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 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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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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