Climate Change Impact - Part 3 - Tonle Sap Lake - Cambodia

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 South-East 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 km2at 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’ add-in 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 cross-referencing 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%.


Comments

MODELLING THE INFLUENCE OF CLIMATE CHANGE ON TONLE SAP WETLAND



Tonle Sap wetland and the influence of climate change

Model of Tonle Sap

Tonle Sap is the largest lake in South-East Asia and 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 km2at its lowest to ten times that figure at its largest.  The bed of lake is close to sea level and its maximum level is normally only 10 m above sea level. 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. 


Figure 1 - Important measuring sites related to the model of Tonle Sap
  
Levels and flows in the Mekong are measured at Kampong Cham and in the channel connecting the lake to the Mekong at Prek Kdam.


Figure 2 - Level measurement on the Mekong at Kampong Cham









Figure 3 - Level measurement on Tonle Sap River at Prek Kdam



The next chart shows the level at Kampong Cham and in Tonle Sap Lake. Two features are worth noting:
  • 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
  • the range of levels in the Mekong, about 15 m, is higher than in the Lake, about 7 m.

As the water levels are recorded relative to local data it is not possible to know from this graph the relative levels between the two measuring locations. 
Figure 4 - Water levels in Tonle Sap Lake and the Mekong at Kampong Cham


The next chart shows the flow at Prek Dam in the Tonle Sap River. During the period of October to April water flows from the lake to the Mekong. During the rest of the year the flow is toward the lake from the Mekong.




Figure 5 - Flow in Tonle Sap River at Prek Kdam




The above data sets, levels in the lake and the Mekong and flow via the Tonle Sap channel give us many of the important elements for a model of Tonle Sap.  However there are a number of other important factors. These are:
  •          Flow into the lake from surrounding rivers
  •          The relationship between depth, volume and area of the lake
  •          Precipitation on the lake
  •         Evaporation from the lake
There are four usable records of flow into the lake. There are shown on the following map and comprise he flows records at Battombong (Stung Sangker), Kampong Kdey (Stung Chikriang), Kampong Chen (Stung Staung) and Pursat (Stung Sen). There are other level records but they do not have an accurate rating curve linking levels and flows.





Figure 7 - Relationship between Level and Volume in Tonle Sap Lake


The curves on the chart were fitted using Excel. In the case of the flood area the relationship is:
Area = 30.061(Level)2+ 1094.1(Level) + 716.69
Where the surface area of the lake is in square kilometres and level is in metres.
The equivalent relationship for volume is:
Volume = 0.914(Level)1.883
...where the volume of the lake is in cubic kilometres.

The final elements for a model of the lake, rainfall and evaporation were based on average values taken from climate stations around the lake. For each daily time step the volume of rainfall and evaporation were based on the amount in millimetres multiplied by the area of the lake. The model also included a further loss mechanism. As Tonle Sap contracts in size during the October to April period water evaporates from the exposed soil which, given there is little rain in this period, becomes very dry.   When, later, Tonle Sap again expands the water flows from the lake over land which has been dry for, in some cases, several months. This water then sinks into the voids in the soil. The model applied this loss cumulatively. If the area of Tonle Sap was expanding the loss was equivalent to the total evaporation during the period of expansion at that point in time. The maximum loss by this mechanism was 200 mm. Once the lake started contracting then the loss from this component was set to zero.

The basic formula for the lake model was:

Volume[t+1] = Volume [t] + Inflows – Outflows

The model operated on a daily time step and was developed as an Excel file.

The inflows were: flow from local rivers, flow via the Tonle Sap channel and rainfall on the lake. During calibration it was found the estimate of local inflows based on the above method (adjusted in proportion to the drainage area) overestimated the inflow by a factor of two. A preliminary analysis suggested two reasons for this. One is that the area used refers to the area of the Tonle Sap ecosystem which might be larger than the drainage area upstream of the level measuring point. The second is that the gauging stations receives water from the upland parts of the drainage basin and therefore exaggerates the average runoff. Since the contribution from the surrounding rivers is small compared to the contribution from the Mekong through the Tonle Sap channel this parameter is not of great importance to the overall accuracy of the model.

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’ add-in 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 levels in Tonle Sap Lake.

The outcome of Solver optimisation process is that the formula became:
Flow = 1126 * (Mekong level – Tonle Sap level – 3.98)1.32

The value of ‘d’, relating to the direction of flow,  was 0.59. 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 values of parameter ‘b’, 3.98 m, which allows for the difference in the datum at Kampong Cham and at Prek Kdam is compatible with Figure 4of water levels at the two sites above.
 Another factor not included this model is the time delay between changes in relative water levels. To have included a hydraulic model would have required information on the channel shape and dimensions and a whole project on its own.
The next chart shows the simulated and observed flow of the Tonle Sap channel.


Figure 8 - Simulated and observed flow in Tonle Sap River

At first sight this does not look encouraging. In particular the peak inflows are not well represented.
However examination of the current meter gaugings carried out in 2008 to 2010 suggests a reason. The following chart is for the ‘out’ period only; that for flows in the other direction is very similar. It shows that for mid-range levels there is a reasonably consistent relationship between flows and levels but at low levels and high levels, when the flow direction is changing, the relationship is unclear. In the case of the following chart levels from 6 to 8 metres can be associated with either increasing flow, as in 2009, or falling flow, as in 2010. The flow associated at that level can also vary from 1,000 m3/s to almost 20,000 m3/s.


Figure 9 - Current meter gaugings in Tonle Sap River
This suggests that the calculation of actual flow values at Prek Kdam might not be consistent and that to simulate them might be as much a case of simulating peculiarities of the flow calculation as of representing the underlying flow patterns.
It should also be noted that the simulation of flows in the Tonle Sap channel is not an end in itself. The overall objective is to simulate water levels in Tonle Sap Lake. The following chart shows that very simulation.


Figure 10- 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 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.

Projected levels

In a separate part of the project flows of rivers within Cambodia and the whole of the Mekong  were simulated using the HYSIM rainfall/runoff model (http://www.watres.com/software/HYSIM/).  The hindcast values of all climate models reported on in the IPCC Technical Assessment Report of 2012 were analysed and the models rated on 4 factors: representation of observed monthly temperature, representation of observed monthly rainfall, representation of monthly temperature anomaly, representation of monthly precipitation anomaly. It was concluded that the MIROC model was most 
appropriate for Cambodia.

Using the calibrated hydrological model and the climate projections, flows were projected for a 30 year period centred on 2045.

The following chart shows the observed (or more strictly, simulated using observed meteorological data) and projected levels.  


Figure 11 - Observed and projected water levels in Tonle Sap Lake

The following table shows the change in the level of Tonle Sap Lake for different return periods.
Return period
(years)
Current conditions
Projected 2045
1-in-2
8.60
9.43
1-in-5
9.20
10.13
1-in-10
9.60
10.60
1-in-25
10.11
11.18
1-in-100
10.48
11.62
1-in-100
10.85
12.05

Acknowledgement

The work described above was performed while the author was working for SweRoad under a contract providing support to the Ministry of Rural Development of Cambodia, financed by the Nordic Development Fund and supervised by the Asian Development Bank. Any views expressed or those of the author and do not necessarily represent those of the other parties.




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