Technical Assistance Consultant’s Report · Project Number: 46470-001 . January 2018 . TA8572 (REG): Action on Climate Change in South Asia (Financed by the Asian Development Bank)

Technical Assistance Consultant’s Report

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.

Project Number: 46470-001 January 2018

TA8572 (REG): Action on Climate Change in South Asia (Financed by the Asian Development Bank)

Prepared by: MST Farida Perveen, Md. Golam Mahabub Sarwar, Md. Sirajul Islam, Md. Shameem Bhuiyan, and Abu Hena Md. Mostafa Dhaka, Bangladesh For: South Asia Department Asian Development Bank

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Report on Hazard, Exposure, Vulnerability and Risk (HEVR) Spatial Data

Project No: 46470-001 TA 8572 REG: Action on Climate Change in South Asia

Asian Development Bank

January 2018

Asian Development Bank Government of Bangladesh

Final

Databases

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Report on Hazard, Exposure, Vulnerability and Risk (HEVR) Spatial Data

Project No: 46470-001 TA 8572 REG: Action on Climate Change in South Asia

Prepared by - TA National Consultant Team Asian Development Bank

Dr. Mst. Farida Perveen Remote Sensing-GIS Specialist and Team Leader

Dr. Md. Sirajul Islam Disaster Management Specialist

Dr. Md. Golam Mahabub Sarwar Climate Risk and Vulnerability Assessment Specialist

Dr. Shameem Hassan Bhuiyan Hydro-Meteorologist

Abu Hena Md. Mostafa Climate Change Economist

Project Officer TA National Coordinator Liping Zheng Dr. Nurun Nahar Asian Development Bank Programming Division, Planning Commission

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Table of Contents LIST OF TABLES. ………………………………………………………………………………………...5

LIST OF FIGURES ……………………………………………………………………………………….5

ACRONYMS …………………………………………………………………………………......6

1. INTRODUCTION ……………………………………………………………………………..7

2. COORDINATE AND CONSULT WITH RELEVANT ORGANIZATIONS FOR

SOURCING AND COLLECTION OF SPATIAL DATA…………………………………………….8

2.1 Organization visits: …………………………………………………………………...8

2.1.1 Bangladesh Bureau of Statistics (BBS)……………………………………………10

2.1.2 Department of Agricultural Extension (DAE) …………………………………….10

2.1.3 Soil Resource Development Institute (SRDI)……………………………………...11

2.1.4 Survey of Bangladesh (SOB..……………………………………………………...11

2.1.5 Department of Forest (FD)…………………………………………………………12

2.1.6 Water Resources Planning Organization (WARPO)………………………………12

2.1.7 Local Government Engineering Department (LGED)……………………………..12

2.1.8 Department of Public Health Engineering (DPHE)………………………………..13

2.1.9 Bangladesh Water Development Board (BWDB)….……………………………...13

2.1.10 Department of Disaster Management (DDM)……………………………………13

3. COMPREHENSIVE LIST OF MAPABLE DATA AND MAPS FROM DIFFERENT

SOURCES IN RAW ARCGIS FORMATS……………………………………………………..14

4. UNDERTAKE A DETAILED REVIEW OF EXISTING DATASETS……………………..17

4.1 Administrative data…………………………………………………………………..17

4.2 Climatic data…………………………………………………………………………18

4.2.1 Meteorological Stations in Bangladesh……………………………………19

4.2.2 Present status and future scope of historical climate data in Bangladesh…20

4.3 Hydrographic/ water body/ water quality data………………………………………25

4.4 Natural Features……………………………………………………………………..27

4.5 Geo-Physical data……………………………………………………………………27

4.6 Physical Infrastructure……………………………………………………………….29

4.7 Socio-economic Data………………………………………………………………...30

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4.8 Spatial Data at a Glance……………………………………………………………..31

4.9 List of HEVRI Mappable Data and Maps for Agriculture and Water Sectors………33

4.10 HEV Matching Template For Agriculture and Water sectors……………………...37

5. SPATIAL DATA QUALITY………………………………………………………………….39

5.1 Quality check of existing datasets……………………………………………………40

5.2 Challenges………………………………………………………………………........42

6. CONCLUDING REMARKS………………………………………………………………….43

7. REFERENCES…………………………………………………………………………………………44

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List of Tables

Table 1: Schedule of organization visit ………………………………………………………….9

Table 2: Comprehensive list of mapable data and maps from different sources in raw ArcGIS

formats …………………………………………………………………………………………..15

Table 3: List of Administrative data …………………………………………………………….17

Table 4: Climatic data: Position and date of operation of different BMD stations………….......18

Table 5: List of data from BMD…………………………………………………………………19

Table 6: Comparison of previous and present Climate data observation facilities in

Bangladesh……………………………………………………………………………………….25

Table 7: List of Hydrographic/ water body/ water quality data………………………………….26

Table 8: Inventory of natural features data………………………………………………………27

Table 9: Physical Infrastructure…………………………………………………………………30

Table 10: Socio-economic Data………………………………………………………………….30

Table 11: Spatial Data at a Glance……………………………………………………………….31

Table 12. Inventory/List of HEVRI Mappable Data and Maps for Agriculture and Water

Sector…………………………………………………………………………………………….33

Table 13: HEV Matching Template For agriculture and water sectors………………………….37

List of Figures Figure 1: Meteorological Stations of BMD …..…………………………………………………20

Figure 2: Agro-ecological Zone of Bangladesh (Source: BARC)…..…………………………...21

Figure 3: Operational Meteorological Observatories in Bangladesh…………………………………….24

Figure 4. Seismic zoning map for Bangladesh…………………………………………………..28

Figure 5. Landslide susceptibility of the country………………………………………………..29

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Acronyms ADB : Asian Development Bank

ADP : Annual Development Programme

BBS : Bangladesh Bureau of Statistics

BMD : Bangladesh Meteorological Department

BCCSAP : Bangladesh Climate Change Strategy and Action Plan

BWDB : Bangladesh Water Development Board

CRS : Climate Risk Screening

CRVS : Climate Risk and Vulnerability Screening

CRVA : Climate Risk and Vulnerability Assessment

DDM : Department of Disaster Management

DoE : Department of Environment

DMCs : Developing Member Countries

DSS : Decision-Support System

DAE : Department of Agricultural Extension

DPHE : Department of Public Health Engineering

FD : Department of Forest

GIS : Geographic Information Systems

LGED : Local Government Engineering Department

SRDI : Soil Resources Development Institute

SOB : Survey of Bangladesh

WARPO : Water Resources Planning Organization

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1. Introduction

Bangladesh is frequently affected by cyclones and storm surges (Karim 1995, Alam et al. 2003,

Islam and Peterson 2009). Besides flood and cyclones, Bangladesh is also vulnerable to many slow

onset disasters. It is greatly feared that Bangladesh will be highly affected by sea-level rise and

saline intrusion (Milliman et al. 1989, Sarwar and Khan 2007, Sarwar 2013). The country is also

vulnerable to drought (Habiba et al. 2012), erosion (Sarwar and Woodroffe 2013), salinity (SRDI

2010) and landslide (Sarwar 2008). The geographic status of Bangladesh has made it one of the

most vulnerable countries to climate change among the South Asia developing member countries

(DMCs) of ADB (BCCSAP 2009). Frequently occurring natural disasters, high population density,

poor infrastructure and low resilience to economic variability make Bangladesh even more

vulnerable to climate variability and changes. Different parts of Bangladesh suffer from various

natural disasters such as flooding, river bank erosion, drought, flash floods, forest fires, landslides

and tropical cyclones. The coastal belt has historically well-known large cyclones and surges.

Climate change and sea level rise will worsen this scenario of cyclones in the near future (Sarwar

2013). Other disasters include tornadoes, hailstorms and lightening which are projected to be more

frequent and intense because of global warming. This project will encompass a survey of data

required for climate risk screening and climate risk and vulnerability assessment.

ADB developed a climate risk screening system in 2013 which was applied in South Asia

developing member countries to evaluate the level of risks. The screening report provided by the

ADB tool includes a preliminary evaluation of climate/ weather- related as well as associated

geological and geophysical risks. The medium or high risk projects face further screening through

dedicated screening tools. Temperature increase, precipitation change, wind speed change, sea-

level rise, solar radiation change, water availability, flooding, tropical storms, wildfire, permafrost,

sea ice, snow loading, and/or landslides can be considered as hazard components of risks (ADB

2014). These risk assessments can be in forms of risk overlays, thematic maps generated by index

and/ or criteria-based methodologies and their combinations. Project lifespan, design, and efficacy

can be affected by the risk assessment which may lead to recommendations on strategic and co-

beneficial climate change adaptation and disaster risk reduction and management options.

However, spatial data is very important to carry out such vulnerability assessment. Spatial data in

Bangladesh is scare and maintained by various organization with inadequate cooperation among

themselves (WAPRO 2012).

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The main objective of the report is to develop HEVR spatial databases for CRS and CRVA study.

To perform the task, data from various organizations is required. In order to develop HEVR spatial

databases, the consultant team has visited different organizations and is collecting the spatial and

non-spatial data from different sources such as- Ministries/Departments/Agencies which was

previously listed through scoping study. By this time the team has achieved a significant progress

in collection of data required for development of HEVR spatial databases.

2. Coordinate and consult with relevant organizations for sourcing and collection of spatial data

2.1 Organization visits:

For the development of spatial databases in the areas of hazards, exposure, vulnerability and risks,

a list of HEVR mappable data and maps has been prepared through scoping study. Appropriate

organizations, who are responsible for collecting those data sets were identified in the next step

and 12 organizations were selected for the data collection. Official letters from Planning

Commission has been sent to all those organizations accordingly, describing the list of data

required from that organization with their spatial and temporal extent.

Most of the organizations, which held the required data for the study are government organization.

Anticipating the bureaucracy that may delay the exercise, direct visit to those organizations in

person was planned so the data collection step is expedited. Few sub-groups among the consultants

were formed considering their specialization. The groups, especially liked to meet the personnel

at those organizations who are responsible for data collection and distribution purposes. Few

obstacle and legal constraint that we faced in that connection were,

• Most of the organizations has only provision of providing data with appropriates charge,

i.e. data is not free. However, in this study we have no budget for data collection.

• Well, because the data will be used for another government project for state purposes, there

might be a possibility that if there is any clear direction from the highest authority like

ministry is there to provide it free of charge, they may comply it. This process actually

delayed the task a bit.

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• Most of the organizations are so conservative about their spatial data, which are processed

already for GIS layer, i.e. in Shape file format. They rather willing to provide only the raw

data, even though they have another version with shape file format.

• Some of the organizations wanted for further complicated approach of data sharing by

asking for an MoU, which again took time.

However, realizing the matter the higher authority of the Programing Division of Planning

Commission further has sent the request letter to the concern Ministries for providing the data with

free of charge. Anyway, the authority of the Programing Division has tried its best. Among all

those limitation, the consultant team progressed well in this regard and has collected the required

data from different organizations.

. The following visits were made during data collection to different organizations, by the team

such as LGED, DPHE, DDM, BBS, DAE, WARPO, BWDB, BMD, SRDI, FD, DoE and SoB.

Table 1: Schedule of Organization visits

SL Organizations Officer(s) visited

Visiting Consultant(s)

Visiting Date Data collection status

1 Ministry of Defense (MoD)

Deputy Chief, MoD

Dr. N Nahar Dr. M F Perveen Dr. M G M Sarwar Mr. M S H Bhuiyan

19/09/2017 & 13/11/2017

Obtained

2 BMD Director Mr. M S H Bhuiyan 09/10/2017 Obtained 3 LGED Additional Chief

Engineer Dr. M F Perveen Dr. M S Islam Mr. AHM Kamal Dr. M G M Sarwar

05/10/2017 & 19/11/2017

Obtained

4 BWDB Chief Engineer Dr. M F Perveen Dr. M S Islam Mr. AHM Kamal

09/10/2017 & 16/10/2017

Obtained

5 DAE DG, DAE Dr. M F Perveen Dr. M G M Sarwar

17/10/2017 23/11/2017

Obtained

6 SRDI Director, SRDI Dr. M F Perveen Dr. M G M Sarwar Mr. M S H Bhuiyan

16/10/2017 & 29/10/2017

Obtained

7 BBS DG, BBS Dr. M F Perveen Dr. M G M Sarwar Mr. M S H Bhuiyan

17/10/2017 Obtained

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8 SoB Surveyor General of Bangladesh

Dr. M F Perveen Dr. M S Islam Dr. M G M Sarwar Mr. M S H Bhuiyan

05/10/2017 15/10/2017 & 29/10/2017

Obtained

9 DDM Dr. N Nahar Dr. M F Perveen Dr. M S Islam Mr. M S H Bhuiyan Mr. AHM Kamal

10/15/2017 & 14/11/2017

Under processing

10 FD DG, FD Dr. M F Perveen Dr. M G M Sarwar

18/10/2017 Obtained

11 DPHE Dr. M S Islam Mr. AHM Kamal

10/10/2017 Obtained

12 WARPO Principal Scientific Officer, Ms. Fahmida,

Dr. M F Perveen Dr. M S Islam Mr. AHM Kamal

09/10/2017 Obtained

2.1.1 Bangladesh Bureau of Statistics (BBS):

Bangladesh Bureau of Statistics (BBS) is only the national Statistical institution responsible for

collecting, compiling and disseminating statistical data of all the sectors of the Bangladesh

economy to meet and provide the data-needs of the users and other stake holders like national level

planners and other agencies of the Government. The consultant team visited BBS several times for

data collection and has received the data of crop production of different years, irrigated & non

irrigated agricultural area, loss & damaged data by flood, flashflood, drought and excessive rain.

Besides, we need to collect other data from BBS such as- fisheries, agro-forestry, demography,

settlements, livelihood (Occupation, education, Demography, Gender equity etc.), poverty,

housing capacity, economic capacity and income distribution etc. The received data are not in

shape file format, all are statistical data and it needs to convert to the shape file.

2.1.2 Department of Agricultural Extension (DAE):

The Department of Agricultural Extension (DAE) is a service oriented government organization

responsible for providing efficient, effective, location specific, integrated extension services to all

categories farmers in accessing and utilizing better know how to increase sustainable and profitable

crop production. The available data of DAE is crop production of major crops, loss & damaged

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data by flood, flash flood, drought, salinity, pest and disease affected area of different years, major

cropping patterns, cropping intensity etc. The national consultant team and the ADB Head Quarter

team visited DAE after inception workshop first time and had a meeting with Director General.

Later on the national consultant team again visited DAE and has collected the necessary data. The

collected the data are not available in shape file format, it is in MS Excel and MS Word file format

which can be integrated with GIS format later.

2.1.3 Soil Resource Development Institute (SRDI):

Soil Resource Development Institute (SRDI) is an attached department under the Ministry of

Agriculture. SRDI is responsible for inventory of the soil and land resources of the country in order

to assess their potentials for agriculture and other uses. The available data of SRDI is soil salinity,

water salinity, soil erosion, landslide and soil nutrients. Official letters from Planning Commission

has been sent to SRDI describing the list of data required from that organization. Two of the

consultants visited SRDI and met with Director, head of the organization. SRDI is so conservative

about their spatial data, which are processed already for GIS layer, i.e. in Shape file format. They

are unwilling to provide shape file of their spatial data. They wanted to provide us only the raw

data, even though they have another version with shape file format. The director informed the

consultant that SRDI does not have official data sharing policy, so, they cannot provide the spatial

data. So, SRDI has provided the hard copy maps.

2.1.4 Survey of Bangladesh (SOB):

Survey of Bangladesh (SOB) is the national mapping organization of Bangladesh under ministry

of defense. It has a Digital Mapping Center, modern printing press and well equipped Geodetic

Detachment. Through the concern Ministry, Planning Commission asked SoB to provide the data

such as- Administrative boundaries-(Division, District, Upazila and Union), Geodetic Control

Points, hydrographic feature, relief and vegetation. The consultant team visited SoB several times

for the purposes. SoB has only provision of providing data with appropriates charge i.e. data price

is fixed by the government. After personal visit to SoB GIS section, the spatial data set in the form

of shape files has been collected.

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2.1.5 Department of Forest (FD):

Forest Department is responsible for forest resources management and conservation activities

along with biodiversity & watershed management and development. The available data of FD is

Forest Map, Reserved Forest, Ecologically Critical Areas (ECA), National Park, Wildlife

Sanctuary, Botanical Garden etc. The consultant team visited FD for data collection and has

received the data of Forest Map, Khadimnagar and Lawachara National Park.

2.1.6 Water Resources Planning Organization (WARPO):

Water Resources Planning Organization (WARPO) is a government organization under the

administrative control of Ministry of Water Resources. WARPO is responsible for national level

spatial distribution of water availability, flood related inundation and loss and damage data/map,

DEM, flood frequency map (with different return periods), irrigation coverage, flood mitigation –

structural/non-structural data. The consultant team visited WARPO for the data. WARPO has

provision of providing data with nominal charge which is fixed by the government. The consultant

team visited WARPO for data collection and finally has received the data.

2.1.7 Local Government Engineering Department (LGED):

Local Government Engineering Department is an organization which works at grassroots level,

mostly within the administrative unit ‘Upazila’. It possesses a variety of spatial data set of

grassroots level information related to administrative boundary, socio-economic conditions, health

and education facilities, infrastructure and road network, ecological and hydrological features like

forest, rivers and canals, growth centers, etc. 22 features with several sub-features are there. After

personal visit to LGED GIS section, these spatial data set in the form of shape file were collected

from the Head of the GIS division. The only limitation of the data set is that all the shape files are

as individual upazila level, i.e. for 491 upazila and 22 features. To create a figure for entire

Bangladesh, it requires merging of those upazila files together. Quality of data is good, unless

shortage of appropriate metadata for all the files.

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2.1.8 Department of Public Health Engineering (DPHE):

Department of Public Health Engineering is the government entity responsible for water supply

and sanitation related works. They collect data related to groundwater level for the entire country

along with variation over the seasons and different ground water quality parameters like arsenic,

iron, salinity, etc. The data are not available in shape file format, but values with spatial location

which later can be converted into appropriate format for GIS use. Two of the consultants visited

the Groundwater Hydrology section of DPHE and collected the data in Excel file format. While

the groundwater quality data for the entire country is quite acceptable, there are lots of missing

data for groundwater levels for a number of stations. Conversion of those data to appropriate GIS

format is truly cumbersome.

2.1.9 Bangladesh Water Development Board (BWDB):

Bangladesh Water Development Board is the apex body in Bangladesh with the largest network

of hydrological data collection. Data include precipitation, river stage, river flow, river

morphology including cross-section and catchment boundary, flood inundation map, etc. Under

Flood Forecasting and Warning Center (FFWC), it can create real time inundation level and short

term forecast for a network of water gauge stations distributed all over the country (Figure 2). The

data from BWDB is again not free of charge. During our first visit, they acknowledged the receipt

of the data request and assured that they have all the data, but cannot give it without charge.

Subsequently another request to relevant ministry was sent officially from the planning

commission to provide data free of charge. Finally the consultant team visited BWDB again and

has received the data.

2.1.10 Department of Disaster Management (DDM):

Department of Disaster Management usually do not collect any data directly, but from secondary

sources. All sorts of disaster related data including loss and damage, they collect from different

organizations relevant. In recent years they took a big project as development of Risk Atlas for

different hazards under the project MRVA (Multi-hazard Risk and Vulnerability Assessment). The

consultant team met the then project director of that project. Consultants also met the personnel

responsible for GIS unit of DDM.

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From the very beginning, consultant team was highly interested about the Risk Atlas prepared by

DDM under MRVA project. However, DDM was always reluctant to handover the shape files of

those risk maps prepared for different hazards. Recently, they asked for an MoU to sign for such

data sharing purposes. Consultant team prepared the MoU text and visited DDM official again.

After then a detail discussion with the personnel in charge of the GIS unit revealed that because

the Risk Atlas was prepared by consultants/experts outside department, other than the output in

jpeg or pdf file format the original shape file of those risk maps are still with them and they didn’t

hand over them to DDM yet. DDM itself is struggling with getting the shape file of those map.

However, the data is under processing and will be available soon.

3. Comprehensive list of mapable data and maps from different sources in raw ArcGIS formats

For the development of HEVR spatial databases, the data collected from different organizations has been shown in Table 2.

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Table 2. Comprehensive list of mapable data and maps from different sources in raw ArcGIS formats-

HEVR Database Source Administrative Levels/Spatial

Units

Format (s) (i.e. shp, geotiff, mxd in data and lay-out views, lyr, other ArcGIS formats, excel,

dbf)

Date Remarks/

Status (Receive

d)

1 Administrative boundaries

LGED,

District, Upazila, Union

Shape file 2013

2 Administrative Head Quarters

LGED Division, District, Upazila, Union

Shape file 2013

3 National Road Networks-

LGED National Highway, Regional Highway, District Road, Upazila Road, Union Road

Shape file 2013

4 Railway Network LGED District Shape file 2010

5 Embankment LGED - Shape file 2013

6 River Network LGED - Shape file 2012

7 Growth Center LGED Upazila Shape file 2011 8 Settlement Area LGED Upazila Shape file 2012 9 Water Bodies

(Haor Area) LGED Upazila Shape file 2012

10 Forest Area LGED Upazila Shape file 2010 11 Hill Area LGED Upazila Shape file 2010 12 Hill Forest LGED Upazila Shape file 2010 13 Sandy Area LGED Upazila Shape file 2012 14 Rural Market LGED Upazila 2011 15 Power Transmission

Line LGED - Shape file 2005

16 Socio-economic Infrastructure (e.g. Health center, Cyclone shelter, Police stations, post office, Institutions, Rural market, etc.)

LGED Upazila Shape file 2005

17 Crop Production data

BBS District MS word, PDF

2013-2016

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18 Irrigated & non irrigated agricultural area

BBS Total area of the country

MS word, PDF

2013-2016

19 Total Loss in Crop Productions due to Natural Calamities

BBS & DAE

District MS word, PDF 2008-2015

20 Crop damaged area by Flood, Flashflood & Excessive rain

BBS & DAE

District MS word, PDF 2008-2015

21 Major cropping patterns

DAE District Text format 2016-2017

Inundation report DAE District MS word, PDF 2016

22 Crop damaged area by Flood, Flashflood & Excessive rain

DAE District MS word, PDF 2017

Crop production data for 45 years for major crops

DAE, BBS National level MS Excel 1971-2015

23 Groundwater level

DPHE - Time-series 2016

24 Groundwater quality DPHE - Time-series 2007-2014

25 Forest Map FD - JPEG

2004-2005

26 Khadimnagar and Lawachara National Park

FD - shape file -

27 Reports on Forest area

FD District PDF

-

28 Flood frequency (for 2, 5, 10, 25, 50, 100 Years Return Periods)

WARPO Bangladesh Grid data -

29 Digital Elevation Model

WARPO Bangladesh Grid data -

30 Inundation land type WARPO District shape file - 31 Flood prone area WARPO District shape file - 32 Irrigation demand WARPO District shape file -


SoB Division, District, Upazila

shape file -

34 Dense mixed forest

SoB - shape file

35 Reserved forest SoB - shape file

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4. Undertake a detailed review of existing datasets

4.1 Administrative data Bangladesh is divided in to 8 divisions. These divisions are formed of 64 districts. Districts are divided into 492 upazilas. Upazilas are further divided into unions. However, the study will be conducted based on upazila/district boundary because of time constraints to explore down to union. A list of administrative data has been given below. Table 3: List of administrative data collected

Category Data name Data format Type unit Organization Comments Boundary 1. International Shapefile

(.shp) Polyline LGED, SoB

2. Division Shapefile (.shp)

Polyline LGED, SoB, BARC, WARPO

3. District Shapefile (.shp)


4. Upazila Shapefile (.shp)


HQ 5. Division Shapefile (.shp)

Point LGED

6. District Shapefile (.shp)

Point LGED

7. Upazila Shapefile (.shp)

Point LGED

36 Scrub bush SoB - shape file


BARC Division, District, Upazila

shape file -

38 Water level (monthly average maximum and average minimum)

BWDB - xlsx -

39 Annotation Mapcruize Name of places shape file -

40 Mapcruize Name of Institutes/ Organizations

shape file -

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4.2 Climatic Data A total of 46 synoptic stations are in operation under Bangladesh Meteorological Department (BMD). In addition, BMD operates 10 Pilot Balloon stations and 4 Rawinsonde stations. However, RS data is available for Dhaka, Chittagong and Bogra stations only. There were 15 observatories for basic Meteorological observation in 1947. There were also some part time observatories during that time. The number of observatories increased to 41 but a few of them were closed subsequently and by gradual addition/deletion the total number of observatories were 33 in 1981. Data available for 34 stations as stated in Table 4 below. However, data before 1948 are not available at BMD, although the Table 4 indicates operation period long before 1948.

Table 4: Climatic data: Position and date of operation of different BMD stations (Data available from 1948)

Sl no.

Name of the observatory

Operation Period

International Station No.

Latitude (North)

Longitude (East)

Elevation (m)

1 Barisal 1883 41950 22°43' 90°22' 2.1 2 Bhola 1965 41951 22°41' 90°39' 4.3 3 Bogra 1884 41883 24°51' 89°22' 17.9 4 Chandpur 1964 41941 23°14' 90°42' 4.88

5 Chittagong MMO 1937 41978 22°13' 91°48' 5.5

6 Chuadanga 1986 41926 23°39' 88°49' 11.58 7 Comilla 1883 41933 23°26' 91°11' 7.5 8 Cox’s Bazar 1908 41992 21°27' 91°58' 2.1 9 Dhaka PBO 1949 41923 23°46' 90°23' 8.45 10 Dinajpur 1883 41863 25°39' 88°41' 37.58 11 Faridpur 1883 41929 23°36' 89°51' 8.1 12 Feni 1973 41943 23°02' 91°25' 6.4 13 Hatiya 1965 41963 22°27' 91°06' 2.44 14 Ishwardi 1963 41907 24°09' 89°02' 12.9 15 Jessore 1867 41936 23°12' 89°20' 6.1 16 Khepupara 1973 41984 21°59' 90°41' 1.83 17 Khulna 1921 41947 22°47' 89°34' 2.1 18 Kutubdia 1977 41989 21°49' 91°51' 2.74 19 Madaripur 1976 41939 23°10' 90°11' 7 20 Maijdee Court 1883 41953 22°52' 91°06' 4.87 21 Mongla 1988 41958 22°28' 89°36' 1.8 22 Mymensingh 1883 41886 24°44' 90°25' 18 23 Patuakhali 1973 41906 22°20' 90°20' 1.5 24 Rajshahi 1883 41895 24°22' 88°42' 19.5 25 Rangpur 1883 41859 25°44' 89°16' 32.61

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26 Rangamati 1957 41966 22°22' 92°09' 68.89 27 Sandwip 1966 41964 22°29' 91°26' 2.1 28 Satkhira 1877 41946 22°43' 89°05' 3.96 29 Sitakunda 1977 41965 22°38' 91°42' 7.3 30 Srimangal 1905 41915 24°18' 91°44' 21.95 31 Syedpur 1980 41858 25°45' 88°55' 39.6 32 Sylhet 1952 41891 24°54' 91°53' 33.53 33 Tangail 1982 41909 24°15' 89°56' 10.2 34 Teknaf 1976 41998 20°52' 92°18' 5

Table 5: List of data from BMD

SL Data Category and Data Name Data Type

Comments

1. Temperature (seasonal/ monthly/ daily: mean, maximum and minimum)

Historical

2. Precipitation (annual/ seasonal/ monthly/ daily total) Historical 3. Humidity Historical 4. Drought, heat waves Historical 5. Evaporation Historical 6. Cyclone/ storm surge Historical 7. Nor’wester Historical 8. Historical RS Observation Historical

4.2.1 Meteorological Stations in Bangladesh

Meteorological Stations of BMD has been shown in Figure 1.

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Figure 1: Meteorological Stations of BMD

4.2.2 Present status and future scope of historical climate data in Bangladesh

i) Geographic Context of Bangladesh

Bangladesh is a small country but densely populated in South Asia extending from 20°34'N to 26°38'N latitude and 88°01'E to 92°41'E longitude. It is bordered by West Bengal to the west and

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northwest, Assam and Meghalaya to the north, Assam and Tripura to the east and Myanmar to the southeast whereas; the Bay of Bengal is situated in the southern part of Bangladesh (Figure 1). Except the hilly northeast and southeast, landscape of the country is mainly flat and almost 50% of its landscape ranges within 5 m of mean sea level. The Ganges-Brahmaputra river system plays the key role in the formation of alluvial plain landscape that rises to form forested hills in the northeast and east of the country. It is one of the largest deltas in the world with a total area of 147,570 km2 (BBS 2015). It has 30 Agro-Ecological Zones (AEZ) in Bangladesh.

Figure. 2: Agro-ecological Zone of Bangladesh (Source: BARC).

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Bangladesh is globally recognized as the country of natural disasters because of the frequent occurrence of disasters like flood, cyclone, earthquake, and drought and riverbank erosion. Flood is mostly occurred in rainy season due to excessive rainfall and upstream discharge from surrounding countries whereas, cyclone and drought occurs in summer when the weather is extremely hot and dry. Occasionally earthquakes also occur in Bangladesh. Almost three-fourths of the country is bordered by the mountains and hills, whereas, the funnel shaped Bay of Bengal in the south have made the country a meeting place for catastrophic natural disasters. According to Bangladesh Disaster Statistics, a total of 234 disasters have occurred during the period 1980-2010 in Bangladesh.

ii) Historical climate data of Bangladesh

Bangladesh Meteorological Department (BMD) is the main source of historical climatic data in Bangladesh. It is a government organization under the administrative control of the Ministry of Defence. Meteorological activities have started in Bangladesh since 1867 through the establishment of one observatory in Satkhira, a district in South-western in Bangladesh. In 1947 the service had been renamed as Pakistan Meteorological Services and after the country’s independence in 1971, it became the Bangladesh Meteorological Department (BMD) and has started work in full swing. BMD has a climatic data archive from 1948 and onward for 12 stations. The number of observatories has gradually increased up to 35 with in 1980. Therefore BMD is archiving good quality climate data of 35 observatories from 1980.

The observed climatic parameters are :

BMD are collected in the following data by their observatories:

1. Rainfall 2. Air temperature (Max, Min and hourly) 3. Solar Radiation 4. Bright sunshine hour 5. Relative humidity 6. Dew point Temperature 7. Soil moisture at different depths (5,10,20,30,50 cm) 8. Soil temperature at different depths (5,10,20,30,50 cm) 9. Evaporation 10. Estimation of evapotranspiration with Daily ET equation. 11. Wind speed & direction at 10 meter and 02 meter 12. 03 Doppler RADAR and 02 conventional RADAR 13. 10 Pilot stations for upper air circulation observation 14. 04 Radio sounding stations for upper air observation.

• Recently BMD has installed 04 Air quality monitoring system at o4 mega city and 08 Lightning detection system.

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• BMD has 43 Automatic Weather observation System (AWS) with telemetry (Real time data collection system)

• BMD is using the observation of following satellite with necessary processing and analyzing software of Himawari (Japan), NOAA (USA) and MICAPS (China) and EUMETSAT of European Space Agency for monitoring weather and climate system.

• Remote sensing supports in agro-met services by mapping and monitoring of agricultural crops, crop acreage and yield estimation, crop inventory, crop damage assessment, change detection, wetland mapping etc.

BMD collects information by the observatory team at different places in Bangladesh. The observatory team analyze the data three hourly and its exchange with neighboring countries three hourly interval through GTS (Global Telecommunication System) link. World community collects this data six hourly through WMO link. Bangladesh is under the Delhi hub. Map of different operational meteorological observatories are shown in Figure-3.

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Figure: 3 Operational Meteorological Observatories in Bangladesh.

25

Table 6. Comparison of previous and present Climate data observation facilities in Bangladesh:

Previous Observational Facilities

• Synoptic observatories: 35 • Pilot Observatories: 10 • Rawinsonde Observatories: 3 • Agromet observatories: 12 • RADAR Stations: 03

• AWS: 0 • Earthquake Monitoring Stations: 4

Present Observational Facilities

• Synoptic observatories: 60 • Pilot Observatories: 10 • Rawinsonde Observatories: 4 • Agromet observatories: 20 • RADAR Stations: 5 (all operational,

3 is Doppler Radar out of 5) • AWS :43

Earthquake Monitoring Stations: 10

BMD is recently implementing Bangladesh Weather and Climate Services Regional Project (BWCSRP). This project is to strengthen the capacity of the GOB to deliver effective weather and climate information services and improve the quality and access to such services in priority sectors and communities and service delivery related to water, agriculture and multi-hazard disaster risk management early warning systems. This project supports to modernization of BMD's meteorological observation network over land, air and ocean, weather forecasting capacity and strengthening public weather and climate services. The project will also support installation of High Performance Computing System for Operational Weather Forecasting; state of the art of latest weather prediction models, hardware, software for BMD and divisional offices. Design development and implementation of a National Framework for climate services, computing infrastructure to run climate models, support for urban weather services, design of end-to-end early warning system for severe weather phenomenon (such as Tropical cyclones, thunderstorms and extreme winds) are expected to be supported through the project once it is effective.12 storm surge gauge, 03 Ocean Buoy, 03 RADAR for aviation purposes, 200 AWS and other related equipment will be installed under this project.

4.3 Hydrographic/ water body/ water quality data

Hydrographic data has been collected from Bangladesh Water Development Board (BWDB), Department of Public Health Engineering (DPHE), Local Government and Engineering Department (LGED) and Water Resources Planning Organization (WARPO). List of collected data has been shown in Table 7.

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Table 7: Hydrographic Data

Data name Data format Type Organization Comments 1. Wide river Shapefile

(.shp) Polygon LGED

2. Small river/ khal Shapefile (.shp)

Polyline LGED

3. Water bodies Shapefile (.shp)

Polygon LGED, BWDB

4. Flood-prone area Shapefile (.shp)

Polygon WARPO

5. Irrigation demand Shapefile (.shp)

Polygon WARPO

6. Flood: 2 years Return Period

Raster Grid WARPO


Raster Grid WARPO


Raster Grid WARPO


Raster Grid WARPO


Raster Grid WARPO


Raster Grid WARPO

12. Water level (monthly average maximum and average minimum)

Excel - BWDB

13. Ground Water Arsenic concentration

Excel - DPHE Converted to Shapefile (.shp)

14. Water salinity Shapefile (.shp)

Polygon SRDI Generated from hardcopy map

15. Soil salinity Shapefile (.shp)

Polygon SRDI Generated from hardcopy map

16. River erosion Shapefile (.shp)

Polygon - From satellite image interpretation

17. Coastal erosion Shapefile (.shp)

Polygon - Sarwar and Woodroffe, 2013

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18. Sea-level rise Shapefile (.shp)

Point - Sarwar 2013

19. Waterways Shapefile (.shp)

Polyline - Mapcruiz

4.4. Natural Features

An inventory on the data on natural features has been shown in Table 8.

Table 8: inventory of natural features data

Data name Data format Type Organization

Comments

1. Wide river Shapefile (.shp)

Polygon

LGED

2. Small river/ khal Shapefile (.shp)

Polyline

LGED

3. Water bodies Shapefile (.shp)

Polygon

LGED

4. Forest Shapefile (.shp)

Polygon

LGED

5. Dense mixed forest Shapefile (.shp)

Polygon

SoB

6. Reserved forest Shapefile (.shp)

Polygon

SoB

7. Scrub bush Shapefile (.shp)

Polygon

SoB

4.5. Geophysical Data

The geophysical hazards included in this study are Earthquake and Landslide. Apart from this, Digital Elevation Map (DEM) data is needed for purposes as preparation of other hazard maps, including Flood inundation map. The types and sources of some of the geophysical data are listed below;

- Earthquake : Seismic Zoning Map as prepared by the Geological Survey of Bangladesh. Both Jpg and Shape file. Entire Country is divided into three zones as Zone 1: High hazard Zone 2: Moderate hazard Zone 3: Least hazard

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Figure 4. Seismic zoning map for Bangladesh (GSB, 2018)

- Landslide: There are not many study on landslide occurrences in Bangladesh. Very recently this disaster has become frequent so that included in the list of an important hazard in Bangladesh. Under the MRVA project of the Department of Disaster Management (DDM), a hazard assessment study has been done recently and map prepared showing the spatial distribution of the hazard. The team is in process of having that hazard map.

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Figure 5. Landslide susceptibility of the country (DDM, 2017)

- DEM: from WARPO, Digital Elevation Map (DEM) data has been collected. This data can be utilized for both flood hazard assessment as well as landslide assessment. The data is useful, however, have some problems as;

4.6. Physical Infrastructure

List of physical Infrastructure data has been shown in Table 9.

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Table 9: List of Physical Infrastructure data

Data name Data format Type Organization Comments 1. National Highways Shapefile (.shp) Polyline LGED 2. Regional Highways Shapefile (.shp) Polyline LGED 3. Zila Road Shapefile (.shp) Polyline LGED 4. Upazila Road (Pucca) Shapefile (.shp) Polyline LGED 5. Upazila Road (Katcha) Shapefile (.shp) Polyline LGED 6. Railway Network Shapefile (.shp) Polyline LGED 7. Embankment Shapefile (.shp) Polyline LGED 8. Buildings Shapefile (.shp) Polyline Mapcruize

4.7. Socio-economic Data

Socio-economic data collected so far includes educational institutes, settlement, rural market and growth centres. List of socio-economc data have been shown in Table 10.

Table 10: Socio-economic data

Data name Data format Type Organization Comments 1. Growth Center Shapefile

(.shp) Point LGED

2. Rural market Shapefile (.shp)

Point LGED

3. College Shapefile (.shp)

Point LGED

4. High School Shapefile (.shp)

Point LGED

5. Primary School Shapefile (.shp)

Point LGED

6. Madrasa Shapefile (.shp)

Point LGED

7. Ashrayan/ Abasan Shapefile (.shp)

Point LGED

8. Settlement Shapefile (.shp)

Polygon

LGED

9. Agriculture land Statistical 10. Crop Production data Statistical BBS 11. Irrigated & non irrigated

agricultural area Statistical

BBS

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12. Total Loss in Crop Productions due to Natural Calamities

Statistical BBS & DAE

13. Crop damaged area by Flood, Flashflood & Excessive rain

Statistical BBS & DAE

14. Major cropping patterns Text format DAE 15. Crop damaged area by Flood,

Flashflood & Excessive rain Statistical DAE

16. Land use Shapefile (.shp)

Polygon

Mapcruize

17. Name of places

Shapefile (.shp)

Point Mapcruize

18. Name of Institutes/ Organizations

Shapefile (.shp)

Point Mapcruize

4.8. Spatial Data at a Glance

A set of spatial data has been collected from various organizations. List of data has been given below. (Table 11). Table 11: List of spatial data collected

Category Data name Data format Type unit Organization

Comments

Boundary International Shapefile (.shp) Polyline LGED, SoB Division Shapefile (.shp) Polyline LGED, SoB District Shapefile (.shp) Polyline LGED, SoB Upazila Shapefile (.shp) Polyline LGED, SoB HQ Division Shapefile (.shp) Point LGED District Shapefile (.shp) Point LGED Upazila Shapefile (.shp) Point LGED Natural Features

Wide river Shapefile (.shp) Polygon LGED

Small river/ khal Shapefile (.shp) Polyline LGED Water bodies Shapefile (.shp) Polygon LGED Forest Shapefile (.shp) Polygon LGED Dense mixed

forest Shapefile (.shp) Polygon SoB

Reserved forest Shapefile (.shp) Polygon SoB Scrub bush Shapefile (.shp) Polygon SoB

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Physical Infrastructures

National Highways

Shapefile (.shp) Polyline LGED

Regional Highways


Zila Road Shapefile (.shp) Polyline LGED Upazila Road

(Pucca) Shapefile (.shp) Polyline LGED

Upazila Road (Katcha)


Railway Network Shapefile (.shp) Polyline LGED Embankment Shapefile (.shp) Polyline LGED Socio-Economic Infrastructures

Growth Center Shapefile (.shp) Point LGED

Rural market Shapefile (.shp) Point LGED College Shapefile (.shp) Point LGED High School Shapefile (.shp) Point LGED Primary School Shapefile (.shp) Point LGED Madrasa Shapefile (.shp) Point LGED Ashrayan/

Abasan Shapefile (.shp) Point LGED

Settlement Shapefile (.shp) Polygon LGED Land use Shapefile (.shp) Polygon Mapcruize Hydrological Arsenic

contamination Shapefile (.shp) Point Mg/l DPHE Excel file

converted to shapefile

Annotation Name of places Shapefile (.shp) Point Mg/l Cruzmap Name of

Institutes/ Organizations

Shapefile (.shp) Point Mapcruize

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4.9 List of HEVRI Mappable Data and Maps for Agriculture and Water Sectors

Table 12. Inventory/List of HEVRI Mappable Data and Maps for Agriculture and Water Sector

Sectors

HEVR Categories and

Variables

Indicators Year Sources Administ-rative Levels/ Spatial

Units

Format(s) (i.e. shp, geotiff, mxd in data and lay-out views, lyr,

other ArcGIS formats, excel,

dbf)

Comments (Sector)

Hazards Agriculture & Water

Flood % of inundation/ crop area damage

20 years

DDM/BWDB

District .shp / excel Agriculture, Water

Drought SPI 2017 BMD/DDM District .shp Agriculture, Water

Cyclone Wind speed 2011 DDM/BMD District .shp Agriculture, Water

Storm surge Wave height/ Inundation

2011 DDM/BWDB

District .shp Agriculture, Water

Soil Salinity ppt 2009 SRDI District .shp / excel Agriculture, Water

Surface water Salinity

ppt BWDB District .shp / excel Agriculture, Water

Sea laver rise cm/year 2013 Ref. Sarwar 2013


River erosion m/year or area 2010 BWDB District .shp Agriculture, Water

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Sectors

HEVR Categories and

Variables


Units



dbf)

Comments (Sector)

Coastal erosion m/year or area 2013 Ref. Sarwar & woodroop 2013


Land slide No.of occurrence/ damage area

2017 DDM District .shp / excel Agriculture

Earthquake

Richter scale 2017 GSB District .shp Water

Exposures Crop Area in hectare 2015-

2016 BBS District excel Agriculture,

Water Population Density 2011 BBS District excel Agriculture,

Water Settlement % area 2016 LGED District .shp / excel Agriculture,

Water Transportation

infrastructure (Roads/Railway/Airport/seaport)

Length in km 2016 LGED District .shp Agriculture, Water

Forest Area in Hectare 2012 FD District .shp Agriculture, Water

Vulnerability

(inc. adaptive capacity)

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Sectors

HEVR Categories and

Variables


Units



dbf)

Comments (Sector)

Socio-economic Education % of population 2015 BBS District excel Agriculture,

Water Poverty % of population 2015 BBS District Excel Agriculture,

Water Gender Female education

rate 2015 BBS District Excel Agriculture,

Water Livelihood % of employment 2015 BBS District Excel Agriculture,

Water Physical Irrigation Area in hectare - BWDB District .shp Agriculture,

Water Cyclone shelter Numbers 2010 LGED District .shp Agriculture,

Water Medical facilities No of facilities 2016 LGED District .shp Agriculture,

Water Embankment Length of

embankment 2016 BWDB District .shp Agriculture,

Water Risks (maps) Flood Criteria or matrix

based Agriculture,

Water Drought Criteria or matrix

based Agriculture,

Water

36

Sectors

HEVR Categories and

Variables


Units



dbf)

Comments (Sector)

Cyclone Criteria or matrix based

Agriculture, Water

Storm surge Criteria or matrix based

Agriculture, Water

Soil Salinity Criteria or matrix based

Agriculture, Water

Surface water Salinity

Criteria or matrix based

Agriculture, Water

Sea laver rise Criteria or matrix based

Agriculture, Water

River erosion Criteria or matrix based

Agriculture, Water

Coastal erosion Criteria or matrix based

Agriculture, Water

Land slide Criteria or matrix based

Agriculture

Earthquake


Water

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4.10 HEV Matching Template For agriculture and water sectors

Table 13. HEV Matching Template For agriculture and water sectors

Sectors Hazards Exposures Vulnerabilities (e.g. CBA) Risk Impacts

Variable Indicator Variable Indicator Variable Indicator Variable Indicator Variable Indicator

Agriculture & Water

Flood % of inundation/ crop area damage

Crop Area in hectare

Socio-economic

Flood Criteria or matrix based

Life loss, Production loss, property loss

Reduced production, Damage value, no. of death/ injury

Drought SPI Population Density Education % of population

Drought Criteria or matrix based

Production loss

Reduced production

Cyclone Wind speed Settlement % area Poverty % of population

Cyclone Criteria or matrix based



Storm surge Wave height/ Inundation

Transportation infrastructure (Roads/Railway/Airport/seaport)

Length in km

Gender Female education rate

Storm surge




Soil Salinity ppt Forest Area in Hectare

Livelihood % of employment

Soil Salinity


Production loss

Reduced production

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Water Salinity

ppt Physical Water Salinity



Reduced production. morbidity

Sea laver rise

cm/year Irrigation Area in hectare

Sea laver rise


Production loss, property loss

Reduced production, Damage value

River erosion

m/year or area

Cyclone shelter

Numbers River erosion




Coastal erosion

m/year or area

Medical facilities

No of facilities

Coastal erosion




Land slide No. of occurrence/ damage area

Embankment Length of embankment

Land slide Criteria or matrix based


Damage value, no. of death/ injury

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5. Spatial Data Quality

Data quality is the degree of data excellency that satisfy the given objective. In other words,

completeness of attributes in order to achieve the given task can be termed as Data Quality.

Production of data by various mapping agencies assesses the data quality standards in order

to produce better results. Data created from different channels with different techniques can

have discrepancies in terms of resolution, orientation and displacements. Data quality is a

pillar in any GIS implementation and application as reliable data are indispensable to allow the

user obtaining meaningful results.

Spatial Data quality can be categorized into Data completeness, Data Precision, Data accuracy

and Data Consistency.

• Data Completeness: It is basically the measure of totality of features. A data set with

minimal amount of missing features can be termed as Complete-Data.

• Data Precision: Precision can be termed as the degree of details that are displayed on a

uniform space.

• Data Accuracy: This can be termed as the discrepancy between the actual attributes

value and coded attribute value.

• Data Consistency: Data consistency can be termed as the absence of conflicts in a

particular database.

Assessment of Data Quality:

Data quality is assessed using different evaluation techniques by different users.

• The first level of assessment is performed by the data producer. This level of assessment

is based on data quality check based on given data specifications.

• Second level of data quality assessment is performed at consumer side where feedback

is taken from the consumer and processed. Then the data is analyzed / rectified on the

basis of processed feedback.

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5.1 Quality check of existing datasets Quality check of existing datasets has been done. There are few observations which are given

below:

a) LGED Data:

1) Some shape files of the specific districts are missing. 2) The attribute tables are not properly described. 3) All the shape files are as individual Upazila based and it needs merging of those upazila

files together. 4) Khals name is missing. 5) River data is not clear. 6) Road Type (Kaccha, Pacca) is missing. 7) DTM (RL) points is missing (To make DEM & Contour). 8) Metadata is not documented for spatial datasets. 9) Except these mentioned above, the shape files are in good quality.

b) DPHE Data:

1) All the data are raw data and on MS Excel format, so it needs to make shape file with it. 2) Some error when converting x,y data (Lat / Long values) from a table in excel to

shapefile. 3) If we get later the details water table data with a time series data then it would be more

appropriate. 4) Water Quality data are not time series data. So, we need to ask them the time series data

up-to 2016. 5) Water Quality data only for ground water. We need to ask them for surface water quality

data. 6) Metadata is not documented for spatial datasets. 7) There are some data gaps. 8) Three (03) longitudinal coordinate are mistaken and need to be corrected. 9) Some anomalies of Arsenic data have been observed and need to recheck. 10) The difference between As_Wp_fieldkit_mg/l and Arsenic_mg/l is not clear and need to

clarify. c) BBS data: All data on MS Word and PDF format, so need to process for developing shape files with it. d) DAE data: All data on MS Excel and MS Word format, so need to process for developing shape files with it.

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e) FD data: Shape file of Khadimnagar and Lawachara National Park are in good quality. The forest map is in JPEG format but it needs in shape file format, so need to digitize. f) WARPO data:

1) The dataset has not any defined projection. 2) The data source and absolute resolution of DEM is not given in metadata. 3) In flood frequency data grid, the definition of fm 2, 5,10,25,50 and100 is not given. 4) Inundation data could be used for national scale that’s why small scale data might be

required for District/Upazila level mapping. 5) No data unit is given for irridmn.shp.

g) BWDB data:

1) All the data are in raw format and on MS Excel configuration, so it needs to make shape file with it.

2) Station ID wise coordinate will be required for both salinity and water level data to convert excel file into GIS environment.

3) Water level data is separated station wise in different excel file, so need to merge the data into single excel file so that the data could be converted into shape file easily.

4) There is no reference unit mentioned in the salinity data.

h) BMD data: All the data are in raw format and need to clean and organize them on MS Excel configuration, then it needs to make shape file with it. i) SoB data:

1) Administrative boundary shape file is in line format.

2) Metadata is not documented for spatial datasets.

3) Data producing year has not been mentioned in River, Agricultural Land and Forest

polygon data.

4) It is not clear that whether the river polygon is including or excluding the char area.

j) BARC data: Projections of the data are undefined and metadata are not available. k) SRDI data:

1) The data are in hard copy format 2) Need to be scanned the data 3) Need to be georeferenced the data 4) Need to be digitized them.

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5.2 Challenges

The data obtained from different sources are of different types as;

- Direct shape file in GIS environment and output maps

- Data in Excel form with absolute location/coordinates

- Hard copy data of most of the socio-economic factors for the different administrative

units like District or Upazila either in pdf format or photocopy. These data need to input

manually in excel format and convert to shape file later.

- Some maps are in pdf format or JPEG/TIFF format or hard copy- which need to scan as

well as geo-referencing and digitization to produce shape files which would be a time-

consuming task.

While a number of organizations produce GIS-based maps and background data in Bangladesh,

still there are number of problems exist as;

- Lack of coordination among different organizations in terms of projection used. A

number of projections are broadly used by different organizations as BTM, LCC, BUTM,

Geographic Coordinate Systems (GCS). The further complication arises while a map with

particular projection used but different Datum.

- For overlaying of these layers, it is difficult to use these data/shape files with different

projections and datum used. Homogenization of these layers with varying projection and

datum is again a troublesome task.

- In most of the cases, no metadata descriptions of maps/shape files are given. This causes

complexity to understand the source of data, projection and datum type, year of data

generation, the scale of data generation and usability, etc.

- Discrepancy/disagreement exists in Administrative boundary data by different

organizations. A significant amount of deviations are seen for admin boundaries

developed by different organizations. This is an extremely embarrassing condition. If

harmonization will not be done, again the maps will be faulty, while using shape files

from different organizations.

43

- Boundary data for different administrative units like District or Upazila, shape files are in

polyline rather than polygon format. These line shapes sometimes could not be converted

to polygon shape due to data error like discontinuity, missing data, common boundary,

improper format, etc. In that case it becomes difficult to use them for overlaying

purposes.

- Improper labeling: In many cases, identification of objects or attributes are not clear in

the attribute table so that it is creating confusion.

- For elevation related data, there is confusion on reference level/datum. Again, as

mentioned earlier, different datum is used by different organizations create a mismatch in

manifesting the real altitude values.

6. Concluding remarks

For establishing Climate Risk and Vulnerability Assessment (CRVA) tools, the HEVR spatial

datasets have been collected from different sources. Collection of data was a major challenge

during data collection as each organization has a specific data sharing policy and collecting these

data needs administrative procedure. Again, most of the organizations sell data, and getting them

free of cost required further bureaucracy, as we had no fund for data collection purposes. Amid

those limitations, we get quite a good number of sources for data, either in GIS format or shape

file or raw format like Excel, word or pdf.

Getting GIS based data with shape file was very difficult. One of the organizations, Department

of Disaster Management (DDM) recently did a very useful study as Multi-hazard Risk and

Vulnerability Assessment (MRVA). The project team is very eager to collect that data in shape

file format, at least for hazard assessment part. Most of the formalities are completed and

Programming Division of Planning Commission is pursuing DDM to get the data as soon.

While processing the data, further problems are faced because of their poor quality. In Bangladesh,

the spatial data managing organizations are using different map projections, so, the map projection

of the collected data needs transformation again. Quality of some data is good but the shape files

are as individual Upazila based and it needs merging of those Upazila files together to create a

44

figure for entire country. On the other hand, metadata is not documented for spatial datasets and it

needs to be documented for spatial datasets.

Overall, amid all those limitations, the team progressed well. This is part of life here in developing

countries and the team proceeding accordingly with all those obstacles.

8. References

ADB 2014. Climate Risk Management in ADB Projects. Publication No. ARM146926-2, Climate Change Adaptation Focal Point, Asian Development Bank (ADB), Manila, the Philippine. Alam MM, Hossain MA, Shafee S (2003) Frequency of Bay of Bengal cyclonic storms and depres-sions crossing different coastal zones. Int J Climatol 23(9):1119–1125. Bangladesh Climate Change Strategy and Action Plan 2009. BBS 2015. Yearbook of Agricultural Statistics of Bangladesh-2015. Habiba U, Shaw R, Takeuchi Y 2012. Farmer’s perception and adaptation practices to cope with drought: Perspectives from Northwestern Bangladesh, International Journal of Disaster Risk Reduction 1: 72-84. Islam T, Peterson RE (2009) Climatology of landfalling tropical cyclones in Bangladesh 1877–2003. Nat Hazards 48:115–135. Karim N (1995) Disasters in Bangladesh. Nat Hazards 11(3):247–258. Milliman JD, Broadus JM, Gable F 1989. Environmental and Economic Implications of Rising Sea Level and Subsiding Deltas: The Nile and Bengal Examples, Ambio18: 340-345. Sarwar MGM 2008. Landslide Tragedy of Bangladesh, The First World Landslide Forum, United Nations University (UNU), Tokyo, Japan. Sarwar MGM 2013. Sea-level Rise Along the Coast of Bangladesh, In Shaw R, Mallick F and Islam A (ed.), Disaster Risk Reduction Approaches in Bangladesh, Springer. Sarwar MGM, Khan MH 2007. Sea Level Rise: A Threat to the Coast of Bangladesh, Internationales Asien Forum: International Quarterly for Asian Studies 38(3/4): 375-397. Sarwar MGM, Woodroffe C 2013. Rates of shoreline change along the coast of Bangladesh, Journal of Coastal Conservation 17(3): 515-526. SRDI 2010. Saline Soils of Bangladesh, Soil Resources Development Institute (SRDI), Ministry of Agriculture, Government of the People's Republic of Bangladesh, Dhaka. WARPO 2012. Data Inventory and Data Needs Assessment Report for NWRD & ICRD, Water Resources Planning Organization, Ministry of Water Resources, Government of the People’s Republic of Bangladesh.

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Documents

Technical Assistance Consultant’s Report · Project Number: 46470-001 . January 2018 . TA8572 (REG): Action on Climate Change in South Asia (Financed by the Asian Development Bank)