33
i Relative Sea Level Rise Scenarios Cauvery delta zone, Tamil Nadu, India Rev.3 Ali Dastgheib (PhD, MSc) Roshanka Ranasinghe (PhD) May 2014
i
Relative Sea Level Rise Scenarios
Cauvery delta zone, Tamil Nadu, India Rev.3
Ali Dastgheib (PhD, MSc) Roshanka Ranasinghe (PhD)
May 2014
ii
Table on Content Table on Content ......................................................................................................................................ii
Executive Summary ................................................................................................................................. iii
1. Introduction ..................................................................................................................................... 1
2. Study Area ....................................................................................................................................... 2
3. Current trends in local relative sea level rise (RSLR) at CDZ ........................................................... 2
3.1. Data Collection ........................................................................................................................ 2
3.2. Current trends based on the available tide gauge data .......................................................... 4
3.3. Relative sea level rise rates from NOAA .................................................................................. 6
3.4. Global Average trend of SLR .................................................................................................... 7
3.5. Discussion ................................................................................................................................ 7
4. Scenarios for local relative sea level rise by 2100 ........................................................................... 8
4.1. Background .............................................................................................................................. 8
4.2. RSLR Scenario based on extrapolating measured trends ........................................................ 9
4.3. RSLR Scenarios based on the Special Report on Emissions Scenarios (SRES) Climate change
scenarios ............................................................................................................................................ 10
4.3.1. SRES Climate change scenarios ..................................................................................... 10
4.3.2. Global-mean sea-level change for SRES Climate change scenarios .............................. 11
4.3.3. Regional (local) spatial variations in sea-level change .................................................. 13
4.3.4. RSLS Scenarios based on SRES Climate change scenarios ............................................. 15
4.4. Scenarios for local RSLR until year 2100 at CDZ .................................................................... 16
5. Estimating the sea level change associated with extreme events ................................................ 17
5.1. Available data ........................................................................................................................ 17
5.2. Calculation method ............................................................................................................... 17
5.3. Extreme value analysis (EVA) of storm surge data ................................................................ 19
6. Recommendations on using the presented relative sea level projections within the CASDP ...... 21
7. References ..................................................................................................................................... 24
iii
Executive Summary This report documents the data, methodology, and the outcome of the analyses undertaken for
developing sea level scenarios based on different climate change (CC) scenarios and provides a set of
scenarios for sea level for the Cauvery delta of Tamil Nadu, India. The document consist of four main
sections : i) Determining the current trends in local relative sea level rise (Baseline); ii) Developing the
scenarios for local relative sea level rise by the year 2100; iii) Determination of storm surge statistics;
and iv) recommendations on using the presented relative sea level rise projections within the CASDP.
Sea level projections for the Cauvery Delta Zone (CDZ)
The current trend of relative sea level rise (RSLR) was determined by analyzing available tide gauge
data in the study area. Tide gauge data for Cochin, Chennai and Vishakapatnam obtained from the
PSMSL were used in this study. The RSLR rates estimated by the National Oceanic and Atmospheric
Administration of USA (NOAA) for the same locations were also considered. Based on this analysis
the most appropriate rate of local relative sea level rise for the Cauvery delta was considered to be
the same as the closest PSMSL station, CHENNAI: 0.29 ± 0.56 mm/year (Figure E1). The extrapolation
of this rate of RSLR provides one RSLR scenario for the future.
Figure E1. Monthly and annual mean seal level and corresponding trends at CHENNAI
Next, the guidelines provided by NICHOLLS et al. (2011) were used to derive RSLR scenarios linked to
six (6) IPCC SRES scenarios (B1, B2, A1B, A1T, A2 and A1FL). The 'intermediate' assessment
methodology suggested by NICHOLLS et al. (2011) was adopted herein. In this approach, RSLR
projections for a specific location take into account the different contributions from the components
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
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iv
at the global, regional and local scales, as relevant to the study area. These components are then
integrated using following equation:
∆RSL = ∆SLG + ∆SLRM + ∆SLRG + ∆SLVLM
Where :
ΔRSL is the change in relative sea level
ΔSLG is the change in global mean sea level
ΔSLRM is the regional variation in sea level from the global mean due to meteo-oceanographic
factors
ΔSLRG is the regional variation in sea level due to changes in the earth’s gravitational field
ΔSLVLM is the change in sea level due to vertical land movement
Note that in these calculations 0.2m was added to the upper range values of the IPCC global average
SLR values to account for the uncertainty in climate-carbon cycle feedback and ice sheet flow, as
suggested by IPCC (2007). The ranges of RSLR thus calculated for the six SRES scenarios are shown in
Figure E2.
The same information plus the scenario developed based on the rate of RSLR from observed data are
tabulated in Table E1 for 4 different points in time. The highest and lowest values of RSLR by 2100
relative to 1990 projected for the study area are 0.87m and -0.03m respectively.
Table E1. The ranges of RSLR (m) at CDZ calculated for the six SRES scenarios at different times
CC Scenarios
B1 B2 A1B A1T A2 A1Fl Extrapolated
historical
data
2025
lower 0.07 0.06 0.07 0.08 0.07 0.08 -0.01
upper 0.19 0.18 0.20 0.21 0.16 0.17 0.03
2050
lower 0.13 0.10 0.12 0.15 0.13 0.14 -0.02
upper 0.33 0.33 0.36 0.37 0.32 0.35 0.05
2075
lower 0.18 0.15 0.17 0.21 0.18 0.21 -0.02
upper 0.48 0.50 0.54 0.53 0.53 0.58 0.07
2100
lower 0.24 0.18 0.21 0.27 0.24 0.29 -0.03
upper 0.63 0.69 0.74 0.70 0.78 0.87 0.09
v
Figure E2. The ranges of RSLR at CDZ calculated for the six SRES scenarios.
Recommendations
The CASDP intends to implement improved drainage facilities, flood control measures and land use
strategies (among others) to mitigate the potential impacts of climate change in the Cauvery Delta
Zone (CDZ). In designing these interventions, the precautionary principle would advocate the use of
the highest projected sea level rise value (including the 1:100 yr storm surge) of 1.61m obtained in all
designs/strategies. However, this may, on occasion come at a significant and unwarranted cost and
thus be not the most cost-effective approach. Therefore, the following simple approach is
recommended to ensure a balance between safety and cost effectiveness.
Before deciding on a higher or lower RSLR scenario to work with, stakeholders need to make a
decision to take on board climate change projections at all. Therefore, the cost of not incorporating
any climate change projections needs to be estimated, in most cases, to inform this decision.
Assuming that this leads to a decision to indeed consider RSLR, at the design stage of all
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interventions, two separate designs for the highest and mid-level projected sea level scenarios (RSLR
plus storm surge) relevant for the planning horizon of the interventions may be developed. For
example, if the design lifetime of a planned flood protection measure is until 2100, then develop two
separate designs to accommodate RSLR values of 0.87m and 0.29m, plus at least the 1 in 100 yr
storm surge estimate (0.74 m) (i.e. sea level scenarios of 1.61m and 1.03m by 2100). In this example
both the highest and mid-level RSLR values are associated with the A1Fl projections, but this need
not always be the case. Then evaluate the costs associated with both design options as well as the
costs of damage if either design were to fail by overtopping. The way in which damage costs are
estimated differ widely from country to country and local governance unit to local governance unit,
but would ideally account for not only property (or economic) damage but also environmental, and
societal damage and loss of life, or any combination thereof. Say, for example:
Cost of highest sea level design = C1
Cost of mid-level sea level design = C2
Cost of damage due to failure of highest sea level design = D1
Cost of damage due to failure of mid-level sea level design = D2
Then (D1-D2) is the value of additional protection gained by investing (C1-C2). If (D1-D2) is
significantly greater than (C1-C2) then the decision should be to adopt the higher sea level design.
However, if this is not the case the lower sea level design maybe taken under consideration.
Nevertheless, the choice to adopt the less safe option is not only a matter for managers and planners
but one for multi-stakeholder (including politicians and special interest groups) deliberation.
Flood risk maybe more elegantly optimized by balancing the cost of the intervention versus the
potential damage using advanced probabilistic calculations such as those used in flood management
spheres. In the presence of efficient risk-sharing arrangements, investments in risk mitigation can be
evaluated through net present value computations with the cost of risk mitigation on the one hand,
and expected loss (or: the actuarially fair insurance premium) on the other. This approach has been
used in the Netherlands since the 1950s to inform decisions about flood protection (Figure E3).
vii
Figure E3. The optimization of flood protection: total cost equals the cost of dike heightening plus the present
value of expected loss (assuming stationary conditions: no sea level rise, economic growth or degradation).
However, such an approach requires estimates of the exceedance probability of RSLR (which the IPCC
do not provide at present) and detailed spatial information on the damage function. While a
Gaussian distribution maybe assumed for the probability of occurrence for values lying between the
lower and higher RSLR projections per SRES scenario, the availability of sufficiently detailed spatial
data is often the stumbling block when attempting this type of risk optimization approach. However,
if such data can be provided for the CDZ, it is possible to optimize flood risk due to future RSLR (as
part of a separate project).
A further point to note is coastal recession due to RSLR. When the mean sea level rises the coastline
position will move landward due to erosion. A rule of thumb for such RSLR driven recession dictates a
recession of between 50-100 times the RSLR over a 100yr period. Thus, it is also crucial that any
interventions take this recession effect also into account. For example, major new developments or
infrastructure should not be placed within this recession zone. However, the same principle
discussed above for flooding also applies for coastal recession: while the potential damage due to
coastal recession can be large, so can be the forgone land use/commercial opportunities in the
coastal zone. In recent years numerical modeling methods have been developed to assess the coastal
erosion risk and determine optimal coastal setback lines (a setback line is defined as the position
seaward of which developments should be restricted or prohibited). An example application of these
models to a site in Australia is shown below in Figure E4 .
Cost (present value)
Cost of dike
heightening
Present value of expected loss
Total cost
Dike height Optimal height
viii
Figure E4. Coastal erosion risk due to sea level rise by 2100 at Narrabeen beach, Sydney, Australia. The
economically optimal setback line is shown in black while presently adopted setback line is shown in blue.
If sufficiently detailed spatial data can be provided, it is possible (as part of a separate project) to
produce similar output for the CDZ which would be of critical value for determining the appropriate
placement of CASDP intervention measures and/or developing land use policies for the future.
1
1. Introduction In June 2012, under the Asian Development Bank (ADB) partnership with UNESCO-IHE, UNESCO-IHE
was invited to join the group of consultants in the project of Climate Adaptation through Sub-Basin
Development program (CASDP). This project is initiated by The South Asia Dept. of The Asian
Development Bank, which aims to enhance the resilience to climate change in the Cauvery delta of
Tamil Nadu, India. The main goals of the project are : i) to develop integrated programs and
infrastructure for management of ground water, surface water and salinity; ii) to establish and
strengthen the institutions and systems for sub-basin IWRM; and, iii) to implement sustainable
agriculture systems to support high water use efficiency, productivity and incomes of farmers. In
order to fulfill the main goal of this project and support the hydrological analyses needed for this
project, it is necessary to provide robust information on current and future state of climate and sea
level. Also, to integrate climate change and sea level risk management into the design of sub basin
development and investment projects, the level of uncertainties associated with climate change and
sea level rise should be adequately characterized.
As one of the critical inputs to climate risk assessment and adaptation planning, hydrologic,
hydrodynamic and coastal processes modeling, UNESCO-IHE is asked to provide consulting services
and develop a set of sea level rise scenarios.
This report documents the data, methodology, and the outcome of the analyses undertaken for
developing sea level rise scenarios based on different climate change (CC) scenarios and provides a
set of scenarios for sea level rise for Cauvery delta of Tamil Nadu, India. This document consists of
three main sections : i) Determining the current trends in local relative sea level rise (Baseline) ; ii)
Developing the scenarios for local relative sea level rise by the year 2100; iii) Determination of storm
surge statistics; and iv) recommendations on using the presented relative sea level rise projections
within the CASDP. In each section, first the employed methodology is described which is followed by
its application to the study area.
2
2. Study Area Cauvery Delta Zone (CDZ) is located in the eastern part of Tamil Nadu between the latitudes of 100-
11.30 North and between the longitudes 78.150 – 79.450 East. Its coastline is bounded by the Bay of
Bengal to the East and the Palk straight on the South (Figure 1).
Figure 1. Cauvery delta of Tamil Nadu, India. (From ALAPPAT et al. 2011)
3. Current trends in local relative sea level rise (RSLR) at CDZ To determine the current trend in local relative sea level rise (Baseline) for CDZ, first, all the available
data from different sources were collected. These data were then analyzed to determine the current
trend in local relative sea level rise.
3.1. Data Collection The main source of data and information for local sea level is tide gauge records. The major global
data source for tide gauge records is the Permanent Service for Mean Sea Level (PSMSL).
Instrumental records of sea-level change measured with tide gauges are available both locally and
globally. This database is up-to-date and in addition to new measurements, important long-term
historic measurements are sometimes added to the archive (DOUGLAS, 1997; HAIGH et al., 2009;
WOODWORTH et al., 2009). Other sources of sea-level data such as the World Ocean Circulation
Experiment (WOCE) may also offer suitable data, while national and port and harbor authorities can
be consulted for data as well. In this project, data were obtained from the above-mentioned global
data bases. Based on the length and the quality of data, the PSMSL recorded were deemed to be the
best source of tide gauge data for this study. Figure 2 and Table 1 show the available data for tide
gauges at or around the study area.
3
Figure 2. Location of relevant tide gauge stations : Red (+75 years of data), Green (+50 years of data), Blue (-30
years of data)
Table 1. Available tide gauge records
Station name ID Lat Lon Start end Duration(Yrs) Coverage
COCHIN (WILLINGDON IS.) 438 9.967 76.267 1939 2007 68 92.5%
TUTICORIN 1072 8.75 78.2 1964 1991 27 75.6%
TANGACHCHIMADAM 1258 9.283 79.25 1969 1983 14 81.7%
NAGAPPATTINAM 1308 10.767 79.85 1971 1989 18 77.6%
CHENNAI / MADRAS 205 13.1 80.3 1916 2009 93* 59.8%
VISHAKHAPATNAM 414 17.683 83.283 1937 2009 72 86.3%
*31 years of Data for 1921-1952 is missing and therefore only 57 years of data is usable
4
3.2. Current trends based on the available tide gauge data The closest station to the study area is NAGAPPATTINAM, Figure 3 shows the available data for this
station. The duration of the data series is only 18 years and within this 18 yr period a large number of
data points are missing. Therefore the trends determined based on this set of data (-150 and -195
mm/100 years; negative values indicates a lowering of mean sea level) are not reliable. (Data sets
shorter than 36 years or two lunar cycles should not be used in determining the long-term trends of
sea level change, NICHOLLS et al. 2011).
Figure 3. Available annual and monthly tide gauge records at NAGAPPATTINAM station
Subsequently, the relative sea level rise (RSLR) trend was calculated for stations close to the study
area with data spanning over 50 years (i.e. COCHIN, CHENNAI and VISHAKHAPATNAM). Figures 4 to 6
show the annual and monthly tide gauge records of these stations with the trend line of RSLR at each
station based on annual and monthly data. Table 2 summarizes the current local trend of RSLR for
these stations.
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Figure 4. Monthly and annual mean seal level and corresponding trends at COCHIN
Figure 5. Monthly and annual mean seal level and corresponding trends at CHENNAI
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95% Bound for Annual Trend
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Annual Mean Trend 29 mm/Cent.
95% Bound for Annual Trend
6
Figure 6. Monthly and annual mean seal level and corresponding trends at VISHAKHAPATNAM
Table 2. Current local trend of relative sea level rise based on available data
Name Years of data used
RSLR based on monthly data (mm/ years)
+/- 95% CI
RSLR based on Annual data (mm/ years)
+/- 95% CI
COCHIN 69 1.40 0.28 1.44 0.44
CHENNAI 56 0.13 0.53 0.29 0.56
VISHAKHAPATNAM 71 1.01 0.56 0.74 0.53
3.3. Relative sea level rise rates from NOAA National Oceanic and Atmospheric Administration of USA (NOAA) provides rates of RSLR based on
global tide gauge records. (http://www.tidesandcurrents.noaa.gov/sltrends/sltrends.html). NOAA
has used all the available data and filtered them for average seasonal cycles. Table 3 shows the RSLR
rates suggested by NOAA for the above 3 stations in the study area.
Table 3. Local rates of relative sea level rise suggested by NOAA
Name Years of data used
RSLR (mm/ years)
+/- 95% CI
COCHIN 69 1.71 0.36
CHENNAI 93 0.32 0.37
VISHAKHAPATNAM 71 0.79 0.45
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Monthly Mean Data
Annual Mean Data
Monthly Mean Trend 101 mm/Cent.
Annual Mean Trend 74 mm/Cent.
95% Bound for Annual Trend
7
3.4. Global Average trend of SLR IPCC have estimated that the global average mean sea-level rise in the 20th Century was 0.17 ± 0.05
m at an average rate of 1.7 mm/year (BINDOFF et al., 2007). From 1961 to 2003 the average rate of
SLR was 1.8 ± 0.5 mm/yr, while the rate was greater between 1993 and 2003 with satellite
measurements showing a rate of SLR of 3.1 ± 0.7 mm/yr. However, it is unclear if this post-1993
trend reflects short-term variability in global-mean sea-level rise or indicates a systematic
acceleration in the rate of global-mean sea-level rise. (NICHOLLS et al. 2011)
3.5. Discussion Table 4 compares the different rates of RSLR from different sources. Excepting the NOAA estimation
for COCHIN, all estimated rates of RSLR in the study area are lower than the IPCC estimated global
average value. This is likely due to regional spatial variations in sea-level differences, which maybe
driven by, for example, Meteo-oceanographic factors, changes in the regional gravity field of the
Earth, and natural or human induced land uplift and subsidence.
An uplift of 0.27 mm/year due to glacial isostatic adjustment (GIA) is reported for the south of India
(for NAGAPPATTINAM station it is 0.34) by Prof. Richard Peltier of the University of Toronto using
ICE-5G (VM2 L90) model (version 1.3) for PSMSL tide gauge
(http://www.atmosp.physics.utoronto.ca/~peltier/data.php). Such an uplift would result in a local
RSLR that is less than the global average SLR. While this could partly explain the difference between
global average and local relative sea level rise in the study area, no reliable data is available for the
possible vertical land movement in Cauvery delta.
Table 4 shows some differences between the RSLR rates suggested by NOAA and those calculated
based on the available data. The main reason for these discrepancies lies in that fact that NOAA had
filtered the average seasonal cycles for all stations, and specifically for the CHENNAI station, despite
a large gap of 30 years in data at, NOAA had used the available data before and after the data gap.
The filtering of seasonal cycles is not recommended by more recent SLR studies (NICOLLS et al., 2011)
and was therefore not undertaken in the present study. For CHENNAI, the present analysis only
considered the continuous data after 1952 to ensure that the data gap does not affect the estimated
trend in RSLR.
Table 4. Different rates in Sea Level Rise from different sources (mm/year)
Name RSLR By NOAA
RSLR based on monthly
data
RSLR based on Annual
data
SLR Average of 20th
century
SLR Average of 1961-2003
COCHIN 1.71 1.40 1.44
1.70 1.80 CHENNAI 0.32 0.13 0.29
VISHAKHAPATNAM 0.79 1.01 0.74
8
4. Scenarios for local relative sea level rise by 2100 NICHOLLS et al (2011), in their Table 3, summarize 3 different options for the development of RSLR
scenarios for local impact and adaptation assessments. The 3 levels of assessments (detailed;
intermediate; and minimum) vary in the level of rigor required in calculating the different
components of RSLR and are dependent on the level of data available for a given location. Taking into
account the reliability and availability of data for the study area and the budgetary and time
constraints of the study, the 'intermediate' assessment methodology suggested by NICHOLLS et al.
(2011) was adopted.
4.1. Background Relative sea level rise (RSLR) over the next 30-100-year period is the sum of two major components:
1. Global-mean sea-level change (ΔSLG)
The Global-mean sea-level change results from the change in the global volume of the ocean. This is
mainly due to: i) thermal expansion of the ocean as it warms; ii) the melting of glaciers and ice caps
due to human-induced global warming (BINDOFF et al., 2007; MEEHL et al., 2007); and, iii) changes in
the mass balance of the Greenland and Antarctic ice sheets, which is less certain (SHEPHERD and
WINGHAM, 2007). Different Scenarios for the global-mean sea-level change are determined based
on climate change scenarios developed by SRES (1990).
2. Regional ( local ) spatial variations in sea-level change
These local variations are mainly due to :
a. Meteo-oceanographic factors (ΔSLRM), including differences in the rates of oceanic thermal
expansion, changes in long-term wind and atmospheric pressure, and changes in ocean circulation
(such as the Gulf Stream - e.g. LOWE and GREGORY, 2006 and in the Indian Ocean - HAN et al.,
2010). The contributions from these phenomena could be significant, causing large regional
departures of up to 50-100% from the global average value of RSLR for the thermal expansion
component of sea-level change. However, coupled atmosphere-ocean climate models of these
effects under global warming do not agree where these larger-than-average changes will occur
(MEEHL et al., 2007; PARDAENS et al., 2011).
b. Changes in the regional gravity field of the Earth (ΔSLRG) due to ice melting (caused by
redistribution of mass away from Greenland, Antarctica as well as glaciers). The global sea-level
change caused by the melting of an ice sheet will not be evenly distributed as a single “global
eustatic” or global-mean value (see Section 5.5.4.4 in BINDOFF et al., 2007). If a polar ice sheet melts,
then the volume of water in the oceans increases, but at the same time, the gravitational pull from
the ice sheet on the oceans close to the ice sheet falls. The net effect of these processes is that sea-
level rise occurs faster in areas further away from the source of the melting. For example, in the case
of melting Greenland ice, there would be less sea-level rise than the global average in the North
Atlantic, near Greenland, while an enhanced sea-level rise (compared to the global eustatic value)
will occur at low latitudes and in the southern oceans (PLAG, 2006). Each potential mass source or
sink (Greenland ice sheet, Antarctic ice sheet, glaciers, water storage on land) will produce its own
pattern or “fingerprint” of sea-level change measured at the coast (e.g. MITROVICA et al., 2001).
9
c. Vertical land movements (uplift and subsidence) (ΔSLVLM) due to various natural and human-
induced geological processes (CHRISTENSEN et al., 2007, Box 11.5; EMERY and AUBREY, 1991;
ERICSON et al., 2006; PELTIER, 2004; SYVITSKI, 2008). Vertical land movement occurs in most places.
Natural causes include: i) neotectonics, ii) glacio-isostatic adjustment (GIA), and iii) sediment
compaction/consolidation. These changes can be regional, slow and steady, as in the case of GIA, but
can also be localized, large and abrupt, for example due to earthquakes.
The inclusion of regional components of relative sea-level change is important when developing
scenarios for impact and adaptation assessment as they provide a critical link between (global)
climate change and (regional to local) coastal management strategies (CHRISTENSEN et al., 2007;
NICHOLLS et al., 2007).
Relative sea-level change projections for a specific location should take into account the different
contributions from the components at the global, regional and local scales described above, as
relevant to the study area. These can be integrated for a given site using following equation:
∆RSL = ∆SLG + ∆SLRM + ∆SLRG + ∆SLVLM (1)
Where:
ΔRSL is the change in relative sea level
ΔSLG is the change in global mean sea level
ΔSLRM is the regional variation in sea level from the global mean due to meteo-oceanographic
factors
ΔSLRG is the regional variation in sea level due to changes in the earth’s gravitational field
ΔSLVLM is the change in sea level due to vertical land movement
4.2. RSLR Scenario based on extrapolating measured trends One of the useful methods for creating relative sea-level scenarios for local impact assessments is the
extrapolation of historical sea-level trends from observed data. This direct method is very useful
mainly due to the fact that historic records will include changes in relative sea level due to both
vertical land movements and changes in the level of the sea surface. Based on the analyses and
comparisons undertaken in the previous sections the most appropriate rate of local relative sea level
rise for the Cauvery delta was considered to be the same as the closest PSMSL station, CHENNAI:
0.29 ± 0.56 mm/year. The sea level elevation at the NAGAPPATTINAM station in the year 1990 was
considered as the starting point and the sea level till 2100 is predicted and visualized in Figure 7. The
global average SLR is also shown in Fig. 7 for comparison. Note that the lower 95% confidence limit
associated with the local projection indicates a relative decrease in MSL over the 21st century,
relative to 1990.
10
Figure 7. Predicated Sea Level at NAGAPPATTINAM station in Cauvery delta for the scenario based on
extrapolation of tide gauge records (red), compared with global average SLR, based on the average rate of
global SLR (Blue). The upper and lower limits refer to the 5th
and 95th
percentiles of the sea-level distribution.
4.3. RSLR Scenarios based on the Special Report on Emissions Scenarios
(SRES) Climate change scenarios Global-mean sea-level change and Regional (local) spatial variations in sea-level change can be
calculated using equation 1. As Equation is a linear equation, it can easily be used for estimating the
rate of RSLR.
As discussed above in Section 4, here we have followed the ’intermediate’ assessment methodology
summarized by NICHOLLS et al. (2011) to estimate the rate of each RSLR component for the CDZ. This
methodology allows development of RSLR scenarios during the 21st century (rather than only at the
end of the century.
4.3.1. SRES Climate change scenarios
SRES Climate change scenarios are described in the IPCC Special Report on Emissions Scenarios (SRES,
1990). The SRES scenarios are grouped into four groups (A1, A2, B1 and B2) that relate to alternative
development paths, including a wide range of demographic, economic and technological driving
forces and resulting GHG emissions. The SRES scenarios do not include additional climate policies
above current ones. The A1 assumes a world of very rapid economic growth, a global population
that peaks in mid-century and rapid introduction of new and more efficient technologies. A1 is
divided into three groups that describe alternative directions of technological change: fossil intensive
(A1FI), non-fossil energy resources (A1T) and a balance across all sources (A1B). B1 describes a
convergent world, with the same global population as A1, but with more rapid changes in economic
structures toward a service and information economy. B2 describes a world with intermediate
population and economic growth, emphasizing local solutions to economic, social, and
environmental sustainability. A2 describes a very heterogeneous world with high population growth,
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 21006950
7000
7050
7100
7150
7200
7250
Year
Se
a L
eve
l (m
m)
Local predicted RSLR (95 % conf.)
Global Avr. SLR (95 % conf.)
11
slow economic development and slow technological change. No likelihood has been attached to any
of the SRES scenarios. (SRES 1990)
4.3.2. Global-mean sea-level change for SRES Climate change scenarios
As described in section 4.7 of NICHOLLS et al. (2011), a simple method to predict the global averaged
sea level change during the 21st century is to use Equation 2; assuming sea-level rise in 1990 is zero.
This form of curve was chosen because it has the same number of tunable parameters as the
constraints to which the curve can be fitted; namely the estimated rate and amount of sea-level rise
at the end of the 21st century. This information is not available directly from AR4 but it is possible,
using a variety of methods, to create interpolated sea level curves. Here the curves presented in
NICHOLLS et al. (2011) are reproduced and used.
∆𝑆𝐿𝐺 = 𝑎1𝑡 + 𝑎2𝑡2 (2)
Where:
ΔSLG is the change in global mean sea level
t is number of years from 1990
a1 is rate of sea level change (per year)
a2 is change in the rate of sea-level change (per year)
a1 and a2 are calculated based on estimates for global averaged SLR over 21st century reported in
IPCC AR4 (Table 10.7 in MEEHL et al., (2007)) for 6 IPCC SRES scenarios (Table 5). Figure 8 shows the
sea-level change curves generated via Equation 2 for the scenarios reported in AR4 (estimated upper
and lower limits are based on the 5th and 95th percentile reported).
Table 5. The rate of sea level change and change in the rate of sea-level change for IPCC SRES
scenarios
a1
(m/year)
a2
(m/year2)
a1
(m/year)
a2
(m/year2)
B1
upper 0.003370 2.32e-6
A1T
upper 0.003897 3.57e-6
lower 0.001885 1.75e-6 lower 0.002300 2.29e-17
B2
upper 0.002652 1.39e-5
A2
upper 0.001412 3.28e-5
lower 0.001688 -1.40e-6 lower 0.002000 -2.51e-13
A1B
upper 0.003179 1.32e-5
A1Fl
upper 0.001424 4.01e-5
lower 0.002000 -1.99e-6 lower 0.001897 5.56e-6
12
Figure 8. The range of possible global averaged sea-level rise change during the 21st
Century using Equation 2
based on estimates reported in IPCC AR4 (Table 10.7 in MEEHL et al., (2007) for 6 IPCC SRES scenarios). The
upper and lower limits refer to the 5th
and 95th
percentiles of the sea-level distribution; which is assumed to be
Gaussian (Reproduced after NICHOLLS et al. 2011).
However Models used to date to produce the above-mentioned tables and graphs do not include
uncertainties in climate-carbon cycle feedback nor do they include the full effects of changes in ice
sheet flow, because a basis in published literature is lacking. The projections include a contribution
due to increased ice flow from Greenland and Antarctica at the rates observed for 1993-2003, but
these flow rates could increase or decrease in the future. For example, if this contribution were to
grow linearly with global average temperature change, the upper ranges of sea level rise for SRES
scenarios shown in Figure 8 would increase by 0.1 m to 0.2 m at year 2100. Larger values cannot be
excluded, but understanding of these effects is too limited to assess their likelihood, (IPCC 2007). In
this study the upper bound of global sea level rise is adjusted for this effect by 0.2 m in 2100 for
developing Scenarios for local RSLR. (Figure 9)
2000 2020 2040 2060 2080 21000
100
200
300
400
500
600
700
years
incre
ase
in
se
a le
ve
l (m
m)
B1
2000 2020 2040 2060 2080 21000
100
200
300
400
500
600
700
years
incre
ase
in
se
a le
ve
l (m
m)
B2
2000 2020 2040 2060 2080 21000
100
200
300
400
500
600
700
years
incre
ase
in
se
a le
ve
l (m
m)
A1B
2000 2020 2040 2060 2080 21000
100
200
300
400
500
600
700
years
incre
ase
in
se
a le
ve
l (m
m)
A1T
2000 2020 2040 2060 2080 21000
100
200
300
400
500
600
700
years
incre
ase
in
se
a le
ve
l (m
m)
A2
2000 2020 2040 2060 2080 21000
100
200
300
400
500
600
700
years
incre
ase
in
se
a le
ve
l (m
m)
A1fl
13
Figure 9. The range of possible global averaged sea-level rise change during the 21st
Century using Equation 2
based on estimates reported in IPCC AR4 (Table 10.7 in MEEHL et al., (2007) for 6 IPCC SRES scenarios) and
adjusted for the effect of climate-carbon cycle feedback and changes in ice sheet flow.
4.3.3. Regional (local) spatial variations in sea-level change
To develop local RSLR scenarios, the regional variations in RSLR should also be estimated. The
different regional RSLR contributions described in Section 4.1 are computed for the CDZ below.
Regional variation in sea level from the global mean due to meteo-oceanographic factors
IPCC AR4 provides ensemble outputs of general circulation models, showing a sea-level rise that is
smaller than the global average in the Southern Ocean and larger than the global average in the
Arctic. This variation has been attributed to enhanced freshwater input from precipitation and
continental runoff, steric changes or wind stress change (LANDERER et al., 2007) or spatial variation
in thermal expansion (LOWE and GREGORY, 2006). Figure 10 shows the spatial variations in local sea-
level change (m) from the global average as the difference between averages for 2080 to 2099 and
1980 to 1999.
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
B1
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
B2
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A1B
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A1T
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A2
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A1fl
original bounds
adjusted upper bound
14
Figure 10. Spatial Variations in local sea-level change (m) from the global average (i.e., positive values indicate
greater local sea level change than the global average) during the 21st century with the SRES A1B scenario.
Variation is due to ocean density and circulation and is calculated as the difference between averages for 2080
to 2099 and 1980 to 1999 as an ensemble mean of 16 AOGCMs. Stippling indicates where the variation
between the models is less than the ensemble mean. (Source: Figure 10.32 in MEEHL et al., 2007))
It is assumed that the variations in local sea-level change due to this phenomenon are also in the
same range for other SRES scenario. For the east coast of India where the CDZ is located this
variation is about+ 0.05 m in 100 years or +0.5 mm/year.
Changes in the regional gravity field of the Earth
This factor is significant under deglaciation of Greenland or Antarctica. A few studies are now starting
to develop scenarios of future sea level that recognize that changes in the global and regional gravity
field associated with mass exchange with the ocean. This is particularly important for future
scenarios with a large ice melt component, but less so for those dominated by thermal expansion.
However, due to the unavailability of reliable projections of the potential RSLR contribution due to
this phenomenon at present, this effect is not taken into account in this study.
Vertical land movements
Detailed information on the possible land subsidence in the study area due to consolidation and
ground water extraction is not freely available. However, by Prof. Richard Peltier of the University of
Toronto using ICE-5G (VM2 L90) model estimated that the glacio-isostatic uplift at the
NAGAPPATTINAM station in the CDZ is 0.34 mm/year
(http://www.atmosp.physics.utoronto.ca/~peltier/data.php)
Combining the above values, the regional variations in sea-level change at the CDZ is estimated as:
∆𝑆𝐿𝑅𝑀 + ∆𝑆𝐿𝑉𝐿𝑀 = 0.50 − 0.34 = 0.16 (𝑚𝑚/𝑦𝑒𝑎𝑟) (3)
15
4.3.4. RSLR Scenarios based on SRES Climate change scenarios
By adding the local variations of SLR to the global averaged rates of SLR for each CC Scenario, the
upper and lower bound for RSLR over the 21st century at the CDZ for all the CC scenarios relative to
the sea level in 1990 are calculated and shown in the Figure 11.
Figure 11. RSLR during 21st century at CDZ (Red) compared with the Global SLR (Black) for different SRES CC
Scenarios, including the effect of climate-carbon cycle feedback and changes in ice sheet flow.
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
B1
Global SLR
RSLR at CDZ
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
yearsin
cre
ase
in
se
a le
ve
l (m
m)
B2
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A1B
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A1T
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A2
2000 2020 2040 2060 2080 21000
200
400
600
800
1000
years
incre
ase
in
se
a le
ve
l (m
m)
A1fl
16
4.4. Scenarios for local RSLR until year 2100 at CDZ In total seven different RSLR scenarios for the 21st century were developed for the CDZ. One
scenario was based on the extrapolation of historical rate of RSLR derived from tide gauges, while the
other 6 were based on the application of the ‘intermediate’ assessment methodology given by
Nicholls et al. (2011) to six different SRES scenarios (spanning both low and high emissions
scenarios). Both global average SLR and regional variations in SLR were taken into account in deriving
these RSLR scenarios. The derived RSLR projections are shown graphically in Figures 7 & 11 and
partially tabulated in Table 6. The highest and lowest values of RSLR by 2100 relative to 1990
projected for the study area are 0.87m and -0.03m respectively.
Table 6. Estimates of the RSLR at CDZ based on different scenarios relative to 1990 in meters
CC Scenarios
B1 B2 A1B A1T A2 A1Fl Extrapolated
historical
data
2025
lower 0.07 0.06 0.07 0.08 0.07 0.08 -0.01
upper 0.19 0.18 0.20 0.21 0.16 0.17 0.03
2050
lower 0.13 0.10 0.12 0.15 0.13 0.14 -0.02
upper 0.33 0.33 0.36 0.37 0.32 0.35 0.05
2075
lower 0.18 0.15 0.17 0.21 0.18 0.21 -0.02
upper 0.48 0.50 0.54 0.53 0.53 0.58 0.07
2100
lower 0.24 0.18 0.21 0.27 0.24 0.29 -0.03
upper 0.63 0.69 0.74 0.70 0.78 0.87 0.09
17
5. Estimating the sea level change associated with extreme events In this section the storm surge for different return periods at CDZ is determined and the limitation of
these estimates is discussed. These storm surge estimates (considering the limitations) can be
combined with different RSLR scenarios to inform the decision making processes.
5.1. Available data To have a robust estimation of storm surge values for return periods upto 100 years, a long-term,
high resolution (hourly) time series of water level from a tide gauge at the study area is required. For
this study 19 years data of hourly water levels at the Chennai port was purchased. The coverage of
data is different for different years and Table 7 shows the missing data for each year in days. The
missing data has a negative effect on the accuracy of the extreme value analyses performed in this
study. This effect will be more if the missing data is due to difficulties in measurement during high
energy events (storms) leading to a systematic error. A 5 day example of data is shown in Figure 12
(Blue line). As all measured water levels are above zero, it is clear that the recorded water level has a
different datum to MSL. Therefore, based on the website of the port of Chennai, a CD (chart datum)
of 0.54m below MSL is considered.
Table 7. Missing data for each year in days
Year Number of Days without data
1989 76
1990 61
1991 61
1992 15
1993 99
1994 62
1995 0
1996 121
1997 0
1998 0
1999 0
2000 14
2001 48
2002 0
2003 31
2004 0
2005 0
2006 0
2007 12
5.2. Calculation method In order to determine storm surge, the astronomical tide at the tide gauge is subtracted from the
water level data. Astronomical tide is calculated based on all the tidal constituents at the location of
18
tidal gauge (tidal constituents is calculated by fitting tidal components to the water level data data by
T_TIDE method (Pawlowicz et al., 2002)). It should be noted that the astronomical tide is calculated
for the exact same time and with the same datum as the water level data. A 5 day example of such a
hindcasted astronomical tide is shown in Figure 12 (Red line).
The storm surge is then determined by subtracting the calculated astronomical tide from the water
level data. Using this method we generated time series of maximum storm surge data for each year
separately (with above mentioned gaps) and then combined them to have 19 years of surge data for
the study area. The maximum observed storm surge in the data set is 0.71 m (Figure 13).
Figure 12. Five day example of measured hourly water level data from a tide gauge and calculated astronomical
tide for the same time
0 1 2 3 4 5-0.5
0
0.5
1
1.5
2
Time (Days)
Le
ve
l (m
)
Measured water level
Measured water level corrected for datum
Astronomical tide
Surge
19
Figure 13. Nineteen years of calculated hourly storm surge level for CDZ
5.3. Extreme value analysis (EVA) of storm surge data Extreme value distributions can be one of three types; Gumbel, Fréchet and Weibull families. These
distribution families result in quite different extreme value behaviors, and the process of selecting
one is subjective. Once a family is chosen, subsequent inferences do not take account of the
uncertainty associated with the selection. These weaknesses can be avoided by using the Generalized
Pareto (GP) distribution, particularly in the field of coastal engineering (Hawkes et.al. 2002). The
cumulative distribution function (CDF) of GP distribution is given by:
where σ > 0 is the scale parameter; ξ is the shape parameter; u is the threshold. The scale and shape
parameters were estimated by maximum likelihood estimation. The threshold u is obtained by a
root-mean-square-error (RMSE) analysis, which computes the differences between the fitted
distribution and the empirical distribution, i.e.
0 5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Time (Yrs)
Su
rge
Le
ve
l(m
)
20
where the fitted value is the value on the fitted distribution curve which has the same probability
with the observed value, n is the number of the values. For coastal hazard assessment, including
determining surge levels, extreme situations are the most important. Thus, it is reasonable to choose
a u value which will fit the extreme observed data best rather than one that fits all the observation
data. Therefore extreme values are chosen to carry out the RMSE analysis. For the storm surge levels
calculated in section 5.2, the threshold value in which the fitting of GP distribution is converged (for
maximum RSME off 1e-4 ) is u = 0.34(m) . Which means that overall 154 data points ( > 0.34 m ) are
chosen for the GP distribution. The fitted Distribution with 95% confidence range on scale and shape
parameters is shown in Figure 14. Table 8 gives the Storm surge level corresponding to each return
period and their 95% confidence ranges.
Figure 14. GP distribution fitted on the extreme value data with 95% confidence range and the * are the
empirical estimations
100
101
102
0.2
0.4
0.6
0.8
1
1.2
Return Period (Yrs)
Su
rge
Le
ve
l (m
)
21
Table 8. Storm surge level corresponding to different return periods and their 95% confidence ranges
Return Period (yr) Surge level (m)
1 0.44 [0.42 0.49] 5 0.53 [0.46 0.66]
10 0.57 [0.48 0.76] 20 0.62 [0.50 0.87] 50 0.69 [0.52 1.06]
100 0.74 [0.53 1.24]
6. Recommendations on using the presented relative sea level rise
projections within the CASDP The CASDP intends to implement improved drainage facilities, flood control measures and land use
strategies (among others) to mitigate the potential impacts of climate change in the Cauvery Delta
Zone (CDZ). In designing these interventions, the precautionary principle would advocate the use of
the highest projected sea level rise value (including the 1:100yr storm surge) of 1.61m obtained in all
designs/strategies. However, this may, on occasion come at a significant and unwarranted cost and
thus be not the most cost-effective approach. Therefore, the following simple approach is
recommended to ensure a balance between safety and cost-effectiveness.
Before deciding on a higher or lower RSLR scenario to work with, stakeholders need to make a
decision to take on board climate change projections at all. Therefore, the cost of not incorporating
any climate change projections needs to be estimated, in most cases, to inform this decision.
Assuming that this leads to a decision to indeed consider RSLR, at the design stage of all
interventions, two separate designs for the highest and mid-level projected sea level scenarios (RSLR
plus storm surge) relevant for the planning horizon of the interventions may be developed. For
example, if the design lifetime of a planned flood protection measure is until 2100, then develop two
separate designs to accommodate RSLR values of 0.87m and 0.29m, plus at least the 1 in 100 yr
storm surge estimate (0.74m) (i.e. sea levels of 1.61m and 1.03m). In this example both the highest
and mid-level RSLR values are associated with the A1Fl projections, but this need not always be the
case. Then evaluate the costs associated with both design options as well as the costs of damage if
either design were to fail by overtopping. The way in which damage costs are estimated differ widely
from country to country and local governance unit to local governance unit, but would ideally
account for not only property (or economic) damage but also environmental, and societal damage
and loss of life, or any combination thereof. Say, for example:
Cost of highest sea level design = C1
Cost of mid-level sea level design = C2
Cost of damage due to failure of highest sea level design = D1
Cost of damage due to failure of mid-level sea level design = D2
Then (D1-D2) is the value of additional protection gained by investing (C1-C2). If (D1-D2) is
significantly greater than (C1-C2) then the decision should be to adopt the higher sea level design.
22
However, if this is not the case the lower sea level design maybe taken under consideration.
Nevertheless, the choice to adopt the less safe option is not only a matter for managers and planners
but one for multi-stakeholder (including politicians and special interest groups) deliberation.
Flood risk maybe more elegantly optimized by balancing the cost of the intervention versus the
potential damage using advanced probabilistic calculations such as those used in flood management
spheres. In the presence of efficient risk-sharing arrangements, investments in risk mitigation can be
evaluated through net present value computations with the cost of risk mitigation on the one hand,
and expected loss (or: the actuarially fair insurance premium) on the other. This approach has been
used in the Netherlands since the 1950s to inform decisions about flood protection (Figure 15).
Figure 15. The optimization of flood protection: total cost equals the cost of dike heightening plus the present
value of expected loss (assuming stationary conditions: no sea level rise, economic growth or degradation).
However, such an approach requires estimates of the exceedance probability of RSLR (which the IPCC
do not provide at present) and detailed spatial information on the damage function. While a
Gaussian distribution may be assumed for the probability of occurrence for values lying between the
lower and higher RSLR projections per SRES scenario, the availability of sufficiently detailed spatial
data is often the stumbling block when attempting this type of risk optimization approach. However,
if such data can be provided for the CDZ, it is possible to optimize flood risk due to future RSLR (as
part of a separate project).
A further point to note is coastal recession due to RSLR. When the mean sea level rises the coastline
position will move landward due to erosion. A rule of thumb for such RSLR driven recession dictates a
recession of between 50-100 times the RSLR over a 100yr period. Thus, it is also crucial that any
interventions take this recession effect also into account. For example, major new developments or
infrastructure should not be placed within this recession zone. However, the same principle
discussed above for flooding also applies for coastal recession: while the potential damage due to
coastal recession can be large, so can be the forgone land use/commercial opportunities in the
coastal zone. In recent years numerical modeling methods have been developed to assess the coastal
erosion risk and determine optimal coastal setback lines (a setback line is defined as the position
Cost (present value)
Cost of dike
heightening
Present value of expected loss
Total cost
Dike height Optimal height
23
seaward of which developments should be restricted or prohibited). An example application of these
models to a site in Australia is shown below in Figure 16.
Figure 16. Coastal erosion risk due to sea level rise by 2100 at Narrabeen beach, Sydney, Australia. The
economically optimal setback line is shown in black while presently adopted setback line is shown in blue.
If sufficiently detailed spatial data can be provided, it is possible (as part of a separate project) to
produce similar output for the CDZ which would be of critical value for determining the appropriate
placement of CASDP intervention measures and/or developing land use policies for the future.
24
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