et al. 2013). These climate extremes along with sea-level rise
will affect ESL and intensify coastal flood risk (Vousdoukas
et al. 2018).
The recent evolution of extremely high waters along the
severe cyclone-risk coasts of the Bay of Bengal (the east
coast of India and Bangladesh) was assessed using long-term
(24–34 years) hourly tide gauge data available from five
stations by Antony et al. 2016. They have noticed the
highest water levels above mean sea level with the greatest
magnitude towards the northern part of the Bay, which
decreases towards its south-west. Extreme high waters were
also observed resulting from the combination of moderate,
or even small, surges with large tides at these stations in
most of the cases. In the Bay of Bengal, return period and
return level estimations of extreme sea levels have been
provided by Unnikrishnan et al. (2004) and Lee (2013) using
hourly tide gauge data and Unnikrishnan et al. (2011) using
storm surge models, driven by regional climate models. In
the Indian Network for Climate Change Assessment
Report-II, Chap. 4, Unnikrishnan et al. (2010) have shown
that higher flood risks are also associated with storm surge
along the southern part of the east coast of India, where tidal
ranges are low. The study by Rao et al (2015) also suggested
that in an extreme climate change scenario, there is a high
risk of inundation over many regions of Andhra Pradesh,
which is along the east coast of India. Extreme sea-level
projections for Ganga-Brahmaputra-Meghna delta (Kay
et al. 2015) show an increased likelihood of high water
events through the twenty-first century. The First Biennial
Update Report (BUR) to UNFCCC by the Government of
Indian 2015 emphasized the need to promote sustainable
development based on scientific principles taking into
account the dangers of natural hazards in the coastal areas,
and sea-level rise due to global warming.
Long and good quality tide gauge sea-level records are
geographically biased towards the European and North
American coasts, reflecting the historical limitations of the
worldwide tide gauge dataset. Therefore, major ESL analyses cover the ocean’s basin to the extent as possible, but
there is a substantial lack of information especially in the
Southern Hemisphere and the Indian Ocean (Marcos et al.
2015). Extreme sea levels, caused by storm surges and high
tides, can have devastating societal impacts. It was shown
that up to 310 million people residing in low elevation
coastal zones are already directly or indirectly vulnerable to
ESL (Hinkel et al. 2014). Since the extreme sea level is
projected to increase with an increase in mean sea level and
climate extremes, an extensive network of tide gauges with
co-located GPS systems is needed along the Indian coastline,
as our coastline is among the most vulnerable and densely
populated regions of the globe.
The projected rise in mean sea level and ESL is a real
caution for countries off the rim of north Indian Ocean, and
for India, and to a large number of islands in the Indian
Ocean, many of them have fragile infrastructures and population density is projected to become the largest in the
world by 2030, with about 340 million people exposed to
coastal hazards (Neumann et al. 2015). The sea-level
rise-related coastal hazards include loss of land, salinization of freshwater supplies and an increased vulnerability to
flooding. For instance, the Bay of Bengal already witnesses
more than 80% of the total fatalities due to tropical cyclones,
while only accounting for 5% of these storms globally (Paul
2009). The storm surges associated with the cyclone conflates with the climate change-induced sea-level rise (e.g.
Han et al. 2010) to increase vulnerability. In general, the
regional projections of the Indian Ocean mean sea level for
the twenty-first century (see Fig. 9.5b) from climate models
are high compared to other tropical oceans and the consequences could become double-fold considering the fact that
the Indian Ocean basin hosts millions of people on its rim
lands. In further research, an improved understanding of the
factors controlling the Indian Ocean long-term sea-level rise
and continuous monitoring of sea-level variability is essential for better assessing its socio-economic and environmental impacts in a changing climate.
9.6 Knowledge Gaps
Lack of long sea-level observations for the Indian Ocean is a
major caveat to derive the reliable basin-scale pattern of
sea-level rise and multi-decadal variability in this basin. For
example, previous studies suggested that the multi-decadal
oscillations in regional sea level call for a minimum of 50–
60 years of sea-level data in order to establish a robust
long-term trend (e.g. Douglas 1997; Chambers et al. 2012).
There are only two tide gauges in the Indian Ocean that go
back to the nineteenth century: Mumbai (west coast of India)
and Fremantle (west coast of Australia). Figure 9.6 indeed
shows that, except for a few gauge stations along the coastal
India and west coast of Australia, there are no gauge records
available in the interior ocean that spans over a minimum of
40 years. On the other hand, satellite altimetry provides
high-resolution sea-level measurements over the entire basin
since 1992, but the data are so short in terms of providing
reliable estimates of regional sea-level rise trends given the
presence of multi-decadal oscillations (e.g. Unnikrishnan
et al. 2015; Swapna et al. 2017). In the same lines, the
unavailability of long-term hydrographic profiles in the
Indian Ocean limits our knowledge of long-term heat content
and salinity variations in the basin and hence the steric sea
level. In a recent study, Nidheesh et al. (2017) showed that
representation of Indian Ocean decadal sea-level variations
in observation-based sea-level datasets (reanalyses and
reconstructions) is not robust and the inconsistencies are
9 Sea-Level Rise
185
will affect ESL and intensify coastal flood risk (Vousdoukas
et al. 2018).
The recent evolution of extremely high waters along the
severe cyclone-risk coasts of the Bay of Bengal (the east
coast of India and Bangladesh) was assessed using long-term
(24–34 years) hourly tide gauge data available from five
stations by Antony et al. 2016. They have noticed the
highest water levels above mean sea level with the greatest
magnitude towards the northern part of the Bay, which
decreases towards its south-west. Extreme high waters were
also observed resulting from the combination of moderate,
or even small, surges with large tides at these stations in
most of the cases. In the Bay of Bengal, return period and
return level estimations of extreme sea levels have been
provided by Unnikrishnan et al. (2004) and Lee (2013) using
hourly tide gauge data and Unnikrishnan et al. (2011) using
storm surge models, driven by regional climate models. In
the Indian Network for Climate Change Assessment
Report-II, Chap. 4, Unnikrishnan et al. (2010) have shown
that higher flood risks are also associated with storm surge
along the southern part of the east coast of India, where tidal
ranges are low. The study by Rao et al (2015) also suggested
that in an extreme climate change scenario, there is a high
risk of inundation over many regions of Andhra Pradesh,
which is along the east coast of India. Extreme sea-level
projections for Ganga-Brahmaputra-Meghna delta (Kay
et al. 2015) show an increased likelihood of high water
events through the twenty-first century. The First Biennial
Update Report (BUR) to UNFCCC by the Government of
Indian 2015 emphasized the need to promote sustainable
development based on scientific principles taking into
account the dangers of natural hazards in the coastal areas,
and sea-level rise due to global warming.
Long and good quality tide gauge sea-level records are
geographically biased towards the European and North
American coasts, reflecting the historical limitations of the
worldwide tide gauge dataset. Therefore, major ESL analyses cover the ocean’s basin to the extent as possible, but
there is a substantial lack of information especially in the
Southern Hemisphere and the Indian Ocean (Marcos et al.
2015). Extreme sea levels, caused by storm surges and high
tides, can have devastating societal impacts. It was shown
that up to 310 million people residing in low elevation
coastal zones are already directly or indirectly vulnerable to
ESL (Hinkel et al. 2014). Since the extreme sea level is
projected to increase with an increase in mean sea level and
climate extremes, an extensive network of tide gauges with
co-located GPS systems is needed along the Indian coastline,
as our coastline is among the most vulnerable and densely
populated regions of the globe.
The projected rise in mean sea level and ESL is a real
caution for countries off the rim of north Indian Ocean, and
for India, and to a large number of islands in the Indian
Ocean, many of them have fragile infrastructures and population density is projected to become the largest in the
world by 2030, with about 340 million people exposed to
coastal hazards (Neumann et al. 2015). The sea-level
rise-related coastal hazards include loss of land, salinization of freshwater supplies and an increased vulnerability to
flooding. For instance, the Bay of Bengal already witnesses
more than 80% of the total fatalities due to tropical cyclones,
while only accounting for 5% of these storms globally (Paul
2009). The storm surges associated with the cyclone conflates with the climate change-induced sea-level rise (e.g.
Han et al. 2010) to increase vulnerability. In general, the
regional projections of the Indian Ocean mean sea level for
the twenty-first century (see Fig. 9.5b) from climate models
are high compared to other tropical oceans and the consequences could become double-fold considering the fact that
the Indian Ocean basin hosts millions of people on its rim
lands. In further research, an improved understanding of the
factors controlling the Indian Ocean long-term sea-level rise
and continuous monitoring of sea-level variability is essential for better assessing its socio-economic and environmental impacts in a changing climate.
9.6 Knowledge Gaps
Lack of long sea-level observations for the Indian Ocean is a
major caveat to derive the reliable basin-scale pattern of
sea-level rise and multi-decadal variability in this basin. For
example, previous studies suggested that the multi-decadal
oscillations in regional sea level call for a minimum of 50–
60 years of sea-level data in order to establish a robust
long-term trend (e.g. Douglas 1997; Chambers et al. 2012).
There are only two tide gauges in the Indian Ocean that go
back to the nineteenth century: Mumbai (west coast of India)
and Fremantle (west coast of Australia). Figure 9.6 indeed
shows that, except for a few gauge stations along the coastal
India and west coast of Australia, there are no gauge records
available in the interior ocean that spans over a minimum of
40 years. On the other hand, satellite altimetry provides
high-resolution sea-level measurements over the entire basin
since 1992, but the data are so short in terms of providing
reliable estimates of regional sea-level rise trends given the
presence of multi-decadal oscillations (e.g. Unnikrishnan
et al. 2015; Swapna et al. 2017). In the same lines, the
unavailability of long-term hydrographic profiles in the
Indian Ocean limits our knowledge of long-term heat content
and salinity variations in the basin and hence the steric sea
level. In a recent study, Nidheesh et al. (2017) showed that
representation of Indian Ocean decadal sea-level variations
in observation-based sea-level datasets (reanalyses and
reconstructions) is not robust and the inconsistencies are
9 Sea-Level Rise
185