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HAE MODEL
Panut Manoonvoravong
Bureau of research development and hydrology
Department of water resources
BACKGROUND TO THE HYDROLOGICAGRONOMIC-ECONOMIC
MODEL (HAE MODEL)

This model was funded by World Bank in
May 2009 for the Lower Mekong Basin
(LMB) to assist the Thai Government to
develop policy tools for
 adapting
to climate change impacts on the water
and natural resources of the Mekong River
Basin
 building local knowledge and capacity on
climate change
The pilot area for the HAE model development
project was set in Kong-Chi-Mun the Northeast of
Thailand basins (the sub-basin areas of lower
Mekong tributaries).
The models are required to assess the impact of
climate change on:
a) the hydrologic regime,
b) water usage,
c) agricultural production and
d) socio-economic conditions.
Topography
ECHAM4 GCM
The ECHAM climate model has
been developed from the
ECMWF atmospheric model.
It provided a future climate
scenario data of a spatial
resolution approximates to about
2.8º longitude/latitude and the
time-step of 24 minutes.
The model condition based on
the emission scenario of IPCC
SRES A2 & B2.
ROLE OF PRECIS MODEL ON THE PROJECT
To provide the future climate scenario data based
on ECHAM4 GCM’s future Climate data and the
emission scenarios of IPCC SRES A2 & B2.
 6 variables used in the HAE model are

precipitation (mm)
 max & min temperature (Degree Celsius)
 solar radiation (MJ/m2)
 Relative humidity
 Wind speed (m/s)

Future climate projection using PRECIS
model
 Precipitation

 PRECIS
vs. Observation
 Future Precipitation Change A2 & B2

Other Meteorological Parameters
 PRECIS
vs. Observation
 Future Change A2 & B2

Evapotranspiration
Baseline PRECIS
data
Observation in term
of climatology
PRECIS’ baseline &
future climate
scenario data
comparison
DATA PREPARING FOR HAE MODEL
To find the bias
adjustment
coefficient for
PRECIS data
To apply for the future climate
scenario data
HAE model
Bias adjusted
climate
scenario data
PRECIS VERIFICATION

13 observation
stations of TMD
were selected to
compare with
PRECIS simulation
WAY TO ADJUST PRECIS: PRECIPITATION
0.2 Grid
PRECIS
Observed
To Interpolate into
each grid
an approach in which variable bias adjustment was applied to the
PRECIS data depending upon the ranking of the PRECIS simulated
data amongst all rain-day events in a particular month
WAY TO ADJUST PRECIS: OTHER METEOROLOGICAL
PARAMETERS
The approach developed was based on monthly
corrections to PRECIS data determined from the
differences between the mean monthly observed
and PRECIS simulated data at the 13 TMD stations.
By using adjusted PRECIS meteorological data, Daily
potential evapotranspiration was calculated using
the FAO Penman method. Data were calculated on
each of the PRECIS grids.
FUTURE CLIMATE SCENARIO PROJECTIONS
Summary of future climate change in Kong-Chi-Mun basin
Kong
Chi
Mun
- Higher mean annual rain
- Rainy season: higher
rainfall intensity
- Increase rainfall in annual
A2
heaviest rainy day
- Longer hot duration
- Higher mean annual rain
on eastern side
- Rainy season: higher
rainfall intensity
- Increase rainfall in annual
heaviest rainy day
- Longer hot duration
- Mean annual rain is close
to present
- Rainy season: seasonal
rain is close to present
- Increase rainfall in
annual heaviest rainy day
- Longer hot duration
-Mean annual rain is close to
present
- Increase variation in
annual rainfall
- Rainy season: seasonal
B2
rain is close to present
- Dry season: Less rainfall
during dry season
- Longer hot duration
-Mean annual rain is close
to present
- Increase variation in
annual rainfall
- Rainy season: seasonal
rain is close to present
- Dry season: Less rainfall
during dry season
- Longer hot duration
-Mean annual rain is close
to present
- Increase variation in
annual rainfall
- Rainy season: seasonal
rain is close to present
-Dry season: Less rainfall
during dry season
- Longer hot duration
FUTURE CLIMATE SCENARIO
Summary of future climate scenarios
19802009
A2
20402069
Kong
Chi
Mun
Annual Rainfall
 1600 mm
 1200 mm
 1300 mm
Max temp. (Mar-May)
 34.5C
 35C
 35C
Min temp. (Dec-Feb)
 16C
 17.5C
 18 C
Annual Rainfall
(+9%)
(+7%)
Rainy seasonal rain
(+12%)
(+10%)
Dry seasonal rain
(-6%)
Annual heaviest rainday
Max temp. (Mar-May)
Hot duration
(+1C)
(+1C)
(+30-45 days)
(+30-45 days)
(+1C)
(+45-60 days)
Annual Rainfall
Rainy seasonal rain
B2
20402069
Dry seasonal rain
(-14%)
(-11%)
(-7%)
(+1C)
(+1C)
(+1C)
Annual heaviest rainday
Max temp. (Mar-May)
Hot duration
(+30-45 days)
(Increase)
(+30-45 days)
No change
(+45-60 days)
(Decrease)
FLOOD DAMAGES
SWAT Model
Time Series Routed
Mean Daily Flows at
Selected Locations
Associated Rating Curve
Associated Flood Depth
Flood Depth-Area Table
For Location
Associated Flood Area
and Duration
Annual Loss Calculations
Data requirement to estimate yield and crop water uses by DSSAT crop model
2.1 Weather data
2.2 Soil data
2.3 Crops and crop management practices
Time
Frame
Baseline
(BS,
1980-2009)
Future A2
(2050s,
2040-2069)
Future B2
(2050s,
2040-2069)
Data requirement to estimate yield and crop water uses by DSSAT crop model
2.1 Weather data
2.2 Soil data
2.3 Crops and crop management practices
Figure 2.5 Soil group in the basins
ECONOMIC ANALYSIS OVERVIEW
Agriculture
Time Series
Net Production
Values
Present Value of
Production
Flood Damage
Time Series
Flood Damages
Direct/Indirect
Annual Damages
Hydropower
Time Series
Net Production
Values
Present Value of
Production
Present Value Adaptation and Mitigation Costs
Revised Present Value Production and Damages with Mitigation
Benefit : Cost Ratios of Adaptation and Mitigation Measures
THANK YOU FOR YOU ATTENTION