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Cyclone Nargis Case Study Geography Online

Low pressure systems - formation of tropical storms

The figure shows  how cyclones form. The green arrows show where warm air is rising. The red arrows indicate where cool air is sinking. 

•         Tropical cyclones form only over warm ocean waters near the equator. 
•         To form a cyclone, warm, moist air over the ocean rises upward from near the surface. As this air moves up and away from the ocean surface, it leaves is less air near the surface. So basically as the warm air rises, it causes an area of lower air pressure below.
•         Air from surrounding areas with higher air pressure pushes in to the low pressure area. Then this new “cool” air becomes warm and moist and rises, too. And the cycle continues…
•         As the warmed, moist air rises and cools the water in the air forms clouds. The whole system of clouds and wind spins and grows, fed by the ocean’s heat and water  evaporating from the ocean surface.
•         As the storm system rotates faster and faster, an eye forms in the centre. It is very calm and clear in the eye, with very low air pressure. Higher pressure air from above flows down into the eye.

When the winds in the rotating storm reach 39 mph (63 kmph), the storm is called a “tropical storm”. And when the wind speeds reach 74 mph (119 kmph), the storm is officially a
“tropical cyclone” or hurricane. Tropical cyclones usually weaken when they hit land, because they are no longer being “fed” by the energy from the warm ocean waters. However, they often move far inland, dumping many centimeters of rain and causing lots of wind damage before they die out completely.


Case study 7: Cyclone Nargis, Burma

The weather event:
Violent storms that form overwarm seas above 27 degrees Celsius. They are areas of intense low pressure around which very strong winds
over 200km/h and heavy rain rotate. 
The centre of the storm is a calm ‘eye’ of sinking air. The winds produce wave surges 7 metres high, which flood low-lying areas like
the Irrawaddy Delta. 
Once in a while, a tropical thunderstorm grows and grows, becoming a giant hurricane 

How it affected people:             
  –      130,000 people died - Farmers 
  –      Buildings damaged – 800,000 homes destroyed 
  –      260,000 people displaced – including fishermen and
children could not go to school 
  –      Shortages of food as the land was flooded 
  –      Spread of disease due to sewage – water sources become
polluted 

How it affected the environment: 
  –      Rice fields were flooded in the Irrawaddy Delta – damage to farmer’s livelihoods 
  –      2008 & 2009 harvests of rice were destroyed 
  –      Strong winds up to 135 mph 
  –      Storm surge of 7.6m 
  –      Heavy rainfall & flooding 
  –      Farmland, livestock, fisheries & animal habitats were all destroyed 

Responses to the hazard:
People were rescued
Clearing of flood debris
Giving people fresh water, tents and food
Building of a new flood levee


1. Introduction

Coastal regions face an immense threat of property damage and loss of life from cyclones. Together with sustained winds and heavy rains, cyclone-induced surges are one of the main sources of casualties and damage. Surge water can penetrate up to 50 km from the coastline, causing salinisation of fertile farm lands and resulting in death tolls in the thousands (Russo, 1998; Conner et al., 1957). Improved forecasting of the devastating effects of cyclones provides for early warning opportunities and more timely disaster management strategies (Joseph, 1994; TCG, 2008; Madsen and Jakobsen, 2004; Katz and Murphy, 1997).

Early studies on surge estimations date back to the 1950s and used the central pressure of cyclones to predict maximum surge height (Hoover, 1957; Conner et al., 1957); Tancreto (1958) improved on these methods by stratifying the cyclones according to their wind direction to better estimate surge heights, and Chan Walker (1979) improved surge height estimations by using approach paths. Pore (1964) used multistation surge models to predict surge heights, and Jelesnianski (1972) developed a methodology for estimating maximum surge heights via nomograms that used maximum wind, radius of the maximum wind, direction of landing, bathymetry, and central pressure data. Russo (1998) attempted to formalise Jelesnianski's nomographs by defining a bathymetric correcting factor based on geographical coordinates. Within the past several years, Artificial Neural Network (ANN) models have been applied to estimate surge heights using maximum wind velocity, wind direction, and central pressure (Lee, 2006). On the basis of tidal records, Jan et al. (2006) used the distance between tidal station and typhoon centre in conjunction with the central pressure, maximum wind velocity, and maximum wind radius, to estimate surge heights. Huang et al. (2007) proposed a regressive surge model based on the Jan et al. (2006) approach, however, it is difficult to reconstruct a surge profile using this type of model because calculated surge heights are valid for only a specific location.

Cyclone advisories, which are issued by different agencies, generally provide maximum wind velocity, central pressure, radii of at least three wind velocities, and landing point/direction estimation (Vatvani et. al., 2002; Joseph, 1994). Using such parameters from cyclone advisories in spatially distributed Geographic Information System (GIS) models is crucial for building timely operational models and providing information to guide early warning and response efforts. Most GIS-based models of cyclone risk are based on storm probability and provide only a general estimation for probable cyclone damage (Puotinen, 2007; Hossain and Singh, 2003). Event-based GIS models typically assume uniform surge height and are applied for the actual landing point which is known only after the cyclone has made landfall (e.g. Gorokhovich and Doocy, 2008). Their simplicity and speed are advantageous, however, they bear significant uncertainties which are very important drawbacks for emergency managers (Zerger, 2002; Zerger et al., 2002). Even though estimations of event-based models can be improved by using Light Detection and Ranging (LIDAR) elevation data (Webster et al., 2004), the assumption of uniform storm surge height remains a principal drawback for these models. With the exception of complex hydrodynamic models that require heavy parameterisation, calibration data, and skilled personnel, non-GIS-based numerical analyses have focussed on estimation of the surge height rather than its spatial distribution (Huang et al., 2007; Jain et al., 2006; Jan et al., 2006; Kumar et al., 2003). Though hydrodynamic models are spatially distributed and physically based (Jain et al., 2006; Madsen and Jakobsen, 2004; Flather, 1994), they cannot be readily applied in emergency contexts and are more effective in later stages of post-disaster analysis and mitigation planning.

The GIS methodology proposed in this study estimates affected areas and population, using a spatially distributed surge profile during a cyclone. The method aims to increase the reliability of real-time GIS models by using cyclone wind velocity distributions generated from advisory data for estimation of the surge profile throughout the coastline. The methodology was applied for cyclone Nargis, which landed in Myanmar on 2 May 2008 and which is considered the deadliest cyclone since 1970, causing over 138 000 deaths and widespread damage and displacement (TCG, 2008). The surge profile was estimated for different time advisories and discretised to make it compatible with the Shuttle Radar Topographic Mission (SRTM) data. Affected area and populations were estimated from digital elevation and population data within surge buffers that were created using the actual landing point and surge profile derived for the latest advisory. Results were compared with the output of a simple GIS model relying on a constant surge height that was estimated by the central pressure of the cyclone. The proposed GIS methodology was also applied for surge profiles estimated from different time advisories, using both actual and advised landing points to analyse the effects of different surge profiles and landing-point locations/directions on estimates of the affected area and population.

2. Methods

2.1. Study area and data

Coastal regions of the Bay of Bengal are frequently affected by cyclones with a decadal frequency of tropical cyclones (with wind velocities of 34 knots or greater) in the Bay of Bengal, of approximately 50 (Joseph, 1994). In the case of cyclone Nargis, damage assessments and relief efforts were restricted by the military junta and survivors faced many challenges in the months following the cyclone because of limited humanitarian assistance. Simple GIS models relying on post-disaster data provided a useful tool for disaster managers to identify the most affected areas and populations.

National Hurricane Center (NHC) data archive (NHC, 2008) and cyclone advisories provided by the Indian Meteorological Department (IMD) were used to build wind velocity distributions. Table I shows winds velocities and radii provided by different time advisories during the Nargis development. Nargis's original and advisory paths are shown in Figure 1. Since historical records for Myanmar's coastline do not contain parallel wind velocity and storm surge measurements, the data for the Gulf of Mexico were used to define the regressive structure of surge models based on the wind velocities (NDBC, 2008; CFHC, 2008). Only few stations have valid data (Table II), since many stations were damaged during strong winds, or because the distance between available station and hurricane centre was too far to measure extreme wind velocities. Archiving, Validation and Interpretation of Satellite Oceanographic Data Center (AVISO) produced the sea anomalies product for the cyclone Nargis that was used to calibrate the regressive surge model (hereafter called the wind-based surge model). The SRTM 3 arc-second (90 m resolution) data provided by CGIAR-CSI was used to create a digital elevation model of the affected coastal region. Population data from the Gridded Population of the World, version 3 (GPWv3), which is based on the most recent census and has spatial resolution of 1 km, was used to estimate the affected population (SEDAC, 2008).

114.486.939464683157468316746831394683130
215.087.536444674157467417646741674674157
315.889.839463774176377419437741763774157
415.990.746575683185568320456831855683167
515.792.651645611120456120241561022044693194
615.993.7597256111194561302415613024156111213
PTAT2Hurricane Bret8/18/19998/24/199965944
 Hurricane Emily7/10/20057/21/200569925
GDIL1Hurricane Lili9/21/200210/4/200265940
DPIA1Hurricane Katrina8/23/20058/30/200578902
 Hurricane Rita9/17/20059/24/200578897
 Hurricane Ivan9/2/20049/24/200474910
LONF1Hurricane Wilma10/15/200510/25/200578882
 Hurricane Katrina8/23/20058/30/200578902
 Hurricane Charley8/9/20048/15/200465941
SMKF1Hurricane Wilma10/15/200510/25/200578882
 Hurricane Jeanne9/13/20049/27/200454950
 Hurricane Katrina8/23/20058/30/200578902

2.2. Methodology and application

Simple GIS models of storm surge height generally rely on a constant surge height calculated from the central pressure of the cyclone. However, the surge height is dependent on a multitude of factors including central pressure, wind velocity, forward speed of the cyclone, bathymetry, and tidal level of the coastline (Conner et al., 1957). Central pressure has a minor direct hydrostatical effect on surge height, but there are also major indirect effects from winds. Surge height is not uniform along the coastline due to the physical structure of a cyclone and the effects of both bathymetry and topographic irregularities. To account for wind distribution and variable surge height, wind models calibrated by advisory data can be used to estimate the surge profile for any cyclone position; GIS models can subsequently be applied to estimate the affected land area and population. This methodology is illustrated in Figure 2 and includes the following sequence of procedures: (i) create a mathematical model of relationship between wind velocities and surge heights (i.e. wind based surge model), using pre-cyclone data; (ii) calculate the wind velocity distribution using advisory data; (iii) derive the surge profile from the wind velocity distribution, using the wind-based surge model; and (iv) estimate affected land area and population from digital elevation and population data using surge buffers that were created for the discrete surge profile according to the advised landing point and direction.

2.3. Storm surge estimation

2.3.1. Wind velocity distribution

Various models have been developed to estimate wind velocity distribution during cyclones by Xie et al. (2005), Bao et al., (2004), Houston et al. (1999), Shapiro (1983), Anthes (1982), Holland (1980) and Depperman (1947). Simple parameterised models of Holland and Anthes are widely used in recent simulations of wind fields (Hsu and Babin, 2005; Xie et al., 2005; Bao et al., 2004; Vatvani et al., 2002). In this study, both Holland (1980) and Anthes (1982) models (Equations (1) and (2), respectively) were used to obtain the wind velocity distribution. In the Holland and Anthes models, parameters B, x, Rmax and vmax should be determined empirically or estimated from the advisory data.

(1)
(2)

where: v(r) is the wind velocity at radius r;

  • vmax is the maximum wind velocity;

  • Rmax is the radius of maximum wind;

  • B and x are model parameters;

  • Po and Pc are ambient and central pressures;

  • ρ is the density of the air;

  • f is the Coriolis factor.

Table III summarises empirical equations given in the literature for the estimation of the maximum wind velocity during cyclones. Efficiencies of these maximum wind models were tested using the data from the global hurricane archive (NHC, 2008) and compared with the following model's least square estimations derived from this archive: vmax = a + b(PoPc)0.5 + cVf + d.f where: Vf is the forward speed of the hurricane; a, b, c and d are parameters to be determined empirically.

Literature1vmax = 3.44(1010 − Pc)0.644 Atkinson & Holiday (1977)93.098.3293.318.16
 2vmax = 6.3(1013 − Pc)0.5 Athens (1982)92.814.3393.284.83
 3Po = 1010 mbKumar et al. (2003)91.2311.691.4311.84
    
 4vmax = 7(Po − Pc)0.5Po = 1010 mbNatarajan & Ramamurthy (1995)91.684.2892.514.38
 5vmax = 0.856Umax + 0.5Vf;Umax = 0.447[14.5(Po − Pc)]0.5Rmax(0.31f)Rmax = 59 km; Po = 1010 mbUSACE (1984)90.926.0393.076.49
 6vmax = [B(Po − Pc)/ρe]0.5Po = 1010 mb; ρ = 1.15 kgm−3; B = 1Holland (1980)91.687.7592.517.31
Fitted7vmax = 4.78 + 6.58(Po − Pc)0.5 + 0.142Vf − 55225fPo = 1010 mb 92.023.9 4.43
 8vmax = 2.89 + 6.52(Po − Pc)0.5Po = 1010 mb; c = 0; d = 0 91.54.02 4.42
 9vmax = 2.55 + 6.51(Po − Pc)0.5 + 0.068VfPo = 1010 mb; d = 0 91.514.02 4.38
 10vmax = 6.79(Po − Pc)0.5 + 0.283VfPo = 1010 mb; a = 0; d = 0 91.344.12 4.22
 11vmax = 7.1(Po − Pc)0.5Po = 1010 mb; a = 0; c = 0; d = 0 91.54.23 4.36

Model efficiency was evaluated by the determination coefficient R2 and standard error S between the observed and estimated maximum winds. Maximum wind velocity changes with the migration of the cyclone position (Figure 3). In order to approximately determine wind velocity distribution as closely as possible to the values provided by advisories, four different models were used and are summarised in Table IV. Ideally, the wind velocity distribution perpendicular to the cyclone path should be used to calculate the surge profile. Because data required to calculate the wind velocity distribution perpendicular to the cyclone path is not always available, wind velocity distributions calculated for the advisories directions closest to the direction perpendicular to the cyclone path were used. The wind velocity distributions calculated by the models described above for the northeastern direction of Nargis advisories 5 and 6, which were the closest advisories to the Nargis landing point, are shown in Figure 4. The fourth model was the simplest and best-fitting model and was used to estimate wind distribution of cyclone Nargis (Figure 5).

114.486.938593821.51.201.171.291.3317.517.517.517.50.500.480.540.5734.534.035.536.0
215.087.536593621.51.231.151.191.2317.017.017.017.00.490.470.480.4937.037.037.037.0
315.889.839593921.51.010.971.011.0615.015.015.014.50.460.460.460.4628.028.528.027.5
415.990.746594621.51.251.161.251.3420.019.520.020.00.540.500.540.5829.027.029.030.5
515.792.651595121.51.131.031.151.0721.522.521.018.50.460.460.470.4621.522.522.018.5
615.993.759595921.51.151.031.031.1221.523.523.522.00.470.460.460.4616.517.017.016.0

2.3.2. Wind-based storm surge model

Empirical methods of storm surge height estimation available from the literature (Huang et. al., 2007; Hsu and Babin, 2005; Chan and Walker, 1979) do not provide a surge profile in a form that can be used in GIS analysis, and it is difficult to build a hydrodynamic surge model for the affected coastal region before cyclone landfall. However, the regressive relationships between cyclone wind velocities and surge heights can be used to derive a surge profile prior to landfall and are advantageous because they are applicable for any point along the coastline and do not require intensive calculations. In cases where historical data are not available to build regression models, they can be developed using output from hydrodynamic models of the region prior to cyclone season; several hydrodynamic models characterising the Bay of Bengal have been developed (Jain et al., 2006; Madsen and Jakobsen, 2004; Vatvani et al., 2002; Flather, 1994)

Meteorological data of the stations in Table II were used to determine the relationships between the observed values of wind and surge. To eliminate the effects of wave-generated fluctuations on regressive models, 12-h moving average of surge records were applied. Only two stations were found to have more than one significant hurricane record; on these stations, observed wind velocities and surge heights were plotted reciprocally. Fitted regression models for reciprocal plots are shown in Table V indicating that surge height can be readily modelled by linear regressive models with high-determination coefficients obtained especially for velocities greater than 12.5 m/s. Lower wind velocities are not effective for GIS modelling since they produce surge heights less than one meter that are outside of the vertical precision of SRTM data.

LONF11h = 0.0016v2 + 0.040796.29v > 0
 2h = 0.0643v − 0.573694.48v > 12.5
 3h = 0.0018v2 − 0.011394.69v > 12.5
DPIA11h = 0.0511v + 0.205691.77v > 0
 2h = 0.0432v + 0.357493.04v > 12.5
 3h = 0.0011v2 + 0.754491.38v > 12.5

Simultaneous historical in situ wind and surge measurements along the Myanmar's coastline are not available; synthetic wind velocity and surge height data from hydrodynamic models of the region were also unavailable. The sea anomalies merged altimetry product from the AVISO data centre created for the period 28 April to 2 May 2008 (NHC 2008) was used to calibrate the wind-based surge model of the Myanmar coast. The profile extracted for the northwestern–southeastern direction of Advisory 6 was used for this purpose. The calibrated wind-based surge model (Equation (3)) was assumed to be valid along the Myanmar coast, and was used to determine surge profiles for each advisory. Figure 6 shows the comparison between surge profiles calculated for each advisory and surge profiles extracted from the AVISO product.

(3)

where h: surge height (m), and V: wind velocity (m/s). Before using calculated surge profiles in GIS analysis, they should be discretised to create a set of input elevation values for spatial analyses. The proposed set of discretisation points includes 0.5, 1.5, 2.5, and 3.5 m surge heights.