Climate Change Impact Part 6: River Tagus - Tajo - Tejo

Sun, Aug 13 2017 07:16 | Alcantara, Almourol, Climte change, Hydrological Simulation, hygrology, HYSIM, impact, Portugal, Spain, Tagus, Tajo, Tejo | Climate Change, Climate Scenarios, Climate Data

Climate Change Impact


Part 6: Example – Tejo-Tajo-Tagus


Summary

The Tagus (Tejo or Tajo) is one of the most important rivers in Europe. It rises in Spain and flows through Portugal to the Atlantic Ocean. The river basin is highly developed with several large dams and abstractions for irrigation and urban water supply. Data on reservoir storage and abstractions were used to estimate the natural flow of the river. A hydrological model of the river (HYSIM) was calibrated to observed flows. The calibrated model was then run with projected climate changes. The conclusions were that flows in the river would reduce but that the changes associated with human activity were of a similar order of magnitude.

Introduction

The Tagus River (in English, Tajo in Spanish and Tejo in Portuguese) rises in the hearth of Spain and flows into the Atlantic close to Lisbon. The total basin area is 80,000 km2 and the length of the river is 1060 km.

The general approach to hydrology and water resources of the Tejo/Tajo basin was to calibrate hydrological model (HYSIM) which uses precipitation and potential evapotranspiration as input data and flow data for model calibration. Once the model had been calibrated the observed precipitation and climate data could be replaced with projected values related to climate change scenarios and the impact of climate change on river flows assessed.


Figure 1 shows the Tejo/Tajo basin and the principal flow stations.



 The Tejo/Tajo is a highly developed basin and this development has been ongoing for several decades. The total reservoir storage is 9 billion m3 in Spain and 11 billion m3 in Portugal. For comparison, the average natural flow at Alcantara is 6.5 billion m3/year.

Current climate

The simulation started with Spain and then moved on to Portugal. Some of the data sources had data for both countries and others were only for one country.

In Spain, comprehensive climate and flow data were available from a variety of web sites run either at a national or European level. These included precipitation and the climate parameters necessary to calculate potential evapotranspiration. Data were also available for Madrid water supply, one of the principal users. Annual data were available at province level for irrigation. 

The following shows the cumulative effect of artificial influences on flow.



Figure 2 River Tajo at Alcantara - Spain - artificial  influences on flow

The red line shows the influence of reservoirs. Basically, during winter, the reservoirs fill and during summer water is released – principally for hydropower. The Blue line shows the effect of irrigation with abstractions in the summer. The green line represent others uses of water, principally for urban populations. These data, combined with the observed discharge of the Tajo at Alcantara, were used to calculate the natural flow in the river.

The following chart shows the simulated and naturalised daily flow for the River Tajo at Alcantara.


Figure 3 Simulated and naturalised flow - River Tajo at Alcantara - Spain


Simulation of the naturalised flow was not straightforward. Normally peak flows have a major influence on the calibration but in this case the effects of storage masked some of the variation. However, the general pattern of low and high flows is well represented.

For the Tejo river basin in Portugal data on flow, climate, storage and abstractions was also available at the locations in figure 1. The station furthest downstream with flow data for the Tejo is at Almoural. To calculate the naturalised flow at this point it was necessary not only to include the influence of reservoirs and abstractions in Portugal but the cumulative effect including those in Spain as well. The following chart shows the simulated and naturalised flow.

Figure 4 Simulated and naturalised flow - River Tejo at Almoural - Portugal

Considering the many factors that went into simulating flow, the accuracy was good. The correlation coefficients were 0.91 for daily flows and also 0. 91 for monthly flows. The simulated and observed means were 353 m3/s in both cases.  The simulated and observed standard deviations of daily flows were 717 and 716 m3/s and for monthly flows were 550 and 563 m3/s.

Climate change

To examine the effect of climate change six scenarios were considered: A1B, A1B max, A1B min, A2 and B1. The A1B is considered the most probable projection.


Figure 5 Flow duration curve - observed and project flow - River Tajo at Alcantara

A flow duration shows the percentage of the time that a flow was higher than a given value. On this curve, the flows are plotted using a logarithmic axis. Some inferences which can be drawn from this graph are:
  •         Downstream of Alcantara flows are zero for more than 15% of the time.
  •         The difference between the natural and observed flow is of a similar order of magnitude to the effect of climate change.
  •         Average flows will be about 30% lower by the mid-century.

The next chart shows the average monthly flow for the River Tejo at Almourol. This is the lowest point of the Tejo with flow measurement.



Figure 5 Average monthly  observed, naturalised and projected flow - River Tejo at Almourol

This chart confirms that climate change will, not surprisingly, have a similar impact on flows at Almourol as at Alcantara. As with Alcantara, the effect of abstraction and reservoirs is of a similar magnitude to the effects of climate change. It also shows that under the A1B scenario, average flow will be reduced.






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Climate Change Impact - Part 5 - Great Lakes of Africa (Lakes Victoria,Tanganyika and Malawi)

Sun, Aug 13 2017 02:49 | Climate change, cloud cover, fish, Great Lakes of Africa, impact, Lake Malawi, Lake Tanganyika, Lake Victoria, Temperature | Climate Change, Climate Scenarios, Climate Data

Climate Change Impact


Part 5: Example – Great Lakes of Africa


Summary

The Great Lakes of Africa are an important source of fish. The United Nations Food and Agriculture Organisation (FAO) wished to know to what extent climate change would influence the water temperature in the lakes. A study was carried out which found that lake temperature would increase by around 1°C by the middle of the 21st.century.

Introduction

FAO initiated an activity to investigate the possible effects and impacts of climate change on fish and fisheries production on the African Great Lakes; Lakes Victoria, Tanganyika and Malawi.
Figure 1Great Lakes of Africa

Figure 1 Great Lakes of Africa


Current climate

The Joint Research Centre (JRC) at Ispra, Italy, maintains databases of unpublished satellite data including water surface temperatures on the African Great Lakes on an 8-day basis. A time series of a number of decades was required to be analysed in order to present the temperature fluctuations on the Great Lakes
.
The satellite data from which temperatures are estimated are held in TIF (tagged image format) files, one for each year from 1985 to 2008. Each file holds data for 45 passes of the satellite.  For each pass a value is recorded for each cell on a 400 by 250 grid, provided there is no cloud cover and provided it is over water. Each cell is approximately 10 km by 10 km.

One major problem with the data is cloud cover.  For each of the lakes the approximate percentage of time for which temperature can be calculated is:

  • Lake Malawi – 70%
  • Lake Tanganyika – 60%
  • Lake Victoria – 30%
For all lakes, the problem is seasonal and is related to the rainy season.

The method adopted was as follows:
  •          Calculate the average lake temperature for each of the 45 passes and develop a temperature profile.
  •          Assume that although temperature in different parts of the lake would be higher or lower than the average, the distribution throughout the year would be the same.
  •          If data were missing, due to cloud cover, for one of the satellite passes find the value of the previous and of the following passes which had data for that cell.
  •          Base the temperature that satellite pass on the weighted average of the previous and following pass.

This enabled a complete grid of ‘observed’ surface temperature data to be prepared for all three lakes.

The following chart shows an example, for one cell of Lake Malawi.

.

Figure 2 Example of infilled lake temperature data

Climate change impact

As a first stage in assessing impact, an air temperature record was established for each of the lakes. The data from climate stations was of limited availability; few stations and long gaps in the data. As an alternative, the temperature data based on RSS (Remote Sensing Systems) estimates was used. This is one of two the ‘standard’ temperature records based on (Advanced) Microwave Sounding Unit data (AMSU/MSU). Comparing the limited observed data and the satellite derived data, showed similar trends but less variation. The difference in the temperature variation was due to the fact the satellite data were based on a 2.5 ° grid, not a single point, and therefore represented values over an area. In fact, this data was in that way more suitable than point data.

For each of the lakes a relationship between air temperature and water temperature was developed.

The climate change projections were based on the average of six climate models using the A1B (‘business as usual’) scenario. The models were those used in the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”. This gave the projected change in air temperature
.
The final stage was to use the relationship between lake surface temperature and air temperature to estimate the change in surface temperature of the lakes.

This shows that, for all lakes, the increase in surface water temperature would be around 1°C in the middle of the century.



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Climate Change Impact - Part 4 - River Mekong (China, Thailand, Myanmar, Lao DPR, Vietnam)

Sat, Aug 12 2017 10:50 | Cambodia, Climate change, flow, hydrological model, HYSIM, impact, Lao DPR, Laos, River Mekong. Mekong, Thailand, Vietnam | Climate Change, Climate Scenarios, Climate Data

Climate Change Impact

Part 4: Example – River Mekong


Summary


A component of a study of the impact of climate change in Cambodia examined how flows in the Mekong River will change in the future. Climate data on precipitation, temperature and other climate variables were used as input to a hydrological model, HYSIM, of the Mekong Basin. The model was calibrated to observed flows at six gauging stations on the main river. The calibrated hydrological model was then used with climate projections to estimate future flows in the River Mekong.

Introduction

The Mekong River Basin has a drainage area of 795,000 km2and the river is 4350 km in length. The river rises in China at an elevation 5224 m. The river, or its tributaries, also flow through Myanmar, Laos, Thailand, Cambodia and Vietnam. The river’s flow is highly seasonal, dictated by snow melt in the upper reaches and by the Monsoon in the middle and lower reaches.

(At the point where the flow was simulated, Kompong Cham, the basin area is 660,000 km2. It is interesting to compare this with the smallest basin in this series simulated by HYSIM, Pago Stream in US Samoa, which is only 1.52 km2.)

The primary objective of the project was to estimate the impact of climate change on flooding of rural communities and of rural roads in Cambodia. As part of this study a hydrological model of the Mekong River at a daily time step was developed.  The study of the Mekong was necessary for two reasons. Firstly, for communities bordering the river and secondly due its interaction with the inland lake of Tonle Sap.

Figure 1 Mekong River Basin


Current climate

There is a wide variation in the climate over the basin. In particular the temperature in the headwaters of the river are much lower than those closer to the mouth of the river. At Phnom Penh in Cambodia the annual average temperature is 27.4 °C and the range of monthly temperatures is 4.5 °C. At the headwaters of the Mekong the equivalent values are an average of -4.8 °C and a range of 22.7 °C.


Figure 2 Monthly average temperature - Mekong River Basin


To estimate the impact of climate change it was decided to simulate the flows of the Mekong using the HYSIM rainfall runoff model. This model simulates the hydrological and hydraulic process in a river basin with a high degree of physical realism. The model can operate at a daily or shorter time step and in this case, it was decided to simulate the flows at a daily time step. Given the very low temperatures in the upper basin the fact that HYSIM can simulate snow accretion and snow melt was important. The input data required are daily rainfall and daily or monthly potential evapotranspiration (PET). The calculation of PET in turn requires data on temperature, humidity, solar radiation and wind speed.

Thee data came from a variety of sources including:
·         The Ministry of Water Resources and Meteorology of Cambodia - MOWRAM (Flow and climate for Cambodia.)
·         The National Climatic Data Center of the USA. (Daily precipitation and temperature for the whole basin.)
·         The TuTiempo web site (Daily precipitation, temperature, wind speed and relative humidity for the whole basin.)
·         The Climate Research Unit of the University of East Anglia (average monthly values of temperature, relative humidity, wind speed and solar radiation on a 10-minute grid for the whole basin.)

HYSIM has a number of built in data processing apps, these included double-mass plots, infilling of gaps in the data series and the calculation of PET.

Flow data for Cambodia came from MOWRAM and for the rest of the basin from the Global Data Runoff Centre (GRDC).

Simulation

The first flow measuring station for which data were available was for Chiang Saen, in Thailand immediately downstream of the border with China. The total catchment area at this point is 186,000 km2. However, given the large difference in climate in this part of the basin the catchment was divided in three sub-catchments, each with its own climate data. The following chart shows the simulated daily flow for the period 1988 to 1993 (1993 being the last year with flow data from GRDC site.)

Figure 3 Simulated and observed flow at Chiang Saen


As can be seen, with the exception of 1992 when simulated flows were too high, the simulation is generally accurate.

The simulation was continued downstream with intermediate calibration points at Chiang Khan (Thailand), Mukdahan (Thailand), Pakse (Laos), Stung Treng (Cambodia), Kratie (Cambodia) and Kampong Cham (Cambodia). The following chart shows the flow simulation at Kampong Cham.

Figure 4 Simulated and observed flow at Kampong Cham


For the site in Cambodia data had been ordered for 3 calendar years, 2011 being the last. As can be seen the simulation is generally accurate. There are, evidently, some small differences but given the limited data availability the simulation can be considered satisfactory. There is no doubt that had more time and data been available the simulation could have been improved, in particular if major tributaries had been simulated.

It should also be recognised that the aim of the exercise was to estimate the impact of climate change and the difference in flows.

Climate change

At the time when this study was carried out the latest climate projections based on Representative Concentration Pathways were not available. The earlier SRES projections were used. In this case, based on earlier work in Cambodia, the ECHAM05 model with the A1B projection was used. This option was chosen as the A1B scenario is considered to be the ‘business as usual’ scenario which, given the absence of a successor to the Kyoto protocol limiting CO2 emissions, was appropriate.

The precipitation and temperature were adjusted using projected values of these two parameters. A second form of projection was included based on the work of O’Gorman (Sensitivity of tropical precipitation extremes to climate change. Geophysical Research Letters, published online: 16 September 2012). The paper quantifies the increase in intense precipitation associated with an increase in temperature in the tropics. To use this relationship the daily precipitation values for each calendar year were ranked and the highest precipitation was increased by 10% for each degree of temperature increase and the next two by 6%.

The following chart shows the change in average monthly flow for the River Mekong at Stung Treng, the most upstream flow station in Cambodia.

Figure 5 Projection change in monthly average flow - River Mekong at Stung Treng



This chart shows that, on average, flows in the Mekong will increase as a result of climate change. In particular the flood peak will be higher.

The final chart shows daily simulation of observed flow for the year with average flow, 1987, and projected flows adjusted to represent the increases expected in 2050.

Figure 6 Projected (2050) and observed (1987) flow - Stung Treng



Conclusion

The Mekong is one of the major rivers of the world. This study showed it was possible to accurately simulate flows using data largely in the public domain. When the hydrological model was used with climate projections it was also possible to estimate future flows in the river.


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

Sat, Aug 12 2017 10:12 | Cambodia, Climate change, impact, Mekong, road, Tonle Sap, vulnerability, Wetland | Climate Change, Climate Scenarios, Climate Data

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%.


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Climate Change Impact - Part 2 - Southern Bangladesh

Sat, Aug 12 2017 08:50 | Bangladesh, Climate change, crop, evaporation, impact, Precipitation, Temperature, water demand | Climate Change, Climate Scenarios, Climate Data



Climate Change Impact

Part 2: Example – Southern Bangladesh


Summary

This posting is based on a study of the impact of climate change on Southern Bangladesh. It examines current climate related problems which include storm rainfall, drought duration and sea level rise. It concludes that the main changes in climate in the region will be increases in temperature, storm rainfall, sea level and drought severity. Average rainfall will remain similar to the present.

Introduction

The project area is shown in the following map. It covered 13 Upazilas in southern Bangladesh. The total population of the project areas was 17.6 million. In some Upazalis the population density was more than 1000 people per square kilometre; about the average for Bangladesh.

Figure 1 Map of project area



The project tackled three areas of vulnerability: roads, cyclone shelters and markets. In terms of climate vulnerability, a significant factor in the project area was the fact that much of the it was only a few metres above mean sea level.

Current climate

Climate data were obtained from three main sources shown in the following table.

Table 1Sources of climate data used in report
Data source
Data
National Climatic Data Centre (part of the National Oceanographic and Atmospheric Administration of the USA)
Daily values of rainfall, maximum and minimum temperature.
Climate Explorer, a site run by the Netherland Meteorological Service.
Monthly values of temperature and rainfall.
TuTiempo, a weather site run by a Spanish company.
Daily values of temperature, rainfall, wind speed, atmospheric pressure, relative humidity.

Other data analysed included sea levels (from the Permanent Service for Mean Sea Level (PSMSL)) and Cyclone intensity (from the ‘Joint Typhoon Warning Center’ web site.)

The observed climate data were analysed and some of the conclusions were:
  •         Annual rainfall in the project area is reducing. For the period 1947 to 2015 the annual average is 2072 mm/year and it is falling by 1.3 mm each year.
  •         The monsoon rain, defined as the rainfall in the three wettest consecutive months in a year, is not decreasing.
  •         The monsoon season starts earlier with the wettest month being June in recent years but July in earlier years.
  •         The average temperature in the project area, based on the period 1890 to 2015, is 26.1 °C. It is increasing slowly and over the 125 years rose by 0.35 °C.
  •          Sea level data were obtained for 6 stations in the project area or from nearby sites in the Bay of Bengal. The data were downloaded from www.psmsl.org. Most sites had around 20 years of data but one had 60 years. The conclusion was that sea levels were rising at 4 mm a year but in places the settlement of the sediments in the delta gave an apparent increase of more than 10 mm a year.
  •      Data on cyclones were analysed for the Northern Indian Ocean, an area which encompasses the Bay of Bengal, using the Accumulated Cyclone Index. The data covered the period 1972 to 2014. It showed that cyclone energy was increasing by 1% a year – though there was a lot of inter-annual variability. Additional analysis of this data showed that the increase in energy was mainly related to an increasing number of storms – the energy in individual storms showed little increase. Cyclones are a major problem in the study area and in one incident it is estimated that 300,000 people were killed.

Climate projections

In many countries, you are given a lead as to where to go for climate projections. For example, certain climate models might have been shown to perform well or special models might have been developed. In the case of Southern Bangladesh, I could find no such guidance. I therefore decided to evaluate the accuracy of different climate models. In this case I established a set of criteria related to both temperature and precipitation. For the period of available data, the simulation of the climate models was compared to observed values. The criteria included absolute values (some models are biased having values which are consistently higher or lower than observed) and relative values (based on trend and seasonal variation). There were 8 criteria in all; temperature and precipitation, monthly and annual, bias corrected and uncorrected.

From this analysis, a few points stood out:
  •         Average monthly values were more accurately simulated than annual time series.
  •         Temperature was more accurately simulated than precipitation.
  •         No model scored consistently higher than others on all criteria.
  •         Some models were highly rated for temperature or precipitation but poorly for the other one.

Four models were selected for more detailed analysis. These were:

  •         NOAA GFDL-CM3, had the best average accuracy when all criteria were considered.
  •         MIROC-ESM-CHEM, was ranked 2nd overall.
  •         CESM1-CAM5, was ranked 5th overall. The model ranked 3rd was from the same source as model 1 and it was decided against using two models from the same source.
  •         MPI-ESM-LR, was ranked 6th based on all criteria but it was never lower then 11thon any criteria. This means it was less likely to produce a ‘bad’ projection.


The following chart shows the monthly simulated and observed average temperature. Value for five models are shown – the four retained as the best overall and the model which was best for this criterion.  As can be seen all models performed well on this criterion.


Figure 2 Observed and projected monthly temperature


The next chart shows the simulated and observed annual precipitation for the study area. This supports the statement above the annual values and precipitation are not as accurately simulated as temperature.

Figure 4 Observed and projected annul precipitation


For the projections, the highest value of RCP (RCP 8.5) was used. There were two reasons for this. Firstly, as the value associated with the maximum change in climate, it represents an outer envelope of projections. Secondly, the lower values of RCP assume a more stringent agreement on emissions than the ones achieved so far.

The next chart shows the projected change in average monthly temperature for the period 2040-2060 relative to 1985-2015. Whilst there is a range of projections, about 0.6 °C, the model are generally consistent in projecting a lower temperature increase during the rainy season, May to September, than for the rest of the year.
Figure 4 Projection of monthly temperature in 2050


The chart for annual precipitation shows much less consistency among the models. For each model, there are two traces – a faint one for annual values and a heavy one for the 10-year moving average. The range of value suggest the change could be from minus 150 mm to plus 150 mm. For comparison, the observed annual rainfall is around 2000 mm/year.

Figure 5 Projection of annual rainfall


The projection of other parameters was also considered.

The same models were used for the projection on maximum daily rainfall in each year (RX1 projection from the ETCCDI set).  This showed that intense rainfall would increase. All four models were consistent in projecting this increase. As the precipitation is that for the whole area of the model cell which most closely corresponded the study area, the difference in absolute values is likely to be due to difference in model cell size.
Figure 6 Projection of annual maximum daily rainfall




The study area is a rural area where agriculture is important. To study the impact of climate change on agricultural productivity the projections of projections of variables such as temperature, humidity and others were used to calculate changes in potential evapotranspiration. These values along with precipitation projections were used with a simple irrigation model to show the change in water demand. This shows that annual water demand is likely to increase from 600 mm/year to 800 mm/year for the mid-century
Figure 7 Projection of unsatisfied crop water demand

Conclusions

The main changes in climate in the region will be increases in temperature, storm rainfall, sea level and drought severity. Average rainfall will remain similar to the present.



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Calculating the Impact of Climate Change - Part 1 - Introduction

Sat, Aug 12 2017 07:58 | Africa, Asia, Climate change, Europe, impact, Pacific, RCP | Climate Change, Climate Scenarios, Climate Data

Climate Change Impact

Part 1. Background

 

Summary

It is widely accepted that the climate is changing, and will change more in the future, as a result of human activity. I have carried out many studies where I have quantified the impact of changes to climate. These have been in Europe, Asia, the Pacific and Africa. This posting is an introduction. Other postings will examine specific studies.


Introduction

There is widespread acceptance the climate is changing and that humans are driving, at last part of, the change. As a consequence, it is normal for infrastructure projects to examine the potential impact of climate change and then adjust the design to take account of it. This involves developing a quantified timeline of the changes.

So, in this and a series of following posts I am going to describe how to quantify the impact of climate change based on my experience in many parts of the world: Europe, Asia, the Pacific and Africa.

The purpose of these posts is two-fold:

  •         Firstly, to pass on my experience to others who are required to quantify climate change.
  •          Secondly, and unashamedly, to advertise my skills and experience.

This post is introductory. Following posts will be more detailed and specific.

Impact of Climate Change

NASA lists the projected impacts of climate change[1]as:
  •         Change will continue through this century and beyond
  •         Temperatures will continue to rise
  •         Frost-free season (and growing season) will lengthen
  •         Changes in precipitation patterns
  •         More droughts and heat waves
  •         Hurricanes will become stronger and more intense
  •         Sea level will rise 1-4 feet [0.3 to 1.2 m] by 2100

I have examined all these types of impact – and a few more.

Climate Models

A climate model represents the earth as a series of cells (or boxes).

  •          These cells are of the order of 100 km by 150 km horizontally and have around ten levels of atmosphere and a similar number of levels of the ocean.
  •          The models simulate the interaction between each of the model cells about once every hour.
  •         The execution time of climate models is of the order of 1 minute of computer simulation for one day of simulation. Typically, a model will simulate the climate for a period of more than 200 years and the execution time will be a few months.

The above figures are a generalisation for global climate models and individual models will have different values for the above parameters. In particular, regional models, which on represent part of the earth’s area, will have a finer grid.

Representative Concentration Pathways (RCPs)

The whole purpose of climate models is to calculate the changes in climate due to human activity and if these are found to have negative consequences, to evaluate mitigation options. The changes in human activity can lead to an energy imbalance – with more energy being absorbed by the earth than is radiated back into space. The best-known factor is the production of Carbon Dioxide, which allows more energy in to the earth’s atmosphere than out of it, but others include the effect of soot particles in the atmosphere and changes to the reflection of radiation.

Exactly what humans will do to the atmosphere in the coming century is unknowable so four possible trends have been considered. These are known as Representative Concentration Pathways (RCPs). They are labelled by the associated energy imbalance in watts per square metre at the end of this century: RCP 2.6, RCP 4.5, RCP6.0 and RCP 8.5. The first of these would occur if humans severely curtailed their emission of greenhouse gases. The last of the four assumes a future with little or any limitation of emissions.

The use of RCP values was introduced in 2013. Before that the equivalent was SRES (Special Report on Emissions Scenarios) values. Some of the studies I worked on used SRES values.

Downscaling

As stated above, global climate models work at a grid size of the order of 100 km side. (The phrase ‘of the order of’ is used as there is variety of scales between different models.) However, it is sometimes necessary to consider areas that are smaller than this, for example a specific length of proposed road. Going from a model cell to the specific area is known as ‘downscaling’. In theory, there are two methods: dynamic and statistical. However, the ‘dynamic method’ effectively requires a climate model with a reduced grid size developed for a specific study which in all but a few cases is impracticable.

The alternative, known as the ‘statistical’ method or the ‘delta method’, assumes that the changes in climate projected for a model cell apply uniformly over the whole cell. For example, assume that a model cell projects a temperature increase of 2°C but that the observed temperature within the area of the cell is from 9°C to 15°C. The projection will be that that in all locations the increase will be 2°C. 
This method places reliance on observed climate data. Source of such data will be discussed later.

Source of climate projections

For climate projections, I use almost exclusively the Climate Explorer site (https://climexp.knmi.nl) run by the Netherland’s Meteorological Service. The only exception has been in a few cases when ‘pre-digested’ projections were provided by the client.

Use of the web site is free and if you sign up it facilitates use by ‘remembering’ your previous selections.

In terms of projections I use mostly two sets of projections:
  •         Monthly CMIP5 scenario runs
  •         Annual CMIP5 extremes

The acronym ‘CMIP5’ refers to the ‘Coupled Model Inter-comparison Project Phase 5’.
The ‘scenario runs’ part of the site has output from climate models under four groups: Surface variables, Radiation variables, Ocean, Ice & Upper Air variables, and Emissions. In most cases for impact analysis it is the variables in the first group that are important. These include temperature and precipitation.

The ‘extremes’ part of the site has a second set of projections.  These were developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Values are provided for 31 variables. These include maximum daily precipitation, number of frost days (when the minimum was zero or below), number of ice days (when the maximum was zero or below) and growing season length.

Selection of climate projections

The climate explorer site has projections for more than 20 climate models. In addition, some models are run for multiple ‘experiments’ in which slightly different but credible model parameters are used. So, which one to use?
 In some cases, there might be guidance on the choice of climate models, for example from previous studies. Often however a decision has to be made on which models to use. What I have often done is to compare the simulated climate model output with observed values. This is rarely simple. For example how do you choose between a model which is biased (with values consistently higher or lower than observed) but which represent the inter-annual variation with a different model which is less biased but does not represent annual variations?

Sources of observed climate data

The best source, if available, is from the meteorological and hydrological services in the country you are working in.  For various reasons that is not always possible. Sometimes, for example, the meteorological service requires payment which the project has no funds for. Other sources of data include:
  •         The Climate Explorer site (https://climexp.knmi.nl) mentioned above. This has monthly data on precipitation and temperature.
  •         The National Climatic Data Center (https://www.ncdc.noaa.gov/cdo-web/datasets). This site has daily data on precipitation and temperature.
  •         The Climatic Research Unit (http://www.cru.uea.ac.uk/data). This has a range of data including monthly temperature and average values of several meteorological variables on a 10’ grid.

Climate change impact

Quantifying how the climate will change is but the first step to estimating the impact of climate stage. For example, for the impact on water resources it necessary to run a hydrological model with, firstly, observed climate data and, secondly, projected climate data.

Climate change impact studies

The following is a list of the climate change impact studies to be covered in other posts.
  •         Southern Bangladesh. The impact of climate change on rural communities including temperature and rainfall changes and the effect of sea level rise.
  •         Tonle Sap is a shallow lake/wetland in Cambodia. The hydrology is complicated as at times the lake receives water from the Mekong river and at times discharges to the river. A model of lake levels was developed which calculated changes in level due to climate change.
  •         The Mekong River Basin. A hydrological model was developed for the whole of the Mekong basin from the Himalayas in China down to the final flow measuring station in Cambodia. A hydrological model was used to estimate changes in flow due to climate change.
  •         Great African Lakes. The three ‘Great’ lakes (Lakes Victoria, Malawi and Tanganyika) are important for their fisheries. Data on lake temperature was decoded and the impact of climate change on water temperature was estimated.
  •         Hydrology of the Tagus river basin. The Tagus (Tejo/Teju) is one the most developed major river basins in Europe. A water resources/hydrological model of the basin was developed and the impact of climate change evaluated.
  •         Road flooding in Vanuatu. The impact of climate change on road flooding and rural economy was studied.
  •         Road flooding in Samoa. Data from different sources were combined to estimate flooding at different elevations. The impact of climate change was also studied.
  •         Road flooding in Kyrgyzstan. In this case flooding was but one of the potential problems the other one being icing during winter months. Again, the impact of climate change was studied.
  •         Variation of climate change in Zambia.
  •         The Yesilirmak Basin in Northern Turkey is highly developed for hydropower and irrigation. It was projected that average flows would decrease and, equally importantly, the seasonal distribution would change. At present, as a result of snow melt, the peak flow is in early summer at the start of the irrigation season; in future the peak flow will be in December.
  •      The Kagera Basin flows through 4 countries (Rwanda, Burundi, Uganda and Tanzania) before entering Lake Victoria. An extensive data base of flow, rainfall and climate was available this was sufficient for a hydrological model, HYSIM, to be calibrated. It was concluded that the increase in evaporation and in precipitation would to some extent cancel each other out.




[1] https://climate.nasa.gov/effects/
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The Canary in the Coal Mine

Sat, May 14 2016 08:27 | Climate Change, Climate Scenarios, Climate Data

In days of yore, coal miners would take a caged canary into the mine with them as the birds were more sensitive to poisonous gases than humans; if the canary died then the miners got out – alive.

‘Climate sceptics’ have long accused ‘climate activists’ of (to continue the metaphor) breeding highly sensitive canaries and looking for dangerous coal mines. Up to now I’ve studiously respected this site's motto as being a place where ‘numbers count’ and stayed out of debate. After a recent paper on ‘vanishing islands’ in the Solomon Islands archipelago I felt I had to comment. I was partly spurred on to do this by a guest post by David Middleton on the wattsupwiththat.com web site.
The paper in question is “Interactions between sea-level rise and wave exposure on reef island dynamics in the Solomon Islands” (Albert et al, Environmental Research Letters, Volume 11, Number 5). The headline message of the paper was

“..we present the first analysis of coastal dynamics from a sea-level rise hotspot in the Solomon Islands [and] have identified five vegetated reef islands that have vanished.”

That message got widespread coverage. At breakfast this morning in my hotel in Dhaka (Bangladesh) a fellow guest (a curriculum development specialist – nothing to do with climate) asked me if I had heard of the seven (sic) islands which had disappeared.

The total land area of the Solomon Islands is 27,990 km2 (World Bank figure). The area of 5 islands which have disappeared is given in the paper as 160,310 m2. Why did the authors use square metres? Why not hectares or square kilometres? More usual surely for an island? Perhaps it was because 160,310 m2 is 0.16 km2; that is 0.0006 % of the total area of the Solomon Islands.

OK. We are talking about canaries in coal mines so perhaps they are justified in a little sleight of hand. Let’s look further.

Their introduction starts:

“How islands and the communities that inhabit them respond to climate change and particularly sea-level rise is a critical issue for the coming century. Small remote islands are viewed as particularly vulnerable.” The authors do acknowledge a role for wave action but this is seen as secondary to sea level rise.

The following table is taken from the paper.

Area (m2) of the 5 islands which have disappeared
Island
1947
1962
2002
2011
2014
Kale
48,890
43,070
12,572
509
0
Rapita
45,700
21,250
0
0
0
Rehana
38,330
21,800
0
0
0
Kakatina
15,150
3,580
nd
0
0
Zollies
12,240
4,980
0
0
0
Total
160,310
94,680
12,572
509
0

What the table shows is that there was significant loss of area between 1947 and 1962.  The loss was 41% in that period. Expressed in m2 per year the rate was 4100 m2/year for the period 1947 to 1962 and 1800 m2/year for the remainder of the period. I recognise that defining two years just because they have data might bias the answers but when the rate in the second period is less than half that in the first period it is hard to accept that loss of island area is due to increasing sea levels.

Let’s now take a look at sea level rise. The following chart shows sea levels from two sources. The first is from the Permanent Service for Mean Sea Level and covers the period 1975 to 2015. Levels were measured at two locations with a short, 5-month, overlap. The second record from 1992 to the present is from the University of Colorado Sea Level Research Group and is based on satellite altimetry. 




The PSMSL record has a rate of rise of 2.7 mm/year. The University of Colorado gives a rate of 5.9 mm/year much less than 7 mm/year quoted in the paper; the difference in rate is in part probably linked to the recent drop in sea levels due to the El Nino effect. One drawback of these data is that they do not cover the whole period 1947 to the present used for analysis of the area of the islands.

In January of this year I was in Samoa – looking at the impact of climate change on roads. There it is something to be concerned about. On both of the two main islands there are few inland roads but they do have roads all the way round each of the islands. In places these roads are on a narrow coastal band and barely above the current high tide level. So, a modest increase over the next decade or so could have serious consequences. While there I prepared estimates of sea levels from 1948 to 2014. These, together with the PSMSL figure for Solomon Islands are shown on the next chart.


The amplitude of the sea level estimates is higher for the Solomon Islands than for Samoa but they show a similar trend. I’ve also plotted a quadratic trend line through the Samoa data when shows that for the early period sea levels were more-or-less constant but in recent decades have been rising more rapidly.

In other words, if sea levels in the Solomon Islands have followed a similar trend to Samoa, the most rapid loss of area coincided with the least change in sea level.

I mentioned above that I am working in Bangladesh. At the northern end of the Bay of Bengal the 2 metre contour is 100 km from the coast. A typical spring tide has a range of 4 metres. In that part of the country most agricultural land is behind embanked polders and when they are overtopped the land becomes saline. So creeping sea level rise has a real impact there.

The paper that is the basis of this posting has, of course, succeeded in the author’s terms; it has got wide publicity for the potential impact of climate change. But whether describing the disappearance of five small islands, whose total area is that of 20 soccer pitches, has advanced climate science is a moot point.
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Glabal Sea Level Rise

Sat, Apr 9 2016 06:05 | accelerating, CSIRO, Global sea level rise | Climate Change, Climate Scenarios, Climate Data
GLOBAL SEA LEVEL RISE
Summary
  • The average rate of sea level rise from 1880 to 2013 is 1.6 mm/year
  • The rate of sea level rise is not constant. It is increasing at 0.014 mm/year/year.
  • Superimposed on the rising sea levels is a cyclical component with a periodicity of about 50 years which is synchronous with the Atlantic Multidecadal Oscillation.
CSIRO Estimate
Sea levels have risen more than 100 m since the end of the last ice age and they are still rising. This post looks at the rate of rise over the last century or so and, based on sea level data, and answers the question "Is the rate of level rise increasing?",

The CSIRO provide one of the main estimate of global mean sea levels (Church, J. A. and N.J. White (2011), Sea-level rise from the late 19th to the early 21st Century. Surveys in Geophysics, doi:10.1007/s10712-011-9119-1.). The data run from 1880 to 2013. They are available as monthly or annual values. The annual values have been analysed here.

This chart shows the CSIRO sea level data. The data are in millimeters relative to an arbitrary datum. The data show that global sea levels have risen by just over 200 mm in the period 1880 to 2013. Plotting a trend line through the graph gives an average rate of rise of 1.6 mm/year. This is 160 mm  century, much less than most of the climate change projections.








The above chart gives just one rate of sea level rise - the one for the whole period. So what if we look at year-on-year sea level change.

Year-on-year rate of sea level rise

The next chart plots the difference between the value of sea level in the given year and the value in previous year, for each year from 1981 to 2013. So, the first value is the difference between sea level in 1881 and in 1880, and so on. Looking at the chart there is a lot of year-to-year variation, from minus 17 mm/year to plus 21 mm/year. A trend line through the data shows that the rate of sea level rise has increased, by 0.0141 mm/year/year. That means the underlying rate of sea level rise was 1.9 mm/year higher in 2015 than it was in 1880. In other words, the rate of sea level rise is increasing.



Smoothed rate of sea level rise

One way of observing underlying trends more clearly when the data have a lot of year-on-year variation is to use a moving average. This takes the average of the values of the data for a number of years before and after each point plotted. As the data are so variable a long period has been used for averaging, 31 years. The first point plotted is for 1896 and is the average of the sea level from 1881 to 1911, the next is the average from 1882 to 1912 and so on.

This chart confirms that the rate of sea level rise is increasing but not in a uniform way. From a peak of 1.66 mm/year in 1900 to fell to minimum of 0.51 mm/year in 1920. It then rose to another peak of 2.2 mm/year in 1946 before falling to to a minimum of 1.33 mm/year in 1978. The rate of sea level rise then increased again to another peak of 2.84 mm/year 1997.

The trend line on the chart gives a slightly different value for the rate at which the rate of sea level rise in increasing, 0.0117 mm/year/year, to that in the chart above. This is due to effect of the averaging.



Cyclical component to sea level rise

The final chart plots the difference between the rate of sea level rise and the trend line. This is described as the detrended rate of sea level rise. For example, the peak in 1946 was 2.20 mm/year, the value on the trend line for that year was 1.63 mm/year so the value plotted was the difference between them 0.57 mm/year.

This chart shows that the rate of sea level rise has two components. The first is the underlying increase in the rate of sea level rise, this is 0.0141 mm/year/year as seen in the first chart. The second is a cyclical component with an amplitude of plus or minus 0.6 mm/year and a periodicity of around 50 years.





And the orange line? Climate scientists have detected a number of cycles in observed climate data. One of these is called the Atlantic Multidecadal Oscillation (AMO). It is based on sea temperatures in the northern part of the Atlantic Ocean. When the values of this oscillation are plotted along with the detrended rate of sea level rise they show a high degree of synchronicity. It cannot be argued that the AMO causes the variation in the rate of sea level rise. On other hand, it could be argued that both phenomena share an unknown forcing agent.

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SEA LEVELS - PACIFIC ISLANDS

Mon, Oct 19 2015 08:08 | Climate Change, Climate Scenarios, Climate Data

Sea Levels - Pacific Islands

SEA LEVELS - PACIFIC ISLANDS
There is concern that sea level rise might threaten the existence of some small island communities.

Since the early 1990s the Australian Bureau of Meteorology has been running the Pacific Sea Level Project. The continually monitor sea level, air temperature and water temperature among other parameters. Given the motto of this site “Where numbers count” this is something of which we fully approve.

Figure 1 shows the location of the monitoring sites.



Figure 2 shows a schematic layout of a typical station.



In a recent update of our web site
 “www.climatedata.info/impacts/sea-levels/pacific-islands/ 
... we plot the values of sea level for a twelve stations in the network. The data of these stations were summarised by the following figure 3.



Two factors are very evident. Firstly sea levels are rising: a trend line through the average of all stations gives a rate of rise of 5 mm/year. The second very noticeable feature is the way in which sea levels were influenced by the strong El Nino of 1997.

Since I since first set up the web site I have looked at the impact of climate change on rural roads in Vanuatu. This was one of the photos I took – on the island of Ambae. It shows clear signs of coastal erosion with dead tree stumps up to 50 metres out to sea. Such erosion is common on the south coast of that island and the sea was encroaching by about 3 metres every year. However given that the problem is localised to one side of the island the reason is unlikely to due to sea level rise.

The above photo was on the south-east side of the island. This one was on the north coast. Here there is no sign of erosion – indeed vegetation seems to moving close to the sea.


The National Geographic web site recently carried an article with the “a growing body of evidence amassed by New Zealand coastal geomorphologist Paul Kench, of the University of Auckland's School of Environment, and colleagues in Australia and Fiji, who have been studying how reef islands in the Pacific and Indian Oceans respond to rising sea levels. They found that reef islands change shape and move around in response to shifting sediments, and that many of them are growing in size, not shrinking, as sea level inches upward. The implication is that many islands—especially less developed ones with few permanent structures—may cope with rising seas well into the next century.”


Figure 6 show the equivalent of figure 3 but for sea temperature. They are plotted as variation about the mean to show trends more clearly. This plot also shows the influence of the El Nino with a drop in sea temperature. A trend line through the average sea temperature shows an increase of 0.011 C per year.






The average sea temperature for the twelve islands is given in the table below. The range is from 25.4 °C to 30.5 °C.

Island
Mean Sea Temperature - °C
Cook Islands
26.0
Fiji
28.4
Kiribati
29.6
Marshall Islands
28.7
Nauru
28.1
Papua New Guinea
30.5
Solomon Islands
29.4
Samoa
29.1
Tonga
25.4
Tuvalu
29.4
Vanuatu
27.2
Federated States of Micronesia
29.9



Figure 7 is complementary to figure 6 and shows air temperature for the 12 islands and the moving average for the mean of all twelve islands.




This shows that, as expected, islands further from the equator have larger seasonal variation in air temperature. They also show a very low rate of increase in temperature for islands; the annual rate is 0.018 C per year.




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MODELLING THE INFLUENCE OF CLIMATE CHANGE ON TONLE SAP WETLAND

Tue, Oct 13 2015 10:49 | Climate change, Model, Ramsar, Wetland | Climate Change, Climate Scenarios, Climate Data


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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