1. Assessment of design flood characteristics for ungauged permafrost basin
Olga Semenova1,2, Lyudmila Lebedeva3,4,
Anatoly Zhirkevich5
1Gidrotehproekt, 2St. Petersburg State University, 3Nansen Centre, 4 State Hydrological Institute, St.Petersburg
5 Hydroproject Institute, Moscow, Russia

2. Motivation
Sparse hydrometric and meteorological network of Eastern Siberia
Hydrologic regime affected by permafrost
Changing climate conditions
Practical needs of reliable assessment of design flood characteristics for present and future
Goal
Develop parameterization scheme for assessment of possible hydrologic changes in the Timpton River basin
Make preliminary assessment of future runoff characteristics at the Kanku hydropower plant (under construction) by different approaches using the hydrological model Hydrograph
Compare the results of three approaches: PMF (possible maximum flood), traditional frequency analysis and deterministic-stochastic modelling

3. Approaches
Frequency analysis of observed runoff characteristics (annual mean, maximum, minimum). It is based on assumption of hydrological processes stationarity, long-term observational series and basin-analogues.
Probable Maximum Flooding (PMF) method includes identification of crucial meteorological factors of maximum flooding and assessment of runoff characteristics according to them (in our case, by hydrological modelling)
Deterministic-stochastic modelling – combined use of stochastic weather generator and deterministic hydrological modelling

4. Study object – the Timpton river basin№
Gauge
Distance from river mouth, km
Basin area, km2
Period of runoff measurements 1
Ust’-Baralas
337
13 300
1954 – cont. 2
The Kanku hydropower plant
201
27 300 - 3
Ust’-Timpton 20
43 700
1952 – cont.
Altitude varies from 600 to 1700 m
Continental climate
Bare rocks, tundra and larch forest
Zone of discontinuous and sporadic permafrost

5. Variety of landscapes Bare rocks Bush tundra Larch forest
Deep active layer,
Subsurface runoff
Larch forest
Shallow active layer,
surface runoff
Riparian vegetation

6. initially developed by Prof. Yury Vinogradov
The Hydrograph model
Process-based (explicitly includes all processes)
Observable parameters, minimum calibration (can be obtained apriori)
Common input daily data (air temperature and moisture, precipitation)
Free of scale problem (from soil column to large basin)
initially developed by Prof. Yury Vinogradov

7. Basin schematization and model parameters
Basin area was divided into three landcover types:
Bare rocks
Tundra
Larch forest
Moss and
lichen
Peat
Clay with
inclusion of rocks
Bedrock
Density, kg/m3
500
1720
2610
Porosity, % 90 80 55 35
Water holding capacity, % 60
20-40 13 7
Infiltration coefficient, mm/min 10
0.0005
0.05-1
Heat capacity, J/(kg oC)
1930
840
750
Heat conductivity, W/(m oC)
0.8
1.2
1.5
Wilting point, % 8
6-8 4
2-3

8. Model validation on two adjacent basins
1. Ust’-Timpton (43700 km2), 1970 – 1973
observed
simulated

9. Model validation on two adjacent basins
2. Ust’-Baralas (13300 km2), 1970 – 1973
observed
simulated

10. Stochastic Weather Generator
Simulation of daily precipitation, temperature and relative humidity
Simulation of annual and intra-seasonal variations
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

11. Projected scenarios of air temperature (left) and precipitation (right) by changes 2100 (two models, two scenarios)
Reference period –
CanESM2 model
NorESM1-M model

12. Probable Maximum Flooding approach
Main factors of maximum flooding:
Snow water equivalent
Rain on snow during snowmelt
Intensity of air temperature increase during snowmelt
Date of the temperature transition from positive to negative values in autumn antecedent year
Precipitation of the last warm month of antecedent year
The values of 1, 0.1, and 0.01% probability of chosen factors were used to generate artificial meteorological series
Generated meteorological data were used as the forcing for the Hydrograph model to simulate probable maximum flood

13. Results – the Kanku hydropower gauge
PMF FA
Frequency analysis
Probable Maximum Flooding

14. Conclusions
Three different approaches were used to access extreme flood characteristics for an ungauged basin in permafrost area. The results of three approaches are highly inconsistent. Probable Maximum Flooding approach relies on estimation of crucial meteorological factors values of low probability. Combination of the latter in a forecast results in significantly overestimated discharge. Conventional frequency analysis is based on the assumption of hydrological processes stationarity, which is undermined by the ongoing environmental change in high latitudes. DS-modelling approach is seen as a preferable tool for the assessment of flood characteristics in ungauged basins. It allows explicitly accounting for the factors causing extreme runoff events.

15. Thank you for attention!