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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

3

178-208 km/h Devastating damage will occur

4

209-251 km/h Catastrophic damage will occur.

5

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.

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