Climate Change Impact - Part 11 - Turkey

Climate Change Impact

Part 11: Example –Turkey


The Yesilirmak Basin in northern Turkey drains into the Black Sea.  The basin is currently highly developed for hydropower and irrigation. The impact of climate change will be to reduce annual flow. Currently snow melt in late spring provides water at the start of the irrigation season; critically, the biggest reduction in flow will be during this period. To maintain current levels of irrigation will require additional storage. 


The Yeşilirmak basin in northern Turkey has a drainage area of around 36,000 km2 and flows into the Black Sea. The basin is mountainous with parts of the basin reaching elevations in excess of 2,000m. The aim of the study was to develop an understanding of the water balance of the basin and then to examine the potential effects of climate change on the basin. The basin is heavily developed, mainly for irrigation and hydropower.

Figure 1 Yesilirmak River Basin

Current situation

Meteorological data were available from two sources. The first source was supplied locally and covered the period 1961 to 2007. The second was from an internationally supported internet site which provided data from 1931 to 2006. There was considerable overlap in data from the two sources and we were able to create a consistent data set from 1931 to 2007. Annual precipitation reaches 1000mm/annum near the coast and is less than 500 mm/annum inland. Average annual temperature is around 14 °C to the north of basin and less than 10 °C in higher areas inland.

From 1931 to 2007 the data showed little trend in annual precipitation or temperature.

Figure 2 Monthly precipitation - Yesilirmak basin
 For both precipitation and temperature there were differences in seasonal values. Winter precipitation and spring temperature both showed increases.

Figure 3 Seasonal temperature - Yesilirmak basin

Reservoirs in the basin play an important part in flow regulation. The total storage is over 5,000 million m3. This is close to the annual average flow of 5,500 million m3/annum. The calculation of the effect of reservoirs was complicated by three factors:

  •         Little information was available on their operating rules.
  •         There are transfers of water between basins
  •         When a reservoir is newly constructed water is used to fill the reservoirs.

Annual irrigation use is 500 million m3/annum and is, of course, concentrated in the summer months when flows are at their lowest.

Climate change

The basin was modelled in two ways. First a water resources model was developed which simulated as far as possible the progressive changes in water use and reservoir storage over the period with data. This model was used to estimate the natural flow in the basin. Secondly the monthly rainfall/runoff model HYSIMM was used to simulate the flows in the basin.

The climate projections were based on the IPCC task force on Data and Scenario Support 2007 report “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”. The SresA1B scenario, which assumes rapid growth and a balanced use of energy sources, was used

As an initial appraisal of the potential impacts of climate change, the simulated flow for the period 1980 to 1999 was compared with simulated flow for projected values for the period 2080 to 2099, i.e. 100 years in the future relative to observed.

The following chart shows three lines. The blue line is the observed average monthly flow – or, more accurately, the estimated natural flows after accounting for storage and abstractions. The red line shows the flow simulated by the hydrological model. The third line, in green shows the projected monthly flow after the impact of climate change. The impact of climate change is represented by the difference between the red and green lines.

Figure 4 Monthly flows - with and without climate change

This shows that two importance changes are projected:

  •         Overall flows will be lower,
  •         Currently the peak of flow is in late spring which coincides with the start of the growing season. In effect, snow in the mountains is acting as a reservoir. In the future, the peak flow will be in winter.

The conclusion is that to maintain current levels of agriculture additional reservoir storage will be needed.


Climate Change Impact - Part 8 - Samoa

Climate Change Impact

Part 8: Example – Samoa


Some of the roads on Samoa had been damaged in recent storms and the objective of the project was to prepare the rehabilitation taking account of climate change. Climate data, including rainfall at a 10-inute time step for two stations were obtained. The data showed that there was a significant increase in rainfall with elevation (which might explain why the most severe damage to the roads was at highest elevations). A methodology was developed to estimate the storm intensity for a range of durations and return period taking account of climate change.


Samoa consists of two main islands shown on the following map. Both islands have a road network. On Upolo there are roads around and across the island. On Savai’I the roads run around the island.
There is a third island, to the east of and smaller than these two, which is a US territory.

Figure 1 Map of Samoa

The main aim of the project was to upgrade some of the roads on the two Islands taking account of climate change. In particular one of the cross-island roads on Upolu had been damaged during a storm and it considered that its reconstruction should not suffer from the same problem.

As the roads around the islands are often close to sea level, the possibility of sea level rise also had to be considered.

Current climate

There are three types of data available in digital format:

  •         10-minute data from 2010 to 2015 for two stations, Nafanua and Afiamalu.
  •         Daily data from 1984 to 2014 for two stations, Faleolo and Apia.
  •         Monthly data from the early 1980s and in some cases earlier for four stations on Savai’i and one on Upolu.

These data were measured by the Samoa Meteorology Division (SAMET). Other data were abstracted from reports. Additional data on climate and flow were also obtained for US Samoa.

The weather of Samoa is influenced by four main factors:

  •         The sub-tropical high-pressure zone in the Eastern Pacific is a large semi-permanent anticyclone.
  •         Trade winds which blow from between east and south-east which contributes to a rain shadow effect to the north and west of the islands.
  •         The South Pacific Convergence Zone whose position helps to determine the seasonal pattern of the rain in which rain from November to March is above the monthly average.
  •         The Southern Oscillation which when in positive mode leads to increased rainfall.

The rain shadow effect is illustrated by Figure 2 which shows isohyets (contours) of the mean annual rainfall. Areas to the north and west of both of the main islands have less rain than areas to the south and east. The map also shows the effect of elevation on rainfall, with higher rainfall being associated with higher elevations.

Figure 2 Mean annual rainfall Samoa

Climate change projections

  1. The only daily record available for Samoa, of good quality and a long duration, is for Apia. This record was used to estimate the daily rainfall of a given frequency of occurrence.
  2. Two records of rainfall measured at 10-minute intervals are available for a period of up to 6-years, to the south of Apia. The rainfall stations are at different elevations and the one at the higher elevation records more rain than the other. However, when the rainfall at a short duration (from 10 minutes to a few hours) is expressed as a proportion of the daily value, the results are almost identical for both stations. Combining the two records, enables a single curve relating rainfall at a short duration to be calculated, as a proportion of the daily rainfall.
  3. The 10-minute rainfall records are at different elevations (796 m and 128 m) and have different daily storm rainfall (331 mm/day and 206 mm day). This implies that storm rainfall is higher at higher elevations. This is potentially an important conclusion but the 10-minute rainfall records are of short duration. These two records were combined with daily data from Apia, and charts and tables from earlier reports covering both islands, to arrive at a justifiable value for this effect.
  4. The data presented in some earlier reports implied that aspect is an important factor in storm rainfall, with storms on a south-facing slopes having twice the rainfall of slopes on the north or east. It was concluded that the limited data available do not allow an accurate value to be ascribed to this effect.
  5. The relationship between monthly and hourly rainfall was examined. The correlation for the two stations was weak at one station and non-existent at the other.
  6. Two methods are used to calculated the flow resulting from the storm rainfall: The Rational Method for small catchments and the Generalised Tropical Flood Model for larger catchments. These were complemented by the use of a hydrological model of American Samoa.
  7. Climate projections were based on 4 climate models: CSIRO, GFDL, HadGem and MIROC. These had been found to perform well in the region.
  8. Projections were provided for 3 time-horizons: 2030, 2055 and 2090.

 The 2055 projection this represents the highest intensities in this century. And was used for drainage design.
For daily values, this represents an increase of 17% on the current daily rainfall figure and for the standard deviation an increase of 7%. Both the daily values and the standard deviation are used to calculate the rainfall intensities for different frequencies of occurrence.
It was mentioned above that flow and climate data were used for a stream on US Samoa. This is very small catchment, 1.52 km2. (It is interesting to note that in another posting in this series that same model was able to successfully simulate flows in the Mekong River at a point where its drainage area was 660,000 km2.) The HYSIM rainfall/runoff model was run at an hourly time step, though the raingauge was outside the catchment. Figure 3 shows the simulated and observed daily flow.

Figure 3 Simulated and observed flow - Pago Stream - US Samoa

The rainfall and flow data were analysed to estimate an appropriate runoff coefficient. It was found that the coefficient increased for storms of higher return periods and was higher for 2-hour storms that 1-hour storms.
Sea level data were also analysed and it was found that in recent decades sea levels had been increasing by 5 mm a year. This is comparable with the projected values.


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

Climate Change Impact

Part 4: Example – River Mekong


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.


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).


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


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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