1. Looking for universality...
Deterministic-stochastic modelling as a potential tool for the assessment of climate change impacts on hydrological regime in polar regions
Olga Semenova
State Hydrological Institute
St. Petersburg, Russia

2. Objectives
The goal of the research is to develop a tool for assessment of the possible changes in the annual, seasonal and extreme runoff characteristics of watersheds of Eastern Siberia on the base of deterministic-stochastic modelling
Necessary conditions:
Process-oriented deterministic model with physically observable parameters, minimum of calibration and ability to port calibrated parameters to other watersheds
Stochastic model providing the meteorological inputs according to climate change projections
State Hydrological Institute, St. Petersburg, Russia

3. Research strategy Deterministic hydrological model
Parameters of observed daily meteorological series
Physically observable parameters
Simulated scenarios of daily meteorological data
Deterministic hydrological model
Stochastic Model of Weather
Runoff generation processes simulations
Climate change projections
Numerical evaluation of runoff characteristics changes in probabilistic mode
State Hydrological Institute, St. Petersburg, Russia

4. Looking for universality...
Study area
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State Hydrological Institute, St. Petersburg, Russia 4

5. Hydrograph model (deterministic modelling)
Looking for universality...
Hydrograph model (deterministic modelling)
Single model structure for watersheds of any scale
Adequacy to natural processes while looking for the simplest solutions
Minimum of manual calibration R
Forcing data: precipitation, temperature, relative humidity
Output results: runoff, soil and snow state variables, full water balance
State Hydrological Institute, St. Petersburg, Russia

6. Weather model (stochastic modelling)
Looking for universality...
Weather model (stochastic modelling)
Simulation of daily precipitation, temperature and relative humidity
Simulation of annual and intra-seasonal variations
Simulation for hexagonal system of representative points
Temporal correlation of meteorological elements
Spatial correlation of meteorological elements
Parameters are estimated from observed series of meteorological data
Parameters may be modified according to climate change projections R
State Hydrological Institute, St. Petersburg, Russia

7. Looking for universality...
DM: state variables
0.8 m depth
0.4 m depth
Soil temperature at different depths
Kolyma
Water balance
station
Snow characteristics
State Hydrological Institute, St. Petersburg, Russia

8. Looking for universality...
DM: small and middle scale basins
simulated
observed
Timpton at Nagorny, basin area 613 km2
Vitim at Bodaybo, basin area km2
State Hydrological Institute, St. Petersburg, Russia

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DM: Large-scale basin
Lena at Kusur basin area 2,4 million km2
simulated
observed
State Hydrological Institute, St. Petersburg, Russia

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SM: annual values
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Chara meteorological station. Annual precipitation (mm)
• Calc • Obs mm
Exceedance probability, %
State Hydrological Institute, St. Petersburg, Russia

11. SM: correlation of annual values
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SM: correlation of annual values
Spatial correlation of annual temperature, Lena river basin
State Hydrological Institute, St. Petersburg, Russia

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SM: seasonal values
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Monthly distribution of precipitation
Monthly distribution of temperature
Bodaybo station
Vostochnaya station
State Hydrological Institute, St. Petersburg, Russia

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SM: daily values
Daily precipitation, Suntar-Hayata station
State Hydrological Institute, St. Petersburg, Russia

14. SM: correlation of daily values
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SM: correlation of daily values
Spatial correlation of daily temperature, Lena river basin
State Hydrological Institute, St. Petersburg, Russia

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DSM: maximum flows
Timpton river at Nagorny, 613 km2
State Hydrological Institute, St. Petersburg, Russia

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DSM: daily flows
Ebetiem river at Ebetiem, 114 km2
State Hydrological Institute, St. Petersburg, Russia

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Conclusions
The deterministic hydrological model Hydrograph performs well in arctic region at different landscapes and scales
Most parameters are estimated from physical characteristics and do not require any calibration
The stochastic model takes into account annual, seasonal and daily variation of meteorological elements and their spatial and temporal correlation
Next step would be
generation of ensembles of precipitation and temperature forcings according to IPCC climate change projections to obtain probabilistic estimates of annual, seasonal and daily extreme hydrological variables for large scale basins
State Hydrological Institute, St. Petersburg, Russia

18. Looking for universality...
Acknowledgements
The research is being conducted within the research grant funded by the German-Russian Otto-Schmidt laboratory for Polar and Marine Research
The IPY Oslo Stipend granted by the Research Council of Norway is appreciated
State Hydrological Institute, St. Petersburg, Russia