Risk assessment on cyclone based flooding on the socio-ecological system
of the Irrawaddy river delta, Myanmar
Author: Martin Vergouw; Annabel Isarin; Thomas Hofman; Laurens Beets
Majors: Earth Science, Biology, Human Geography
Tutor: Anneke ter Schure
Expert Supervisor: Andres Verzijl
Date: 22-12-2017
Table of content:
Introduction
Theoretical Framework: Problem Definition:
Selected Methods and Data: Results:
Conclusion:
Discussion & Recommendations: Reference List:
Abstract
Floods are the most common environmental hazard due to the fast amount of people living in low level coastal areas. This paper reviews cyclone induced flooding in the Irrawaddy river delta in Myanmar. In the Irrawaddy river delta flooding is a common occurrence, with the most notable occurrence being cyclone Nargis which struck in 2008. The flooding caused loss of life, destruction of infrastructure and salinization of agricultural land. For this interdisciplinary research, a quantitative risk assessment is conducted by integrating the viewpoints of different disciplines on the effects of
flooding due to cyclones. Theories from Earth Science, Biology and Human Geography were used to gain an integrated understanding of the ecosystems and it’s value within the delta. This is done by calculating the probability of the hazard, the economic evaluation of elements at risk and vulnerability of the delta.
Introduction
The Irrawaddy is the biggest river in Myanmar, stretching across the entire length of the country. It originates in the north of Myanmar in the Hengduan Shan mountains and debouches 2170
kilometers downstream in the Irrawaddy delta in Southern Myanmar. Myanmar’s climate is dominated by the dry and the wet season (Brewin et al., 2000). Furthermore, tropical cyclones are common in Myanmar. They develop above the Bay of Bengal south of the country during the spring and the fall (Wang et al., 2013). Three category four tropical cyclones struck the country in 2004, 2006 and 2008. Cyclone Nargis that struck Myanmar in 2008 caused severe damage to the social ecological system of the country comparable to the 2004 tsunami disaster in South Asia (Wang et al., 2013). It is predicted that the number of tropical cyclones and their potential intensity will increase due to the rising air temperature which will have severe consequences for Myanmar (Yu and Wang, 2009). The Irrawaddy delta is of utmost importance to Myanmar since it provides approximately one third of thel rice production in the country, which is the most important crop produced in Myanmar
(SeinnSeinn et al., 2015; Giosan, 2014). The Irrawaddy delta is the largest, most dense agricultural area of Myanmar. Flooding caused by tropical cyclones in this area can cause serious damage to the socio-ecological system. This paper will provide a risk assessment of flooding caused by cyclones in the region. This means calculating the hazard probability, the elements at risk, and the vulnerability of the area.
An interdisciplinary approach was necessary for this research, as a risk assessment crosses the boundaries of different disciplines and connects them. The perspectives from the disciplines Human Geography, Earth Sciences and Biology will be integrated to achieve a more complete and less one-sided view of the complex system of the Irrawaddy Delta. To make a correct risk assessment the following sub question have been made: “What is the hazard probability of flooding in the
Irrawaddy Delta?”, “What are the elements at risk?” and “What is the vulnerability of the Irrawaddy Delta?”. This to answer the main question: How does flooding affect the socio-ecological system of the Irrawaddy river delta?”. This will be presented by first elaborating on the theoretical framework that was used and explaining the methods that were used. Subsequently, the results will be presented followed by a conclusion and discussion.
Extreme floods are caused by two events, monsoons and cyclones. For this risk assessment the focus will be on floods caused by cyclones. Floods caused by monsoons are less extreme and can have positive effects on wetlands and agriculture. With monsoons and regular flooding it would thus be too difficult to separate ‘good’ flooding from ‘bad’ flooding with the limited data available, and thus can the impact not be estimated correctly (Endo, 2009). Cyclones will always have a negative impact on the delta, due to storm surges which lead to loss of life, destruction of infrastructure and salinization of the agricultural fields (Murty, 1986). Therefore, the focus will be on flooding due to cyclones.
The objectives of this research are to determine what the potential effect of a flood caused by a cyclone is in the entire delta and what the change it that a certain flood occurs. If this is determined, is can be made clear what measures are needed to reduce the impact and where these measures are needed most. Only with a proper risk assessment steps can be made towards protecting the
population and the social ecological system from the hazard. If Myanmar will be able to implement suitable adaptation and mitigation measures in the delta they can reduce the risk of flooding.
Risk assessments on flooding in delta’s have been done before, for example in the Mekong delta. However, this has never been done before in the Irrawaddy river delta. In this delta there has only been done research on the impact of all kinds of flooding on the social ecological system,
although not to a large extent. Risk assessments of other delta’s could contribute to the understanding of how flooding affects the social ecological system but, it is not sufficient to decide on which
mitigation and adaptation measures to implement. To do this it is necessary to know which parts of the delta are most vulnerable and where intervention can be most effective. The added value of this research for Myanmar is to give the country the required information to decide on which mitigation and adaptation measures to take to reduce the risk to flooding. The added value for further research is to show how comparing knowledge on the country’s social ecological system with risk assessment methods and other researches can result in valuable information for a country even if there is not a lot of data available.
Theoretical framework
Risk assessment method selection
Floods are extreme hydrological events. They can simply be defined as the rising of a body of water and its overflowing of normally dry land (Princeton University, 2005) (Cooley, 2006). This is ofcourse a broad definition that needs to be specified in order to investigate the impact of flooding in certain region. Only floods negatively affecting humans, infrastructure, agriculture and ecosystems can be perceived as hazards (Cooley, 2006). There are also floods that can be beneficial to humans as they can result in fertile alluvial soils as is the case for many soils in the Irrawaddy delta (Zaw et al., 2011). To exclude floods that are beneficial to humans there will be focussed on the impact on the Irrawaddy delta of floods caused by cyclones as discussed in the introduction.
This paper aims to provide a quantitative risk assessment following the Krewski et Al. (1982) risk assessment method. This method calculates the risk of flooding by multiplying the Hazard probability with the Elements at risk and the Vulnerability. This method reviews different, mutually exclusive, events (E1 .. En) (the hazard) and multiplies those with the probability and loss equivalents. The effectiveness of this method relies on a good database of the events. Hence, this method is not completely adequate for rare events, since less data on these events is available.
This method focuses on the objective risk and excludes the perceived risk. Distinctions are being made between objective and perceived risk because individuals often perceive risks intuitively and quite differently from objective assessments (Starr & Whipple, 1980). Perceived risk is seen as crucial in assessing risk, alongside objective research, because most people make decisions about hazards based on their perceived risk. Risk perception therefore has to be regarded as a valid component of risk management. Overcoming the gap between a perceived risk analysis or an objective risk assessment proves to be difficult. Rohrmann (1994) states that perceived risk varies between individuals and is affected by lifestyle, age, gender, occupation and other variables.
Therefore, it is seen as impossible to include perceived risk in an quantitative risk assessment (Smith & Petley, 2008). The quantitative risk assessment comprises of three parts. Firstly, there is the Hazard probability, secondly there has to be researched what elements are at risk. Lastly, the vulnerability of the river delta has to be investigated.
Hazard probability
In order to calculate the probability of a hazard the probability based approach will be used as described by Smith & Petley (2008). The probability of a hazard is based on the probability that a hazard will occur. With this information the size of floods in this case can be determined, since magnitude and frequency of a hazard are closely related (Smith & Petley, 2008). A flood that has a probability of occurring once every 100 years results often in a bigger disaster than a flood that occurs every year (Smith & Petley, 2008). With this information correct measures can be taken, like building dams that can withstand an once in a 100 year flood. The next step is using the historical data and ranking those events. Rank one is the largest flood with the highest water level, rank two is the second largest flood and so on. When looking at cyclones it can also be ranked by wind speed or pressure in mbar. Subsequently the return period (Tr) will be calculated with the formula: Tr = (n+1)/m, where n is the number of events in the period of the record and m is the rank of the event.
Then the percentage probability will be calculated by dividing 100 by Tr. With this can a plot be made that can estimate the return period for any desired flood level or visa versa. The downside of using the method is that in order to determine the probability of the flooding hazard it must be assumed that the past processes and events can give an accurate representation of the future. This method also requires a correct database.
What are the elements at risk?
The valuation of the elements at risk is a function of the value of the environment itself multiplied with the fraction of elements in the environment that are threatened to be lost by flood. The valuation of the elements at risk is actually a form of risk assessment in monetary units and the equation can be simplified to probability(p) * loss (L) (Smith & Petley, 2013).According to Smith & Petley (2013) the elements at risk in case of flooding are divided in primary and secondary elements. Primary elements of risk being deaths and diseases causing direct harm. The infrastructure and agriculture are
secondary elements and can harm the population on the long term.
The most effective way to determine the elements at risk is by monetary units (Meyer, Scheuer & Haase, 2009). This however becomes problematic when assessing primary elements at risk, for it is ethically challenging to put a value on human lives. In case of secondary elements at risk however, the expression in monetary units suits the interdisciplinary approach to risk determination. Monetary units provide a unifying valuation of elements that would be unconnected and
uncomparable in other ways. It poses an opportunity in comparing and summing up different elements at risk.
Another way of determining the elements at risk is by a Multiple Criteria Analysis (MCA). Meyer, Scheuer & Haase (2009) made such an approach, capable of integrating the economical, environmental and social factors. The identification of elements at risk is the most determining factor in this approach. As the identification of elements is location specific, the elements in this proposal will be based on previous events. This approach also requires a definition of risk for each of these
aforementioned elements, a certain threshold value that, when surpassed will determine the element as being at risk. The downside of using an MCA in comparison to monetary units is the information it provides for future research or decision making. The MCA provides information about the area’s prone to flooding, while monetary units give information about the damage that is done by flooding expressed in values that are more commonly understood. Thus to determine the value of the elements at risk required for our risk assessment, a monetary expression is prefered.
One of the elements at risk that will be used in this risk assessment is agriculture. Official statistics indicate that agriculture is the largest economic sector in the country, accounting for nearly 43 percent of GDP and providing the main source of livelihood for nearly 70 percent of the population (Haggblade et al 2014). The majority of Myanmar’s farmers are engaged in the production of rice, which occupies nearly 50 percent of total sown area (Shrestha, 2014). Reduced rice yield has a big impact on the population, in 1995 48.3% of the average population’s daily calorie intake consisted of rice (Kyaw & Routray, 2006). Floods caused by cyclones can be disastrous for the cultivation of rice. Heavy flooding of rice fields can directly destroy standing crops through heavy flows of water that cannot be coped with by the crops (Goyari, 2005). An example of a flooding event that destroyed a lot of agricultural land in Myanmar is the “Super tide” caused by the cyclone “Hudhud” in October 2004 (Swe & Ando, 2016). This exceptionally high tide devastated a large part of the Irrawaddy delta through overflowing villages and rice fields. In addition, the rice yield will decrease due to the salinization which delays and reduces heading and disturbs growing processes in the plant which is the most important for yield (Sheldon et. al., 2017). Floodwater with a conductivity of 2dS/m
Myanmar experiences high levels of biodiversity in comparison with other countries in Southeast Asia. Due to a combination of climate, habitat and geography multiple ecosystems are present (Rao et. al., 2013). According to the wildlife conservation society (2012), Myanmar supports 233 endangered species whereof 23 critically endangered, living in those ecosystems. The
Ecosystems most susceptible to flooding in the Irrawaddy delta are; Mangroves, Freshwater swamp forests, and flood plains/ Flooded grasslands/Wetlands. Politically isolation of Myanmar however makes the data available on biodiversity and ecosystems incredibly scarce (Rao et. al., 2013). Different ways of ecosystem identification and valuation will be presented for the three most susceptible environments.
Freshwater bodies in the Irrawaddy delta are especially vulnerable to biodiversity loss (Rao et. al., 2014). Unfortunately, freshwater biodiversity is very poorly documented in Myanmar (Rao et. al., 2014), however Allen et. al. (2010) projected the entire Irrawaddy freshwater region to contain 119 to 195 endemic species, this region however includes the Irrawaddy delta upstream river. To express biodiversity in monetary units is very difficult as each species has its own impact on its ecosystem.
In addition, ecosystems have several services which can be provided in provisioning,
regulating, supporting and recreational services (Dempsey, Robertson, 2012). The provisioning value of ecosystems primarily focuses on products, obtainable from the environment such as food and medicine. The regulating and supporting value includes services such as coastal protection by mangroves, or the importance of sustained biodiversity for the ecosystem as a whole. The
recreational value includes the services provided for future generations such as species extinction which can’t be undone. The Economics of Environment and Biodiversity (TEEB) project provides a way to express these services in monetary units (van der Ploeg & de Groot, 2010). TEEB project has done this for multiple regions in the world already. Although TEEB does not have figures for Myanmar in specific, the world average valuation of Wetland biodiversity protection 214$/ha/y (van der Ploeg, de Groot, 2010). As the biodiversity is high in the delta this valuation is potentially higher. The freshwater bodies itself won’t be affected by flooding thus the value of water bodies as flood protection mechanism does not have to be accounted for. The TEEB has significant advantages for determining ecosystem values for instance the ability to evaluate specific traits of an ecosystem such as the biodiversity protection, this makes it possible to evaluate these traits without having to derive and weight them from an existing database. There are however multiple properties of the TEEB in debate, for instance the expression of species loss/extinction in monetary units, which could be counterproductive in conservation of biodiversity according to Rodríguez-Labajos & Martinez-Allier (2013). Overall the contribution of TEEB on interdisciplinary research can be of great value for it combines the economics which are of great importance for decision making with the science behind ecosystems.
Freshwater swamp forests gradually arise from the mangrove forests downstream the
Irrawaddy river (Rao et. al., 2014). The freshwater however makes these ecosystems more vulnerable because salt intrusion due to flooding affects plant growth. The TEEB valuation of freshwater swamps worldwide concerning biodiversity protection 439 USD/y/ha (van der Ploeg, de Groot, 2010).
Vulnerability
Following the Vulnerability-Resilience Indicators Model (VRIM), socio-ecological sensitivity and resilience to flooding in the Irrawaddy river delta will be assessed. This method comprises of five sectors of sensitivity to flooding, and three sectors of coping capability that measure the resilience of the irrawaddy river delta to flooding. The five sectors of sensitivity are; settlement and infrastructure sensitivity, food security, ecosystem sensitivity, human health sensitivity and water resource sensitivity. The three sectors of coping capacity are; economic capacity, human and civic resources and environmental capacity. Indicators are aggregated and serve as proxies for their sectors. (Ibarrán
et Al., 2008). The eight sectors have 18 indicators in total. Ibarrán et Al. (2008) states that the selection of sectors and variables is based on a wide-ranged literature review and includes variables that can be measured, even though other more qualitative aspects are explicitly left out due to measurement issues or to a lack of a clear variable to represent specific concepts. The indicators can be seen in figure x. Hence why a large dataset needs to be available for the use of the VRIM model. After gathering the data the ranges of data will be normalized. Subsequently the means of each sector will be computed. This makes the data comparable to other regions and countries by using
comparative measurement (Brenkert and Malone, 2005). Measuring the vulnerability is a key part in a risk assessment as it shows what the strong and weak points are in relationship to the risk (Smith & Petley, 2008). The reason VRIM is useful is because it aims to compare and integrate impacts between different sectors and populations. The IPCC stated that this is an important feature that should be more integrated in impact assessments (Watson et Al., 1996). Luers et al. (2003: 257) criticises the uses of proxy indicators to measure vulnerability, the paper states; “While the indicator approach is valuable for monitoring trends and exploring conceptual frameworks, indices are limited in their application by considerable subjectivity in the selection of variables and their relative weights, by the availability of data at various scales, and by the difficulty of testing or validating the different metrics”.
Problem Definition
It is clear that flooding in the Irrawaddy delta damages infrastructure, human lives, ecosystems and agriculture (Lone, 2017; Swe & Ando, 2016). However, no research has been done on the exact risk of these elements in the delta. This knowledge gap complicates the development of suitable
adaptation and mitigation strategies in the Irrawaddy delta. Hence, why it is important to assess this risk in the delta. Therefore, the research problem of this paper is defined as the risk of flooding for the socio-ecological system of the Irrawaddy river delta.
The risk of flooding for the socio-ecological system of the Irrawaddy river delta is a complex problem.This can be concluded when comparing the properties of the problem with the different properties of a complex system as defined by Menken & Keestra (2016). Hereby a complex problem is described as problems occurring on different system levels, in which different factors are involved and there is no consensus about the problem definition and the most adequate way to solve the problem. One property of a complex problem that is applicable to this problem is connectivity. The case studied in this paper is complex because the Irrawaddy delta is built up from infrastructure, human lives, ecosystems and agriculture, which are four parts of the system that are connected, but operate independently and react differently to flooding. In addition, this problem is nonlinear to some extent as flooding is caused by weather events and climate change. Weather events and climate change can hardly be predicted, there will always be a degree of uncertainty as weather events are different every year and climate change may be reinforced or balanced by many factors. Climate change can be defined as emergent which is another property of complex systems. Moreover, robustness and resilience play an important role in this problem as ecosystems and agriculture will show robustness to flooding in a certain way and once damaged their resilience will determine part of the vulnerability of ecosystems and agriculture.
A complex problem can be answered through an interdisciplinary approach, as is the case in this paper (Menken & Keestra, 2016). The problem can not be solved from one disciplines viewpoint, as the problem spans over different disciplines. An interdisciplinary approach is needed when the view of the different disciplines differs regarding a complex problem, or when a deeper understanding is needed of the problem, when the driving factors of the system span over different disciplines, or when the problem simply can not be answered from one disciplines viewpoint (Menken & Keestra, 2016). The complex problem of this paper touches three disciplines Earth Sciences, Biology and Human Geography.
While Earth Sciences focuses more on the dynamics of the flood and the salinization that occurs, focusses Biology on the impact on the ecosystems and Human Geography on the elements at risk. The different systems within the problem are interlinked and require an integrated view to be answered (Menken & Keestra, 2016). Combining the different disciplines leads to an integrated approach to a complex problem. For this paper is a risk assessment chosen to describe a complex problem, the impact of flooding on the socio-ecological systems of the Irrawaddy river delta. A risk assessment combines the views of the disciplines and therefore answers the complex. So, in order to achieve a full understanding of the problem and its systems are the different insights from the
disciplines needed to lead to an integrated result.
To integrate multiple disciplines in an interdisciplinary research, several integration
techniques are available. This paper tries to redefine concepts such as flooding, which has different definitions between the disciplines, to create a common conceptual language and get on the same level. By integrating the vulnerability approach as Ibarrán (2008) explains it, the TEEB method for evaluating ecosystems,as Van der Ploeg & de Groot (2010) explain it with the Krewski et al. (1982) method for a quantitative risk assessment. This paper tries to organize the existing theory and relate
them in a new framework of a risk assessment to integrate the different disciplines, and a cross table to highlight the interconnections between the different parts of the risk assessment. A schematic overview of the risk assessment can be seen in figure 1 in the appendix.
Selected methods and data
Risk assessment
The quantitative risk assessment will be used. This method comprises of the hazard probability, elements at risk and the vulnerability. This research will be conducted using secondary data as the timeframe and resources of the research do not allow for primary data to be collected in the Irrawaddy river delta. In addition, collecting primary data would not be necessary since much secondary data is already available. This secondary data is used because there is not much data of the area available and this cannot be gathered in this research.
Hazard probability
The hazard probability will be measured by using the method of Smith & Petley (2008). In this method, historical data of flooding is studied and the events are ranked, after which the probability of flooding is calculated.The information needed to calculate the hazard probability comes from historical data. The historical data of the hazards will be gathered from the website reliefweb
(https://reliefweb.int/country/mmr) and peer reviewed papers such as Needham et Al (2015), Brakenridge et Al (2017) and Kotal et Al (2008).
The hazard probability as described by Smith & Petley (2008) uses two formula. The explanation of the formulas used for this method is described in the theoretical framework, the formulas are Tr = (n+1)/m and P = 100/Tr. This calculation will be conducted for every observation separately. The hazard used for this probability calculation is the cyclone. The reasoning behind this is that significant flooding in the Irrawaddy river delta that causes salinization is caused by high waves from cyclones and monsoons (Miyaoka, et Al, 2012). Cyclones develop often in the Bay of Bengal: 54 have been observed since 1954 till 2015. However, this paper will only focus on flooding caused by cyclones that struck the Irrawaddy river delta directly. The cyclones will be ranked based on the lowest measured pressure in mbar during their lifespan as pressure influences the wind speed of the cyclone. The Saffir-Simpson scale is the most commonly used cyclone intensity scale, but it only consists of four categories, so multiple cyclones will be in the same category and thus can not be ranked correctly. Thus, will the pressure in mbar be used for a correct ranking. Using a combination of these sites and papers should be enough to make a correct database on which a risk assessment could be made. It must be noted that the events in the database are drawn from the same statistical population and are independent.
Elements at risk
The elements at risk consist of primary and secondary elements at risk. The primary elements are human deaths and diseases, the secondary elements are infrastructure, agriculture and environment. These elements must be expressed in monetary terms. This proves to be difficult for the primary elements, as these are not easily expressed in economic value. The question of how much a human life is worth cannot be answered in this paper. Due to the subjectivity of the loss of value per diseased person or death, these are not taken into account in this research.
The damage a flood caused by a cyclone will cause to the social ecological system in the Irrawaddy delta can be expressed in monetary units. To be able to do this the damage of a flood caused by a cyclone on the Irrawaddy delta’s agriculture and ecosystems will be approximated. The hazard probability will be calculated for cyclones with a magnitude between 3 and 5 on the scale of Saffir-Simpson. Therefore, for approximating the damage on the social-ecological system the impact of a cyclone with a magnitude of 4 on the scale of Saffir-Simpson will be used. First, the value of the
ecosystems and agriculture in Myanmar’s Irrawaddy delta will be estimated through using TEEB. For the agriculture only rice production will be taken into account as this is the most important crop in the country and no data could be found on the production of other crops. Then, the damage a certain flood will cause to the agriculture, ecosystems and infrastructure will be approximated by using literature, data on floods that occurred in the delta, and data of flooding in comparable regions such as the Mekong delta. This damage will be expressed in monetary units. Finally, the damage on all three sectors in the delta will be added up to conclude what the total damage of a cyclone with magnitude 4 on the Irrawaddy delta is.
Vulnerability
For the Vulnerability-Resilience Indicators Model (VRIM) most of the information will be included from the national population census and administrative data of organisations such as The World Bank, Myanmar’s government websites such as websites of the Ministry of Agriculture, Livestock and Irrigation and the Ministry of Natural Resources and Environmental Conservation, and the FAO. This is sufficient because most of the data needed in the VRIM model are measurable entities for the river delta that are already available. 18 of these indicators, divided into 8 sectors, will measure the vulnerability in a region as they represent characteristics relevant to human well being and can all be found in secondary data (Ibarrán et Al., 2008). The indicators are shown in figure 2 in the appendix. The data of the Irrawaddy delta will be indexed with the world average to create a ratio for myanmar, that shows the data from the indicators and sectors and can be compared to other areas, or countries around the world. This is done by multiplying the Myanmar data by 100, and dividing this number by the world average. After this the geometric average has to be calculated per sector, and after that the average for both the total sensitivity and coping capacity as well. This way the data from each sector can be compared to the sectors in other areas and countries. The total vulnerability will be calculated by subtracting the total sensitivity from the total coping capacity. This number can also be compared to the vulnerability of other countries, analysis on the points that differ the most from world averages shows what areas are strong, and what areas are sensitive. This information is crucial in policy making regarding the improvement of the vulnerability.
Results
Hazard probability
For every observed cyclone that hit Myanmar only six struck the Irrawaddy river delta directly and caused a flood, for the time period 1982-2017. This time period is chosen because accurate data is only available since 1982. This means that for a 35-year time period six times a cyclone occurred. That shows that every 5.83 years a cyclone hits the Irrawaddy river delta, the calculation being 35/6 = 5.83 years. The first cyclone in this list is the Gwa cyclone in 1982, with a pressure of 914 mbar. The second Hudhud, with a pressure of 951 mbar. Thirdly is cyclone Mala, with a pressure of 954 mbar. Fourthly is cyclone Nargis, with a pressure of 962 mbar. Fifthly is cyclone Giri, with a pressure of 950 mbar. Sixth is cyclone Maarutha, with a pressure of 966 mbar. The rankings, respective pressure, date, return period (Tr) and percentage probability is seen in table 1.
Ranking Pressure (mbar) Date Tr Percentage Probability (1) Gwa 914 1982 7.0 14.2 (2) Giri 950 2010 3.5 28.58 (3) Hudhud 951 2004 2.34 42.73 (4) Mala 954 2006 1.75 57.14 (5) Nargis 962 2008 1.4 71.42 (6) Maaruth a 966 2017 1.17 85.48 Table: 1
The Tr value indicates the return period for each hazard in years. As seen in the table reduces the Tr with a lower ranking, as a higher ranking means a rarer event (Smith & Petley, 2008). The most important data from this is the percentage probability, this indicates the probability of a cyclone occurring. The percentage probability is increasing with a lower ranking. Since there are six cyclones in the 35-year time period it means that this is the probability of a hazard occurring every 5.8 years. So, in order to achieve the probability per year must the percentage probability be divided by 5.8. Thus, the probability that it occurs every year is calculated in table 2 below.
Ranking Yearly Probability
Gwa 2.45 %
Giri 4.90 %
Mala 9.80 % Nargis 12.25 % Maaruth a 14.74 % table: 2
Table 2 gives the yearly probability of each event. An interesting note is that cyclone Gwa for example did not have a 100% chance of occurring in the 35-year time span. If the 2.45% yearly probability is multiplied with the time span of 35 years, the answer is 85.75% probability. This means that a cyclone with the same pressure as cyclone Gwa has an 85.75% chance of occurring in the 35-year time span. With the yearly probability for each event can the risk assessment be completed. Since the formula for the risk assessment is multiplying the hazard probability of the event with the elements at risk and the vulnerability. The elements at risk and the vulnerability will be calculated in the next part.
Elements at risk
Of the 67.7 million hectares contained within the boundaries of Myanmar, 11.84 million hectares (18 percent) were sown in the 2012-2013 cropping season, which is the most recent data that has been found (Tun et al., 2015). The most important crop that is produced in Myanmar is rice. Rice production covers approximately 40 percent of the agricultural land in Myanmar, which is 4.74 million ha
(Shrestha, 2014). One third of this land used for rice production is located in the Irrawaddy river delta which makes this region important for Myanmar (SeinnSeinn et al., 2015). The amount of land that is used for this rice production is 60 percent of the entire delta (SeinnSeinn et al., 2015). This
approximates 1.42 million ha.
The calculations of TEEB have been used to assign a value to the rice production. The most applicable calculations are those from the agriculture in China. This was the only land in Asia for which the Total Economic Value (TEV) of the agriculture was calculated. As rice production is important both in China and Myanmar this TEV was used for the valuation of the agriculture in Myanmar (SeinnSeinn et al., 2015). The TEV of the agriculture in China was calculated to be 14080,90 CNY/ha/yr (Ploeg et al., 2010). When multiplying this amount with the amount of ha cultivated with rice in the delta the value of rice production in the Irrawaddy delta per year. This is approximately 20 billion CNY (19 994 878 000). Which is 3,04 billion USD.
The category four tropical cyclone Hudhud struck Myanmar in October 2004. It devastated a large part of the Irrawaddy delta including a lot of agricultural land. Agricultural land was damaged by the overflowing of water and the effects of salinization on the long term (Swe and Ando, 2016). It is assumed that the damage of other tropical cyclones in category four is comparable. A survey of Swe and Ando (2016) showed that 92,5 percent of respondent rice farmers in the delta were affected by the cyclone. Furthermore, 10 percent of the farmers experienced total yield loss due to this cyclone, 27.5 percent experienced more than 50% of their yield affected, 55 percent experienced less than 50% of their yield affected and only 7.5 percent experienced no damage. Calculating the exact damage appeared to be too complicated since there is no data on what part of the delta will be flooded due to the cyclone.
Ecosystem valuation
The results of the ecosystem valuation, as described in the method section, are derived from the TEEB method. The wetland biodiversity has a value of 214$/ha/y (van der Ploeg, de Groot, 2010), The TEEB valuation of freshwater swamps worldwide concerning biodiversity protection had a value of 439$/y/ha (van der Ploeg, de Groot, 2010). These values are based on world averages as the specific valuation of these ecosystems has not been measured for Myanmar. NASA (2014) however dit estimate the value of mangrove forests specifically for Myanmar. They estimated the value of mangroves to be 100.000-277.000$ per square kilometer (1000-2.770$ha/y) and projected it to be a stable source of income if managed sustainably. Satellite data showed the Irrawaddy delta contained 462 km-2 mangrove forest in 2013. The mangrove forest area was determined by the landsat infrared and near infrared bands that are suitable to distinguish different spectral signatures of land cover types. The amount of biomass was derived from the canopy height in the delta: (Height (m) = 1.12 x Hsrtm – 2.19 Biomass = 10.8 x H (m) + 34.9) (NASA ,2014). The total value of the mangrove forests in the Irrawaddy delta thus amounts to an estimated 90.000.000$/y.
Vulnerability
To measure the vulnerability the VRIM is used to measure vulnerability, this method counts 18 indicators, divided over five sectors of sensitivity, and three sectors of coping capacity. The indicators serve as proxies for indicators that are harder to measure (figure 2 in the appendix). The data from myanmar is indexed based on the world average. The base value being 100, and the index numbers are the ratio of the world average. The indicators are all divided into sectors of sensitivity and coping capacity. An average for each sector, and an average for the overall coping capacity and the overall sensitivity of the Irrawaddy delta can be seen in table 3. To calculate the overall vulnerability, the sensitivity has to be subtracted from the coping capacity. Table 3 shows that the vulnerability in the delta is -52.74. The vulnerability is influenced most by the sectors ‘environmental capacity’ and ‘settlement sensitivity’. What immediately stands out is that the population in the irrawaddy delta is 103 times more at risk from flooding than the world average (Nicholls et al., 1999; GFDRR, 2017 ). This means that the potential extent from disruption from sea level rise is extremely high. The population density is 8.03 times higher than the world average, which puts a lot of pressure and stresses on ecosystems. The GDP per capita is very low, being only 0.08 that of the world average. This means that access to markets, technology and other resources useful for adaptation are not readily available. These are the sectors and indicators that influence the vulnerability the most, and deviate the most from world averages. While comparing the total vulnerability with that of other regions, the vulnerability of -52.74 can be compared to other regions in the world.
Sector Average per sector
Sectors of Sensitivity Settlement sensitivity 1737,59
Food security 92,96
Ecosystem sensitivity 41,81 Human health sensitivity 110,15 Water resource sensitivity 105,60 Sectors of Coping capacity Economic capacity 41,53
Human and civic resources
72,89
Environmental capacity 272,90
Overall sensitivity: 151,02 Overall coping capacity: 98,29
Total vulnerability: -52,74
Table 3: Vulnerability
Concluding this risk assessment is the Irrawaddy river delta extremely vulnerable. Determining the flood risks threatening the inhabitants of the Irrawaddy delta requires a deep understanding of the dynamics of such floods with the elements at risk. The information required to adequately determine the vulnerability of elements at risk appeared to be difficult to obtain. We therefore will not be able to express the final risk in monetary units. We were however able to determine the value of elements at risk, the chance of a flood caused by a cyclone and the vulnerability of the delta compared to other countries.
The Irrawaddy river delta is less susceptible to floods than for example bangladesh (Needham, 2015), however compared to the world average, it scores low. The yearly hazard probability for the cyclone with the highest pressure is 14.74%. But the impact of this storm will not have an enormous impact. The cyclone with the lowest pressure, Gwa, will have a larger impact as lower pressure means higher cyclone intensity. Fortunately, it only has a 2.45% chance of occuring every year. This means that it has a 100% chance of occuring every 41 years, with the calculation being 100/2.45. But this does not mean that it will occur, this is only the predicted chance. But the delta is still susceptible for floods due to cyclones since on average a cyclone will hit the Irrawaddy river delta every 5.8 years.
The vulnerability compared to other regions in the world is -52.74. This is mainly due to the sectors ‘environmental capacity’ and ‘settlement sensitivity’. These two values deviated significantly from the global average and therefore influence the vulnerability heavily. With the delta being 103 times more susceptible to floods than the rest of the world indicates the importance of this risk assessment as it can help develop future mitigation strategies. With the GPD also being below average becomes it apparent that developing mitigation strategies will be difficult since inhabitants will not have sufficient access to technology.
The vulnerability is a function of coping capacity and sensitivity. The most influential factor concerning coping capacity turned out to be environmental capacity. The most important environment being the mangrove forests in the Irrawaddy delta, due to the high ecosystem value and their
abundance (NASA, 2014) . The mangrove forests have a strange interaction with flooding as the roots of mangrove forests help to retain sediments and break waves they are part of the protection itself (Nibedita et. al., 2014). However, when heavy floods hit, the mangrove forests become part the elements at risk themselves for they are the most carbon rich forests in the tropics, containing 1023 mg C per hectare, making them an important supporting factor in the delta (Nibedita et. al., 2014). The mangroves provide habitat for fishes influencing fisheries and biodiversity, they are the most important source of fuel (timber wood), and they prevent erosion. However the coping capacity of the
environment is quite high, the sensitivity appears to be much higher as a result of settlement sensitivity.
The sensitivity has an effect on the elements at risk for if the sensitivity is higher, the more likely it will be that damage will occur. The sensitivity of food security is quite high, this is a concern worth taking into account considering the total economic value of 3.04 billion dollars a year of the rice fields present in the delta.
It has become clear that a risk assessment like this is quite complicated. Firstly, there was less data available than anticipated, which was mostly problematic for the calculation of the elements at risk. This lack of available data became clear at a point during the research where it was not possible anymore to modify our research. Furthermore, we have not been able to integrate the separate parts of the quantitative risk assessment. The calculation of the vulnerability has been done successfully but the result cannot be used for further calculations due to a lack of research on the integration part.. Therefore, the vulnerability is not suitable for calculating the final risk. In addition, there has been some problems with communicating between group members. This led to an unexpected last-minute switch from focussing on floods caused by monsoons to floods caused by cyclones. The lack in data on regular flooding should have become apparent during the first moment of searching for data in the Irrawaddy delta. Thus, due to the late switch there is not much elaboration on cyclones in this
research. This switch to focussing on cyclones also made the calculation of the elements at risk a lot more complicated. Beforehand we were already struggling with lack of available data, but it turned out to be impossible to distinguish the damage done by the flooding from the damage done by the rest of the cyclone. Moreover, it was hard to determine which part of the delta will flood when a cyclone strikes the delta. There was no data of previous events found that described this. In addition, further research should have been done on the valuation of ecosystems as the monetary valuation method that was used appeared not to be very accurate. Values of other countries were used to calculate the value of the ecosystems and agriculture in the Irrawaddy delta. Using a MCA might have been more accurate. Finally, the objective risk as stated in this research is not entirely objective as several choices had to be made to calculate this risk. These choices have been made based on insights from all group members and are therefore, subjective to a certain extent.
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Appendix
Figure 1: Schematic overview of the risk assessment
Figure 2: Vulnerability sectors
Sector Indicator Proxy for Data
Myanmar World average Index Settlement sensitivity
Population at flood risk, (GFDRR, 2017;
Nicholls, 1999)
Potential extent of disruptions from sea level rise
3,10% 0,03% 10333,
3
Population without access to clean water (%) (WaterAid, 2017; Water.org, 2017) Access to basic services to buffer against climate variability 32,14 11,00 292,2 Food security Cereals production (KG/Ha) (Tradingeconomics.com Degree of modernization in the agriculture 3714,00 3907,00 95,1
, 2017;
Data.worldbank.org, 2017a)
sector
Protein consumption per capita (Chartsbin, 2017) Access of a population to agricultural markets 70,00 77,00 90,9 Ecosystem sensitivity Land irrigated (%) (Tradingeconomics.com , 2017) Degree of human intrusion into the natural landscape
24,76 21,00 117,9
Use of fertilizer (KG/ha) (Tradingeconomics.com , 2017; Data.worldbank.org, 2017b) Nitrogen/phosph orus loading of ecosystems and stresses from pollution 20,47 138,04 14,8 Human health sensitivity Completed fertility (Statista.com, 2015; OECD, 2017) Composite of conditions that affect human health 2,23 1,70 131,2
Life expectancy (years) (Data.worldbank.org, 2017c; World Health Organization, 2017) Life expectancy 66,04 71,40 92,5 Water resource sensitivity
Renewable supply and inflow of water (Fao.org, 2017a; World in Data, 2017) Ratio of water supply from renewable resources and withdrawals 2,80 5,00 56,0 Percipitation (mm) (Tradingeconomics.com , 2017; Pidwirny, 2008) Precipitation amount in mm 2.091,00 1.050,0 0 199,1 Economic capacity GDP per capita (Tradingeconomics.com , 2017; CIA.gov, 2017) Distribution of access to markets and other resources useful for adaptation 1.420,50 16.400, 00 8,7
Income equity (Data.worldbank.org, 2017c; Roser, 2017) Realization of the potential contribution of all people 38,10 64,90 58,7 Human and civic resources Dependency ratio (indexmundi, 2017; Data.worldbank.org, 2017b) Social and economic resources available for adaptation 49,05 54,20 90,5 Literacy (%) (UNICEF, 2017) Human capital and adaptability of labor force 92,70 86,25 107,5 Environme ntal capacity Population density (km2) (De Lacerda, 2002) Population pressure and stresses on ecosystems 442,00 55,00 803,6 Air polution (ppm) (Lelieveld et al., 2001; WHO, 2017)
Air quality and other stresses on ecosystems 200,00 85,00 235,3 Unmanaged land (%) (Tradingeconomics.com , 2017) Landscape fragmentation and ease of ecosystem migration 19,65 20,74 94,7
