Climate Change Impact
Part 3: Example – Tonle Sap Lake  Cambodia
Summary
This study related to the estimation
of vulnerability (as a function of ‘importance’ and ‘risk’) to climate change
of roads and communities surrounding Tonle Sap lake.
A mathematical model of Tonle Sap
lake and the channel linking it the River Mekong was developed. This model was
able to accurately simulate the lake levels and hence the extent of flooding
around the lake; this defined the ‘importance’. Another component of the study
estimated the change in levels of the Mekong due to climate change; this
identified the ‘risk’. The study drew on the simulation of the Mekong river
described elsewhere.
The conclusion was that events which
had a rare frequency of occurrence in the past would occur more frequently in
the future.
Introduction
Tonle Sap is the largest lake in SouthEast Asia, is a
wetland of international importance and is recognised by the Ramsar convention.
Like most wetlands its area varies significantly through the year, from 2000 km^{2}
at its lowest to ten times that figure.
The bed of lake is close to sea level and its maximum level is normally
only 10 m above sea level. The distance from the lake to the sea is more than
400 km. The channel from the lake to the Mekong can flow in either direction.
When levels in the lake are higher than those in the Mekong water flows out of
the lake toward the Mekong (generally from October to April) and for the rest
of the year it flows in the opposite direction.
The following map shows three significant locations for
level and/or flow measurement. Levels in the lake are recorded at Kampong
Loung. Flow and level in the River Mekong
are measured at Kampong Cham and in the Tonle Sap channel are measured at Prek
Kdem.
Figure 1
 Cambodia and Tonle Sap
Figure 1  Tonle Sap Lake and Cambodia 
Current climate
The fluctuation of levels in Tonle Sap is very much
influenced by levels in the Mekong. The levels in the Mekong vary by around 15
m and in the lake by around 7 m. The following chart shows daily water levels
in the Mekong at Kampong Cham and in the lake at Kampong Loung. There is
approximate synchronicity in the timing of the two sets of levels but with
peaks in the Mekong generally being a bit earlier than those in Tonle Sap. This
shows that levels in the lake are driven levels in the Mekong.
Figure 2  Water levels in Tonle Sap Lake and the Mekong at Kampong Cham 
A model was then developed which used the levels in the
Mekong and the local inflow to the lake to simulated the levels in Tonle Sap.
The flow via the Tonle Sap channel was based on the
following equation:
Flow = a * (Mekong level – Tonle Sap level – b)^{c}
If the flows were toward to the lake then this formula was
used as above. If it was toward the Mekong then it adjusted by a further factor
d.
The values of the four parameters a, b, c and d were
obtained by using the ‘Solver’ addin of Excel. ‘Solver’ adjusts each of the
four parameters to see how they change the accuracy of the model. In this case
the accuracy of the model is defined as the sum of the squares of the errors in
the estimation of the flows in Tonle Sap channel.
The outcome of Solver optimisation process is that the
formula became:
Flow = 1126 * (Mekong level – Tonle Sap level – 3.97)^{1.18}
^{}
The value of ‘d’, relating to the direction of flow, was
0.64. In reality, this parameter is compensating for some hydraulic factors not
included in this model. A full solution of the equations would take account of
the inertia of the water in the Tonle Sap channel; in simple terms when the
relative levels in the lake and the Mekong change they first have to stop the
river flowing in one direction before they can increase its flow in the
opposite direction.
The value of parameter ‘b’, 3.97 m, which allows for the
difference in the datum at Kampong Cham and at Prek Kdam is compatible with the
figure of water levels above.
The following chart shows the simulated and observed water
level in Tonle Sap.
Figure 3 Simulated and observed levels in Tonle Sap Lake 
As can be seen the simulation is generally accurate. Many of
the peaks of water level are slightly underestimated but otherwise it is good.
The correlation coefficient between observed and simulated levels is 0.967.
It can therefore be concluded that the simulation of water
levels in Tonle Sap Lake is sufficiently accurate for the model of lake levels
to be used to study flooding around the lake.
Vulnerability
The aim of vulnerability mapping is to identify locations at
risk where interventions to reduce vulnerability are needed. Two factors are
involved. The first is the importance of the risk; if a road connects large
communities, for example, it is more important that it continues to function
and serve a wider community than a road with lower importance.
A simple definition of vulnerability is:
Vulnerability = Importance x Risk …………………… Equation 1
Importance of road segments
To evaluate the importance of a road, a scoring system was
developed. The aim was to be able to identify
the importance of a road. It is appreciated that such an ideal will never be
completely achieved; whatever the algorithm says each would have to be examined
using the calculated value as a guide. The scoring was applied to each road
section, defined as a section of road between two junctions. In all there were
5263 road sections. They covered 8 provinces.
The Ministry of Rural Development of Cambodia (MRD) already
has a system of road classification which goes from 1, the most important, to
4, the least important. As the aim was to have a higher weighting for higher
importance this number system was reversed.
Another factor is what the road connects to. A road with a
low ranking could be considered more important if it joined a road of higher
rank.
The length of a road is also a factor – the longer the road
the more important it could be considered.
To have a scoring system compatible
with the numbers associated with road category, the logarithm of the length in
metres was used. This would go from 2 for a road of 100 metres up 4 for a road
of 10,000 metres.
The population served by the road is also a significant
factor. As the data on communes identifies the area and the coordinates of the
centre, the algorithm identified communes based on the square root of the area
(approximately the distance from the centre to an edge of the commune and
distance from the road. To have a score compatible with other elements the
logarithm of the population was used as a score. In this case the population
was the total of all communes adjoining the road.
The data base also lists wats (pagodas), mosques and
churches. Since wats are usually built on high ground and provide refuge during
a flood the presence of a wat was given a score of 3.
Health centres, which are important to the whole community
but not specifically relate to flooding were given a score of 2.
The final element was the presence of a school for which the
score was 1.
The following table summarises the scoring system
Table 1  Summary of road importance scoring
Item

Description

Score

Road segment identification

Coordinates and brief description

For crossreferencing only.

Road category

MRD categories from 1 to 4

Score in inverse order. Class 1 has 4 points, class 4 has 1 point.

Length

Kilometres

Logarithm of the road length in metres. For example, a segment 10,000
metres long world have a score of 4

Category of road joined to

e.g. National Road, MRD 3

As for road categories. If connected to a national road then 5
points. Points are given for both ends.

Population in communes adjoining road

This is the total population of all the communes the road passes
through.

Score is based on the logarithm of the population. For example, if
the population is 30,000 the score is 4.5

Schools

The presence or otherwise of a school

Score is 1 or 0

Wat/Church/Mosque

The presence or otherwise of a Wat (Pagoda)

Score is 3 or 0. Higher than a school as it relates to the whole
community and often provides a refuge during a flood.

Clinic or health centre

The presence or otherwise of a clinic.

Score is 2 or 0. Higher than a school as it relates to the whole
community.

The scoring system was applied to MRD roads in eight
provinces: Battambong, Kampong Cham, Kampong Chhnang, Kampong Speu, Kampong
Thom, Pursat, Siem Reap and Thboung_Khmom. Tis established the
‘importance’ element of equation 1.
Impact of climate change
The model of Tonle Sap, combined with modelling of flow in
the Mekong under current project (climate change) values established the
‘risk’.
In general terms, the following summarise the projected
changes in road vulnerability:
 What was a 1 in 5 year flood is projected to occur every 2 years.
 What was a 1 in 10 year flood is projected to occur every 3 years.
 What was a 1 in 25 year flood is projected to occur every 6 years.
 What was a 1 in 100 year flood is projected to occur every 9 years.
 A 1 in 100 year flood is projected to increase in area by 10%.
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