3. 2005.09 – 2009.06 B.Sc. China University of Mining & Technology
2009.09 – 2011.11 M.Sc. (Equivalent) Hohai University
2011.12 – 2015.08 Ph.D. University of Oslo (NVE)
2015.07 – 2015.12 Scientist Norwegian Meteorological Institute
Education
3
4. 4
Hydrological and Climate Modelling
Multiple modelling tools
HBV, SimHYD, XAJ, WASMOD, WRF
Good programming skills
FORTRAN, C++
, R, Matlab, Shell script, Java
Statistical analysis
time series analysis and data-driven models
Experience in spatial analyst softwares
ArcMap
Expertise
5. 5
Research
1) Li, H. et al., 2014. Implementation and testing of routing algorithms in the
distributed HBV model for mountainous catchments. Hydrology Research, 45(3),
pp.322–333. doi: 10.2166/nh.2013.009.
2) Li, H. et al., 2015. How much can we gain with increasing model complexity with
the same model concepts? Journal of Hydrology, 527, pp.858–871. doi:
10.1016/j.jhydrol.2015.05.044.
3) Li, H. et al., 2015. Integrating a glacier retreat model into a hydrological model
– Case studies of three glacierised catchments in Norway and Himalayan
region. Journal of Hydrology, 527, pp.656–667. doi: 10.1016/j.jhydrol.2015.05.017.
4) Li, H. et al., 2015. Water Resources under Climate Change of Himalayan Basins.
Water Resources Management. doi: 10.1007/s11269-015-1194-5.
5) Li, H. et al., 2015. Stability of model performance and parameter values on two
catchments facing changes in climatic conditions. Hydrological Sciences Journal,
60(7-8), pp. 1317-1330. doi: 10.1080/02626667.2014.978333.
10. 10
Flow Routing (A1 & A2)
A1: Implementation and testing of routing algorithms in the distributed HBV model for mountainous
catchments
A2: How much can we gain with increasing model complexity with the same model concepts?
11. 11
Flow Routing (A1 & A2)
Methods: 7 model variants
LWhole: lumped model
SBand: semi-distributed with elevation bands
GRZero: grid-based model; no routing
GROne: grid-based model; hillslope routing
GRTwo: grid-based model; hillslope and channel routing
A1: Implementation and testing of routing algorithms in the distributed HBV model for mountainous
catchments
A2: How much can we gain with increasing model complexity with the same model concepts?
12. 12
Monthly mean runof simulations by the five model variants.
Flow Routing (A1 & A2)
Results: Runof
A1: Implementation and testing of routing algorithms in the distributed HBV model for mountainous
catchments
A2: How much can we gain with increasing model complexity with the same model concepts?
13. 13
Flow Routing (A1 & A2)
Conclusions
The source-to-sink and channel routing do not add value in daily runof
simulation.
The model performance in runof simulation is improved by slope routing,
particularly in the low flow.
The model performance at the interior points increases with larger area.
The models are similar in reproducing the internal variables, such as
evaporation, snow and groundwater.
A1: Implementation and testing of routing algorithms in the distributed HBV model for mountainous
catchments
A2: How much can we gain with increasing model complexity with the same model concepts?
14.
14
Scheme of the Δh model
HBV
Huss et al. 2010
Glacier Retreat (A3 & A4)
A3: Integrating a glacier retreat model into a hydrological model – Case studies of three glacierised
catchments in Norway and Himalayan region
A4: Water Resources under Climate Change of Himalayan Basin
15. 15
Glacier Retreat (A3 & A4)
Study Sites (Area; Glacier; P/T)
Nigardsbreen in Norway (65; 73%; 3,736/-0.5)
Beas in India (3,202; 30%; 1,116/-1.0)
Chamkhar Chhu in Bhutan (1,353; 15%; 1,786/1.8)
16. Basin Variable Criteria Calibration Validation
Nigardsbreen
Q
NSE 0.90 0.90
RME 4.61 5.38
M R 0.90 0.92
Chamkhar
Chhu
Q
NSE 0.87 0.85
RME -0.02 10.32
Beas Q
NSE 0.65 0.73
RME 2.07 -22.38
Model performance in three basins
Glacier Retreat (A3 & A4)
Results
17. 17
Annual mass balance simulation of Nigardsbreen
Glacier Retreat (A3 & A4)
Cal. 1991 – 2002 Val. 2003-2012
Results: Nigardsbreen
19. 19
Ten-year moving average of annual temperature and precipitation of the Chamkhar Chhu Basin
Glacier Retreat (A3 & A4)
Results: Future Climate
A3: Integrating a glacier retreat model into a hydrological model – Case studies of three glacierised
catchments in Norway and Himalayan region
A4: Water Resources under Climate Change of Himalayan Basin
20. 20
Chamkhar Chhu Beas
Water resources per capita in the future
Glacier Retreat (A3 & A4)
Results: Water Resources
A3: Integrating a glacier retreat model into a hydrological model – Case studies of three glacierised
catchments in Norway and Himalayan region
A4: Water Resources under Climate Change of Himalayan Basin
21. Conclusions
The HBV model with Δh-model can reproduce the hydrological and glacial
processes.
The model with easily accessible input data can be applied in large areas for
climate change.
There is significant warming in the Himalayan region and the warming efects
are more obvious with higher greenhouse gases emissions.
There is more uncertainty in precipitation than in temperature.
Population growth is roughly responsible for 40% of the decline in water
availability.
21
Glacier Retreat (A3 & A4)
A3: Integrating a glacier retreat model into a hydrological model – Case studies of three glacierised
catchments in Norway and Himalayan region
A4: Water Resources under Climate Change of Himalayan Basin
24. 24
Non-stationarity (A5)
A5: Stability of model performance and parameter values on two catchments facing changes in
climatic conditions
Study Sites (Area; P/T)
Wimmera in Australia (2000; 560/13.5)
Lianshui in China (5499; 1360/17)
26. 26
Cumulative frequency of deviations of parameters calibrated on all sub-periods. The
optimized parameters on the whole period are the benchmark values. All models have seven
selected parameters.
Non-stationarity (A5)
A5: Stability of model performance and parameter values on two catchments facing changes in
climatic conditions
27. 27
Non-stationarity (A5)
Conclusions
Stability of model performance and parameter values depends on model
structure as well as the climate regime.
Models with higher performance scores are more stable in changing conditions.
Diferences in stability among models are larger in terms of model efficiency
than in model parameter values.
A5: Stability of model performance and parameter values on two catchments facing changes in
climatic conditions
28. 28
Ongoing …
The response of the hydrological system in India to climate change
WP1 Climate modelling & Hydrological Modelling
WP2 Empirical statistical downscaling
WP3 Field measurements
WP4 Socioeconomic effects of climate change
