30 U.S.A. 3.1

HadCM3 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

-5

0

5

10

15

20

25

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

A1F1 B 1

0,0

1,0

2,0

3,0

4,0

5,0

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

A1F1 B 1

Precipitation change (%)

Temperature change (degree C)

25

75

125

175

225

275

0 20 40 60 80 100

Probability, %1-Nov

11-Nov

21-Nov

1-Dec

11-Dec

21-Dec

31-Dec

0 20 40 60 80 100

Probability, %

25

75

125

175

225

275

0 20 40 60 80 100

historical curve A1F1 В1

Maximum SWE, mmMaximum SWE, mm Date of snow establishmentDate of snow establishment

MeltingMelting

Nizhnedevickaya station

Podmoskovnaya station

Modelling results: snow (by 2039)Modelling results: snow (by 2039)using generated ensembles of meteorological inputusing generated ensembles of meteorological input

0

40

80

120

160

200

0 20 40 60 80 100

Probability, % 1-Feb

21-Feb

13-Mar

2-Apr

22-Apr

12-May

0 20 40 60 80 100

Probability, %

Date of complete snow meltingDate of complete snow meltingMaximum SWE, mmMaximum SWE, mm

15.5

4.6

24.6

14.7

3.8

23.8

12.9

0 20 40 60 80 100

HistoricalcurveA1F1

B1

Modelling results: minimum soil moisture (by 2039)Modelling results: minimum soil moisture (by 2039)using generated ensembles of meteorological inputusing generated ensembles of meteorological input

50

100

150

200

250

300

350

0 20 40 60 80 100

Probability, %

Podmoskovnaya station: soil moisture in the 1 meter layer (mm)

Significant decrease of minimum soil moisture

Nizhnedevickaya station

Podmoskovnaya station Valday station

25

75

125

175

225

275

0 20 40 60 80 100

historical curve A1F1 В1

Modelling results: maximum soil temperature at 0,4 m depth (by 2039)Modelling results: maximum soil temperature at 0,4 m depth (by 2039) using generated meteorological input according to chosen scenariosusing generated meteorological input according to chosen scenarios

Rise of soil temperature in the forest zone and no change in the steppe zone – need to be verified

ConclusionsConclusions•The deterministic hydrological model Hydrograph simulates the processes in snow and soil

well for the European zone of Russia using the historical data

•The stochastic model takes into account annual, seasonal and daily variation of meteorological elements and their spatial and temporal correlation

•Deterministic-Stochastic Modelling System can be used for the assessment of possible changes in soil and snow processes

•Verification of the modelling results based of their analysis is required

Next step would be…Next step would be…Probabilistic estimates of annual, seasonal and daily extreme runoff variables for small watersheds

Thank you for attention!Thank you for attention!

Acknowledgements1) The support granted by the ERB conveners is highly appreciated2) The research was conducted with partial support by the German-Russian Otto-Schmidt laboratory