Manzul Hazarika Ph.D.

Associate Director, Geoinformatics CenterAsian Institute of Technology

E-mail: manzul@ait.ac.th

Education, Research, Training and Capacity Building Activities in AIT

(Research & Training Node for Sentinel Asia)

Asian Institute of Technology (AIT)

AIT Academic Structure (Schools and Extension)

Computer Science

School of Engineering and Technology

AIT Extension(Non-degree

training, consultancy and

services)

School of Environment Resources and Development

Geotechnical and Geoenvironmental

Engineering

Agricultural Systems and Engineering

International Business

Industrial Engineering and Management

Management of Technology

Remote Sensing and Geographic

Information Systems

Gender and Development Studies

Urban Environmental Management

Construction,Engineering and

Infrastructure Management

Structural Engineering

Natural Resources Management

Regional and Rural Development Planning

Environmental Engineering and Management

Pulp and Paper Technology

Aquaculture and Aquatic Resources Management

Food Engineering and Bioprocess Technology

Transportation Engineering

Design and Manufacturing Engineering

Water Engineering and Management

Service Marketing and Technology

International Public

Management

Environment, Infrastructure and

Urban Development

Development Management

Agriculture, Resources and

Rural Development

School of Management

Business Management

Education and Training

Development

Information and Communications

Technology

Information and Communications

TechnologiesMechatronics

Telecommunications

InformationManagement

Executive MBAEMBA Bangkok EMBA Vietnam

EMBA-HRM

Energy

Microelectronics

Remote Sensing and GIS Field of Study

EducationMaster and PhD ProgramsDiploma and Certificate Programs3 Regular Faculties, 1 JAXA Seconded FacultyApproximately 60 students (2006)

Multidisciplinary Programs (SET & SERD)New Master Program on Disaster ManagementDisaster Management (starts in August 2007)

Research/Training CenterGeoinformatics Center carries out research, training and capacity building activities

Capacity Building : Mini-Projectsby

Geoinformatics Center (GIC)

Mini-Projects - Characteristics

Training and comprehensive capacity building through real-world problems such as flood, drought, landslide, etc.Involve data/service provider agencies and services/products user agencies,Explore the theoretical aspects and identify most appropriatedata analysis and integration techniqueCalibration/validation through field observationsGenerate products with participation of both users and service providersDevelop case studies to share in the region

Mini-Projects in 2006

Capacity building projects are being sponsored by the Japan Aerospace Exploration Agency (JAXA) in developing countries.

Projects:

Flood – 5 Projects (Bangladesh, Cambodia, China, Laos & Nepal)

Drought – 1 Project (Philippines)

Landside – 3 Projects (Philippines, Sri Lanka & Vietnam)

Activities:1. Workshop and Training in AIT– Aug/Sep, 2006

2. Field Visit – Nov/Dec, 2006

3. Data Analysis and Report Writing in AIT– Jan/Feb, 2007

Flood Projects

Sl. No.

Country Organizations

Flood Forecasting & Warning Center (FFWC)

Local Government Engineering Dept. (LGED)

Bangladesh Disaster Preparedness Center (BDPC)

Geography Department, Ministry of Land AdministrationUrban, Planning and Construction (MLUPC)

2 Cambodia

Hydrology and Water River Works Dept., Ministry of Water Res. and Meteorology (MOWRAM)

3 China PR Beijing Normal University

Environmental Research Institute (ERI), Science Technology and Environment Agency

4 Lao PDR

Department of Meteorology and Hydrology (DMH)Department of Water Induced Disaster Prevention (DWIDP)

Survey Department

Department of Hydrology and Meteorology (DHM)

5 Nepal

1 Bangladesh

Drought and Landslide Projects

Sl. No.

Country Organizations

6 Philippines(Drought)

Philippine Rice Research Institute (PhilRice)

Philippines Inst. of Volcanology & Seismology (PhiVolcs)7 Philippines(Landslide)

National Mapping & Res. Info. Agency (NAMRIA)

National Building Research Organisation (NBRO)8 Sri Lanka

Survey DepartmentInstitute of Geography, VAST9 Vietnam

Min. of Natural Resources and Environment (MONRE)

Bangladesh: Flood Mapping by Integrating Remote Sensing Data & MIKE 11 Model

Munshiganj

Cambodia: Flood Hazard Mapping in Three Provinces of Cambodia under Mekong Basin

Kompong Cham, Prey Veng and Kandal Provinces

China: Flood Risk Assessment using Remote Sensing & Hydrologic model in the Xiang Jiang River

Laos: Application of RS-GIS for Flood Extent Study in Savannakhet Province

Nepal: Rainfall-Runoff Modeling of Bagmati Basin & Flood Loss Estimation of Gaur Municipality

BagmatiWatershedBagmatiWatershed

Gaur 3D view

Philippines-I: Detection of Drought Prone Areas Using Remote Sensing and Meteorological Approach in Iloilo

Philippines-II: Modeling of Rain and Earthquake Triggered Landslides using RS and GIS-based Slope Stability Models

Southern Leyte, Philippines

LANDSAT TM IMAGE OF LEYTE ISLANDBANDS 452

124.24°11.60°

124.8°10.82°

125.28°10.15°

125.36°9.87°

STUDY AREAPHILIPPINES I

GUINSAUGON, SOUTHERN LEYTE

N

ASTER DATA

ALOS DATA

Sri Lanka:Use of a Slope Stability Index Based Predicting Tool for Landslide Hazard Mapping

Ratnapura Town Area

Vietnam: Application of Remote Sensing and GIS for Landslide Hazard Mapping in a Mountainous Areas

Yen Chau & Bac Yen Districts

IntroductionIntroduction

Mini ProjectsMini Projects

LandslideLandslide

FloodFlood

DroughtDrought

ContactContact

JAXAJAXA

Sentinel AsiaSentinel Asia

Data Collected through Field Visits …

Soil sampling at a shallow landslide location in Huoi

Thon

Scar of Huoi Thon major landslide

A wide open crack in the road produced by an active landslide in Hong Ngai

Typical land cover A shallow slide in Bac Yen Point positioning using GPS

Cambodia

A Detail Example for Flood Hazard Mapping

Flood Hazard Mapping in Three Provinces of Cambodia under Mekong Basin

Study Area

Study area covers 3 provinces (Kompong Cham, Prey Veng, and Kandal) with a population of 245,000.

2000 Flood (Dartmouth Flood Observatory)

• Aug., 2000 • 208,200 sq. km area flooded; 1,139 dead; 6.5 Million displaced• Property Damage: 78 Million US$

Main Objectives of the Study

• To integrate a flood simulation model and remotely sensed data with the available topographic and socio economic data

• To validate the model by comparing the simulated flood inundation area and depth with the available flood maps and remote sensing image.

• Prepare a hazard map using depth map and the socio-economic data

Data Used

1. Hydrologic Data 2. Vector Data- Water level - River network- Discharge - Road network

- Administrative boundary- Location of schools

3. Topographic Data 4. Satellite Images- Spot height - RADARSAT (2000)- WGS84 Ellipsoidal heights - LANDSAT ETM (2005)- Hydrological Atlas /Bathymetry- GPS Survey data

5. Ancillary Data- Population density in 2004- Settlement in 2004- Flood Depth in 2000 from MIKE 11

Available Satellite Data of Study Area

Landsat : Jan, 2005 Radarsat : Sep. and Oct., 2000

Methodology

Hydrological Data

Flood hazard map

Topographic and GPS Data

TIN Land use map

Population density

Road network

Vulnerability Assessment

HEC-RAS

Satellite ImageLANDSAT ETM

Field Survey

Right Bank: 95.15KmLeft Bank: 43.62 KmPP-Kg.Cham:

During Field Survey

Comparison of Results

RADARSAT-1 Image, September, 2000

Flood Affected Villages

Village Affected by flood and non-flood

1076

1693

0

500

1000

1500

2000

Flooded Non flooded

No.

vill

age

Population and householdaffected and no affected

267842

1214067

244626

1211019

0

500,000

1,000,000

1,500,000

flooded 1214067 267842

non-flooded 1211019 244626

Total Both Sexes Total Households

Area For Flood Hazard Mapping

Weighted Population Map

+

=Population per commune X Weighted Landuse

Total Weight

Weighted Population per pixel

Enlarged Weighted Population Map

Reclassified Landuse Map

Flood Depth Map

Final Flood Hazard Map

Conclusions for Cambodia

Hazard Population % of totalRank Agriculture Build Up Agriculture Build Up affected (103) population

Low 305 396 0.09 0.65 104 4.31Medium 2387 3400 0.70 6.62 394 16.25High 12646 12734 3.70 21.03 720 29.71

Land Affected, ha % of total land

High/V.High

• The Extent of the flood depth from HEC-RAS is comparable with the flood map derived from RADRASAT data. Hence, approach could be replicated in other parts of the basin.

• Non-availability of sufficient elevation data for DEM generation was felt as the main problem during the study. ALOS data could be useful generating accurate DEM.

NepalFlood Forecasting and Early Warning System

in Bagmati Flood Plain

Objectives

• To compute flood hydrograph by rainfall-runoff modeling using hydrologic and statistical data

• To prepare flood hazard maps for various return periods

• To generate loss functions, estimate flood loss and prepare flood loss map for Gaur municipality

• To suggest a mechanism for flood forecasting and early warning system

Methodology

Rainfall dataRainfall data

Hydrological Hydrological modelmodel

Flood hydrograph Flood hydrograph

Hydraulic modelHydraulic model

Flood mapsFlood maps

Comparison &Comparison &ImprovementImprovement

Direct floodDirect flooddamage assessmentdamage assessment

Population Population datadata Flood risk mapsFlood risk maps

Satellite imageSatellite image

1. DEM1. DEM2. Flood map2. Flood map3. Landuse map3. Landuse map

Flood maps forFlood maps forDiff. Return periodsDiff. Return periods

Rating curveRating curve

Flood Early Flood Early WarWar ning Systemning System

Flood maps forFlood maps forDiff. Water levelsDiff. Water levels

TopographicalTopographicalDataData

(1. TRMM & 2. Rain gauge data)

(1. HEC HMS and 2. Statistical)(1. HEC HMS and 2. Statistical)

(HEC RAS)(HEC RAS)

Flood hazardFlood hazardmapsmaps

CommunityCommunitysurveysurvey

Data Available

• Satellite imagery– Aster– Landsat

• Hydrological data– Rainfall data– Discharge data

•• Vector dataVector data– Topographic data– DEM– Landuse data

•• Ancillary dataAncillary data– Socio-economic data– Census data

Rainfall-Runoff Modeling (HEC-HMS)

Simulated vs. Observed Discharges

0

1000

2000

3000

4000

5000

6000

1-Jun-04 21-Jun-04 11-Jul-04 31-Jul-04 20-Aug-04 9-Sep-04 29-Sep-04

Time in Days

Dis

char

ge in

m̂3/

s

Simulated

Observed

Observed peak discharge = 5600cumecsSimulated peak discharge = 5321cumecs

Regression Analysis Approach of Extreme Discharge Prediction

Test Datasets• TRMM 3-hourly rainfall data covering June to September 2004• Daily discharge data of the same period (Dependent Variable)

Validation Data• 2005 Monsoon (June to September)• Predicted Variable is daily discharge data of Monsoon 2005

TRMM Grids in the Study Area

Flood Maps

Input DischargeReturn Period Discharge

2 year 3750

5 year 6150

10 year 7750

20 year 9250

50 year 11250

100 year 12700

Inundated areaReturn period

Area inundated

% area inundated

2 year 363.4 36.9

5 year 403.9 41

10 year 422.9 42.9

20 year 437.7 44.5

50 year 454.8 46.2

100 year 465.6 47.3

Community Based Survey

Mud housesCommunity based survey

RCC frame housesBrick mortar houses

Damage Function Analysis

Depth vs. Damage: Flood duration (B.M)

Depth vs. Damage: Building Age (B.M)

Flood Hazard Map

Type Equation

RCC D=1.4687 * Ln(x) + 1.8713

BM D=4.1053 * Ln(x) + 5.27

Adobe D=15.161 * Ln(x) + 17.502

Type Height (m)

RCC 0.67

BM 0.42

Adobe 0.67

Average plinth level

Damage functions

Flood Loss Estimation

Construction rates

Estimated loss corresponding to Q50 = NRs 225 million

Type Minimum Maximum Mean Count

RCC 0 3.84 3.28 339

BM 0 11.04 8.44 3532

Adobe 0 39.33 29.13 1514

Damaged houses

Replacement ValueNepalese Rupees (NRs) Number of houses

0 – 5,000 739

5,000 – 15,000 1083

15,000 – 25,000 827

25,000 – 50,000 1222

50,000 – 100,000 1040

>100,000 474

Damage values

Type Nepalese Rupees/sq ft Nepalese Rupees/sq m

RCC 1100.00 11830.00

BM 700.00 7530.00

Adobe 200.00 2150.00

Flood Forecasting and Early Warning

Conclusions for Nepal

• Rainfall-runoff model in combination with the flood hazard maps provides a good basis for real-time flood forecasting

• Flood damage functions were generated for buildings and a flood loss map was produced for Gaur Municipality.

• Downscaling of TRMM data could be useful in flood forecasting, especially for ungauged river basins

Thank you for your kind attention