Climate Change Impact - Part 12 - Kagera Basin (Rwanda, Burundi, Uganda and Tanzania)

Climate Change Impact


Part 12: Example – Kagera Basin (Rwanda, Burundi, Uganda and Tanzania)


Summary

The Kagera basin flows into Lake Victoria and as such it forms part of the Nile Basin. An extensive data base of climate and flows was available and was used to calibrate the HYSIM hydrological model to 22 sub-basins. Climate projections show that rainfall is projected to increase but temperature (and hence evapotranspiration) is also expected to increase. Whilst the two changes to some extent balance out, the increase in temperature still has important implications for the future of agriculture.

Introduction

The Kagera River Basin and its tributaries flow within four countries (Rwanda, Burundi, Uganda and Tanzania). The Kagera River flows into Lake Victoria which in turn forms part of the Nile River Basin. The aim of the study was to assess the water resources potential of the basin and also to estimate the potential effect of climate change.

Figure 1 is a map of the river basin with the sub-basins used for hydrological modelling delineated.


Figure 1 Kagera River basin showing sub-basins for hydrological modelling



Current situation

The project team was provided with a climate and hydrometric data base developed in the Lake Victoria Environmental Management Program, Phase I. This included river flow, precipitation, temperature and other variables such as wind speed, sunshine and relative humidity needed to calculate potential evapotranspiration. Data were available up to the year 2000.

The methodology was based on use of a hydrological model, HYSIM (in its monthly variant). A hydrological model requires continuous input data so gaps in the data had to be infilled by reference to nearby stations with data.

Temporal infilling is standard option from the HYSIM program.  The program does this as follows:

  •         Reads the data into a monthly array of data from all stations.  If there are more than a set number of days with data, then the total for those days is adjusted pro-rata upwards to give the month's total (in the case of precipitation) or monthly mean, in the case of other variables.
  •         Calculates a matrix of totals/means for concurrent periods for all pairs of stations.
  •         Uses the above totals to calculate the ratio of the totals/mean for all pairs of stations.
  •         Uses these ratios to estimate the errors in the relationship between all possible pairs of stations.
  •         Infills the monthly data using whichever station with data gives the lowest error and which has not itself been infilled.

Even when all stations with data had been infilled there were still large parts of the basin without measurements. To complement the observed values, data on a 10’ geographical grid from the Climatic Research Units of the University of East Anglia were also used.

The data were used to calculate potential evapotranspiration and precipitation on a 10’ grid. The following figure shows contours of rainfall minus potential evapotranspiration (PET). As can be seen the western parts of the basin are in surplus but those to the east are in deficit.


Figure 2 Precipitation minus potential evapotranspiration

A similar process was used for other climatic parameters.

The data base of rainfall and precipitation were used with the HYSIM hydrological model to simulate river flows in the basin. Flows were simulated for each of the 22 sub-catchments that are shown on figure 1.

The following figure shows the simulated and observed flows for the Rivubu at Gitega.

Figure 3 Simulated and observed flows - River Rivubu at Gitega

For most of the time the simulation is good however the accuracy drops off markedly after the early 1990s. Whilst the data base is good up to the end of the 1980s the number of climate stations becomes much fewer for the later years and this can be considered to explain the change in accuracy.

Climate change

The A1B scenario has a balanced emphasis on all energy sources. It is generally considered to be the projection of temperature that might occur in the absence of any international agreed and binding protocol to reduce carbon emissions. This scenario was therefore used.

The choice of models was based on those listed on Table 6 of the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”, Version 2, June 2007.  Seven models were listed. The projections were based on the average of those models.

The following figure shows the current average annual temperature in the basin and the projected temperature for two different time horizons 2020 to 2049 and 2070 to 2099.

Figure 4 - Observed and projected basin temperature fro two time horizons

For the period 1970 to 1999 the average basin precipitation was 1570 mm/year. For the period 2020 to 2049 it is projected to be 1644 mm/year and the period 2070 to 2099 is projected to be 1740 mm/year.

The overall conclusion was that the increase in precipitation would counter-balance the increased potential evapotranspiration at a basin level but for individual sub-catchments the picture was more complex. Even though the overall water balance might be the same the higher temperature had implication for the types of crops which could be grown and the amount of irrigation required.





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Climate Change Impact - Part 11 - Turkey

Climate Change Impact


Part 11: Example –Turkey


Summary

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. 

Introduction

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.


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Climate Change Impact - Part 10 - Zambia

Climate Change Impact


Part 10: Example –  Zambia


Summary

Zambia has a climate typical of Southern Africa with cool dry winters (June to August) with hot wet summers. The potential impact of climate change was studied as part of a project enhancing the country's skills in Integrated Water Resources Management (IWRM). Temperature are expected to rise throughout the year and over the whole country with slightly higher increases in the south-east. The changes in precipitation are less consistent with some months projected to have an increase and others to have a reduction.

Introduction

The Government of Zambia was fully aware of the principles of Integrated Water Resources Management.  The need for climate change to integrated in water resources planning was recognised in the National Water Plan of 1994. The Water Resources Management Act of 2011 took this a stage further. This act sought to create a National Water Authority. Section 8 of the Act on ‘Functions of the Authority’ said its functions should include:

  •         minimising the effects of climate change
  •         support proactive climate change planning and management
  •         in consultation with the institution responsible for national statistics, establish and maintain an information system, which will be accessible by both gender, in accordance with regulations issued by the Minister providing for the content of the system, which shall include relevant hydrological, hydrogeological, meteorological, climatological, water quality, water storage and supply and use data, and relevant information on potentials for the use of water
  •         publish forecasts, projections and information on water resources

The aim of this project was to assist the Government in the integration of climate change into Integrated Water Resources Management.

The first, and at the time of the project only, National Communication on Climate Change prepared under the auspices of the United Nations Framework Convention on Climate Change was produced by the Ministry of Tourism and Natural Resources (MoTNR) in 2002. The report considered mitigation options. Under the heading ‘Vulnerability and Adaptation Assessment” it was estimated that maize production might fall but sorghum could increase and groundnuts remain steady. There was no clear indication of the effect on livestock. In terms of water resources, it was suggested that southern parts of the country might be particularly vulnerable.

A report on the National Adaptation Programme of Action on Climate Change was produced by the MoTNR. Zambia had experienced a number of climate related hazards over several decades. Using multi-criteria analysis, it had identified most urgent needs to prioritize ten immediate adaptation interventions. Zambia was divided into 3 ecological-climatic regions based on rainfall. The wettest regions were toward the north of the country. According to the report, the projections suggested that the wettest region would have an increase in rainfall but the drier regions would have less rainfall. The driest region is projected to produce less agricultural produce and livestock. Wildlife could be heavily stressed due to reduced rainfall and increased migration. Malaria is likely to increase in areas with increasing rainfall.

Current Climate

The average annual temperatures for four stations are shown on figure 1. All four stations show a similar trend: a maximum around 1930, a general fall until about 1975 and then an increase to a new maximum around 2005. Temperatures during the 5 years 1927 to 1931 were about 0.5 °C higher than temperatures from 2001 to 2004.


Figure 1 Average annual temperature in Zambia - four representative stations


Temperatures are lowest in June and July. In terms of geographical distribution of temperatures, they are highest in the south-east and the north though the variation is not great – most of the is in the range 22°C to 24°C.

Figure 2 shows the seasonal distribution of precipitation. It shows that rainfall is highly seasonal with very little rain in the period June to August.


Figure 2 Average monthly precipitation - three representative stations

There is considerable variation in rainfall from year to year. The wettest station showed a slight increasing trend and the driest station showed a slight decreasing trend. The geographical distribution of rainfall showed it as being higher to the north of the country.

Climate change projections

Climate change projections were based on the A1B scenario. This is considered to be the ‘business-as-usual’ scenario. The projection used was the average of 23 climate models used to inform the IPCC Assessment Report.
Temperatures are expected to increase by from 3.2°C to 3.9°C by the end of the century. Figure 3 shows the geographical distribution of the temperature changes.


Figure 3 Geographical distribution of climate change


In the case of temperature, the increases are fairly uniform throughout the year. In the case of precipitation there is a marked difference in the changes at different times of the year.


Figure 4 Projected change in precipitation

This shows that rainfall will decrease in the currently driest periods of the year and will increase most in the wettest periods.

Conclusions

Temperatures are projected to rise throughout the year and over the whole country with slightly higher increases in the south-east. In the case of precipitation there are seasonal variations in the changes. In January, there are increases in precipitation over the whole country but larger increases in the north. In November, precipitation is expected to fall over the whole country with larger falls in the south.  December could be considered a ‘pivot’ month with increases in the North and reductions in the South.





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Climate Change Impact - Part 9 - Kyrgyzstan

Climate Change Impact


Part 9: Example –Kyrgyzstan


Summary

Kyrgyzstan has a continental climate with cold winters and hot summers. Most of the rain falls in the summer months and temperatures are below freezing for most of the winter months. It is projected that storm rainfall will increase by up to 20% and that the duration of lying snow will decrease.

Introduction

Kyrgyzstan is in central Asia and has severe winters with temperature below zero for many months, particularly in mountainous areas.



Figure 1 Map of Kyrgyzstan showing project road

Climate change can affect roads in many ways. The most obvious is storm rainfall; an increase in storm rainfall could require modification to current design for culverts and longitudinal drains. Other factors include daily temperature range, which could affect expansion joints, and maximum temperature, which could affect the choice of binding agent.

Once current values of these parameters have been determined then the extent to which they will change in the future can be assessed.

The observed climate data were downloaded from internet sites which process data facilitated by international organisations such as the World Meteorological Organisation.

Other documents related to climate change for Kyrgyzstan were downloaded. These included:

  •         UNFCCC Country Brief 2014: Kyrgyzstan
  •         Climate Profile of the Kyrgyz Republic
  •         The Kyrgyz Republic: Intended Nationally Determined Contribution
  •         The Kyrgyz Republic’s Second National Communication to the United Nations Framework Convention on Climate Change

These documents describe the potential change to the climate in general terms but were not specific enough for road design.

Current Climate

Kyrgyzstan has a continental climate with warm summer and cold winters.

 The following chart shows the location of the climate stations which were used to determine current climate parameters. Only stations in the area of the project road are shown. The data were at a daily time step and included: precipitation, depth of snow, average daily temperature, daily maximum temperature and daily minimum temperature. Data were downloaded for the period 1950 to the present.

Figure 2 - Location of sites with climate data


Figure 2 shows the location of sites with climate data. Sites with a solid diamond have precipitation, temperature and snow data. Sites with an open diamond only have precipitation data.

The chart also shows the area covered by the nearest climate model cell as an orange rectangle. It is convenient that the section of road of interest corresponds to one of the climate cells.

Rainfall is highest in the summer months. In winter, average monthly temperatures are often below zero. The road itself passes between two areas with higher elevations and maximum daily rainfall is lower than areas with higher land – around 20 mm per day.

Climate projections

To examine the performance of climate models, their simulation relative to past observed climate for the period 1970 to 1990 was examined. The four models chosen on this basis were:

  •         bcc-csm1-1: Beijing Climate Center Climate System Model (China)
  •         IPSL-CM5A-MR: The Institut Pierre Simon Laplace (France)
  •         CCSM4: The Community Climate System Model Version 4 (USA)
  •         NorESM1-M: Norwegian Earth System Model (Norway)


The conclusions relating to climate change and daily maximum rainfall were:

  •   The average percentage increase for the final 50 years of the present century is 7.3% for the RCP 8.5 projections and 5.5% for the RCP 6.0 projections.
  • The projections of 5-day rainfall, more relevant for rivers which are crossed by the road showed a similar increase which could be translated in into increased flooding.
  • Winter and summer temperatures have been rising in recent years. The rate of increase has been 3.3 °C per century, slightly lower than the projected 5.5 °C per century. This significance of these increases relate to icing in winter and the heat-resistance of the road surface in summer. There is no indication that the diurnal temperature range (important for expansion joints) will increase.
  • On average, the depth of snow reaches 500 mm or even more in an average year.
·         When a range of models was ranked on the projected increase between the present and the year 2100, the difference in projection between the upper and lower quartile was quite modest; of the order of 15%. This applied for both RCP 6.0 and RCP 8.5.

·         When four selected models were compared their difference in projected values for the year 2100 was larger.

·         In most cases the largest percentage increase in daily rainfall for any projection occurred not in the final year of this century but in an earlier year. At any time in the current century the increase in precipitation rarely exceeded 20%, apart from the few models with the highest rate of increase in precipitation. A further consideration following from this is that the maximum increase in rainfall could occur during the projected life of the road.


There are no specific projections related to snow depth. Figure 3 shows two alternative metrics. The first is ‘icing days’; these are days when the daily maximum temperature is below zero. The second is ‘frost days’; these are days when the daily minimum temperature is below zero. The decline in these two variables indicates two things. Firstly, that snow fall will be less frequent and secondly that it will lie for a shorter time.


Figure 3 Days with temperature below freezing for all or part of a day

Conclusions

The main conclusions are that there will be an increase in storm rainfall of up to 20% during the life of the road. The period with lying snow will reduce.








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Climate Change Impact - Part 8 - Samoa

Climate Change Impact


Part 8: Example – Samoa


Summary

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.

Introduction

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.








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Climate Change Impact - Part 7 - Vanuatu

Climate Change Impact


Part 7: Example – Vanuatu


Summary

The project examined projected changes to road flooding for four islands of the Vanuatu archipelago. The conclusion was that rainfall intensities would increase for all islands, particularly at the lower durations and return periods critical for road drainage.

Introduction

This example looks at the estimation of road flooding in Vanuatu. The islands of Vanuatu stretch from 13°S to 20°S and 116.e°E to 170.25°E. They lie about 2000 km from the coast of Australia.

The following chart shows the layout of the islands of Vanuatu. The four islands highlighted in green, Ambae, Pentecost, Malekula and Tanna, were included in the study. The red crosses mark the location of climate measurement sites. The roads on the islands are being upgraded and for this it was necessary to ensure that road drainage would be effective for the whole life of the road taking into account projected climate change.

Figure 1 Vanuatu showing the site of climate stations


Flooding on the islands can occur very suddenly and result in flash flooding with rapid increase in flow depth. The following photograph was taken the day following a storm.  The stream itself can been to the left of photograph. The highlighted area, near the tree, shows floating material trapped in the branches showing the depth of flooding. This indicates the degree of flood problems to tackled.


Figure 2 Material trapped in branches during a flood.


Current Climate

Daily observed climate data were obtained from 3 sources:
  •  The Vanuatu Meteorological and Geohazards Department (MGHD)
  •  The National Climate Data Center (NCDC) which is part of the National Oceanographic and Atmospheric Administration in the USA
  • TuTiempo web site


Data from the Meteorological Office was for Bauerfield, Efate and three stations on Tanna. All stations had daily precipitation and Bauerfield included temperature and wind speed. Data from NCDC was for Pekoa, Spiritu Santo, for daily precipitation and temperature. The data from TuTiempo were available for 6 sites and included precipitation, temperature, wind speed and relative humidity.

Where data were available from different sources for overlapping periods, their values were compared and were found to be compatible.

Data were also obtained on storm rainfall profiles. For road drainage, the critical time of a storm is often of the order of a few minutes so daily data on its own is not sufficient.

Climate projections

Projections were provided from PACCSAP (Pacific-Australia Climate Change Science and Adaptation Planning) and included precipitation projections in NetCDF (Network Common Data Format) for the whole world at the grid spacing of the original models. These are:

  •         ACCESS1-3, 1.875° longitude, 1.25° latitude (220km x 147km)
  •         CNRM-CM5, 1.4° longitude, 1.4° latitude (165km x 165 km)
  •         GISS-E2-R, 2.5° longitude, 2.0° latitude (294km x 235 km)


A further data set of projections was provided for which all the files had RX1Day in their title. They had been produced from the 50-km downscaling CCAM normal-cubic atmospheric model, a stretched-grid atmospheric model. They followed the guidelines of “Expert Team on Climate Change Detection and Indices (ETCCDI)”. These had projections of daily rainfall for three RCPs (RCP2.6, RCP4.5 and RCP8.5) and for 4 time-horizons (2030, 2050, 2070 and 2090).

Downscaling was carried out using the Delta Method.

The baseline period was 1987 to 2013 which had observed data. For consistency, the 2030 projection was based on the same number of years, 2027 to 2043. The 2055 projections used values from 2042 to 2068.

The temperature projections were consistent with different models showing similar increases. For precipitation, the projections were less consistent with higher values of RCP leading to higher rainfall for some models and lower rainfall for others.

The conclusions for the four islands were:

  • Ambae: Rainfall intensity is likely to increase for 2030 by about 15% for the 1-in-2-year storm up to about 30% for higher return periods. The additional increase for 2055 relative to 2030 is about 4%.
  • Pentecost: As with Ambae, the percentage increase is less for small return periods, 20% for 1-in-2, but up to 32% for 1-in-100. The 2055 projection is almost identical to the 2030 projection.
  • Malekula: The projections suggest storm rainfall intensity will increase from the baseline to the 2030 time horizon but after that will remain more or less constant. It is also noticeable that increases in storm intensity for low return periods are small, 18%, but increase for longer return periods, about 33%. The 2055 projection is slightly lower than the 2030 projection.
  • Tanna: The projections for this island were lower than those for the other islands.




Figure 3 Daily rainfall intensity for different  islands and return periods

The above chart shows the daily rainfall intensity for different return period on each island for 2055.
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Climate Change Impact - Part 5 - Great Lakes of Africa (Lakes Victoria,Tanganyika and Malawi)

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
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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.

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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)

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

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




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

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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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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SEA LEVELS

SEA LEVELS

Sea levels have been rising since the maximum of the last ice 20,000 years ago. The rate of sea level rise is regarded as an indicator of climate change. The change in sea levels is driven by two factors: the thermal expansion of the sea water as it warms and the melting of ice over land.

Long Term Sea Level Change

During an ice age, ice covers are large areas around both poles. The amount of water in the ice caps is such that sea levels are markedly reduced. Levels 20,000 years ago, at the maximum of the last ice age, were 140 m lower than they are today. Until about 7,000 years ago the rate of rise was about 100 mm/decade. Since then rate of rise has averaged 10 mm/decade.

Estimation of Sea Level Change

Global sea levels have traditionally been estimated from tide gauges. As can be imagined they show fluctuations of several meters due to tide and wave action. Identifying sea level changes of a few millimetres a year against this background “noise” is problematic. Since 1993, data are available from satellites. There are two other factors which add to the difficulty of estimate changes in sea level. The first is the way the earth has reacted to the melting of the ice caps. Where major ice melt has taken place, in northern Europe and North America for example, land levels have risen; the post glacial rebound (PGR). Conversely where sea levels have risen and encroached on previously dry areas, land levels have fallen under the increased weight of the oceans; glacial isostatic adjustment (GIA). (Some sources use the two terms interchangeably) These changes typically average around 4 mm/decade but can be higher in some locations. The second factor is the influence of atmospheric pressure. The changes in pressure can be seasonal and modify levels by 1 metre; often an allowance is made for these pressure difference by applying what is called “an inverted barometer.” As can be seen the adjustments to be made to sea level are of a similar order of magnitude to change in sea level itself. It is generally considered that the rate of change of sea level cannot be accurately estimated for periods of less than 10 years.

Sea Level Change

Figure 1 shows the sea level changes from 1807 to 2001 using two estimates based on tide gauges (Jevrejeva et all and Church at al). There is broad agreement between the two estimates. The Jevrejeva record show that sea levels fell for the first half of the 19th century. This suggests that the low temperatures recorded in Europe in this period may have been representative of global temperatures. It also follows the Dalton minimum of sunspot activity.

Figure 1



Figure 2 shows a composite record from the two tide gauge estimates and satellite based data from the TOPEX/JASON satellite system. To harmonise the two data sets the satellite data were adjusted to give the same average for the period of overlap. The graph also shows the rate of rise per decade. This was based on subtracting the difference in level for pairs of months 10 years apart. Over the last century or so the rate of rise has fluctuated from -20 mm/decade up to 40 mm/decade. The increase since 1880 has been around 250 mm.

Figure 2

Whilst at first sight the rise in sea level seems almost constant looking at the estimates of the rate of sea level rise shows that this does fluctuate. To clarify this the following graph, figure 3, shows the rate of rise in twent-year periods.

Figure 3

This appears to suggests that there is an underlying increase in the rate of sea level rise of about 0.015 mm/year/year and a fluctuation about this trend of ± 1 mm year.
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