Technical Assistance Consultant’s Report

Project Number: 46496-001 December 2016

Republic of the Union of Myanmar: Transformation of Urban Management - Flood Management Component (Financed by the Japanese Fund for Poverty Reduction)

FINAL REPORT PART 2 (Part 2 of 7)

Prepared by International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI) (Tsukuba, Japan) CTI Engineering International Co., Ltd. (Tokyo, Japan) CTI Engineering Co., Ltd. (Tokyo, Japan) PASCO CORPORATION (Tokyo, Japan)

For: Ministry of Construction and Ministry of Transport and Communications, Department of Meteorology and Hydrology, under the Ministry of Transport and Communications.

This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. (For project preparatory technical assistance: All the views expressed herein may not be incorporated into the proposed project’s design). Chapter 4 TA -8456 MYA: Transformation of Urban Management – Part II Flood Management

CHAPTER 4 HYDRO-METEOROLOGICAL ANALYSIS

4.1 Introduction The consultant team conducted hydro-meteorological analysis for the river basins where the three target cities are located. The Rainfall Runoff Inundation (RRI) Model was applied to flood inundation simulation. The RRI Model is a grid-based distributed runoff model which can simulate rainfall-runoff and flood inundation simultaneously to reproduce flood inundation in low-lying areas. A storm surge simulation model with Myers formula was developed for coastal flood hazards. In this chapter, the key activities of hydro-meteorological analysis, that is, the development of flood inundation and storm surge simulation models, are presented. The models were calibrated and adjusted for each basin. The results of hazard assessment were utilized to create flood hazard maps and to implement agricultural damage risk assessment in the following chapters. In this TA, flood inundation models for the three target cities, Yangon, Mandalay and Mawlamyine, were developed by using the RRI Model. For the continuous support and improvement of the models after this TA, personnel in charge of this task are assigned by DMH for each city (see Table 4.1.1). Though elevation data is essential in modeling, it is difficult to obtain ground survey data or aerial survey data in Myanmar at this moment; therefore, this TA used globally available satellite data for simulation. Such data can be used at the primary stage of simulation modeling. The developed model was calibrated and verified by comparing the result from the reproduction of an inundation area by the model and the satellite images of past flood events. Through this process, DMH staff learned a great deal about not only flood hazard simulation and mapping but also the importance of data quality that should be improved continuously. While the initial products were able to give the basic outline of the inundation condition, some parts of the target area required simulation with accuracy greater than that of globally available satellite data such as HydroSHEDS, which was applied to the initial modeling. Therefore, in March 2016, the consultant team procured more accurate DEM called AW3D with 2 m resolution for the central part of the three target cities, and improved the quality of elevation for the RRI Model. Rainfall obtained is only daily data; therefore the data was divided equally into 24 hours to obtain average hourly data for simulation. Hourly data is essential in simulating the condition of inland inundation in the city area, because inland inundation progresses on an hourly basis while a river flood in a large river basin on a daily or weekly basis. It should be noted, however, that such average hourly data do not show an actual rainfall pattern with distribution and a peak value in a day. Afterward, the consultant team was able to obtain a rainfall intensity formula for Yangon, and applied hourly rainfall data obtained from this formula. The results are presented in sections 4.2.4, 4.2.6 and 4.2.8. In addition to the original target cities, Bago city, which is located in the eastern side of Yangon, was also modeled by the trainees from the Irrigation Department (ID). Furthermore, in response to the request of the Myanmar side, two other areas, Nyaung Don and Kale, were included as additional targets in the model development to reproduce the 2015 flood caused by Cyclone Komen. The training on flood inundation simulation with the RRI and storm surge models is one of the featured outputs of TA-8456 Part II. Thus, this chapter explains the outline of a flood inundation simulation model using the RRI and storm surge models and future challenges in the simulation. Table 4.1.1 Assignment of DMH Staff Target Area Sta ff Assigned Period Remarks RRI Model January 2015 to October 2015 Ms. Myo Myat Thu *Ms. Thu studied abroad (ICHARM, Japan) from October 2015 to Yangon September 2016. Ms. Aye Aye Naing October 2015 to October 2016 Mandalay Mr. Myo Tun Oo January 2015 to November 2015

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Target Area Staff Assigned Period Remarks July 2016 to October 2016 Ms. Khin Min Wun Soe January 2016 to June 2016 January 2015 to October 2016 Mawlamyine Mr. Zaw Myo Khaing *Mr. Khaing and Ms. Aye Aye Naing were in charge of preparation of coastal flood hazard maps in Yangon and Mawlamyine. Storm Surge Model Dr. Than Naing February 2015 to October 2016 Ms. Khine Soe Oo February 2015 to October 2016 Ms. Sanda Wai February 2015 to October 2016 - Ms. Witt Yi Soe February 2015 to May 2015 June 2015 to February 2016 Dr. War War Thein Dr. War War studies abroad in India

4.2 Development of Flood Inundation Simulation Model using RRI Model 4.2.1 Outline of RRI Model (1) Features of RRI Model The Rainfall-Runoff-Inundation (RRI) Model1, developed by ICHARM, was used for flood inundation analysis for the three cities (Mandalay, Yangon and Mawlamyine). The RRI Model is a grid-based distributed runoff model, which can simulate rainfall-runoff and flood inundation simultaneously to reproduce flood inundation in low-lying areas. This type of simulation is difficult if conventional flood prediction models are used. The model is available as free software, which can be easily handled and modified to adjust to actual field conditions. The RRI Model can simulate flood inundation depth and area during floods, as well as river water level and discharge volume during normal flows. The model requires such input data as topographical information, rainfall data and sea tide data. It is designed to calculate flood inundation based on lateral water movement in an inundated area. The RRI Model can be used to identify flood inundation areas and evaluate the effectiveness of structural measures by showing changes in flood inundation area before and after the completion of structural measures (e.g., dam, dike, etc.). Simulation by the RRI Model can be also utilized for non-structural measures (e.g. flood hazard maps to indicate the area and depth of inundation during a flood for the identification of evacuation routes and locations of evacuation shelters) in flood management.

1D Diffusion in River Subsurface + Surface Vertical Infiltration

2D Diffusion on Land

Figure 4.2.1 Conceptual Diagram of RRI Model

1 RRI Model was awarded for its excellence in 2013 by the Japan Society of Civil Engineers and by Japan Institute of Country-ology and Engineering.

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(2) Application examples of RRI Model The RRI Model has already been applied to flood management, providing assistance for governments in various countries and regions as follows: (a) Pampanga River basin, the Philippines To conduct flood disaster risk assessment for agricultural damage, the RRI Model was applied to estimate flood inundation depth, area and duration in the Pampanga River basin of the Philippines. With different scales in return period, inundation was simulated (Figure 4.2.2) to estimate agricultural flood damage (Figure 4.2.3).

25-Year Flood 50-Year Flood 100-Year Flood

Inundated area (>0.5m depth)= Inundated area (>0.5m depth)= Inundated area (>0.5m depth)= 141,264 ha 89,849 ha 115,425 ha Figure 4.2.2 Maximum Inundation Depth (Pampanga River basin)

25-Year Flood 50-Year Flood 100-Year Flood Estimated damage: Estimated damage: Estimated damage: 2248.3 million Peso 1031.13 million Peso 1648.37 million Peso Figure 4.2.3 Distribution of Calculated Agricultural Flood Damage (Pampanga River basin)

(b) Indus River basin, Pakistan Under the UNESCO-ICHARM project, “Strategic Strengthening of Flood Warning and Management Capacity of Pakistan”, which was completed in June 2014, the RRI Model was applied to flood forecasting and flood hazard analysis in the Indus River Basin of Pakistan. The aim of the project was to strengthen their flood forecasting and early warning systems and flood

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hazard analysis capacity with the Integrated Flood Analysis System (IFAS) and the RRI model, both of which were developed by ICHARM. The Flood Forecasting Division (FFD) of Pakistan Meteorological Department (PMD) established a flood forecasting system based on IFAS and the RRI Model. IFAS was applied to mountainous areas (the upper catchment area of the Indus River basin) to calculate river discharge for flood forecasting and to define input boundary conditions for the RRI Model. The RRI model is applied to a relatively flat area (the middle-lower catchment area of the Indus River basin) to calculate flood inundation and discharge for flood forecasting in plain areas and to prepare flood inundation/hazard maps for the lower Indus. The FFD/PMD disseminates the output results of IFAS and RRI model through webpage as shown in Figure 4.2.4 (see http://www.pmd.gov.pk/FFD/index_files/ifashyd.htm).

INPUT DATA : OUTPUT DATA: • Rainfall data (ground- gauges, GSMaP • Rainfall distribution maps and forecasted) • Hydrographs at specified locations • Real-time observed discharges • Inundation extents in mid-low Indus

Control panel for operation settings Hydrograph

[m] 0.0-0.5 Plain view 0.5-1.0 1.0-2.0 of rainfall, 2.0-3.0 Inundation 3.0-5.0 discharge, 5.0-6.0 area by RRI 6.0-7.5 inundation (Animation)

Figure 4.2.4 Flood Forecasting and Hazard Analysis System by using IFAS and RRI Model (Indus River basin)

(c) Chao Phraya River basin, Thailand In response to the 2011 severe flood in Thailand, JICA supported the country to develop a flood forecasting system for the Chao Phraya River basin. The Chao Phraya River basin has a large basin area of more than 160,000km2 and a river length of more than 700km. The RRI Model is employed as the core simulation engine of this system. The system, “Flood Risk Information”, is open to the public through the internet, and can disseminate daily and seven-day forecasts of river discharge, water level and inundation area. Anyone can access and check past, current and future situations of flooding. Figure 4.2.5 shows the webpage of the Flood Risk Information System (see http://floodinfo.rid.go.th/index_en.html).

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Figure 4.2.5 Flood Risk Information System (Chao Phraya River basin)

(d) Agano River in Japan In Japan, flood forecasting is in operation for the main segments of its major rivers while flood forecasting information is rarely available for small tributaries flowing through hilly and mountainous areas. It is critical, however, for local governments to collect as much information as possible that may be useful to practice disaster prevention, because there is often not much time after rainfall until a flood occurs in such areas. Therefore, ICHARM applied the RRI Model to a small river in a mountainous area in Japan in the aim of eventually providing flood risk information, such as flood-prone areas and flood forecasting information, to municipalities in mountainous areas with limited river information before a disaster occurs. The system was developed for the Agano River basin in Aga-machi, Niigata Prefecture. As a result, the effectiveness and usefulness of the RRI Model have been verified with high reproducibility in water level and discharge of past floods.

(3) Requirements of RRI Model Simulation The time required for calculation for a specific area and period is strongly dependent on computer specifications, such as CPU clock frequency and memory capacity. Calculation time with general computer specifications is indicated in Table 4.2.1. Even though the value of a model parameter (e.g., roughness coefficient of river) also affects calculation time significantly, it usually takes roughly 5 hours of calculation time per 10,000 grid cells per 30 days of simulation period.

Table 4.2.1 Calculation Speed of RRI Model Simulation CPU (type, clock Memory Grid Number of Calculation time PC type period frequency (GHz)) (GB) size (m) grid cells (hours) (days) Desktop core i7, 3.60GHz 16GB 500 28,000 180 40 Desktop core i7, 3.60GHz 16GB 500 14,000 90 10 Desktop core i7, 3.60GHz 16GB 500 9,600 90 7

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(4) Functions of RRI_GUI (Graphical User Interface) A Graphical User Interface for the RRI Model (hereinafter, RRI_GUI) has been developed for easy development of a flood inundation simulation model. RRI_GUI is composed of two programs, that is, “RRI_Builder” for developing a flood inundation simulation model and “RRI_Viewer” for viewing simulated results. Figure 4.2.6 and Figure 4.2.7 show typical displays of RRI_GUI. RRI_GUI was provided to train DMH and ID members in flood inundation model development (http://www.icharm.pwri.go.jp/research/rri/rri_top.html).

Figure 4.2.6 Typical Display of RRI_Builder

Figure 4.2.7 Typical Display of RRI_Viewer

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4.2.2 Data/Information Necessary for Development of Model In an early stage of the project, data and information required for the study, such as hydro-meteorological data and DEM data, were collected. These data were used for both flood inundation modeling and storm surge modeling as well as for flood and storm surge risk assessment. Related organizations for data collection were identified through the mission. (1) Target flood Target floods for model calibration were selected from floods with the largest inundation area in recent years. The periods corresponding to the target floods are listed in Table 4.2.2. (2) Meteorological Data The available data are shown in Table 4.2.2. The stations of collected meteorological (rainfall) data from DMH are shown in Figure 4.2.8 to Figure 4.2.10 (hydrological stations are also shown). Rainfall data from the ID’s stations indicated in Figure 4.2.11 were also collected. The RRI Model was developed for flood hazard mapping and risk assessment using these collected data. Rainfall data collected from both DMH and ID were daily basis. Then daily rainfall data was converted into hourly data by equally divided into 24 hours for simulation. But, as is explained at section 4.1, simulation for inland inundation requires hourly observed data to reflect the hourly change of condition (see section 4.2.8). Globally available rainfall data such as GSMaP or 3B42RT can be also used for simulation, especially when ground rainfall observatories are not sufficiently distributed in the area to develop models. Table 4.2.3 shows the features of global rainfall data. GSMaP data was used for the simulation at Kale and Nyaung Don (see section 4.3).

Table 4.2.2 Collected Meteorological and Hydrological Data for the Target Areas Target Number of Period* Remarks Area Available Station Rainfall: 7 (daily)  Target flood: May, 2007 DMH: Bago, Kaba-Aye  Initially the sea water level of 2007 near the river ID: Mahuyar Dam, Paunglin mouth observed by ID was used for the boundary Dam, Kalihtaw Dam, Lagunbyin condition, which was later replaced by the average Dam, Ngamoeyeik Dam sea water level estimated from the astronomical 2007 tidal level data of 2016 (from May to October) in Yangon Water level/discharge: 1 (daily) Apr–Oct Yangon. This average sea water level was revised DMH: Bago based on the results of the benchmark survey by MjTD (see section 4.2.10 and Annex-5).  Model calibration was carried out using satellite images due to lack of observed discharge/river water level data in the target area. Rainfall:7(daily)  Target flood: September, 2004 DMH: Mandalay, Sagaing,  The simulation model for Mandalay was calibrated Monywa, Tadaoo, Kyaukme with observed discharge/river water level. 2004 Mandalay ID: Sedawgyi Dam, Zawgyi Dam Apr Oct Water level/discharge: 4 (daily) – DMH: Mandalay, Thabekkyin, Katha, Sagaing Rainfall: 12 (daily)  Target flood: August, 2013 DMH: Hpaan, Kawkayeit,  Observed water level of 2013 near the river mouth Mawlamyine, Ye, Mudon, provided by DMH is given as the lower boundary Kyeikkame, Thaton, Belin, condition. 2013 Mawlamyine Thieinzayet, Azin Dam  Model calibration was carried out using satellite Apr Oct ID: Shwe Nat Taung Dam, Win – images due to lack of observed discharge/river Pha Non Dam water level data in the target area. Water level/discharge: 1 (daily) DMH: Hpaan *A largest flood event occured in this period

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Legend

Meteorological Pyay Hydrological Meteoro.-hydro.

Collected Rainfall Data Collected Water Level and Discharge

Khamonseik Tide Station (ID)

Tharrawady Henzada

Zaungtu Zalun Bago Hmawbi (Met)

Hmawbi (Agro) Khayan Hlaezeik

Ayeywa Mingaladon

Kaba-Aye

Central Yangon

Figure 4.2.8 Stations of Collected Meteorological and Hydrological Data from DMH (Yangon)

Legend

Meteorological Putao Hydrological Machanbaw Meteoro.-hydro. Collected Rainfall Data Collected Water Level and Discharge

Mogaung Naungcho Myitkyina

Mohnyin Sinbo

Pinlebu Katha Shwegu Bhamo Muse

Myitsone Htigyaing

Kanbalu Thabeikkyin Momeik Lashio

Ye U Mogok Mandalay Hsipaw Shwebo Myinmu Kyaukme

Monywa Pyin Oo Lwin Myitnge

Sagaing Meiktila Ngazu Tadaoo Lungyaw Hlaingdat Yamethin

Figure 4.2.9 Stations of Collected Meteorological and Hydrological Data from DMH (Mandalay)

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Legend

Meteorological Theinzayat Hydrological Meteoro.-hydro. Belin Collected Rainfall Data

Hpa-an Collected Water Level and Discharge Thaton

Mawlamyine Kawkayeit

Mudon

Kyaikkame

Ye

Figure 4.2.10 Stations of Collected Meteorological and Hydrological Data from DMH (Mawlamyine)

Mawlamyine

Mandalay

Yangon

Figure 4.2.11 Stations of Collected Meteorological and Hydrological Data from ID

Table 4.2.3 Features of Global Rainfall Data Name of Data Observation Resolution Time lag Source site dataset source frequency 0.1° ftp://rainmap:[email protected] GSMaP JAXA Hourly 4 hours (approx.10km) .jaxa.jp/ 0.25° http://gdata1.sci.gsfc.nasa.gov/daac-bin/G 3B42RT NASA Every 3 hours 6-10 hours (approx.30km) 3/gui.cgi?instance_id=rt_intercomp

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(3) Hydrological Data The available data from DMH are shown in Table 4.2.2. Stations that provided hydrological data for Mandalay, Yangon and Mawlamyine areas are shown in Figure 4.2.8 to Figure 4.2.10. Figure 4.2.11 shows stations whose rainfall data were collected from ID.

(4) Discharge Data from dams Since the flow regime is affected by the discharge from dams, discharge data from dams were applied as boundary condition to reproduce the past flood condition for calibration. Table 4.2.4 shows the discharge data collected from ID. Figure 4.2.11 shows the locations of the dams. For flood simulation with a 100-year flood, discharge from dams was defined as the same as inflow to dams, which means no flood control by dams in the simulation. Table 4.2.4 Daily Rainfall & Discharge at Dams (ID) Modeling Location No Name of Dam Period Area Latitude Longitude 1 Ngamoeyeik 17°2046.244 96°0902.459 Apr.1, 2007 - Jul.31, 2007 2 Lagunbyin 17°1459.064 96°1838.740 Ditto 3 Yangon Kalihtaw 17°1428.043 96°0719.420 May.1, 2007 - Jul.31, 2007 4 Mahuyar 17°2929.807 96°1119.180 Apr.1, 2007 - Jul.31, 2007 5 Paunglin 17°3030.355 96°0537.280 Ditto 6 Saetawgyi 22°2356.854 96°1942.380 Apr.1, 2004 - Sep.30, 2004 Mandalay 7 Zawgyi 21°2749.835 96°2320.570 Ditto 8 Azin 16°1559.800 97°4505.458 Jun.1, 2013 - Aug. 31, 2013 9 Mawlamyine Winphanon 16°0515.320 97°4622.330 Ditto 10 Shwe Net Taung 16°2437.285 97°3942.160 Ditto

(5) Tidal Data Observed tidal data for 2007 at Yangon (Ayeywa of the mouth of Pan Hlaing River and Hlaezeik of the mouth of Hlaing River, see Figure 4.2.8) were collected from ID. In this TA, since the daily maximum rainfall of 2007 is largest among the past three decades, the flood of 2007 was selected as target flood scale. The tidal data at Ayeywa were used as boundary condition of river water level for inundation analysis by the RRI model. The data were available only three times a day (6:00AM and at the time of maximum and minimum tidal levels). For reference, the astronomical tide level in 2014 at Yangon (Elephant Point) and Mawlamyine were collected from the Myanmar Port Authority (MPA), and used for checking observed tidal levels collected from ID and also used as lower boundary condition for simulation. For Mawlamyine, the astronomical tide data were used as boundary condition of the river level at the mouth of Ataran River for inundation analysis. The data were provided only four times a day (at low and high tide). In this TA, hourly observed tidal data could not be obtained. For proper model calibration for Yangon and Mawlamyine, hourly observed tidal data should be collected.

(6) Elevation Precise topographic data such as ground observed data are important for flood simulation modeling. While satellite data can provide necessary topographic information to simulate inundation area and depth with certain accuracy because relative height is the main factor that decides the inundation condition, precise elevation value itself can only be obtained through the

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calibration of satellite data using ground observed data. In this TA, the consultant team could not obtain ground observed data; therefore satellite data of HydroSHEDS provided by USGS (http://hydrosheds.cr.usgs.gov/index.php) were employed as the basic data set for the RRI Model. HydroSHEDS provides a series of geo-referenced data sets including river networks, watershed boundaries, drainage directions, and flow accumulations. HydroSHEDS is based on high-resolution elevation data obtained during a Space Shuttle flight for NASA's Shuttle Radar Topography Mission (SRTM). HydroSHEDS provides data with resolutions of 3 arc-seconds (approximately 90m), 15 arc-seconds (approximately 450m) and 30 arc-seconds (approximately 900m), which are derived from SRTM data of 1 arc-second and 3 arc-seconds. Though finer and more precise DEM could generate more accurate results from flood inundation analysis, globally available data such as SRTM and ASTER-GDEM (refer to Table 4.2.5) can also produce useful information.

Table 4.2.5 General Description of SRTM and Aster GDEM Items SRTM2 ASTER-GDEM3 Data source Space Shuttle Radar ASTER Provider NASA/USGS METI*/NASA Release Year From 2003 From 2009 revised occasionally by local data revised occasionally by local data Observation Period 11 days (in February 2000) Ongoing (from 2000) Resolution SRTM 1: approx.30m Approx.30 m SRTM 3: approx.90m Coverage North 60 degrees to South 56 degrees North 83 degrees to South 83 degrees Remarks For free For free Digital Surface Model (DSM) Digital Surface Model (DSM) * Ministry of Economy, Trade and Industry, Japan

Afterward, the consultant team procured a finer DEM called AW3D (3.2.3 (2)) to improve the accuracy of simulation for more detailed analysis. The AW3D data was purchased only for the central areas of the three cities because of the limitation of the budget. The central area of each city covered by AW3D was connected to the surrounding area where SRTM was used. Since the value of elevation is different between AW3D and SRTM, adjustment is required for the smooth connection of AW3D and SRTM. Generally, difference in elevation between the two becomes bigger from the seaside to the mountain side. Furthermore, in Mandalay, it was required to compare the height of the dyke and the water level of a 100–year flood to ensure the safety of structures behind the dyke. Since the bench mark was provided by MCDC for Mandalay and used to measure the dyke height, the geoid height values of both AW3D and SRTM were adjusted using the bench mark data. In Yangon and Mawlamyine, on the other hand, there was no reliable bench mark available for the city area. Thus, the difference in height value at each grid of AW3D and SRTM was averaged and equally applied to SRTM to connect the AW3D area and the SRTM area smoothly. In Yangon, the consultant team could get the bench mark data at Thilawa, which is in the opposite shore of Yangon, and found that the elevation value of SRTM was not so different from the bench mark data. Therefore adjustment of SRTM was only applied to the directly connected area to the Yangon central area. In addition, it is necessary to recognize that general global DEM data (except for bathymetry DEM data such as SRTM-Plus) do not include elevation values under water. Therefore users have to note that the water depths of existing water bodies such as ponds, lakes and wetlands cannot be obtained from DEM data. For this reason, the inundation depths of water bodies shown in hazard maps are not water depths from the bottom, but depths from the water surface.

2 http://hydrosheds.cr.usgs.gov/dataavail.php 3 http://gdex.cr.usgs.gov/gdex/

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In general, the river flow is calculated by a 1-dimensional unsteady flow model which is built in calculation grid as rectangle shape (the left figure in Figure 4.2.12) by the RRI Model. However, in the Mandalay case, it is difficult to model the actual river cross section using the rectangle shape because the river width is far larger than the calculation grid size. Therefore, the river flow was firstly calculated by a 1-dimensional unsteady flow model of the rectangle shape at the bottom of the channel, and then the river flow was formulated by a 2-dimensional unsteady flow model by assuming overflow from the 1-dimensional model to the adjacent grids of DEM in the river (the right figure in Figure 4.2.12).

[1-dimensional unsteady flow (rectangle shape)] [Modeling river shapes by DEM]

3D image of river cross section Over flow Calculation grid for river course grid (Elevation of DEM) H (bank height)

H Flow direction (river depth) Vertical cross section Actual river channel B Consider as inundated depth (river width) No river channel Grid cell with river channel Figure 4.2.12 Modeling of River Shape by DEM (in case that actual river width is much larger than calculation grid size)

In this TA, for more accurate modeling of river cross sections, the elevation data were modified based on the riverbed contour lines including riverbed shapes from DWIR (Figure 4.2.13).

Contour Line (2)

Contour Line (1)

a. Contour Line (1) b. Contour Line (2)

Figure 4.2.13 River Bed Contour Lines near Mandalay from DWIR

In addition, the consultant team modified AW3D data for more accurate inundation area/depth. AW3D

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which was procured in this TA-8456 is Digital Surface Model (DSM), which could include heights of ground as well as those of buildings, trees, etc. Therefore, the consultant team prepared the elevation data in central areas by using AW3D except for buildings. Figure 4.2.14 shows the schematic diagram on distribution of elevation values in each calculation grid. If the elevation values include buildings and some other objects, an inflexion point can appear in a distribution curve. Based on the examination on accumulated-elevation distribution, the value of 30% was employed as the representative elevation of grid in this TA.

20 20

18 No buildings 18 No buildings 16 16 Including buildings etc. Including buildings etc. 14 14 12 Buildings 12 Representative 10 10 Inflexion point elevation

8 8 Elevation (m) Elevation Elevation (m) Elevation 6 6 4 4 2 2 0 0 123456789 10 11 12 13 14 15 16 17 18 19 20 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Number of elevation values in a grid Accumulated elevation distribution (%)

Figure 4.2.14 Representative Elevation (AW3D)

(7) River Line and River Cross Section Simulation by the RRI Model has three steps. Firstly, rain water is given to basin grids, and rain water flows into a river channel according to the gradient in terrain. And then the discharge volume of the river is calculated by a one-dimensional unsteady flow model. Finally, the inundation condition is calculated by a two-dimensional unsteady flow model applying the result of the one dimensional model to the grid model. For the calculation of river discharge, the river line is initially set using HydroSHEDS, the river cross section is modeled as a rectangular shape, and the default values of width and depth are defined by the catchment area at each calculation point by using an empirical formula, which is adjusted manually in the process of calibration. In this TA, the consultant team collected some actual cross sections of the target rivers (a part of the Ayeyarwady River at Mandalay, and the Ataran River at Mawlamyine), and utilized them for the arrangement of the simulation model. The river line is set up automatically according to the differences of elevation (gradient in terrain). As default setting of RRI_GUI, a calculation grid that collects water from more than 20 grids is defined as "river calculation grid" (refer to the RRI Model Textbook at page A9), and the river line is determined. If a calculation grid is 1km, a minimum unit of a water catchment area to identify canals and rivers is 20km2, which means that the system is set to recognize no canals or rivers in a catchment area less than 20km2. If a small canal is modeled, the model parameter of "riv_thresh" (threshold in flow accumulation, ACC4) and grid size should be modified. It should be noted that the RRI Model has no component to incorporate water flow in pipelines like rainwater drainage, because the target of the RRI Model is river floods with a large scale of inundation. Normally, when a large-scale flood occurs, drainage pipes have already been filled up to its capacity; therefore the RRI Model can be applied in such a case. But if the target is frequent rainfall of a relatively small scale, the function of drainage pipes should be incorporated and analyzed. There are several models

4 ACC is abbreviation for “Flow Accumulation”. It means the number of accumulated flow as the accumulated weight of all grids flowing into each downslope grid. ESRI: http://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/how-flow-accumulation-works.htm

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available for analysis of pipeline drainage, such as InfoWorks CS5, MOUSE6, SWMM7 and NILIM8 Model (National Institute for Land and Infrastructure Management, Japan). In the simulation of flood inundation by a two-dimensional model, the flow direction (DIR) is determined by difference in height between adjacent grids; therefore, accurate elevation data is required especially for a flat plain. The river line also needs to be modified manually to represent actual river lines.

(8) Land Use Land use, a factor defining the movement of floodwaters on the ground surface, is represented by a roughness coefficient. Therefore, the identification of the most suitable roughness coefficient is essential in simulation. Different roughness coefficients can be used for different land use categories, but in this TA, which involved the introduction of a new procedure to the country, we decided to adopt the same roughness coefficient for the simulation to simplify the process, except for the Detail Model areas of Yangon and Mandalay, where we adopted a different coefficient that is more suitable to the model (see section 4.2.3 (3)). Soil condition defines the discharge volume of floodwaters in association with the permeation of water from the ground surface. In this TA, after the calibration, the same parameter for coefficient of permeability was also adopted for the simulation except for the Detail Model area of Yangon, where we adopted different parameters after categorizing soil condition into three types in reference to the categories of land use listed in the GLC2000 (Figure 4.2.15).

Hydrological Station Meteorological Station Dam site Station (rain, discharge) Large Basin Model Detail Model

1

2

3

1: Mountainous area 2: Agricultural field 3: Artificial surface area

Figure 4.2.15 Land use of Yangon Detail Model and Classification in the GLC2000

5 InfoWorks CS https://www.emori.co.jp/hw/InfoWorks_brochure.pdf 6 DHI https://www.dhigroup.com/ 7 Storm Water Management Model (SWMM) https://www.epa.gov/water-research/storm-water-management-model-swmm 8 National Institute for Land and Infrastructure Management, NILIMhttp://www.nilim.go.jp/english/eindex.htm

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(9) Summary of Model Development Table 4.2.6 shows the summary of model developed corresponding to requirements for reproducibility of simulated inundation. Detailed explanations were described in each section.

Table 4.2.6 Summary of Model Development

Model Elevation Grid size Rainfall Remarks 90 arc-seconds (approx. HydroSHEDS (30 arc-seconds) 2.7 km) Initial Model for Large Basin Model for Large Basin Model Daily (Section 4.2.4) HydroSHEDS (15 arc-seconds) 15 arc-seconds (approx. for Detail Model 450m) for Detail Model HydroSHEDS (15 arc-seconds) Second Model and AW3D (resolution: 2m, Ditto Ditto (Section 4.2.6) only for limited area of Detail Model) Only for Yangon HydroSHEDS (3 arc-seconds) Hourly rainfall data were Third Model and AW3D (resolution: 2m, 6 arc-seconds (approx. prepared by using a rainfall (Section 4.2.8 Hourly only for limited area of Detail 180m) intensity formula provided by and 4.2.9) Model) YCDC HydroSHEDS 3 arc-seconds DEM with AW3D 2m Fourth Model Elevation data of southern part Ditto Ditto (Section 4.2.9) of Yangon were modified by using benchmarks Based on the benchmark survey, Fifth Model the tidal level which is given to Ditto Ditto Ditto (Section 4.2.10) the RRI Model as the lower boundary conditions was revised.

4.2.3 Specification of Flood Analysis Model (1) Target area A two-step model approach was adopted for analysis, which is the combination of the Large Basin Model and the Detail Model. The Detail Model is for the detailed analysis of flood hazard conditions in the Large Basin Model while the Large Basin Model is for the calculation of the boundary condition of the Detail Model area. Firstly, the analysis of Large Basin Model was conducted, which was connected to the analysis of the Detail Model utilizing the result of Large Basin Model simulation such as inflow to the Detail Model area. The Detail Model area was identified from population, socio-economic activities and past disasters. The Detail Model area is also considering administrative boundaries and hydrological boundaries, while the Large Basin Model area is considering the necessity for hydrological analysis to be able to produce the appropriate boundary condition for the Detail Model. For the three cities, areas for the Detail Model and the Large Basin Model were shown in Figure 4.2.16, Figure 4.2.17 and Figure 4.2.18. Table 4.2.7 summarizes the Large Basin Model area and the Detail Model area for the three cities. Figure 4.2.19 indicates the distribution of population density and past inundated area caused by past floods.

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Table 4.2.7 Model Range for Three Cities (Yangon, Mandalay and, Mawlamyine) Large Basin Model Detail Model Model Grid size: 90”×90” Grid size: 6”×6” and 15”×15” (approx. 2.7km×2.7km) (approx. 180m×180m and 450m×450m) Area: approx. 22,700km2 Area: approx.3,220km2 Numbers of grid cells: approx.1,100 Numbers of grid cells: approx.75,000 Upstream boundary (grid size:180m) Upper boundary of catchment area of Hlaing Upstream boundary River and Bago River Location in branch rivers determining to comprise a Lower boundary past inundated area in the model region (Hlaing Yangon River and coastal line River, Bago River, Ngamoeyeik Creek and other City/Township rivers) Yangon/Kayan, Kungyangon, Kyauktan, Syriam, Lower boundary Thongwa, Yangon (Rangoon), Hlegu, Hmawbi, Yangon River and coastal line Yangon Htantabin, Taikkyi, Insein, Kawkhmu City/Township Hinthada/Zalun Yangon/Kayan, Kyauktan, Syriam, Thongwa, Maubin/Danubyu, Irrawaddy, Yandoon Yangon (Rangoon), Hlegu, Hmawbi, Htantabin, Pharpon/Dedaye Taikkyi, Insein, Kawkhmu Pegu/Thanatpin, Waw, Bago (Pegu), Daik-U, Maubin/Irrawaddy, Yandoon Kawa, Kyauktaga Pegu/ Bago (Pegu), Kawa Pyay/Paukkaung, Paungde, Prome, Shwedaung, Thegon Taungoo/Pyu Thayarwardy/Gyobingauk, Letpatan, Minhla, Monyo, Nattalin, Okpo, Tharrawaddy, Zigon Area: approx. 56,250km2 Area: approx. 5,120km2 Numbers of grid cells: approx.7,800 Numbers of grid cells: approx.28,000 Upstream boundary (grid size:450m) Thabeikkyine station of Ayeyarwady River Upstream boundary Lower boundary Thabeikkyine station of Ayeyarwady River, and Sagaing station of Ayeyarwady River and lower location in branch rivers determining to comprise a boundary of its catchment area past inundated area in the model region (Myitnge City/Township River, Pai River, Samon River, Zawgyi River, Mandalay/Amarapura, Mandalay, Maymyo, Paalaung River and other rivers) Patheingyi Lower boundary Kyaukme/Hsipaw, Kyaukme, Mong Mit, Sagaing station of Ayeyarwady River and lower Namhsan, Namtu, Nawnghkio, Kyaukse, Myitha, boundary of its catchment area Singaing, Tada-U City/Township Mandalay Lasho/Hsenwi, Lashio, Mong Yai, Tangyan Mandalay/Amarapura, Mandalay, Patheingyi Loilen/Lai-Hka, Mong Kung Kyaukme/Kyaukse, Myitha, Singaing, Tada-U Magwe Minbu/Natmauk Myingyan/Natogyi Meiktila/Meiktila, Pyawbwe, Thazi, Wudwin Pyin-Oo-Lwin/Madaya, Singu, Thabeikkyin Muse/Kutkai, Mu-Se, Namhkan Sagaing/Ngazun, Sagaing Myingyan/Kyaukpadaung, Mahlaing, Natogyi, Shwebo/Khin-U, Shwebo, Wetlet Taungtha Taunggye/Ye-Ngan Pyin-Oo-Lwin/Madaya, Mogok, Singu, Thabeikkyin Sagaing/Ngazun, Sagaing Shwebo/Khin-U, Shwebo, Wetlet Taunggye/Ho-Pong, Kalaw, Lawksawk, Pindaya, Taunggyi, Ye-Ngan Yamethin/Tatkon, Yamethin Area: approx. 5,830km2 Area: approx. 2,150km2 Numbers of grid cells: approx.800 Numbers of grid cells: approx.11,000 Upstream boundary (grid size:450m) Upper boundary of catchment area of Ataran Upstream boundary River Upper end of historical inundated area Lower boundary Inflow location from Large Basin Model area at Lower end of Ataran River Zami River and Winyaw River Mawlamyine City/Township Lower boundary Mawlamyine/Kyaikmaraw, Moulmein, Mudon, Lower end of Ataran River Thanbyuzayat, Ye Include Mawamyine urban area along riverside of Hpa-an/Kya-In Seikkyi Thanlwin River City/Township Mawlamyine/Kyaikmaraw, Moulmein, Mudon, Thanbyuzayat Hpa-an/Kya-In Seikkyi

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Large Basin Model

Paunglin Dam Mahuyar Dam

Ngamoeyeik Dam

Detail Model Kalihtaw Dam Lagunbyin Dam Hlaing River Ngamoeyeik Creek

Bago River

Pan Hlaing River

Discharge/Water level boundaries (Detail Model) Upstream boundary Lower boundary

Figure 4.2.16 Detail Model and Large Basin Model for Yangon

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Large Basin Model

Thabeikkyin

Detail Model

Saetagwyi Dam

Sagaing

Myitnge River

Zawgyi Dam

Zawgyi River

Paalaung River Samon River

Discharge/Water level boundaries (Detail Model) Upstream boundary Lower boundary

Figure 4.2.17 Detail Model and Large Basin Model for Mandalay

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ShweNet Taung Dam Ataran River

Azin Dam

Large Basin Model Winphanon Dam

Zami River

Winyaw River

Detail Model

Discharge/Water level boundaries (Detail Model) Upstream boundary Lower boundary

Figure 4.2.18 Detail Model and Large Basin Model for Mawlamyine

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a. Yangon model area b. Mandalay model area c. Mawlamyine model area

Large Basin Model Hydrological Station Permanent water area (Lake, Detail Model Meteorological Station Pond, etc.) Dam site Station (rain, discharge) Inundation area Yangon: May, 2007

Mandalay: September, 2004 2 Mawlamyine: August, 2013 (persons/km ) Data source: CIESIN, 2000. Global gridded population database http://www.ciesin.org/ Figure 4.2.19 Population Density Distribution and Past Inundated area in Three Cities

(2) Calculation grid The proposed calculation grid size was 15 arc-seconds (approx. 450m) in all three cities for hazard assessment using the Detail Model (grid size of 6 arc-seconds was later adopted for Yangon). Meanwhile, the Large Basin Model for rough estimation in a wider area used a 90 arc-seconds (approx. 2.7km) grid size.

(3) Model parameters Table 4.2.8 shows values of major parameters used in the RRI Model.

Table 4.2.8 Model Parameters for the Initial Model

Parameter Yangon Mandalay Mawlamyine

Roughness coefficient in 0.20 (Large Basin Model) 0.20 (Large Basin Model flood plain (m-1/3s) 0.06 (Detail Model) and Detail Model)

5.56×10-7 (Large Basin Model) 5.56×10-7 5.56×10-7 Coefficient of permeability9 (Detail Model) (Large Basin Model (Large Basin Model and (m/s) × -6 Forest area : 3.67 10 and Detail Model) Detail Model) Agri. area : 2.00×10-6 Urban area : 1.00×10-7 River cross section9 =5.0, =0.35, = 0.95, =0.2 (Width and depth etc.) (River width and depth were modified where the cross sections were available.) (m) � �� � �� Roughness coefficient of 0.030 rivers (m-1/3s)

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(a) Roughness coefficient for a flood plain The roughness coefficient for a flood plain defines the resistance of land surface to flood flow, which decides the velocity and inundation depth of flood-waters. In the GUI (see section 4.2.1 (4)), the default value of this roughness coefficient is 0.3 (m-1/3s) for all land use categories, though changeable manually. Please note that 0.4 (m-1/3s) is set as the default value in the GUI for simulation if no land use category is applied. In this TA, after the calibration, 0.20 (m-1/3s) was adopted for all land use categories except for the Detail Model areas of Yangon and Mandalay, where 0.06 (m-1/3s) was applied.

(b) Coefficient of permeability The permeability of a surface soil layer defines the infiltration of surface water into subsurface soil. In the calibration stage, the coefficient of permeability should be adjusted to fit the measured river discharge or inundation area. In this TA, 5.56×10-7(m/s) was adopted except for the Detail Model area of Yangon, where we adopted different parameters after categorizing soil condition into three types (see section 4.2.2 (8)). Additionally note that, since the calculation period shortened by only calculating the period of target rainfall with the third and subsequent RRI Models for the Detail Model area of Yangon compared with the previous models, as the newer models used finer grid size and thus required longer calculation time, we assumed that no permeation would occur during the calculation period because the ground would already be fully saturated, which was represented by setting the subsurface soil depth as zero.

(c) River cross section (width, depth, etc.) The RRI Model sets all river cross sections as a rectangle shape for the initial one-dimensional unsteady flow analysis, which is defined by width and depth. The river width and depth can be determined by using river cross section information as shown in Figure 4.2.20 (for details, see Sayama 20149). When detailed geometry information of river channels is not available, the width and depth are approximated by the catchment area at each calculation point by using an empirical formula as follows (Coe et al., 200810; Sayama, 20149).

�� � = �� where and are river width and depth in meter,�� is the upstream contributing area = �� (catchment area) in km2 and , , and are regression parameters, whose values are estimated� from river cross section data. The default values� of parameters in the RRI Model, which were determined by adjusting� �� the� parameters�� based on the available cross section information of some river basins in Asian countries, are: =5.0, =0.35, = 0.95 and =0.2 (Sayama, 20149). However, the values of these parameters are subject to change depending on river basins and available river cross section information.� � � � �� For flood hazard simulation in this TA, firstly river width and depth were estimated by using default values of the parameters of empirical formula, and then the cross sections were adjusted through calibration to reproduce past actual inundation and also based on river width information obtained from google earth. For the RRI Model simulation of Mandalay, river cross section data from DWIR were also used (see section 4.2.2(6)).

9 Sayama, T.: Rainfall-Runoff-Inundation (RRI) Model, Technical Manual, Technical Note of PWRI, No. 4277, 2014. 10 Coe, M. T., Costa, M. H., and Howard, E. A.: Simulating the surface waters of the Amazon River basin: impacts of new river geomorphic and flow parameterizations, Hydrological Processes., 22, 2542–2553, doi:10.1002/hyp.6850, 2008.

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Real cross section River model W

D1 D h r W1 Real cross section (with bank) River model (with bank) H W H e e D1 h D r

W1

Figure 4.2.20 Determination of River Width and Depth to Reflect Actual Cross Section

(d) Roughness coefficient of rivers The roughness coefficient of a river, like the roughness coefficient of a flood plain, defines the resistance of the river that defines the velocity and water depth of the river.

(4) Boundary condition The lower boundary condition for Yangon was initially set by the sea water level of 2007 near the river mouth observed by ID, which was later replaced by the average sea water level estimated from the astronomical tidal level data of 2016 (from May to October) in Yangon. This average sea water level was revised based on the results of the benchmark survey by MjTD (see section 4.2.10 and Annex-5). The lower boundary condition for Mawlamyine was set by the observed sea water level of 2013 near the river mouth provided by DMH, which provides hourly tidal-level changes. In the Mandalay case, time-series changes in discharge at the lowest station, Sagaing, were set as the lower boundary condition. The upstream boundaries of the Large Basin Model were set as the upper end of the Hlaing and the Bago River for Yangon, and the Ataran River for Mawlamyine. In Mandalay, the upstream boundary condition of the Large Basin Model was set as river discharge at Thabeikkyine station. To give sufficient boundary conditions for the Detail Model, time-series changes in discharge from branch rivers were also analyzed. The boundary points of branch rivers were set at the outside of inundation area. Observed discharge data from dam release were used as the boundary conditions in Yangon and Mandalay area for calibration.

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4.2.4 Model Calibration (1) Method Historical inundation areas obtained from satellite images (MODIS: Moderate Resolution Imaging Spectroradiometer) were utilized for model calibration of a detailed model and its parameters. Floods in 2004, 2007 and 2013 were respectively selected as a target historical flood in Mandalay, Yangon and Mawlamyine. In the case of the Mandalay detailed model, an observed hydrograph at Sagaing was checked for reproducibility. Basically, parameters related to roughness or permeability were calibrated. In addition, river geometric data, such as width, depth and location, were adjusted to recreate past flood conditions.

(2) Calculation result Maximum inundation area for the target flood was estimated by RRI Model simulation. Superimposed maps with maximum inundation extent and simulated inundation extent are showed in Figure 4.2.21, Figure 4.2.22 and Figure 4.2.23. In the maps, the mesh area was confirmed as inundation area from satellite images. Simulated inundation area is marked in blue.

(a) Yangon (Target flood: 2007) The default simulation result (indicated as blue colored areas) overestimated the maximum inundation area of the target flood (indicated as blue hatched area) as shown in Figure 4.2.21 a. The inundation area needed to be reduced mainly at the central region of Yangon in the detailed model; therefore the land surface permeability (ksv) for agricultural fields was selected for adjustment. With the increased permeability, the inundated area reduced as shown in the series of simulated results (Figure 4.2.21 a, b and c). Based on this simulation, finally ksv was determined as 2.00×10-6 (m/s).

-6 -6 -6 a. k =1.00x10 b. k =2.00x10 c. k =4.00x10 sv sv sv Legend:

Simulated inundation area Actual inundation area

Reference water (water bodies)

* With the area in the red circle, simulated inundated area decreased with increasing ksv, permeability parameter of Green-Ampt infiltration model. Figure 4.2.21 Result of Calibration for Yangon Detail Model

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(b) Mandalay (Target flood: 2004) The default simulation result overestimated the maximum inundation area in the southern region as indicated in Figure 4.2.22 a. To improve reproducibility, the locations of the river alignments in the southern branch rivers were adjusted corresponding to the actual river alignments. The calibrated result is shown in Figure 4.2.22 b. The overestimated inundation area along the southern branch rivers was successfully reduced by this adjustment. The calculated river discharge at Sagaing also had good agreement with the observed river discharge as shown in Figure 4.2.22 c.

Underestimated

Overestimated

Legend: a. Before adjustment Simulated inundation area Actual inundation area Permanent water area

35,000 ObservedSagaing discharge 30,000 Simulatedcal data discharge

25,000

/s)

3

20,000

15,000

Discharge (m

10,000

5,000

0 9/19/69/119/169/219/26 m/d b. After adjustment c. Hydrograph at Sagaing station

Figure 4.2.22 Results of Calibration for Mandalay Detail Model

(c) Mawlamyine (Target flood: 2013) The default simulation result roughly indicates actual inundation area in the midstream and upstream regions. However, in the downstream region of the model basin, the overestimated inundation area was found in the default simulation result (Figure 4.2.23 a) from comparison with the observed inundation area (Figure 4.2.23 b). To reduce the inundation area in the downstream region and to reproduce the maximum inundation area for the target flood, the river width was adjusted by reflecting the actual width measured on a satellite image. Figure 4.2.23 c

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shows the results of analysis after adjustment, which still showed an overestimation of inundation area in the downstream region.

b. Observed inundation a. Default result [5-6 Aug 2013 by UNOSAT c. Calibrated result (https://unitar.org/unosat/)) Legend: Simulated inundation area Actual inundation area Reference water

*With the area in the red circle, simulated inundation area was overestimated comparing with satellite observed inundation. Figure 4.2.23 Result of Calibration for Mawlamyine Detail Model

(3) Evaluation and final parameters The maximum inundation area for the target flood was roughly reproduced by the RRI Model. (a) Yangon There were some limitations in adjusting the simulated area to the observed inundation extent (Figure 4.2.21 b). For further improvement, rainfall data and more detailed elevation information are necessary as input. Based on rainfall distribution using the Thiessen method, most part of the detailed model area for Yangon was covered by the Kaba-Aye rainfall station (Figure 4.2.24). Due to such rough distribution of rainfall stations, there are limitations to represent actual rainfall conditions. Since the Yangon area is on a very flat terrain, there are also limitations to simulate overland flow without precise topographic data even after adjustment of DEM using the basic function of RRI_GUI called “DEM adjustment” to smoothen surface elevation (see Figure 4.2.25). For more accurate reproduction of inundation area, higher resolution DEM should be used to represent an actual overland flow direction.

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Kalihtaw Dam Lagunbyin Dam

293 mm 246 mm

Kaba-Aye

471 mm

Figure 4.2.24 Rainfall Distribution using Thiessen Method for Yangon Detail Model

Legend: Elevation (m)

a. SRTM DEM (HydroSHEDS provided) b. Adjusted DEM (created in this TA)

Figure 4.2.25 DEM of Yangon Detail Model

(b) Mandalay Maximum inundation area for the target flood was reproduced fairly well over the entire basin (Figure 4.2.22). The discharge hydrograph at the lowest boundary (Sagaing) was also well reproduced.

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(c) Mawlamyine The maximum inundation area for target flood in the mid and upstream region was reproduced with considerable accuracy after the river width was adjusted. However, even after the river width was adjusted, an excessive amount of flood water still remained in the downstream region. One of the reasons for this problem is data quality. Detailed rainfall distribution data should be used for further improvement of accuracy. Another possible reason is that no drainage channels are considered in the model. Reproducibility needs improving to develop the drainage model.

4.2.5 Reflection of Suggestions/Comments for the Improvement of the Simulation Model Through the workshops on flood hazard mapping, which were held at the three target cities during the January 2016 mission (see Chapter 1), the consultant team collected comments and suggestions from the participants on the flood hazard maps. These comments and suggestions were utilized for further improvement of the hazard maps. Since the hazard maps were prepared based on the limited available data, suggestions by the participants on difference in conditions of the inundation caused by a past flood between simulated results and actual observations were important for further improvement of the simulation models.

4.2.6 Improvement on Elevation Data for Flood Hazard Simulation (Second RRI Model) As is explained in the section 4.2.2(6), global elevation data has a certain limitation in reproducibility of inundation area. For the improvement of the model, elevation data “AW3D” with 2m spatial resolution, provided by NTT data, were applied to flood hazard simulation using the RRI Model. As described in 4.2.2 (6), DEM data for the main urban area and flood-prone area in the three target cities were replaced with newly created DEM with 450m square grid based on AW3D. The area painted in pale blue in Figure 4.2.26 indicates the area in which DEM was replaced with the elevation data based on the AW3D (A: Yangon, B: Mandalay and C: Mawlamyine). In addition to the use of AW3D, adjustment of elevation data was conducted by using benchmark data for Mandalay, which were provided by MCDC. In Mandalay, actual river cross-section survey data of the Ayeyarwady River from DWIR, shown in Figure 4.2.13, were also introduced to improve the reproducibility of inundation area. Figure 4.2.27 illustrates improved simulation results regarding the maximum extent of inundation caused by each target flood. The improvements led to more accurate simulation of flood inundation.

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Detail Model

Detail Model Replaced range

Replaced range

a. Yangon b. Mandalay

Replaced range

Detail Model

c. Mawlamyine

Figure 4.2.26 Range Replaced with Finer Elevation Data in Detail Model Area

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a. Yangon

b. Mandalay

c. Mawlamyine

Figure 4.2.27 Simulated Results of Flood Inundation using AW3D

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4.2.7 Statistical Analysis to Identify the Target Flood for Flood Hazard Simulation in Mandalay Statistical analysis is employed to identify the target flood scale. In general, rainfall intensity or resulting discharge volume should be used for statistical analysis. The water level of a river is not appropriate for statistical analysis if the configuration of the river cross section changes frequently, because water level can change even for the same river discharge volume. Figure 4.2.29 shows the river cross section at Sagaing and Yatanarbon on 2013 and 2014, which indicates instability of the river cross section of the Ayeyarwady River in Mandalay. Therefore rainfall intensity or discharge volume should be used for statistical analysis to identify the target flood in Mandalay.

Yatanarbon

Sagaing

Figure 4.2.28 Location of Cross Section Survey

Figure 4.2.29 Cross Section near Mandalay (2013 and 2014)

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In this TA, the observed discharge at the Thabeikkyin Station upstream of Mandalay was employed as data for statistical analysis because: 1) the river cross section at the Thabeikkyin Station is stable and the observed discharge values seem reliable; and 2) the rainfall data collected in the upper river basin were not enough in quantity. The cross section around the Thabeikkyin Station was considered to be stable because the station is located at the narrow section of the river in the mountain side upstream of the alluvial fan of the Ayeyarwady River. As is described at 3.2.3 (2), improvement of the accuracy of simulation was requested to verify the safety of structures near the river in case of a 100-year flood. In response, the consultant team adopted three methods, which are 1) DEM with greater accuracy was newly procured and replaced the previous one, 2) elevation data in the flood plain were modified based on the bench mark data provided by MCDC, and 3) the river bed elevation of the Ayeyarwady River was revised in reference to the recent survey of river-bed contour lines provided by DWIR. As a result, the team verified that the height of the existing embankment that protected the structures was higher than the estimated water level of a 100-year flood by approximately 50 cm (see Annex-6). Estimated water level was calculated by the RRI model using statistical analysis data of discharge volume at Thabeikkyin Station. As is mentioned above, changes in the configuration of the river cross section affect the water level of a river. Therefore the currently estimated water level of a 100-year flood cannot be applicable if the river cross section may change in the future. If the river bed rises due to sedimentation or the river width narrows due to human intervention, water level may rise accordingly. Since the Ayeyarwady River in Mandalay is prone to sedimentation, it is strongly recommended that river cross section should be observed periodically, and that when the river cross section shows certain changes, the effect of sedimentation on the river flow should be re-simulated to verify the safety of the embankment. Such changes may not be detected through the mere observation of water level. Countermeasures such as dredging should also be taken as necessary.

4.2.8 Applying Rainfall Intensity Formula for Yangon (Third RRI Model) The Yangon city area is affected mainly by inland water due to a low drainage capacity rather than flood water from rivers. In order to represent actual inundation conditions more closely, the Yangon Detail Model was reconstructed and flood hazard was re-simulated after resized by adopting 180m square grids and hourly rainfall data. Small differences in land elevation greatly affect the behavior of overland water flow. Grids should have the size to represent an actual direction of water movement due to topography, especially in a flat area, such as the Yangon area. Therefore, using a finer resolution of elevation data, a refined model with smaller-sized grids was created to perform flood inundation simulation. By integrating and combining HydroSHEDS DEM data with a resolution of 3 arc-seconds and AW3D DSM data, which is limited for the central area of Yangon city, new topographic data with 180m grids were created and applied for achieving the objectives described above. Newly created DEM with 180m square grids for Yangon is shown in Figure 4.2.32. Bench mark information for adjusting DEM data were provided by YCDC. In Yangon city, the dominant cause of inundation is inland water flooding. Torrential rainfall for a short period of time would trigger inland water flooding. Therefore, hourly rainfall distribution is necessary for representing such an inundation. Since only daily rainfall data were available for simulation, hourly averaged rainfall was prepared by dividing daily rainfall into 24 hrs and given to the initial model as a model input. A rainfall intensity formula (YCDC formula) was developed in the Study on Drainage System of Mingalar Taung Nyunt Area, which was conducted by Fukken Co., Ltd in cooperation with the

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YCDC11. In JICA’s recent project, the Study Team examined the validity of the YCDC formula. Examination based on daily maximum rainfall data in the recent 20 years (1992-2011) was conducted using the Gumbel formula. They concluded that the YCDC formula shows safer standards and can be recommended to use for drainage works. The YCDC formula is indicated in Figure 4.2.30. The hyetograph has been modified as an input data suitable for the simulation of inland water flooding. Figure 4.2.30 shows the comparison between a 100-year return period hyetograph at Kaba-Aye station developed by the YCDC rainfall intensity formula and hourly averaged rainfall based on available daily data (used in the Initial and Second Model).

[YCDC Rainfall intensity formula] Where, I : Rainfall intensity (mm/hr) = t : Rainfall duration (min) n, K : Constants to be determined for each return period � � � 100 year return period Hyetograph in Yangon Rainfall Intensity Obtained� Using the YCDC Formula 120 Rainfall intensity formula Return Period 100 K n I Hourly averaged rainfall (year) 5 1,115 0.7 63 80 10 1,249 0.7 71 60

20 1,382 0.7 79 40 50 1,785 0.7 102 20 100 1,918 0.7 109 IntencityRrainfall (mm/h) 0 Source: The Study on Drainage System of Mingalar 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Taung Nyunt Area Time(hour) Figure 4.2.30 YCDC Rainfall Intensity Formula and Model Hyetograph (100-year return period) in Yangon

Rainfall Intensity Curve Center – Concentrated Hyetograph

T is rainfall duration

Rainfall intensity (mm/hr) intensity Rainfall

: :

I

: Rainfall intensity (mm/hr) intensity : Rainfall I

time t time t

Source: Ministry of Land, Infrastructure, Transport and Tourism, MLIT, Japan

Figure 4.2.31 Prepartion of Center-Concentrated Hyetograph

11 Preparatory study on Thilawa special economic zone infrastructure development in the Republic of the Union of Myanmar : final report, Japan International Cooperation Agency : Nippon Koei Co., Ltd. , 2014.3

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Simulated results of inundation area in Yangon are shown in Figure 4.2.33. Compared with the previous result (Figure 4.2.27), the inundated area was distributed widely over the entire model region.

Legend: Elevation (m)

Figure 4.2.32 DEM with 180m Square Gird

Figure 4.2.33 Results of Simulation using 180m Square Grid

4.2.9 Adjustment of Ground Elevation in Yangon Area (Third and Fourth RRI Model) In order to improve the simulation results, more accurate topographic data should be prepared because the reliability of flood inundation analysis depends on the accuracy of ground elevation. In the Third RRI Model, the elevation of Yangon except for that of its central area (where AW3D had already been applied) was adjusted to AW3D-based elevation, because AW3D was improved by eliminating abnormal elevation (see section 4.2.2 (6)) and increased reliability. The elevation outside of the AW3D applied area was reduced by 1.1 m as shown in Figure 4.2.34 (left). The AW3D-derived elevation is based on EGM 96, which is a geoid model (see Annex-5) commonly used worldwide and whose datum level (zero level) is regarded as similar to that of the mean sea level (M.S.L). In the Fourth RRI Model, the elevation data of HydroSHEDS used for the area located south-east of Bago River (the green area of Figure 4.2.34 (right)) was checked with the benchmark data provided by the Myanmar Japan Thilawa Development Limited (MjTD), and it was found that the original

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elevation data of HydroSHEDS needs no adjustments (The datum level of the benchmark of MjTD is considered as M. S. L (see Annex-5)). On the other hand, the elevation of the area on the right bank (west side) of the Yangon River could not be checked because of the lack of benchmark data. Benchmark data in this area should be collected to calibrate topographic data for simulation models.

[Third Model] [Forth Model]

[HydroSHEDS] – [1.1m] [HydroSHEDS] [1.1m] – (Adjusted to AW3D) (Adjusted to AW3D)

[AW3D]

HydroSHEDS (No benchmark is HydroSHEDS available) (AW3D] (Checking with MjTD’s benchmarks)

Figure 4.2.34 Calibrated Areas of Yangon with Benchmarks

4.2.10 Revision of Lower Boundary Condition (Fifth RRI Model) The consultant team delivered flood hazard maps and coastal flood hazard maps to organizations and institutes relevant to flood risk assessment in Myanmar and others at the mission of final workshop (May 2016), and received comments from Myanmar Japan Thilawa Development Limited (MjTD), suggesting that the flood inundation area along the coastal area including Thilawa Special Economic Zone (TSEZ) could be overestimated. Through the discussion between MjTD and the consultant team, a benchmark survey was conducted by MjTD on November 2016. According to the results of the benchmark survey, it is found that the datum level (zero gauges) used for tidal level is different from that for elevation around TSEZ by 2.93m (see Annex-5). Therefore, the consultant team revised the tide data which had been given as the lower boundary condition for the RRI Model, and conducted flood inundation analysis again.

Tide level: 0.5m to 7m (low/high) at Yangon River mouth (Astronomical tide level published by Navy. this data was utilized for the Fourth RRI Model

Fourth RRI Model Elevation around TSEZ is approx. 5 - 6 m

Under the condition of the fourth Fifth RRI Model model, sea water comes into the land twice a day (when high tide occurs). -2.93m

Figure 4.2.35 Relationship between Tide Level and Elevation of TSEZ

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4.2.11 Coastal Flood Inundation Simulation By setting the tidal level along the coastal line as the boundary condition, coastal flood inundation simulation was also able to be implemented. After calculation of sea level rise by the storm surge model (based on Myers formula, see section 4.4), time-series changes in sea level as the boundary condition were incorporated to the RRI Model by adding astronomical tide level to calculate storm surge level (instead of observed tidal level, see Figure 4.4.2). Normally, measured tidal level should be used as the boundary condition for simulation. However, in this simulation, estimated hourly tidal level was obtained and used, since such tidal level data was not available from relevant organizations. Figure 4.2.36 shows the initial simulation results of coastal flood inundation in Yangon due to Cyclone Nargis in 2008. In this simulation, rainfall was not considered. Note that grid cells adjacent to the sea were also considered as part of the sea; therefore these cells are not displayed as inundation area, even though these areas are inundated with the water level being the same as the sea level (see Figure 4.2.36 b). In the later stage of this project, the difference in the datum level between the bench mark and the astronomical tidal level was surveyed (see Annex-5) and applied to simulation in Yangon. Figure 4.2.37 shows the final simulated results of coastal flood inundation in Yangon (third version), most of which is cleared of flood waters after a 2.93 m difference in the datum level was applied to the simulation. Users can simulate any cyclone case using the storm surge model. As mentioned in the latter sections of this report, any cyclone with a virtual track and intensity can be simulated. The simulated results of Mawlamyine by applying the same course and intensity of Cyclone Nargis are shown in Figure 4.2.38, which shows that there was no inundation caused by Cyclone Nargis. As such, the storm surge model for the Yangon and Mawlamyine Detail Models were used as the base model for coastal flood inundation simulation.

a. Entire view b. Magnified view

Figure 4.2.36 Simulated Results of Coastal Flood Inundation (Yangon, First Version*) *An explanation on version of coastal flood hazard maps is in section 5.2.3

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a. Entire view b. Magnified view

Figure 4.2.37 Simulated Results of Coastal Flood Inundation (Yangon, Third Version*) * An explanation on version of coastal flood hazard maps is in section 5.2.3

a. Entire view b. Magnified view

Figure 4.2.38 Simulated Results of Coastal Flood Inundation (Mawlamyine)

4.2.12 Findings and Recommendations Findings and recommendations regarding flood inundation analysis are summarized as below:  Based on hydrological/meteorological observed data from DMH and globally available data, flood inundation simulation using the RRI Model could be implemented for flood hazard assessment. Reproduction of the maximum inundation extent roughly represents inundation area based on satellite data.

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 With appropriate parameter calibration and/or model adjustment, simulated inundation extent and river discharge can be optimized to fit to observed data. Therefore, data and information that can contribute to model development, such as local topographic data with contour lines, should be collected as much as possible.  In simulation on flat areas such as Yangon, it is necessary to modify elevation data manually in order to recreate actual inundation conditions. For further model improvement, it is essential to acquire DEM with higher resolution.  In addition, more detailed rainfall distribution data (e.g., utilization of rainfall observed by weather radars) are required for improvement of results. Continuous efforts should be made by DMH to collect and accumulate data and information.

4.3 Flood Inundation Simulation Model for Other Basins 4.3.1 General Description Based on the request from ID, the Bago area was added to the areas to create a flood inundation simulation model. Bago City is located north-east of Yangon and the annual flooding of the Bago River makes this area flood-prone. Agricultural or economical damage due to flooding is one of the serious issues in this area. The RRI Model for the Bago area was developed by the trainer candidates from the ID with the support of the consultant team. Figure 4.3.1 shows the ranges of the Detail Model of the Bago area, and Figure 4.3.3 shows the stations that provided observed data for model development. Furthermore, two other basins were covered as an urgent correspondence. In the summer of 2015, large-scale inundation by river flooding occurred in Myanmar when Cyclone Komen hit the area. DMH requested TA-8456 Part II to support DMH in developing a flood simulation model for areas damaged by the flood. Therefore the consultant team took up this as a part of training for DMH officers. Nyaung Don in the Ayeyarwady region and the Kale area in the Sagaing region were selected as target areas by DMH to analyze flooding and inundation caused by Cyclone Komen. Trainees developed a flood inundation simulation model for the two areas by using their knowledge acquired through this project. Figure 4.3.2 shows the ranges of the Large Basin Model and the Detail Model and Figure 4.3.4 shows the location of hydro-meteorological stations.

Bago

Bago River Detail Model

Figure 4.3.1 Detail Model for Bago

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Ayeyarwady River

Detail Model

Large Basin Model Chindwin River Large Basin Model Manipor River Large Basin Model Ayeyarwady Hlaing River River Nyaung Don Myintthar River

Kale Yangon Detail Model

Detail Model

a. Nyaung Don b. Kale

Figure 4.3.2 Detail Model and Large Basin Model for other Areas

Kodukwe

Wagadok Zaungtu

Bago Shankhaing

Paingkyone

Legend Rainfall station Water level station

Figure 4.3.3 Stations of Collected Meteorological and Hydrological Data (Bago)

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Homali Pyay n

Tamu

Tagondine Mawlaik Khamonseik Henzada Kalemyo Kalewa Tharrawady Ramtholo Falam Ngathainggyaung Mingin Zalun Seiktha Hakh VaarCamp

Gangaw Pathein Maubin Legend MyaungMya Rainfall station Pauk Water level station Minda Paletwa t a. Nyaung Don b. Kale

Figure 4.3.4 Hydro-Meteorological Stations

4.3.2 Bago The available data are shown in Table 4.3.1. The model range and boundary conditions for Bago are defined in Table 4.3.2 and in Figure 4.3.1. Since the target area was not so large, only the Detail Model was created with 30” grid size (approx. 900m). Figure 4.3.5 shows the simulated result of the maximum inundation area for the Bago basin. Comparison with the observed inundation situation was not undertaken, since no satellite image over the Bago basin in August 2014 was available. In the downstream region of the Bago River basin, the inundation area simulated by the RRI Model spreads widely with a maximum inundation depth of more than 2.0 m (Figure 4.3.5). However, in the south-eastern part of this model area, flood water seems to be dammed up at the border of the river basin, which was defined as the model boundary. This causes the overestimation of inundation depth and the underestimation of inundation area (see Figure 4.3.6 (2)). When a huge flood occurs in a low-lying area, flood water could spread beyond the river catchment area. Therefore, simulated inundation area and depth should be examined carefully by comparing actual ground elevation. If the flood water level exceeds the elevation of the river basin boundary, the calculation area should be extended to include the adjacent areas (see Figure 4.3.6 (3)).

Table 4.3.1 Collected Meteorological and Hydrological Data for Bago Target Number of No. Period* Remarks Area available station Rainfall: 6 (daily)  Rainfall data from ID were included. 2014 1 Bago Water level  The model was calibrated with discharge/river water Aug /discharge: 1 (daily) level. *A large flood event occured in this period

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Table 4.3.2 Model Range for Bago Large Basin Model Detail Model Model Grid size: 90”×90” Grid size: 30”×30” (approx. 2.7km×2.7km) (approx. 900m×900m) Not created Area: approx. 2,680 km2 Numbers of grid cells: approx. 5,730 Upstream boundary Upper end of Bago River Lower boundary Lower end of Bago River Bago City/Township Yangon-S/Kayan, Thougnia Yangon-N/Hlegu Pegu/Kyauktaga, Daik-U, Bago(Pegu), Waw, Thanatpin, Kawa Thayarwady/Gyobingauk, Okpo, Minhla, Letpatan

Figure 4.3.5 Result of Bago Detail Model

(1) Water catchment area is determined (2) In case of a large flood, the movement of based on elevation of the ground flood water could be restricted by the basin boundary.

Inundation depth can be overestimated. (3) Calculation area beyond actual water catchment is employed to avoid this kind of issue.

Figure 4.3.6 Issue on Calculation Area in Low-Lying Area

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4.3.3 Nyaung Don Nyaung Don is the city located at the diversion point between the Ayeyarwady River and the Hlaing River, which is north-west of Yangon in the Ayeyarwady region. The Large Basin Model and the Detail Model for Nyaung Don were developed by the trainer candidates from DMH with technical support of the consultant team. Table 4.3.3 shows the model range for Nyaung Don. The Large Basin Model was defined to cover a catchment area of Ayeyarwady River and additionally the eastern and western parts of the flat plain, which are outside the Ayeyarwady River Basin but contiguous with the basin. It is difficult to obtain ground gauged rainfall data from DMH for simulation, and thus global data, such as GSMaP satellite rainfall data, were applied to simulate flood hazard. These global data are available for quick and rough calculation of flood inundation area. Figure 4.3.7 b and c indicate the simulation results of the maximum inundation extent and flood water depth on the same date of the satellite image shown in Figure 4.3.7 a. They show a fairly good agreement with the outline of the inundated area, even though there are some minute differences between the simulated and actual inundation areas because of the limited availability of elevation and rainfall data. However, the simulation results clearly indicate no inundation in the central area of Nyaung Don.

Table 4.3.3 Summary of Nyaung Don Model Large Basin Model Detail Model Model Grid size: 150”×150” Grid size: 30”×30” (approx. 4.5km×4.5km) (approx. 900m×900m) Area: approx. 378,400km2 Area: approx.40,900km2 Numbers of grid cells: approx. 19,100 Numbers of grid cells: approx. 49,600 Upstream boundary Upstream boundary Upper end of Ayeyarwady River Location in branch rivers determining to comprise a Lower boundary past inundated area in the model region (Pyay in the Lower end of Ayeyarwady River Ayeyarwady River) City Lower boundary Myomgmya, Maubin, Hinthada, Thayarwady, Lower end of Ayeyarwady River and Hlaing River. Pyay, Thandwe, Thayetmyo, Minbu, Lower end of some smaller rivers located in the MagweMinbu, Yamethin, Meiktila, Taunggye, lower plain with historically inundated area Myingyan, Pakokku, Mindat, Kyaukse, Sagaing, City/Township Monywa, Mandalay, Shwebo, Kyaukme, Loilen, Pyay/Padaung, Paukkaung, Paungde, Prome, Palam, Kalemyo, Pyin-Oo-Lwin, Tamu, Mawleik, Shwedaung, Thegon, Nyaung Don Katha, Hkamti, Bhamo, Myitkyina, Patao, Muse, Thayarwady/Gyobingauk, Letpatan, Minhla, Lasho Monyo, Nattalin, Okpo, Tharrawaddy, Zigon (Northern part of the Large Basin Model is Hinthada/Danubyu, Henzada, Ingapu, Kyangin, located in Bangladesh) Lemyethna, Myanaung, Zalun Maubin/Danubyu, Irrawaddy, Ma-Ubin, Pantanaw, Yandoon Yangon-N/Hmawbi, Htantabin, Taikkyi Bassein/Bassein West, Kyaunggon, Kyonpyaw, Ngaputaw, Thabaung, Yegyi Myoungmya/Einme, Labutta, Moulmeingyun, Myaungmya, Wakema Pharpon/Kyaiklat, Pyapon, Bogale, Dedaye Yangon-S/Kungyangon, Insein, Kawkhmu Yangon-E/Yangon (Rangoon)

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Nyaung Don

Nyaung Don

0 50 100km a. Actual situation (ERCC) b. Simulation by RRI c. Simulation by RRI (inundated area) (water depth)

Figure 4.3.7 Comparison of Simulated Results and Actual Inundated Area for Nyaung Don

4.3.4 Kale Kale is the city located at the western part of the Sagaing region in Myanmar. The Myintthar River flows out from the basin isolated from the Chindwin River basin by mountains. The Large Scale Model and the Detail Model for Kale were developed by the trainer candidates from DMH. Table 4.3.4 shows the model range for Kale. Like the situation of data availability in Nyaung Don, only global data, GSMaP and HydroSHEDS data were used as model input for hourly rainfall and land elevation data, respectively. Figure 4.3.8 b and c indicate the simulation results of the maximum inundation area and inundated water depth on the same date of the satellite image in Figure 4.3.8 a. They show a good agreement with the actual inundation situation.

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Table 4.3.4 Summary of Kale Model Large Basin Model Detail Model Model Grid size: 30”×30” Grid size: 15”×15” (approx. 900m×900m) (approx. 450m×450m) Area: approx. 24,700km2 Area: approx. 8,800km2 Numbers of grid cells: approx. 31,100 Numbers of grid cells: approx. 44,500 Upstream boundary Upstream boundary Upper end of Manipor River, Myintthar River and Location in branch rivers determining to comprise a Nayintaya River past inundated area in the model region (Vaar Camp Lower boundary in the Manipor River and Gangaw in the Myintthar Confluence with Chindwin River (Kalewa) River) Kale City/Township Lower boundary Palam/Tonzang, Tiddim, Falam, Haka Confluence with Chindwin River (Kalewa) Kalemyo/Kale City/Township Pakokku/Gangaw, Htilin, Saw Palam/Falam, Haka, Tiddim, Tonzang Mindat/Matupi, Mindat Kalemyo/Kale Pakokku/Gangaw Monywa/Kani

Kale Kale

Myintthar River

Chindwin River

a. Actual situation(MIMU) b. Simulation by RRI c. Simulation by RRI (inundated) (water depth)

Figure 4.3.8 Comparison of Simulated Results and Actual Inundated Area for Kale

4.3.5 Findings and Recommendations Findings and recommendations regarding additional flood inundation analysis are summarized as below:  Simulation of Nyaung Don and Kale flood inundation successfully reproduced the actual inundated area of the 2015 flood.  Regarding the simulation of Bago, it was found that the catchment area (equal to rainfall collected area) is different from the actual inundation area. This could cause the underestimation of flood inundation area (see Figure 4.3.6). Basically, RRI_GUI defines the model area based on the

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catchment area. However, in the abovementioned case, users have to modify the model area manually by reference to past inundation conditions for proper evaluation of inundation area.  In general, reproduction of flood inundation area in low-lying areas, e.g., Nyaung Don, is more difficult than that in hilly or mountainous areas because it is difficult to clarify flow directions of flood water. For further model improvement, it is essential to acquire DEM with higher resolution to identify an actual overland flow. More detailed rainfall distribution data (e.g., radar rainfall) are also required.

4.4 Storm Surge Analysis Every year, large cyclones pass through near Myanmar, and some of them hit and wreak serious damage such as loss of human lives. Cyclone Nargis, which formed in the Bay of Bengal in 2008 and caused catastrophic damage, is still fresh in our minds. The purpose of storm surge analysis is to estimate sea level rise due to low pressure and ocean winds, and to evaluate flood risk caused by storm surges. The result of storm surge analysis will be utilized for a coastal protection plan, input data for inundation analysis as the lower boundary condition of the RRI Model and so on. At present, the Meteorological Division of DMH has three (3) storm surge models including the storm surge model developed by CTI Engineering Co., Ltd as shown in Table 4.4.1.

Table 4.4.1 Outline of Storm Surge Model used by DMH Meteorological Division Outline Method Developer Bathymetry Resolution 1) Air pressure field: IIT SRTM Plus 3,600m (Indian Institute of Technology) - Resolution: 900 m Fujita’s formula or Myers formula is employed to Developed in FORTRAN calculate air pressure field. programming code JMA ETOPO 2 2 min (Japan Meteorology Agency) - Resolution: 1,800 m (equal to approx. 2) Storm surge level 3,600 m) Developed in FORTRAN Two-dimensional unsteady programming code flow model is employed CTI Engineering Co., Ltd SRTM Plus 3,600m - Resolution: 900 m (for TA-8456 Developed in FORTRAN training) programming code

4.4.1 Basics of Storm Surge Basically, the behavior of sea water level is dominated by tidal force (Figure 4.4.1). Sea water level can be estimated by the gravitational effect between the earth and the moon, which is thus named astronomical tide level. However, when cyclones or typhoons pass, sea water level will rise due to low air pressure and strong ocean winds. Therefore, the magnitude of air pressure, wind speed and wind direction are essential factors for storm surge analysis. Figure 4.4.2 and Figure 4.4.3 show the definition and mechanism of storm surge, respectively.

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The Sun The Earth

New moon

Full moon

Figure 4.4.1 Schematic Diagrams of Syzygy “Full Moon and New Moon”

Observed sea level

A

Sea water Sea water level(E.L.m) storm surge astronomical tide level

t Figure 4.4.2 Definition of Storm Surge Figure 4.4.3 Mechanism of Storm Surge

1) Low air pressure: Usually, air pressure on the ground is one atmospheric pressure (equal to 1,013 hecto-pascal (hPa)), which means that the weight of air is on the sea surface. Under low air pressure, the sea water level will rise (Figure 4.4.4).

1,013 hPa (1 atm) suction

In general, if air pressure decreases by1 hPa, sea water level will rise approx. 1cm.

Figure 4.4.4 Sea Level Rise by Low Air Pressure

2) Blow up: If strong ocean winds blow from offshore to the coast, sea water will be deluged by ocean winds and the water level near the coast will rise (Figure 4.4.5).

Ocean wind blow up Calm/Lull

Water level will rise in proportion to square of wind speed.

Figure 4.4.5 Sea Level Rise by Ocean Wind

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4.4.2 Development of Storm Surge Model with Myers Formula (1) Outline of Storm Surge Model (a) Air Pressure Field As mentioned in the previous sub-section, storm surges are caused by low pressure and ocean winds (velocity and direction). Thus, the air pressure field and the wind field shall be estimated for storm surge analysis, and several methods have been developed for this purpose. Air pressure can be calculated by using a simple method such as Fujita’s model and the Myers model (Figure 4.4.6). In this TA-8456 Part II, the Myers model is employed for calculation of air pressure.

(hPa)

P

Distribution of air pressure is calculated by Myers model or Fujita’s model Pc r (km) [Myers formula] Here, P (r) : air pressure at r km from center of cyclone   r0  (m/s) rP )(  Pc  P exp  Pc : air pressure at center of cyclone  r  ΔP : degradation of pressure (= P∞ - Pc) (hPa)

r0 : distance from a center of cyclone to a place where maximum wind velocity occurs (km) Here, formula] [Fujita’s P (r) : air pressure at r km from center of cyclone

a P∞ : air pressure very far from center of cyclone rP)(P  2 a : pressure depth ( = P - P ) (hPa) 1 rr0 ∞ c

Pc : air pressure at center of cyclone

r0 : parameter according to scale of cyclone (km)

Figure 4.4.6 Calculation of Air Pressure Field

In general, air pressure field calculated by Myers formula is higher than that by Fujita's formula (Figure 4.4.7).

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1,020

1,000

980 Fujita (Pc = 980 hPa) Myers (Pc = 980 hPa) Fujita (Pc = 960 hPa) 960 Myers (Pc = 960 hPa) Fujita (Pc = 940 hPa)

) Air Air Pressure) (hPa)

r Myers (Pc = 940 hPa)

(

P 940 Fig- Distribution of Air Pressure P (r) 920 0123456789 10

r/r0 Figure 4.4.7 Comparison of Estimated Air Pressure Field

(b) Wind field Regarding the wind field, it should be estimated by composite wind which consists of gradient wind (V10) and wind generated by travel of cyclone (Vp). Gradient wind (V10) is generated by gradient of the constant-pressure line (Figure 4.4.8). The wind is estimated by the following formula:

   rf  4 P  V10   1 1 C1 2  rf22 r    a 

P P  P0 r0  2 r0 exp( ) 合成風Composite wind r r r

Myers formula is differentiated by r 台風の Direction進行方向   r r  rf  4 0  0   V10: V10   1 1 22P exp  C1 V10   傾度風  2 a rf r  r   (gradient wind) Vp:場の風Vp    (wind generated by travel of cyclone) Here, V : gradient wind at 10m from sea surface (m/s) Center台風の of 10 r : distance from a center of cyclone (km) cyclone中心 θ Angle of r0: distance from a center of cyclone to a place where blowing wind maximum wind velocity occurs (km) Constant-pressure等圧線 line f: coefficient of “Coriolis”, C : constant 1 ρ : density of air a Here, V : wind by travel of cyclone (m/s)  1  p V  C V  exp  r C : constant p 2   2    β : constant according to scale of cyclone (km) V : moving velocity of cyclone (m/s) r : distance from a center of cyclone (km) Figure 4.4.8 Calculation of Wind Field

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V10 means a wind velocity at 10m from sea surface (Figure 4.4.9). In general, V10 is representative wind which most affects sea level rise. Figure 4.4.10 shows the distribution diagram of wind velocity.

Vertical distribution of

Altitude wind

V10

10m

Velocity

Figure 4.4.9 Definition of V10

r 0 Left half circle (west) Right half circle (east)

(m/s)

v

r (km)

Figure 4.4.10 Diagram on Distribution of Wind Velocity (North hemisphere)

For reference, model parameters for estimation of air pressure and wind field are shown in Table 4.4.2. Each value should be determined by model calibration.

Table 4.4.2 Model Parameters (Japanese Case)

Parameters Values Remarks

employed in storm surge analysis for Tokyo Bay, C : for gradient wind, V 0.60 1 10 Japan

C2 : for wind, Vp 4/7 Ditto θ : angle of wind blowing 30 degree Ditto Hydraulic formulary by Japan Society of Civil f 8.47 x 10-5 : coefficient of “Coriolis” Engineers (JSCE), 1999

3 ρa : density of air 1.205 kg/m Ditto : constant value according to employed in storm surge analysis for Tokyo Bay, β 500km scale of cyclone Japan

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(c) Calculation of sea water level The behavior of sea water is influenced by not only low air pressure and ocean winds but also the shape of a seafloor (Figure 4.4.11). For example, sea level rise caused by a cyclone at a shallow beach will be higher than in deep ocean areas. In order to analyze storm surges considering features of a sea floor bed, a two-dimensional unsteady flow model should be employed.

Ocean wind Ocean wind

Figure 4.4.11 Influence by Sea Floor Bed

Typically, a theoretical formula on a long wave is employed for calculation of sea water movement, which can be applied to not only storm surges but also tsunamis. Fluid motion on the surface of the earth should be described by the following formulas:

[Flow velocity: u (x - direction)] u u u u 1 P 2u *   2u  2u   u  v  w  fv       2  2 2  t x y z w x wz w  x y  [Flow velocity: v (y - direction)] v v v v 1 P 2v *  2v 2v   u  v  w  fu       2  2 2  t x y z w y wz w  x y 

[Flow velocity: w (z - direction)] w w w w 1 P  u  v  w   g ζ(x,y,t) y t x y z w z x

Here, z f: coefficient of “Coriolis”, ρ : density of sea water w h(x,y) P: atmospheric pressure g : acceleration due to gravity μ: coefficient of eddy viscosity (vertical) μ*: coefficient of eddy viscosity (horizontal)

Figure 4.4.12 Calculation of Sea Water Level (two-dimensional unsteady flow)

(2) Data/Information Necessary for Development of Model In order to develop a storm surge model, data and information shown in Table 4.4.3 are required. Figure 4.4.13 shows the location of tidal stations in Myanmar. In the course of this project, tidal data and meteorological data were not publicly available in Myanmar, and the Part II Consultant Team was not able to collect such types of data, either.

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Table 4.4.3 Data/Information Necessary for Development of Strom Surge Model No. Data/Information Descriptions Best track including Past best track (Cyclone track) and air pressure at center of cyclone are central air pressure, indispensable data for storm surge analysis. Those data can be obtained from radius of cyclone. following websites for free.  India Meteorological Department (IMD) http://www.rsmcnewdelhi.imd.gov.in/index.php?lang=en  Japan Meteorological Agency, Japan 1 http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html  Joint Typhoon Warning Center (JTWC) http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/ioindex.php  National Institute of Information, Japan: http://agora.ex.nii.ac.jp/digital-typhoon/latest/track/  UNISYS: http://weather.unisys.com/hurricane/

Bathymetry data In order to calculate distribution of storm surge by using two-dimensional model, (sea floor bed) bathymetry data is required. This data can be obtained following websites.  SRTM Plus (resolution 30sec = approx. 900m, Free) http://topex.ucsd.edu/WWW_html/srtm30_plus.htm  GEBCO 2014 Grid, General Bathymetric Chart of the Oceans (resolution 2 30sec = approx. 900m, Free) http://www.gebco.net/  NOAA NATIONAL CENTERS FOR ENVIRONMENTAL INFORMATION (resolution 1min = approx. 1,800m, Free) http://www.ngdc.noaa.gov/mgg/global/global.html

Tidal data Regarding astronomical sea tide, Myanmar Navy publishes tide table (high and (astronomical sea low tide, 4 values par day) every year. However, the consultant team could not tide and observed obtain hourly astronomical tidal data. sea level) Sea water level in Myanmar is observed by Myanmar Port Authority (MPA), 3 which is not publicly used and therefore not provided.

Challenges: Hourly tidal data should be collected for model calibration. Meteorological data DMH has observed meteorological data. (air pressure, wind At the present, meteorological stations are not located along the coastal line. 4 velocity, wind direction etc.) Challenges: Wind profile should be collected for model calibration. Others Flood marks/records caused by past storm surge are helpful for model calibration. (flood marks at past 5 storm surge) Challenges: Information which shows past flood condition should be collected for model calibration.

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Reference: Myanmar multi hazard risk Figure 4.4.13 Location of Tidal Stations

(3) Development of Storm Surge Model (a) Target Cyclone In this TA, the trainer candidates and the consultant team conducted storm surge analysis for seven cyclones including Nargis, which formed in April to May 2008 and wreaked biggest-ever catastrophic damage in Myanmar. [Seven Target Cyclones] Mala (2006), Akash (2007), Nargis (2008), Giri (2009), Mahasan (2013), Komen (2015), Roaun (2016) Table 4.4.4 shows the noted cyclones that hit Myanmar in the past.

Table 4.4.4 Killer Cyclones & Associated Storm Surges Peak Surge Death Damage No Name Date Landfall Point (m) toll (Kyats) 800 1. Sittwe Cyclone 7.5.1968 4.25 Near Sittwe 1,037 million 776 2. Pathein Cyclone 7.5.1975 3.00 Near Pathein 304 million 3. Gwa Cyclone 4.5.1982 3.70 Near Gwa 31 38 million Maungdaw 4. 2.5.1994 3.66 Near Maungdaw 10 78 million Cyclone 5. Mala Cyclone 29.4.2006 4.57 Near Gwa 1 N/A Ayeyarwady,Yangon, 6. 2-3.5.2008 N/A 130,000 N/A “Nargis” Mon & Kayin 7 “Komen” 30.7.2016 N/A North of Maungdaw N/A N/A Data Source: DMH Meteorological Division

(b) Target Areas and resolution In this TA-8456, a calculation grid of 3,600m was employed in reference to resolution of other storm surge models (JMA).

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(c) Model Parameters As initial values, model parameters used in Japanese storm surge analysis (see Table 4.4.2) were given to the model.

(4) Model Calibration In general, model calibration has to be conducted in order to reproduce actual storm surge conditions properly. However, calibration of the storm surge model was not conducted in this TA due to lack of data.

4.4.3 Simulation (1) Calculation Condition Table 4.4.5 shows the calculation conditions of storm surge analysis.

Table 4.4.5 Calculation Condition No. Items Description Simple method. Myers formula is employed for estimation of air 1 Methodology pressure field. SRTM Plus (NASA) 2 Bathymetry *Minimum calculation grid is approx. 900m 3 Resolution 3,600m 7 cyclones 4 Target cyclone Mala (2006), Akash (2007), Nargis (2008), Giri (2009), Mahasan (2013), Komen (2015), Roaun (2016) India Meteorological Department (IMD) and Joint Typhoon Warning 5 Best track Center (JTWC). Air pressure at Ditto 6 center of cyclone Commonly used values in Japanese case 7 Model parameters Model calibration by using observed data is necessary

(2) Simulation Result The simulation results are shown in Figure 4.4.14 to Figure 4.4.17.

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Estimated Storm Surge (1/4) (1/4) Storm Surge Estimated

14

Figure 4.4. Figure

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Estimated Storm Surge (2/4) (2/4) Storm Surge Estimated

15

Figure 4.4. Figure

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Estimated Storm Surge (3/4) (3/4) Storm Surge Estimated

16

Figure 4.4. Figure

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Estimated Storm Surge (4/4) (4/4) Storm Surge Estimated

17

Figure 4.4. Figure

4.4.4 Findings and Recommendations Findings and recommendations regarding storm surge analysis are summarized below:  A storm surge model can be developed using global data including cyclone tracks and bathymetry data, which are available for free.  At present, DMH has three storm surge models, IIT (Indian Institute of Technology) model, JMA (Japan Meteorological Agency) model and CTI model (CTI Engineering Co., Ltd). However, those models are not calibrated and verified by observed tide data, etc. due to scarce data availability. Therefore, the following are strongly recommended for model calibration and improvement of storm surge models: 1) data sharing with MPA, Navy, and other projects or installing own tidal gauges; and 2) field survey in coastal areas for the traces of high tide.  All three models used in DMH are programmed in FORTRAN; therefore it is recommended to master FORTRAN programming to be able to manage model operation at DMH (see Table 4.4.1).  The CTI model can estimate wind profiles including wind velocity and direction. Since JICA completed the installation of 30 AWS (Automatic Weather Station) in whole Myanmar on March 2016, the storm surge model developed for each area should be improved based on the 1) observed tidal data and 2) wind profiles observed at AWS.

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