Education, Research, Training and Capacity Building Activities in AIT

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1 Education, Research, Training and Capacity Building Activities in AIT (Research & Training Node for Sentinel Asia) Manzul Hazarika Ph.D. Associate Director, Geoinformatics Center Asian Institute of Technology

2 Asian Institute of Technology (AIT) AIT Academic Structure (Schools and Extension) School of Engineering and Technology Computer Science Design and Manufacturing Engineering Industrial Engineering and Management Information Management Remote Sensing and Geographic Information Systems Telecommunications Information and Communications Technologies Mechatronics Microelectronics Construction, Engineering and Infrastructure Management Geotechnical and Geoenvironmental Engineering Structural Engineering Transportation Engineering Water Engineering and Management School of Environment Resources and Development Agricultural Systems and Engineering Aquaculture and Aquatic Resources Management Energy Environmental Engineering and Management Food Engineering and Bioprocess Technology Gender and Development Studies Natural Resources Management Pulp and Paper Technology Regional and Rural Development Planning Urban Environmental Management School of Management International Business International Public Management Management of Technology Service Marketing and Technology Executive MBA EMBA Bangkok EMBA Vietnam EMBA-HRM AIT Extension (Non-degree training, consultancy and services) Agriculture, Resources and Rural Development Business Management Development Management Education and Training Development Environment, Infrastructure and Urban Development Information and Communications Technology

3 Remote Sensing and GIS Field of Study Education Master and PhD Programs Diploma and Certificate Programs 3 Regular Faculties, 1 JAXA Seconded Faculty Approximately 60 students (2006) Multidisciplinary Programs (SET & SERD) New Master Program on Disaster Management (starts in August 2007) Research/Training Center Geoinformatics Center carries out research, training and capacity building activities

4 Capacity Building : Mini-Projects by Geoinformatics Center (GIC)

5 Mini-Projects - Characteristics Training and comprehensive capacity building through realworld problems such as flood, drought, landslide, etc. Involve data/service provider agencies and services/products user agencies, Explore the theoretical aspects and identify most appropriate data analysis and integration technique Calibration/validation through field observations Generate products with participation of both users and service providers Develop case studies to share in the region

6 Mini-Projects in 2006 Capacity building projects are being sponsored by the Japan Aerospace Exploration Agency (JAXA) in developing countries. Projects: Flood 5Projects(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, Field Visit Nov/Dec, Data Analysis and Report Writing in AIT Jan/Feb, 2007

7 Flood Projects Sl. No. Country 1 Bangladesh 2 Cambodia Organizations Flood Forecasting & Warning Center (FFWC) Local Government Engineering Dept. (LGED) Bangladesh Disaster Preparedness Center (BDPC) Geography Department, Ministry of Land Administration Urban, Planning and Construction (MLUPC) Hydrology and Water River Works Dept., Ministry of Water Res. and Meteorology (MOWRAM) 3 China PR Beijing Normal University 4 Lao PDR 5 Nepal Environmental Research Institute (ERI), Science Technology and Environment Agency Department of Meteorology and Hydrology (DMH) Department of Water Induced Disaster Prevention (DWIDP) Survey Department Department of Hydrology and Meteorology (DHM)

8 Drought and Landslide Projects Sl. No. Country 6 Philippines (Drought) 7 Philippines (Landslide) 8 Sri Lanka 9 Vietnam Organizations Philippine Rice Research Institute (PhilRice) Philippines Inst. of Volcanology & Seismology (PhiVolcs) National Mapping & Res. Info. Agency (NAMRIA) National Building Research Organisation (NBRO) Survey Department Institute of Geography, VAST Min. of Natural Resources and Environment (MONRE)

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

10 Cambodia: Flood Hazard Mapping in Three Provinces of Cambodia under Mekong Basin Kompong Cham, Prey Veng and Kandal Provinces

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

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

13 Nepal: Rainfall-Runoff Modeling of Bagmati Basin & Flood Loss Estimation of Gaur Municipality Bagmati Watershed Gaur 3D view

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

15 Philippines-II: Modeling of Rain and Earthquake Triggered Landslides using RS and GIS-based Slope Stability Models Southern Leyte, Philippines N STUDY AREA PHILIPPINES I GUINSAUGON, SOUTHERN LEYTE ASTER DATA ALOS DATA LANDSAT TM IMAGE OF LEYTE ISLAND BANDS 452

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

17 Vietnam: Application of Remote Sensing and GIS for Landslide Hazard Mapping in a Mountainous Areas Yen Chau & Bac Yen Districts

18 Introduction Mini Projects Flood Drought Landslide Contact JAXA Sentinel Asia

19 Data Collected through Field Visits Scar of Huoi Thon major landslide Soil sampling at a shallow landslide location in Huoi Thon A wide open crack in the road produced by an active landslide in Hong Ngai A shallow slide in Bac Yen Point positioning using GPS Typical land cover

20 Cambodia Flood Hazard Mapping in Three Provinces of Cambodia under Mekong Basin A Detail Example for Flood Hazard Mapping

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

22 2000 Flood (Dartmouth Flood Observatory) Aug., ,200 sq. km area flooded; 1,139 dead; 6.5 Million displaced Property Damage: 78 Million US$

23 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 socioeconomic data

24 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 Settlement in Flood Depth in 2000 from MIKE 11

25 Available Satellite Data of Study Area Landsat : Jan, 2005 Radarsat : Sep. and Oct., 2000

26 Methodology Hydrological Data Topographic and GPS Data Satellite Image LANDSAT ETM Population density Road network HEC-RAS TIN Land use map Flood hazard map Vulnerability Assessment

27 Field Survey Right Bank: 95.15Km Left Bank: Km PP-Kg.Cham:

28 During Field Survey

29 Comparison of Results RADARSAT-1 Image, September, 2000

30 Flood Affected Villages Village Affected by flood and non-flood No. village Flooded Non flooded Population and household affected and no affected 1,500,000 1,000, , Total Both Sexes Total Households flooded non-flooded

31 Area For Flood Hazard Mapping

32 Weighted Population Map + Population per commune X Weighted Landuse Total Weight = Weighted Population per pixel

33 Enlarged Weighted Population Map

34 Reclassified Landuse Map

35 Flood Depth Map

36 Final Flood Hazard Map

37 Conclusions for Cambodia Hazard Land Affected, ha % of total land Population % of total Rank Agriculture Build Up Agriculture Build Up affected (10 3 ) population Low Medium 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.

38 Nepal Flood Forecasting and Early Warning System in Bagmati Flood Plain

39 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

40 Methodology Satellite image Rainfall data (1. TRMM & 2. Rain gauge data) 1. DEM 2. Flood map 3. Landuse map Hydrological model Flood hydrograph (1. HEC HMS and 2. Statistical) Topographical Data Hydraulic model Flood maps (HEC RAS) Rating curve Comparison & Improvement Community survey Flood maps for Diff. Return periods Flood hazard maps Direct flood damage assessment Flood maps for Diff. Water levels Population data Flood risk maps Flood Early War ning System

41 Data Available Satellite imagery Aster Landsat Hydrological data Rainfall data Discharge data Vector data Topographic data DEM Landuse data Ancillary data Socio-economic data Census data

42 Rainfall-Runoff Modeling (HEC-HMS) Simulated vs. Observed Discharges Discharge in m^3/s Simulated Observed Jun Jun Jul Jul Aug-04 9-Sep Sep-04 Time in Days Observed peak discharge = 5600cumecs Simulated peak discharge = 5321cumecs

43 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

44 TRMM Grids in the Study Area

45

46 Flood Maps Return period Input Discharge Return Period Discharge 2 year year year year year year Inundated area Area inundated % area inundated 2 year year year year year year

47 Community Based Survey Community based survey Mud houses Brick mortar houses RCC frame houses

48 Damage Function Analysis

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

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

51 Flood Hazard Map Average plinth level Type Height (m) RCC 0.67 BM 0.42 Adobe 0.67 Damage functions Type Equation RCC D= * Ln(x) BM D= * Ln(x) Adobe D= * Ln(x)

52 Flood Loss Estimation Damage values Replacement Value Nepalese Rupees (NRs) Number of houses 0 5, ,000 15, ,000 25, ,000 50, , , >100, Estimated loss corresponding to Q 50 = NRs 225 million Construction rates Type Nepalese Rupees/sq ft Nepalese Rupees/sq m RCC BM Adobe Damaged houses Type Minimum Maximum Mean Count RCC BM Adobe

53 Flood Forecasting and Early Warning

54 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

55 Thank you for your kind attention