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.
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
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%.
climatedata