Hurricanes and Floods - A Study Case of Myanmar Flood in 2015
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Hurricanes and Floods: a study case of Myanmar flood in 2015
Vitor Vieira Vasconcelos
PhD in Natural Sciences Stockholm Environment Institute – Asia Centre
October 2015
The first section of this study will focus in two disaster types: hurricanes and floods. The
study describes how these natural disasters were predicted in the past, today and the perspective
of the future. The scales to rate these events and the physical precursors of these disasters are
discussed, as well as how modern technology can assess these precursors to forecast their location.
The second section of this study focus on the Myanmar floods that happened from July to
September 2015. This flood was caused by a hurricane (tropical cyclone) and monsoonal rains,
and thus provide a good linkage between the two types of disasters explained in the first part of
this study. Along the second section the damages caused by the flood are discussed, the physical
aspects that triggered the disaster and the extent of damage are described, and then the prevention
and preparedness for this event in Myanmar is evaluated. In the end, some comparisons with other
flood-prone countries in South and Southeast Asia are made.
Section 1: Hurricanes and Floods
Hurricanes
The first hurricane warning systems in North America was implemented by Lt. Col.
William Reid of the Royal Engineers of England in 1847, based mainly on barometric readings
(Sheets, 1990). At that time, it was difficult to forecast hurricanes, because of the lack of radars
and satellites. In 1943, airplanes started to be used for hurricane reconnaissance; in 1955, radars
were installed in the American coast; and in 1960, the first American meteorological satellite was
launched (Sheets, 1990). As telemetric devices became available, in 1954 hurricane tracks started
to be forecasted for a 24-hours intervals, and this forecasted window gradually increased to 120
hours in 2003 (Willoughby et al., 2007). However, long term forecasting of hurricanes is still a
challenge, but it is possible to estimate if a season will have more hurricanes based on ocean
temperatures and in the climate cycles of La Niña, El Niño and the Atlantic and Pacific Decadal
Oscillations (Abbot, 2014). Zhang (2011) opined that Hurricane forecasting will be improved in
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the next decades with advances in hurricane atmospheric models, especially the ones that can
assimilate large amount of data that is becoming available from satellites, Doppler radars and other
types of telemetric devices.
Hurricanes are rated in the Saffir-Simpson scale according to the wind speed, as disposed
in Table 1:
Table 1 – Saffir-Simpson scale (adapted from Abbot, 2014)
Category Sustained Winds Types of Damage Due to Hurricane Winds
1 119-153 km/h Very dangerous winds will produce some damage
2
154-177 km/h
Extremely dangerous winds will cause extensive
damage
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178-208 km/h Devastating damage will occur
4
209-251 km/h Catastrophic damage will occur.
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252 km/h or higher Catastrophic damage will occur
However, one shortcoming of this classification is that most of the damage caused by
hurricanes are not due to the wind speed, but due to the huge amount of rainfall that causes flood
and landslides.
According to Abbot (2014, p. 284-285), the physical precursors of a hurricane include:
- Seawater above 27 oC in the upper 50 meters of the ocean;
- Warm, humid and unstable air that can sustain convection
- Weak upper level winds, preferably blowing on the same direction that the storm is
moving.
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Abbot (2014, p. 284) describes that, given the environmental conditions to start the process,
the future hurricane begins as a low pressure zone, that will attract humid winds as thunderstorms
and be characterized as a tropical disturbance. After the winds go stronger and start to circulate
through a defined center, it is classified as a tropical depression. When the surface wind speed
exceeds 63km/hr, it becomes a tropical storm. If the wind speed go up to 119km/hr, the eye of the
storm becomes much clearer defined, and it is then called a hurricane. Today the use of satellite
images and Doppler radars can monitor the size, location and speed of the hurricane winds and
storms.
Floods
In ancient times, floods were considered as a punishment from God, such as described
about the great flood in the Bible. With the increasing understanding of climate and hydrology,
now people became aware of flood as a natural phenomenon that is part of the hydrologic cycle.
Analyzing time series of stream flow data from a gauging station, it is possible to correlate
the flood extension with the river flow (Baker, 1977). Thus, knowing how the average frequency
of a high flow in the river, it is possible to infer also the average frequency that a flood of certain
magnitude may happen. With this method, it is possible to build flood frequency maps, that
delimitate, for example, the flood extension for the average frequency of 10, 50 or 100 years.
However, the availability of hydrological data is crucial to infer the flood-frequency. 100
years ago, there were no long time-series for many of the rivers in United States, although there
were already longer records in Europe (Abbott, 2014). During the twentieth and twentieth-first
century, with more extensive and detailed hydrological and flood databases, it is now possible to
have a better estimation of flood frequency. In the future, we expect to have even longer
hydrological time-series, which will improve these estimations.
The classification of floods based on their return average frequency is useful, but need
some critical interpretation. If a 100 years flood happens in an area, it does not mean that the next
100 years flood will happen just after more 100 years. Even just after a big flood, every year will
still have 1% chance of having a 100 years flood. On the other hand, the chance of having at least
a 100 years flood during a century is not 100%: because of combinatory probability, the chance
will be 63% (Abbott, 2014). In other words, some centuries may have more than one flood of 100
years magnitude, and other centuries may not have any flood with that magnitude.
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Using detailed hydrological data and high resolution digital elevation models that became
available during the twentieth and twentieth-first century, it is also possible to simulate floods with
hydraulic models. These models simulate the water flow in channels and flood plains, and can
include hypothetical scenarios for unusual flows or structural interventions in the channels and
landscapes, such as dams, levees and retention ponds (Brunner, 1995).
As discussed by Abbot (2014), floods are mainly caused by intense rainfall, such as from
hurricanes and storms, but they can also be triggered by snow and ice melting. If the soils of the
basin are already wet (from some previous rain), then when there is a new rainfall event there will
be less infiltration in the soil and thus much more water is converted into runoff to the rivers,
increasing the flood frequency. Changes in land use, such as more impervious surfaces in
urbanized areas, also increase runoff rate and, henceforth, flood frequency and magnitude. Floods
may also happens when a dam (natural ice dam or human made dam) breaks.
As the water-level in the river channel rises to overtop the river banks (levees), the flood
goes through the stages described in Table 2:
Table 2 – Flood stages. Adapted from Abbot (2014, p. 363)
Flood stage Description
Action stage Water begins overtopping the banks
Minor flood stage Roads, parks, and yards may be covered by water
Moderate flood stage Building inundation occurs; roads are closed and evacuations may be
necessary
Major flood stage Buildings may be completely sub-merged, lives are threatened and
large-scale evacuations may be necessary
Early-warning systems for flood are based on hydrological and/or meteorological
forecasting (Younis et al., 2008). In hydrological forecasting, a gauging station upstream from a
city can warn in advance that the stream-flow is increasing to dangerous levels, and then the city
has time to evacuate while the waters flow from the gauging station to the city. On the other hand,
it is also possible to use meteorological forecasting to estimate how much rain will fall on the basin
for the next days, and then use hydrological modelling to infer how this rainfall will be converted
into stream-flow. As weather models keep being improved, as discussed in the previous section
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about hurricanes, this improvements will also be useful for flood early warning. One possibility
being tested for future early-warning systems is to monitor or model the soil moisture, in order to
anticipate the effects of infiltration or runoff in the case of rainfall events (Norbiato et al., 2008).
As satellite images were also becoming more accessible along the twentieth century,
mapping the extension of these floods also became more precise. The satellites that use radar pulses
are more useful than the ones that rely on solar light, because the radar pulses can pass through the
clouds and give a picture of the flood areas during the storm (Townsend and Walsh, 1998). In the
future, it is expected that the satellite radars will have better spatial resolution and returning period,
being able to monitor the floods around the world in nearly real time.
Section 2: A Report on a Myanmar Flood Disaster in 2015
The hurricane Komen happened from July to September 2015 and its landfall was on
Bangladesh (western neighbor of Myanmar), but most of the rainfall concentrated in the western
part of Myanmar (Figures 1 and 2), causing floods.
Figure 1 – Rainfall estimate in South-Southeast Asia in July, 2015. Data: Weather News (2015)
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Figure 2 – Flood affected townships in Myanmar, 2015. Sorce: OCHA (2015)
According to OCHA (2015), the flood affected more than 34.6 million people (66% of the
population in Myanmar), temporally displaced more than 1.6 million and killed at least one
hundred. The same report states that the flood destroyed more than 21,000 houses, 608 schools
and 840,000 acres of farmland, while also damaged more than 468,000 houses and 4,100 schools.
The number of affected people in each township in Myanmar can be seen in the map of Figure 3.
As the rivers are the main transportation mode in Myanmar, the heavy stream flow also hampered
navigation in most of Ayeyarwady river, causing economic losses and also impeding food and
other supplies to reach the affected people. The flooded cities had to close most of their commerce
and service buildings for many weeks, causing additional economic losses.
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Figure 3 – Number of people affected by flood per township in Myamar, as of 11th September,
2015. Source: ERCC (2015).
According to IFRC (2015), since June of 2015, Myanmar and Bangladesh were already
receiving heavy rainfall from monsoonal storms. In the end of July, the low pressure zone in the
Pacific Ocean near Bangladesh intensified the storms that grew to the tropical cyclone Komen.
According to ERCC (2015), Komen grew from a depression to a deep depression in 29 July, and
became a cyclonic storm in 30 July. GDACS (2015) classified Komen as a tropical cyclone storms
with maximum wind speed of 74km/h, in 30 July, 2015. The landfall of the Cyclone on Bangladesh
occurred on 30th July (Figure 4). After entering Bangladesh, in 31th July Komen lost strength and
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became again a deep depression (ERCC, 2015), but the it rainfall on Myanmar went on through
August and September. Over 500mm rainfall occurred because of the hurricane, being more than
double of the average rainfall for in many areas of Myanmar (Weather News, 2015).
The heavy rainfall over the already wet soils from the previous Monsoonal rains caused
floods on the Ayeyarwady river basin and other coastal basins in Myanmar (Weather News, 2015).
The stream flow in the mid-Ayeyarwady river reached levels of a 25 years flood (Figure 5). The
damage of the flood became worse as the heavy rainfall continued through the months of August
and September (Weather News, 2015, and Figure 6).
Figure 4 – Estimated Precipitation from the tropical cyclone Komen in 30th September, 2015.
Source: The Watchers (2015), with data from NASA/JAXA/SAI.
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Figure 5 – Stream-flow of Ayeyarwady River, at station 29. Source: Flood Observatory
(http://floodobservatory.colorado.edu/SiteDisplays/29.htm, accessed 26th September, 2015)
Figure 6 – Rainfall in the catchment of the Ayeyarwady River, at station 29, comparing 2015 with
previous years. Source: Flood Observatory
(http://floodobservatory.colorado.edu/SiteDisplays/29.htm, accessed 26th September, 2015)
It is difficult to analyze how an impact of such magnitude could be prevented. Myanmar is
a poor country, and 66 % of the population live in rural area (World Bank, 2015) and these farmers
do not have other alternative then practice subsistence agriculture on the fertile flood plains of the
rivers. The author has travelled to Myanmar in September, 2015, and was able to see how they
manage to adapt to this situation (pictures included).
Before the flood starts, the Department of Meteorology and Hydrology used its early
warning system, communicating with the village heads in advance (Thein, 2015). However, 55 %
of Myanmar population live in isolated communities that lack electricity (World Bank, 2015) and
usually do not have means for fast communication.
The farmers in Myanmar usually build elevated houses (Figure 7), that can face the most
usual floods. However, the flood in 2015 overpassed most of these houses (Figure 8). Other
farmers have floating houses, that can cope better with the floods (Figure 9). Many families from
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flooded farms fled to shelters near the temples that are usually built on the highest spots of the
landscape (Figure 10). Usually the farmers store rice from the previous season to eat during the
flood time, but if the flood lasts for a long period, such as in 2015, and the boats carrying food
cannot sail through the rivers, then the situation starts to be critical. Many farmers also turn into
subsistence fishery (Figure 11), to increase their food safety. As the flood got worse, it flooded not
only farmlands but also some riverine cities that were built on the river levees, causing higher
economic losses (Figure 12).
Figure 7 – Suspended farm house on Uru
River, Homalin, September, 2015
Figure 8 – Flooded house in Chindwin River,
Township of Homalin, Myanmar, September,
2015.
Figure 9 – Floating house in the township of Homalin, Myanmar, September, 2015.
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Figure 10 – Population flee from the flood to a shelter near a temple in the township of Homalin,
Myanmar, in September, 2015.
Figure 11 – Farmers fishing in the Uru River, township of Homalin, Myanmar, in September,
2015.
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Figure 12 – Flood in the city of Homalin, aerial view September, 2015
OCHA (2015) estimated that US$75.5 million dollars would be needed for emergency
response to this disaster, but at 16th September, 2015, only 23 million had been funded (30% of
the amount needed). With the available funds, 455,000 people received food assistance, 13,000
people received shelter kits, 5,000 people received dignity kits (clothes, underwear, sanitary
napkins, soap, toothbrushes, towels and other hygiene items), 1,525 mobile health clinics provided
services in the affected areas, and 136,000 water sources had been cleaned. The emergency
response also included training on psychosocial attendance for affected people and a lifeline radio
program to inform people about the flood extension and emergency responses.
Due to the increasing mining activities in Myanmar after 1989 (Earthrights International,
2004), most of the villagers and farmers started to install private wells as source of drinking water,
because they were afraid of heavy metal contamination in the river water. However, one critical
vulnerability is that many of these wells have been flooded in 2015. Although the international
funds covered cleaning some of the community wells of the villages, most of the individuals do
not have awareness about how to deal with their own wells. After the flood, most of them just
pump out the water from their wells until it gets clear again, and then re-start drinking it again.
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The correct procedure, according to Atiles and Vendrell (2012), would be using chlorine to
disinfect the wells after the flood. Without this procedure, most of the villagers are exposed to
bacteria contamination, causing diseases such as diarrhea and cholera.
Floods in Myanmar share similarities with floods faced by other countries in South and
Southeast Asia, such as India, Bangladesh, Cambodia, Vietnam and Thailand. The large flood
plains downstream of the Himalayas are densely populated by poor subsistence rice farmers.
Although these farmers have developed ways to cope with the regular floods, the damage of
extremely high floods are immense, especially because the government of developing countries
do not have money to provide relief, and then have to rely on international donations. In these
extreme floods, many cities that usually are not flooded suddenly have water raising on the streets,
causing high economic damage. One example was the flood in Bangkok, Thailand, in 2011, when
the Chao Phraya river overflowed and caused a damage of 500 billion dollars (Thongsawas, 2013).
Moreover, recent studies (World Bank, 2013) have shown that the frequency of extreme events of
rainfall is likely to increase in South and Southeast Asia, due the ongoing climate change.
In conclusion, the floods in Myanmar in 2015 caused by the tropical cyclone Komen,
resulted in high social and economic losses. The population, although adapted to regular inter-
annual flood, was not prepared enough for this flood that reached levels of a 25 years return period.
More than half of the population of the country was affected, with thousands of houses and farms
destroyed. The international help was not enough for appropriate emergency response activities.
This is a good example of how floods can be disastrous in countries of South and Southeast Asia,
that share similar physical and socio-economical characteristics with Myanmar.
References
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