Report on

Preparation of an inventory of perennial surface water sources close to village with over 80% tube wells Arsenic contaminated

Implementation Phase

March 2006

i

Study Team

Md. Motaleb Hossain Sarker, GIS Specialist/Project Leader Mir Abdul Matin, DSS Expert Ehsan Hafiz Chowdhury, Database Expert Iffat Huque, Remote Sensing Expert Md. Abdul Lahel Shafey Ms. Maktuba Mohid Md. Sajidur Rahman, Remote Sensing Analyst Md. Kamal Hossain, GIS Analyst Sayeefur Rahman Rizvi, Graphic Specialist Md. Firoz Alam, GIS Analyst and Field Coordinator M. Habibur Rahman, GIS Analyst and Field Coordinator Mst. Fahmida Khatun, Junior GIS Analyst Md. Billal Hossain Majumder, Junior GIS Analyst Md. Ahmadullah Kamal, GIS Technical Assistant Md. Mahmud Hassan, GIS Technical Assistant

GPS Operator, Comilla region

Md. Anowar Hossain (Field Supervisor) Md. Shahadat Hossain Manik Md. Mosharef Hossain Md. Saiful Islam (T) Md. Saiful Kabir Md. Saidur Rahman Md. Abu Kauser Md. Kamal Pasha

GPS Operator, Khulna region

Md. Mamunur Rahman Khan (Field Supervisor) Md. Mohashin Md. Saiful Islam (P) Md. Abul Kalam Azad Md. Abdus Salam Sayed Shohel Ali Md. Shiratur Rahman

iii

Acknowledgement

This report is the result of a study conducted by CEGIS for Arsenic Policy Support Unit (APSU) to make an inventory for perennial surface water sources as an alternative source of safe drinking water closes to the villages where more than 80% tubewells are arsenic contaminated. CEGIS expresses gratefulness to DFID for providing the financial support to carry out the study. CEGIS also express the gratefulness to DPHE especially to Mr. Ehtishamul Huque, Superintendent Engineer for his dynamic role to formulate the project and providing the detail information of the arsenic contaminated villages of the country. Precious and cordial cooperation from Mr. Sudhir Gosh, Executive Engineer, Research and Development, DPHE is gratefully acknowledged. Mr. Tushar Mohan, Executive Engineer, Training Division of DPHE is also acknowledged for providing GPS. The team is also thankful to Dr. Ahmmadul Kabir, Local Consultant of APSU for their valuable suggestions and co-operations in project execution and for reviewing the interpretations made by the team. The guidance from Mr. Giasuddin Ahmed Choudhury, Executive Director of CEGIS through participating in important discussions is also acknowledged with duely honor.

Contribution from the short-term GPS operators and two experienced field supervisors (Md. Anowar Hossain, and Md. Mamunur Rahman Khan) are notable and are highly appreciated. The sincere contribution from two field coordinators (Md. Firoz Alam and Mr. Habibur Rahman) for assisting the project leader in various technical data preparation and GIS mapping activities. Mr. Motaleb Hossain Sarker has given expertise input to complete the whole study as a Project Leader and GIS Specialist for setting up field offices, initial mobilization of survey team, design and development GIS database and generation of keen full idea of Automated Mapping (AM) and data exploration software development. The remarkable inputs from Mr. Abdul Lahel Shafey for giving input as GIS Programmer to develop the autamted map printing and data exploration software. Partial contribution from Mrs. Muktoba Mohid for software development is also appreciated. Technical guidance and time-to-time feed back from Mr. Mir Abdul Matin, Division Head, GIS division is also greatly acknowledged. Valuable input in technical editing of the report from Mr. Ehsan Hafiz Chowdhury, Principal Specialist of CEGIS are also gratefully acknowledged. Mr. Awlad Hossain is also thankfully acknowledged for contribution in report editing. Contribution from Mrs. Iffat Huque, Division Head, Remote Sensing for giving input as a Remote Sensing Expert through advising in methodology development for identification of perennial surface water sources from the satellite images is also thankfully acknowledged. Rigorous input for image processing and methodology development from Mr. Sajidur Rahman as Remote Sensing Analyst are also acknowledged. Translating the field staff report by Ms. Fahmida Khatun is acknowledged. Thanks are also due to Mr. Sayeefur Rahman Rizvi for formatting the report.

The report was written by

Md. Motaleb Hossain Sarker Ehsan Hafiz Chowdhury Mir. Abdul Matin Md. Sajidur Rahman

v

Table of contents

Study Team............................................................................................................................... i Acknowledgement .................................................................................................................. iii Table of contents ......................................................................................................................v List of tables........................................................................................................................... vii List of figures........................................................................................................................ viii Acronyms ................................................................................................................................ ix Chapter 1 Introduction............................................................................................................1

1.1 Background..............................................................................................................................1 1.2 Objective..................................................................................................................................2 1.3 Description of the study area ...................................................................................................2 1.4 Outputs & Deliverables ...........................................................................................................3 1.5 Structure of the Report ............................................................................................................3

Chapter 2 Methodology...........................................................................................................9 2.1 Introduction .............................................................................................................................9 2.2 Study approach ........................................................................................................................9 2.3 Materials / Tools......................................................................................................................9

2.3.1 Satellite data................................................................................................................10 2.3.2 Topographic maps.......................................................................................................10 2.3.3 Finn map for coastal area............................................................................................10 2.3.4 GPS .............................................................................................................................10

2.4 Other GIS data.......................................................................................................................14 2.5 Software.................................................................................................................................14 2.6 Image processing ...................................................................................................................14

2.6.1 Pre Processing.............................................................................................................14 2.6.2 Resolution Merge........................................................................................................15 2.6.3 Post Processing ...........................................................................................................15

2.7 Feature extraction from satellite images................................................................................16 2.7.1 Waterbodies ................................................................................................................17 2.7.2 River & Khals .............................................................................................................17

2.8 Detail Pond Survey................................................................................................................17 2.8.1 Methodology of pond survey ......................................................................................19

2.9 Data processing and editing...................................................................................................19 2.10 Database preparation .............................................................................................................19

2.10.1 Water bodies ...............................................................................................................19 2.10.2 River ...........................................................................................................................20 2.10.3 Pond ............................................................................................................................21

2.11 Map preparation and tools development................................................................................22 Chapter 3 Detailed Pond Survey ..........................................................................................23

3.1 Introduction ...........................................................................................................................23 3.2 Field of office and team formation ........................................................................................23 3.3 Field data and map preparation .............................................................................................23 3.4 GPS Training for field staffs .................................................................................................26 3.5 Field survey and quality control ............................................................................................27 3.6 Quality control of field data collection system......................................................................27 3.7 Field data entry and processing .............................................................................................27 3.8 Field observation ...................................................................................................................28

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Chapter 4 Data Analysis........................................................................................................29 4.1 Introduction ...........................................................................................................................29 4.2 Analysis of waterbodies and river data..................................................................................29

4.2.1 Accuracy assessment of waterbodies and rivers .........................................................29 4.2.2 Summary of waterbodies ............................................................................................30 4.2.3 Summary of river data ................................................................................................31

4.3 Analysis of pond data ............................................................................................................32 4.3.1 Summary of pond data ................................................................................................32 4.3.2 Pond size .....................................................................................................................33 4.3.3 Water availability from pond data ..............................................................................35 4.3.4 Dry season water depth...............................................................................................36 4.3.5 Usages of the ponds ....................................................................................................36 4.3.6 Vegetation coverage of pond ......................................................................................40 4.3.7 Physical water quality (color) .....................................................................................43

4.4 Identification of potential ponds for safe water options ........................................................45 4.5 Result and discussion.............................................................................................................49

4.5.1 Waterbodies ................................................................................................................49 4.5.2 River and khals ...........................................................................................................50 4.5.3 Pond survey.................................................................................................................52

Chapter 5 Map preparation and Software Development...................................................53 5.1 Map preparations ...................................................................................................................53 5.2 Software development ...........................................................................................................53 5.3 Training on developed software ............................................................................................54

Chapter 6 Conclusion and Recommendation......................................................................55 6.1 Conclusion.............................................................................................................................55 6.2 Recommendation...................................................................................................................56

References...............................................................................................................................57 Annex – A: Field forms for pond survey Annex – B: Sample maps of perennial surface water sources Annex – C: Sample attributes of pond survey Annex - D: Union wise ponds location map Annex – E: Field observation from field staff

vii

List of tables

Table 1.1: Percentage of contaminated tube wells including demography of surveyed unions .............7 Table 2.1:Images used for the study .....................................................................................................10 Table: 2.2: Specifications of digitizing grading of rivers and khals. ....................................................17 Table 2.3: Additional surveyed union...................................................................................................18 Table 2.4: Attribute of waterbodies data...............................................................................................19 Table 2.5: Attribute of waterbody data .................................................................................................20 Table 2.6: Attribute of River data .........................................................................................................20 Table 2.7: Attribute of pond data ..........................................................................................................21 Table 4.1: Summery table of digitization accuracy (in percentage): ....................................................29 Table 4.2:District wise number and areas of waterbodies for arsenic affected unions .........................30 Table 4.3: District wise river information for affected unions..............................................................31 Table 4.4: Union wise summary of pond surveyed ..............................................................................32 Table 4.5: Union wise pond area class..................................................................................................34 Table 4.6: Union wise number of pond under different water volume class ........................................36 Table 4.7: Union wise number of pond under different dry season water depth class .........................38 Table 4.8: Summary result of uses of the pond.....................................................................................39 Table 4.9: Percentage % of vegetation coverage of pond.....................................................................41 Table 4.10: Status of different type vegetation found in the pond........................................................42 Table 4.11: union wise pond water color information. .........................................................................43 Table 4.12: Union wise apparently potential ponds..............................................................................47

viii

List of figures

Figure 1.1: Arsenic contaminated upazilas where more than 80% tubewells are affected .....................4 Figure 1.2: Arsenic affected unions where more than 80% tubewells are affected ................................5 Figure 1.3: Union selected for detailed pond survey ..............................................................................6 Figure 2.1: Flow diagram of the study Approach .................................................................................11 Figure 2.2: Sample topographic map of Karanja union, Santhia upazila, Pabna ..................................12 Figure 2.3: The sample Finn map of Karapara union at Bagerhat Sadar Upazila.................................13 Figure 2.4: The GPS used in the field for data collection.....................................................................14 Figure 2.5: Flow chart of for image processing the methodology ........................................................16 Figure 2.6: Methodology for detailed pond survey...............................................................................18 Figure 2.7: The database system of the project.....................................................................................21 Figure 3.1: Sample of base map for detail pond survey........................................................................24 Figure 3.2: Sample union base (without image) map for pond survey .................................................25 Figure 3.3: Sample union base (with image) map for pond survey ......................................................26 Figure 3.4: Pond survey data entry interface ........................................................................................28 Figure 4.1: Statistics of pond area of surveyed pond. ...........................................................................33 Figure 4.2: Dry season water volume of ponds for surveyed unions....................................................35 Figure 4.3: Dry season water depth (meter) at surveyed ponds............................................................38 Figure 4.4: Percentage of total surveyed pond under different vegetation category.............................40 Figure 4.5: Pond with good quality of water ........................................................................................44 Figure 4.6: Pond with normal quality of water .....................................................................................44 Figure 4.7: Pond with bad quality of water...........................................................................................45 Figure 4.8: Status of potential ponds ....................................................................................................48 Figure 4.9: An integration view of Gher areas......................................................................................49 Figure 4.10: Waterbodies and its use at haor areas in north-east (Sylhet) zone. ...............................50 Figure 4.11: Water body adjacent to the large irrigation area in Hobiganj district...............................50 Figure 4.12: A sample of a IRS 1D panchromatic image over low laying areas of Gopalganj Sadar, Gopalganj district..................................................................................................................................50 Figure 4.13: Field observation for river analysis. .................................................................................51 Figure 5.1: Interface of automated map printing software....................................................................53 Figure 5.2: Data entry interface of automated map printing software ..................................................54

ix

Acronyms

APSU Arsenic Policy Support Unit

BAMWSP Bangladesh Arsenic Mitigation Water Supply Project

BBS Bangladesh Bureau of Statistics

BIWTA Bangladesh Inland Water Transport Authority

BTM Bangladesh Transverse Mercator

BWDB Bangladesh Water Development Board

CEGIS Center for Environmental and Geographic Information Services

DFID Department For International Development

DGPS Differential Global Positioning System

DLRS Directorate of Land Records and Surveying

DPHE Department of Public Health Engineering

DTW Deep Tube-wells

ERDAS ERDAS IMAGINE image processing software

ETM Enhanced Thematic Mapper

Finn Map Coastal Area Map (Map prepared by BIWTA and funded by FINNIDA)

FINNIDA Department for International Development Cooperation

GCP Ground Control Points

Germin 12XL Germin 12 Channel Global Positioning System

GIS Geographical Information Systems

GOB Government of Bangladesh

GPS Global Positioning System

ICRD Integrated Coastal Resources Database

IKONOS Satellite-based imagery acquisition systems, 4m multi spectral and 1m panchromatic

IRS Indian Remote Sensing Satellite

ISPAN Irrigation Support Project for the Asia and Nearest

JPEG Joint Photographic Experts Group (A set of file compression techniques)

LANDSAT Satellite-based imagery acquisition systems

LISS Linear Imaging self-Scanning Sensor

MSS Multi Spectral Scanner

NAMIC National Arsenic Mitigation Information Center

NWRD National Water Resources Database

PAN Panchromatic

x

RADARSAT A Canadian radar satellite launched in 1995.

SDC Swiss Development Corporation

SoB Survey of Bangladesh

SPARRSO Bangladesh Space Research & Remote Sensing Organization

SPOT System Pour Observation de la Terre

STW Shallow Tubewell

TM Thematic Mapper

ToR Terms of Reference

WARPO Water Resources Planning Organization

WB World Bank

1

Chapter 1

Introduction

1.1 Background

Arsenic contamination of drinking water is a significant health problem in Bangladesh (BMRC, 2002). In Bangladesh more than 90% of rural households depend on water supply from the groundwater source for drinking and other domestic usage. Tube wells have been introduced for safe drinking water and domestic purposes for more than 50 years. As surface water carries lot of bio-logical and chemical waste, groundwater is considered to be the best option for drinking water. More recently arsenic contamination in ground water was discovered. The extent and severity of this problem has placed a large part of the country in a most vulnerable situation. Screening of 4.7 million tubewells in arsenic affected Upazilas showed that 29% of these have arsenic in excess of the Bangladesh standard. It is estimated that 20-25 million peoples are at risk from arsenic in these Upazilas.

Since 1997, the Government of Bangladesh (GoB) through the Department of Public Health Engineering (DPHE) has carried out various studies for the investigation of the problem to identify the sources and causes of contamination. A national program has been initiated with assistance from the World Bank (World Bank, 1997) and the Swiss Development Cooperation (SDC) to address the arsenic problem under the Bangladesh Arsenic Mitigation Water Supply Project (BAMWSP). The objective of the project is to provide arsenic free water supply to rural and urban communities.

A National Arsenic Mitigation Information Center (NAMIC) has been established within BAMWSP aiming to be the bank of all arsenic related information. This information center will work as a technical and operational entity to facilitate decision making by providing necessary information. It is also responsible for collating all arsenic related information, which are collected or generated by different stakeholders and for integrating, analyzing, storing, and disseminating this information.

NAMIC has compiled survey data on arsenic contaminated tube-wells in the country from various studies. The compiled databank shows that around 8500 villages have been identified where over 80% of tubewells are arsenic contaminated. Figure 1.1 shows the extent of arsenic contamination by upazilas where 80% of the tube-wells within each village are contaminated.

The report of the committee on “Surface Water Development and Management for Drinking Water Supply in the Arsenic affected areas of Bangladesh” proposes to seek alternative sources of surface water in the arsenic affected areas of the country (BGS, 2001). The sources that could be considered to meet the demand for drinking water supply are mainly rainfall, river flows during the dry season, surface water bodies like beels, ponds, lakes and groundwater within the suction level of STWs. To understand surface water sources during the dry periods it is necessary to know the extent and availability of water along with its quality. The best way to find possible surface water perennial sources is dry season high-resolution images, which give a potential coverage of the water extent of rivers, khals, water bodies and ponds.

In this regard, APSU took initiative to identify the perennial sources of water in the affected villages in terms of extent from surface resources. CEGIS has been carried out study to develop methodology for identification of perennial surface water from satellite images during the feasibility phase of this project with the assistance of DFID. Following the recommendations from the feasibility phase, the

Introduction

2

implementation phase was executed to prepare perennial surface water database from satellite images and detailed pond database from the field survey using GPS.

The methodology to extract perennial water sources from images and detailed pond survey is described in Chapter 2. Maps showing the extent of surface water sources in arsenic contaminated villages will give a clear picture about the availability of alternative sources of surface water. The maps could be helpful in planning pilot areas where surface water sources could be utilized and drinking water needs from different sources could be prioritized.

1.2 Objective

The objective of the project is to prepare GIS maps and a database on perennial surface water sources (rivers, water bodies and ponds etc.) from available satellite images and detailed pond survey using GPS in severely arsenic affected villages of selected unions.

The outcome of the feasibility study demonstrated the procedure and methodology for the implementation phase describing the work plan and budget provision for safe water option technology. The output of the implementation phase of the project will be used to provide support to DPHE in the preparation of an inventory of perennial surface water sources close to villages where over 80% of the tube-wells have arsenic contamination in excess of 50 mg/l. The inventory will facilitate identification of alternate surface water options in arsenic affected areas.

However, the overall objective of the project is to identify perennial surface water sources using satellite images as well as GPS surveys and to prepare GIS based maps along with a database.

The specific objectives are as follows:

Identification of perennial surface water sources from satellite images following the

methodology developed during feasibility phase.

Extraction of perennial water bodies including rivers from available satellite based images in

highly arsenic affected areas

Production of digital maps of the surface water bodies for highly arsenic affected unions

displaying available data on a GIS platform at 50,000-60,000 scales.

Preparation of pond database for selected 35 unions in highly arsenic affected areas.

Development of a system to assist DPHE for identification and screening of potential pond to

optimize the water quality monitoring activities.

1.3 Description of the study area

The study area for delineation of waterbodies during the implementation phase of the project has been selected discussing with the Arsenic Policy Support Unit (APSU) and Government officials of DPHE. The study area for delineation of water sources is shown in Figure 1.2 covering the unions where more than 80% tubewells are arsenic affected.

The selection of the study area was based on several criteria or factors:

(i) percentage of affected tubewells in the unions;

(ii) number of affected villages;

Introduction

3

(iii) area suitable or not suitable for deep tube wells based on aquifer characteristic;

(iv) distribution of selected unions throughout the study area; and

(v) other associated geographic attributes concerned.

The unions selected for detailed pond survey are presented in Figure 1.3. The percentage of arsenic affected tube well for each union including the number of households in each village are presented in Table 1.1. From the table it has been observed that lowest percentage of tube well affected at Karanja union under Santhia upazila of Pabna district, which is 85% and number of affected villages is three. Highest percentage of tube well affected at Betmore Rajpara union under Mathbaria upazila of Pirozpur district, which is 100% and number of affected village is one. The highest number of village affected at Paschimgaon union under Laksham upazila of Comilla and the population density is also higher, which is more than 1200.

1.4 Outputs & Deliverables

The following deliverables/outputs were made available to APSU at the end of the study:

• Methodology to identify perennial surface water sources from satellite images;

• Maps of surface water bodies including rivers in highly arsenic affected unions (where more than 80% tube well are affected) in GIS platform;

• Perennial surface water database for highly arsenic affected unions

• Detailed pond database for selected 35 unions

• A standalone software for automated map production and displaying the available data

• Final technical report mentioning the methodology, database development, outputs and further recommendations

1.5 Structure of the Report

The purposes of this study was to develop GIS based perennial surface water database for highly arsenic contaminated unions and detailed pond database for 35 unions through methodology development, field investigation and database development. Chapter 1 of the report contains the background, study area descriptions, objectives, deliverables and approach of the study. Overall methodology of the study is briefly described in Chapter 2. Chapter 3 of this report deals with detailed pond survey, which includes the survey methodology, pond database development and field observation. Chapter 4 deals with the data analysis, which includes pond sizes, water availability, usage of the ponds, vegetation and vegetation coverage percentage of the pond and apparent physical water quality (color and odor) etc.

The description of map preparation and simple software for automated map preparation are described in Chapter 5. Finally the conclusions and recommendations are incorporated in Chapter 6.

Introduction

4

Figure 1.1: Arsenic contaminated upazilas where more than 80% tubewells are affected

Introduction

5

Figure 1.2: Arsenic affected unions where more than 80% tubewells are affected

Introduction

6

Figure 1.3: Union selected for detailed pond survey

Introduction

7

Table 1.1: Percentage of contaminated tube wells including demography of surveyed unions

Source: NMAIC

District Upazila Union No. Affected Village

% of ContaminatedTW

Population No. of Household

Area (Sq. Km)

Population density

Bagerhat Bagerhat Sadar Kara Para 11 89.22 28440 5404 25.86 1100

Bagerhat Mollahat Gaola 13 94.60 13599 2478 41.03 331

Bagerhat Morrelganj Daibagnyahati 5 93.96 16816 3223 20.88 805

Brahamanbaria Banchharampur Salimabad 17 96.14 31138 4894 27.02 1152

Brahamanbaria Nabinagar Shibpur 15 97.91 26093 4462 23.93 1090

Chandpur Hajiganj Dakshin Rajargaon 17 99.14 21186 3775 15.22 1392

Chandpur Kachua Kadla 28 97.40 31487 5218 26.99 1167

Chandpur Kachua Pathair 20 98.45 38857 6753 31.75 1224

Chandpur Matlab Durgapur 26 95.80 26212 4546 25.19 1041

Chandpur Shahrasti Uttar Meher 24 98.03 21622 3791 16.39 1319

Comilla Chandina Maijkhara 25 93.87 37356 6672 30.27 1234

Comilla Homna Paschim Chander Char 20 98.10 20510 3582 16.06 1277

Comilla Laksam Paschimgaon 30 97.48 26814 4639 21.19 1265

Comilla Muradnagar Sreekail 23 98.02 32255 5308 30.76 1049

Gopalganj Gopalganj Sadar Suktail 13 95.01 13213 2343 20.95 631

Gopalganj Kashiani Parulia 10 94.10 9838 1775 16.56 594

Gopalganj Kotali Para Radhaganj 23 93.24 21578 4129 29.22 739

Gopalganj Tungi Para Patgati 13 97.00 29810 5369 28.79 1035

Jessore Abhaynagar Sreedharpur 2 95.92 24444 4159 34.46 709

Khulna Paikgachha Kapilmuni 18 91.62 28578 5366 37.89 754

Khulna Terokhada Sachiadah 10 89.25 16102 2900 36.64 440

Madaripur Rajoir Badar Pasha 24 93.93 22555 4092 23.79 948

Narail Kalia Kalabaria 20 95.48 17024 3180 27.78 613

Pabna Bera Masundia 3 85.78 19146 3146 28.86 663

Pabna Santhia Karanja 3 85.34 30483 4929 32.30 944

Pirojpur Mathbaria Betmore Rajpara 1 100.00 18859 3614 25.08 752

Pirojpur Pirojpur Sadar Sikdar Mallik 3 98.33 16629 3255 25.19 660

Rangpur Pirgachha Tambulpur 1 87.55 39011 9541 36.89 1058

Satkhira Assasuni Kadakati 16 97.05 25746 4688 52.03 495

Satkhira Kalaroa Keralkata 18 94.91 17187 3331 24.31 707

Satkhira Kaliganj Champaphul 6 95.65 14391 2577 30.25 476

Satkhira Tala Khalishkhali 22 92.90 22227 4182 36.65 606

Sirajganj Royganj Chandaikona 1 88.57 28678 5266 32.76 875

Sylhet Balaganj Paschim Pailanpur 10 95.27 11605 1884 20.90 555

Sylhet Kanaighat Purba Dighirpar 11 95.37 13490 2339 33.88 398

9

Chapter 2

Methodology

2.1 Introduction

During the feasibility phase different multi-spectral and panchromatic satellite data with the highest available resolution were used to develope the methodology. The satellite images are LANDSAT TM/IRS LISS and IRS Pan, merged after pre-processing to get higher resolution multi-band images. Following the recommendation of feasibility phase and as per ToR the study methodology has been approached and briefly discussed in section 2.2 of this chapter.

Visual (on screen) interpretation was performed to evaluate manual methods for identifying water features such as water bodies, rivers, khals and ponds etc., from images. In addition, the merged images were digitally enhanced using various techniques to identify the water bodies in the area. The enhanced images were checked using other available secondary data (GIS data, topographic maps, Finn Map and field information, etc.) for extracting water bodies including potential ponds. Finally, the results were compared with the field data collected under this project.

2.2 Study approach

Implementation phase of the project was carried out through step wise approaches following extraction of perennial surface water sources (waterbodies and river) from satellite images and preparation of detailed pond data collecting through GPS field survey. The extraction of surface waterbodies from satellite images includes image processing, ground truthing, data extraction and data preparation. Further the detailed data preparation included collection of pond information through field survey using GPS and field forms etc. The methodological approach of the study is shown in Figure 2.1 in a simplified flow diagram. Furthermore methodology for data extraction from satellite images and detailed pond survey were presented in Figure 2.1 and 2.2 respectively.

2.3 Materials / Tools

Following materials and tools including the satellite images have been used for carrying out of the study:

(i) Satellite data

(ii) Topographic maps

(iii) Finn Maps

(iv) Global Positioning System (GPS)

(v) Other GIS data layers from National Water Resources Database (NWRD) and

(vi) GIS and Image processing software.

Methodology

10

2.3.1 Satellite data

Repetitive and synoptic viewing facilities of satellite remote sensing system offers an effective means for acquiring valuable and timely information of the earth surface and its natural resources at large scale. In recent years satellite images have been used for inventorying, monitoring and managing of earth’s resources. Considering all these facilities it was decided to use remote sensing data for this project. The size and distribution dynamics of individual major classes such as soil, vegetation and water is a major factor for interpreting remote sensing data. Percent of area and spectral response of individual classes both are important in the determination of overall response of a pixel.

High spatial resolution data shows more classes than coarse spatial data. Generally High-resolution optical satellite images are suitable for visual interpretation of small and linear features such as ponds and khals etc.

Considering the idea of spatial and spectral resolution and project objectives, a set of cloud free digital images covering the proposed areas of different satellite systems such as LANDSAT TM multi spectral, IRS 1D LISSIII multi spectral and IRS 1D panchromatic were selected for the study. LANDSAT TM multi-spectral image has a spatial resolution of 30 meter, IRS 1D LISS multi-spectral image has a spatial resolution of 24 meter and IRS 1D panchromatic image has a spatial resolution of 6 meter.

Table 2.1:Images used for the study

Image Name Sensor Type (Pan/MS) Spatial Resolution Frame Size

IRS PAN 1D 5.8 m 70 x 70

IRS P6 (Multi spectral) LISS III 24 m 140 x 140

Landsat-7 Multi Spectral 30 m 185 km x 185 km

Merged Multi Spectral 5.8m ≈ 6.0m --

2.3.2 Topographic maps

Topographic maps of the Survey of Bangladesh at the 1:50,000 scales were used as associate base maps to identify waterbodies in the field. Topographic maps have also been used to identify the villages for pond survey. These maps were found to be the most accurate, in terms of location and scale and very useful in identifying features in the field. However, these maps were created in the 1960s and have not been revised since then. Hence, this maps are used as base map for locating the affected villages for GPS survey. The sample topographic maps of Karanja union under Santhia upazila of Pabna district are shown in Figure 2.2

2.3.3 Finn map for coastal area

High-resolution topographic maps at the 1:10,000 scale were produced from aerial photograph for coastal areas by FINNIDA. Thess are very useful maps from which detailed water bodies including the ponds can be delineated and easily identified. These maps however are available for coastal areas only. Finn maps have been used to identify the waterbodies in the coastal area. The sample Finn Map for Krarapara union under Bagerhat Sadar upazila has been shown in Figure 2.3

2.3.4 GPS

Different types of Global Positioning System (GPS) were used to collect pond locations from the field. Different hand held GPS were used for this study, which are eTrex Venture and Garmin 12

Methodology

11

Channel etc. were presented in Figure 2.4. The accuracy of Germin 12XL are comparatively better which is between 5-10 meter. While the accuracy of the eTrex Venture GPS has been found 8 - 15 meter. However, the overall accuracy of the GPS has been used for the survey work is 10 to 15 meters. The field GPS operators / data enumerators were strictly instructed to follow and maintain satellites that are more accurate and available for distribution in GPS.

Figure 2.1: Flow diagram of the study Approach

Identification of waterbodies & river from satellite images

Methodology development for image processing and data capturing from images Satellite image processing Extraction of waterbodies & rivers

Field visit for ground truthing

Preparation of perennial surface water database Development of software for

exploring data and automated map preparation

Project Implementation Plan

Output Surface water database

GIS Map Software

Documentation (Final Report Preparation)

Detailed pond data collection Field mobilization and field material

preparation GPS Training to the field staff

Detailed pond data collection Data entry and processing

Preparation Pond Database

Methodology

12

Figure 2.2: Sample topographic map of Karanja union, Santhia upazila, Pabna

Methodology

13

Figure 2.3: The sample Finn map of Karapara union at Bagerhat Sadar Upazila

Methodology

14

Figure 2.4: The GPS used in the field for data collection

2.4 Other GIS data

Several GIS data layers were used at different stages during this study from available sources such as NWRD and CEGIS. Other GIS data that has been used for this study are on perennial water bodies including rivers from NWRD, settlements, roads etc., from CEGIS’ archive. The data was prepared from satellite images. This water body coverage was visually interpreted from 1:50,000 scale maps prepared from a SPOT multi spectral image, as well as LANDSAT and IRS images.

2.5 Software

The ERDAS Imagine software was used for image processing and analysis. The ArcGIS and ArcView GIS software were used to capture the data on water bodies as well as for data and map preparation. The MS Access software was used for field data entry, processing and analysis.

2.6 Image processing

Satellite images are enriched with the earth surface related information but they never represent any physical parameter directly. It needs to go through a number of processing steps in order to be used for analysis and interpretation. The stepwise procedure of image processing has been followed according to the flow chart provided in figure 2.5.

2.6.1 Pre Processing

All images were geometrically corrected and geo-referenced to Bangladesh Transverse Mercator (BTM) with spheroid and datum Everest. All IRS 1D images were geo-referenced by ground control points (GCP) collected by DGPS. And all multi spectral (MSS) images were georeferenced using Landsat TM (2003) image mosaic, which was previously georeferenced by a sufficient number of collected ground control points from the field using DGPS. During resampling a nearest neighbor method with relevant pixel size (6mx6m for IRS 1D panchromatic, 24mx24m for IRS 1D LISS III and 30m x 30m for LANDSAT TM) was used. The geo-referencing accuracy was approximately ±10 meters, ±24 meters and ±30 meters for IRS 1D panchromatic, IRS 1D LISS III and LANDSAT TM

Methodology

15

images respectively. Before conducting the resolution merge process good co-registration between the input images was ensured.

2.6.2 Resolution Merge

High resolution IRS 1D panchromatic images may be used to identify perennial water bodies. But it has some limitations is that it express all classes in gray scale which develop tonal conflicts among classes like water and vegetation, moist land and waterbodies. These tonal conflicts make it complex to identify different surface features. Due to the spectral constraint it is difficult to conduct further digital processing on the panchromatic images. To overcome all these limitations it was decided to use the combination of high resolution IRS 1D panchromatic and medium resolution multi-spectral images (IRS 1D LISS III and LANDSAT TM) to extract perennial water bodies. This process of combination is termed resolution merge.

Images with medium spatial resolutions like IRS 1D LISSIII multi spectral, LANDSAT TM multi spectral were merged with high resolution IRS 1D panchromatic images to convert in high resolution (6m) image with multi-spectral characteristics for more visibility and further digital processing to make more prominent the classes that are of interest. It can be mentioned that LANDSAT TM multi spectral images have been used for those areas where LISS III are not available.

During resolution merge all the methods (Principal Component, Multiplicative and Brovey Transform) for varying resampling techniques (like Multiplicative and Nearest Neighbor, Multiplicative and Bilinear Interpolation and Multiplicative and Cubic Convolution) were conducted to find the best combination for our study. In accordance with the variation of resampling techniques the combinations of spectral bands also varied. It was found that for IRS 1D panchromatic and multi spectral IRS 1D LISSIII image the combination of Brovey Transform method with Cubic Convolution resampling technique produces best results. And for IRS 1D panchromatic and multi spectral LANDSAT TM image the combination of Principal Component method with Cubic Convolution resampling technique produces best result.

2.6.3 Post Processing

Resolution merge improves the image quality especially in reference to spatial and spectral properties. But this is not enough for our purpose. Further digital processing have also been conducted in order to enhance the images for easier interpretation with the help of image processing software.

Studying different band combinations of the resolution merged images threshold values were identified in the different bands to separate water bodies from moist soil. A ‘water bodies’ layer was generated using the selected threshold values, which was further combined with the merged images by layer stack techniques. This does not necessarily identify all water bodies but can be used to enhance the water bodies for viewing and that helps digitization of water bodies. In some cases enhancing brightness of merged images produced expected results. Finally processed data were used to digitize targeted object/class through the process of on screen digitization.

Methodology

16

Figure 2.5: Flow chart of for image processing the methodology

2.7 Feature extraction from satellite images

A satellite image always represents the condition of earth surface when the satellite passes over an area. For this reason it is very difficult to confirm perennial surface water bodies from a single date satellite image. Considering the project cost the generalized idea ‘as area containing water in the dry season can be considered as a perennial water body’ was accepted and highest priority was made to select dry season images from the available images in the CEGIS’s achieves.

Water bodies were generally identified by their black or blackish signatures in IRS 1D panchromatic images and black, dark blue and bluish color in multi spectral images when images were viewed for digitization with the combination of Medium NIR, NIR and red spectral bands.

Using on screen-digitization all required features were extracted from IRS 1D panchromatic images with support from the resolution merged images and the enhanced images for identification of the

High Resolution Panchromatic IRS

Images (Raw image)

Coarse Resolution LANDSAT/LISS Images

(Raw image)

High ResolutionPanchromatic IRS Images (Geo-referenced image)

Coarse ResolutionLANDSAT/LISS

Images (Geo-referenced image)

Geo-referencing

Resolution merge methods

Feature (waterbodies andriver) extraction

Resolution merged images

High Resolution Panchromatic IRS Images (Geo-referenced image)

Field Verification (GPS survey)

Final output

Methodology

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features. Expert knowledge is used for visual interpretation and identification of classes that is of interest. ArcView GIS for Windows was used to capture the data. The data extraction was done using the following guidelines.

2.7.1 Waterbodies

Naturally depressed areas filled with water were classified as water bodies. Haors, baors, beels, ghers and small lakes are considered as water bodies. Water bodies are generally located outside or near settlements. The irregular shapes of water bodies is the main criteria to separate it from ponds which have regular rectangular shapes in most cases. The exposed water in perennial water bodies presents a tonal contrast with the surrounding areas, but in some cases moist lands make it difficult to identify water bodies. In this study therefore, expert knowledge as well as an enhancement technique was used in confusing or complex cases. The technique is one of several ways of visual enhancement of features available in the ERDAS Imagine image processing software.

2.7.2 River & Khals

Water bodies with linear continuous and irregular shapes and a few bends in their path of travel were considered as Rivers and Khals. Due to the presence of water they usually shows clear contrast with surrounding areas which appears in darker tones. In this study both rivers/khals were digitized either as a single line or as a polygon. The specifications for polygons and lines are based on the width and are presented in Table 2.2

Table: 2.2: Specifications of digitizing grading of rivers and khals.

Code River/Khals width (m) Types

1 0 – 10 Single Line

2 10 – 25 Single Line

3 25 – 50 Double Line

4 50 – 100 Double Line

5 > 100 Double Line

2.8 Detail Pond Survey

Pond data were collected from selected unions using GPS survey and field survey form. The field survey form was designed in such a way so that important physical parameter can be collected from the field. The field staffs were also trained to collect the required quality information efficiently. Photograph of each surveyed pond data were also collected.

Following the ToR, thirty-five unions were selected for detailed pond survey after discussing with APSU/DPHE. Data preparation for pond survey has been done based on the BBS union and village database. From the survey data it has been found that 4 additional unions have been surveyed as per listed affected villages. Thus the total number of surveyed union became 39 instead of 35. Union boundary that followed for this study is BBS 1991 census data and DLRS mauza database, because 2001 census of BBS has not been published yet for all the unions or upazilas. Therefore, pond mapping has been done for 35 unions using the census information of 1991. Name of the additional unions with the arsenic affected villages were collected from field survey is presented in Table 2.3 Further Kadakati union under Assasuni upazila which has been newly created from Durgapur union and there is no union boundary of Kadakati union in 1991 BBS census and DLRS mouza map. Thus all the affected villages under Durgapur has been surveyed and incorporated in database as Durgapur / Kadakati union.

Methodology

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Table 2.3: Additional surveyed union

District Upazila Union Number of

affected village

Remarks

Comilla Daudkandi Majidpur 1

Comilla Homna Chanderchar Purba 16

Comilla Laksam Bagmara 1

Comilla Laksam Ward No-02 1

Figure 2.6: Methodology for detailed pond survey

Field data collection using GPS and pond photograph using digital camera

Quality control of field data collection

Field Preparation Field form development

Field data preparation

Selection of unions for detail pond surveyPercentage of Arsenic contaminated

tubewell Demographic data

Data preparation and upload into database system

Data entry and processing

GPS Training to field staff Preparation of Field Maps

Methodology

19

2.8.1 Methodology of pond survey

Detailed pond survey were carried out for 35 unions following (i) Selection of unions for pond survey, (ii) Field preparation which included field form development and data preparation, (iii) GPS training to the field staff, (iv) Field data collection and quality checking, (v) data entry and processing and (vi) Data preparation and uploading into the database system. Those steps were presented in a flow diagram and have been shown in Figure 2.3. During the GPS survey of pond some essential physical parameters of pond were collected are:

(i) Physical size, shape and color of water (ii) Water availability of the ponds (During dry and wet season) (iii) Vegetation coverage surrounding the pond (iv) Dry season water depth and quality of water (v) Usage of the ponds (drinkable, washable, other domestic use) (vi) Detailed physical quality of water in the ponds (e.g. algae, presence of water hyacinth

etc.)

2.9 Data processing and editing

After collection of all the pond and surface water (river and water bodies) data were then cleaned to make them error free. The waterbodies and river data layer were cleaned using the field information and satellite images. Senior GIS professionals also checked the captured data. The pond data were also checked and edited through visualization interpretation.

2.10 Database preparation

After checking and cleaning of water bodies, river and pond data, all the data were processed. All the attribute data were organized and kept in MS Access. Further, the spatial data of waterbodies and river were kept in Arcview Shape file format in GIS platform. A database has been developed for this study comprises with all the attributes and GIS data which has been presented in Figure 2.7.

2.10.1 Water bodies

Earlier it has been mentioned that union wise waterbodies data were created in Arcview and ArcGIS platform. This steps followed to create waterbodies database are (i) clipping the GIS shapefile by union boundary, (ii) assigning the unique user identifcation number (id) for each waterbodies of the particular union and (iii) assigning the name of beel/baor where these were known from secondary sources into Arcview and ArcGIS environment. Thus all the attributes were incorporated to waterbodies for further data analysis. The attributes of waterbodies data are presented in Table 2.4.

Table 2.4: Attribute of waterbodies data

Item name Description Remarks ID Unique identification number District Name of district Thana Name of upazila Union Name of union Mauza Name of mauza WbsName Name of waterbodies If available Area_sqm Area in square meter Remarks Any comments

Methodology

20

2.10.2 River

The river data has also organized as union wise and available attribute information was incorporated. The river database was created following the same process as the waterbodies data. It can be noted that river data were categorized as two distinct classes are (i) river with double line (polygon) are those have width greater than 25 meter and (ii) river line those have width less than 25 meter. The river with double line was further categorized into three class (i) river width 25 – 50 meter, (ii) river 50 – 100 meter width and (iii) river width greater than 100 meter. One of the important attributes added to this river class is water availability. The attribute information of river with double line (polygon) has been presented in Table 2.5

Table 2.5: Attribute of waterbody data

Item name Description Remarks ID Unique ID NWRD_ID NWRD river ID ID given by National Water

Resources Database Width_code River width code 3 – River width 25m – 50m

4 – River width 50m – 100m 5 – River width > 100m

RivName River name Area_sqm River segment area in sqm Water Water availability 0 – Water

1 – Char (for big river) 2 - Seasonal dryness / char

Remarks Any special information

River single line has also been categorized into two category are (i) River width < 15 meter and (ii) River width 15 – 25 meter. The attribute information of river with double line (polygon) has been presented in Table 2.6.

Table 2.6: Attribute of River data

Item name Description Remarks

ID Unique ID

NWRD_ID NWRD river ID ID given by National Water Resources Database

Width_Code River width code 1 – River width < 15m

2 – River width 15m – 25m

RivName River name

Length River segment length in m

Len_km River segment length in km

Water Water availability 0 – Water

1 – Char (for big river)

2 - Seasonal dryness / char

Methodology

21

Figure 2.7: The database system of the project.

2.10.3 Pond

The pond data were collected from the field using the questionnaire form. A user friendly pond data entry frame work has been developed under the software development of the current project. Thus all the collected data were entered into the software and quality checking and editing were done in the field. The attribute information including GPS reading of ponds were then exported to dbf format. Then union wise dbf file were imported into Pathfinder (GPS data conversion system) software to make union wise GIS or shape file. The attributes table of pond data is presented in Table 2.7

Table 2.7: Attribute of pond data

Field name Field description Remarks PID Village wise pond id PondID Mouza wise pond identification number InvName Name of the interviewer SDate Survey date VillageID Unique village identification number OwnerName Pond owner name OwnerFatherName Father’s name RespName Responder name P_Len_F Pond length in feet P_Width_F Pond size width in feet P_Depth_F Pond size depth in feet

GIS data (GIS: ArcView, ArcGIS)

Other (Document, Photograph and

maps etc.)

Tabular data (MS Access)

Perennial surface water sources database

Photographs Report Maps

Mouza and Village data Percentage of Arsenic contaminated tubewell

Demographic data

Pond location Water bodies, river,

Administrative boundary

Waterbodies and River data (from satellite images)

Detailed pond database (from field survey)

Methodology

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Field name Field description Remarks Cur_Depth_F Current water depth Feb_Depth_F Dry season water depth Use_Drink Water use for drinking use_Fish Water use for fish culture Use_Irri Water use for irrigation Use_Bath Water use for bath wash Use_Not Water not used VegCovarage Percentage of Vegetation coverage VT_Water_Hy Vegetation type (water hyacinth) VT_Algae Vegetation type ( algae) VT_Others Vegetation type (other) WaterColor Color of the pond water WaterOdour Odor of pond water Remarks Any comment for the pond GPS Accuracy Accuracy of the GPS * GPS reading Latitude and longitude for the pond

2.11 Map preparation and tools development

The major goals of this project is to identify perennial surface water sources and production of union wise surface water source maps. Thus all the data were organized as union wise to produce maps. Union wise surface water sources maps were prepared in Arcview GIS environment. An avenue script was also developed for automated and speedy map production. All the maps were converted into graphics (JPEG) format for further usage and kept into the database. Further, user friendly software was developed to print the maps. The software has six components (i) Data entry module, (ii) Data Explorer, (iii) Export (iv) Query and (v) Analysis and (vi) Map Browser. The description of the software development is given in Chapter 5.

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Chapter 3

Detailed Pond Survey

3.1 Introduction

The main objective of the pond survey was to collect GPS coordinates of ponds and to record its essential physical parameters to develop a detailed pond database. The pond database will be used to identify potential ponds for alternative source of safe drinking water in highly arsenic affected unions. The methodology of pond survey has already described through a flow diagram in 2.4 of Chapter 2. Further brief descriptions of pond survey activities are given in this chapter.

3.2 Field of office and team formation

Thirty-five unions in different districts were selected for detailed pond survey. The selected unions were categorized into two main zones (i) Greater Comilla zone which covers Comilla and Sylhet districts and (ii) greater Khulna zone which include Khulna, Jessore and Faridpur districts. In this regard two field offices were hired at Comilla and Khulna to conduct and monitor field survey activities. Two field survey teams were formed to collect field data from these zones. Each team comprised of seven GPS operators led by a field supervisor. Two field supervisors were engaged for smooth operation of the field activities. Beside these some scattered unions those located at Pabna, Serajganj and Rangpur district were also surveyed through other field surveyors. The location of surveyed union has been shown earlier in Figure 1.3.

3.3 Field data and map preparation

Union wise base maps including satellite image showing the location of village (settlement pattern) were produced for 35 unions. In all these base maps administrative boundaries (upazila, union, mauza), rivers, roads (national, regional, feeder roads, rural roads) and some important locations of the area were also included. The field base map was very useful for the identification of villages, ways of travel within the area, planning of site logging and relevant management at the field offices. The sample of field maps shown in figures 3.1, 3.2 and 3.3 which were produced for the execution of pond survey activities.

3.1 Field form preparation

For the execution of fieldwork of detailed pond survey, a field form was designed in such way so that essential physical parameters of pond can be collected. The important parameters of field questionnaire were: (i) Identification of Pond owner (ii) Location of pond, (ii) Pond size (length, width and depth), (iii) present water depth, (iv) dry season water depth (i.e. possible lowest depth), (v) usage of the pond, (vi) vegetation coverage of pond, (vii) water color, (viii) GPS reading (latitude, longitude and accuracy) and (ix) remarks etc. The pond survey form is presented in Annex – A.

Detailed Pond Survey

24

Figure 3.1: Sample of base map for detail pond survey

Detailed Pond Survey

25

Figure 3.2: Sample union base (without image) map for pond survey

Detailed Pond Survey

26

Figure 3.3: Sample union base (with image) map for pond survey

3.4 GPS Training for field staffs

A short training course on “use GPS for field data collection” was arranged for field staffs. The training contained the concept of GPS, application of GPS and operation of GPS for field data collection. Field staffs were advised to properly collect data and maintain quality for better output.

Detailed Pond Survey

27

Earlier it has already been mentioned different types of handheld GPS were used in the field and the accuracy of these GPS are between 8 to 15 meters.

3.5 Field survey and quality control

All the field staffs were trained on completing the field form during the GPS training. Field staffs were properly guided and instructed by field supervisors, field coordinators and the project leader. During data collection field operators were visited pond areas physically to observe the current status of ponds and to get more information through interviews of pond owners or adjacent residents of pond.

3.6 Quality control of field data collection system

The quality control of the field data collection was done through very close monitoring of field activities. Different measures were taken to execute quality control:

• Field supervisors, field coordinators and project leader closely monitored the field work

• The field maps and supplied affected village list were followed properly

• Regular discussion meetings were arranged

• High accuracy levels were maintained

• Local field guides were also hired in necessary cases for properly identify village and pond location

• Discussions over cell phones were conducted to instantly solve problems during data collection.

3.7 Field data entry and processing

The field data that was collected and recorded in the field form and finally the hard copy of data were digitized in the computers. The software is developed with data entry interfaces to digitize the field level information. The developed data entry interfaces are given in Figures 3.4. Field data were updated every day after field works. During entry, some parameters were automatically entered using lookup tables with interface such as location (Upazila, union, mauza etc). All other parameters were then manually entered into the data entry software. Quality control of the data entry was done through random checking and graphical and visual interpretation. For data consistency, the names of administrative boundaries (union, mauza, etc.) were checked with BBS administrative names. Furthermore, all attribute data were processed in MS Access database and spatial data were processed in ArcView and ArcGIS.

Detailed Pond Survey

28

Figure 3.4: Pond survey data entry interface

3.8 Field observation

Field staffs were instructed to note down the people’s perceptions, social approaches on usage of surface water as an alternate drinking water in the affected areas and any other important observations. Information and observation of the peoples’ were analyzed and placed in Annex-D. The observation reports contain the geographical attribute of specific mauzas or unions, problems and severity of arsenic contamination, impact on that area and field staff’s own observation including people’s perceptions and demand.

29

Chapter 4

Data Analysis

4.1 Introduction

After processing and preparation of waterbodies, river and pond data, different analyses were done. The data analysis includes summary of river, waterbodies, pond data and as well as tabular information of physical parameters of pond data. Percentage of accuracy of waterbodies extraction from satellite images has also been incorporated.

4.2 Analysis of waterbodies and river data

The most important perennial surface waters are rivers and waterbodies. Therefore it is crucially important to know the potential number of perennial waterbodies and their area under each arsenic affected union. Summary information of these waterbodies and river including accuracy assessment is briefly discussed in this chapter.

4.2.1 Accuracy assessment of waterbodies and rivers

Perennial water bodies have been digitized from IRS panchromatic images and categorized into two classes, perennial and uncertain classes (perennial/seasonal). Multi spectral resolution merged images, SoB topo sheets and field validation data were used to guide the digitizing quality. Primarily the resolution-merged images and SoB topo sheets were used for defining waterbodies as seasonal or perennial. Finally survey for field validation was conducted for two fold purposes: i) to check the perennial water bodies, which were delineated from images, and ii)to define uncertain classes like perennial, seasonal or other. The summary of statistics for field validation data is presented in Table 4.1. The percentage of identified waterbodies as perennial during digitization is 87%. Remaining 13% is seasonal water bodies. From an image it can identify water bodies but difficult to define whether the waterbody is seasonal or perennial.

Table 4.1: Summery table of digitization accuracy (in percentage):

Types of waterbodies

Perennial

(from Image)

Perennial

(from Field)

Accuracy

Perennial 94 82 87

Rivers and khals were also digitized from IRS panchromatic images with the help of resolution merged multi spectral images. To maintain the digitization quality, delineated rivers and khals were further checked with respect to NWRD details river database, information of water development board (BWDB, 2005), topographic maps in associated with coastal area maps (Finn Map) and Integrated Coastal Resources Database (ICRD). Field verification for river and khals were conducted during the field validation for surface water and information from field also incorporated. It can be mentioned that accuracy level of river and khals were more than 90% with respect to the reference information as well as field validation data.

Data Analysis

30

4.2.2 Summary of waterbodies

Attributes of waterbodies data has been analyzed using MS Access to generate summary information. From the analyzed data a summary table of waterbodies containing the numbers and area were produced and is presented in Table 4.2. It has been found that a total of 723 perennial waterbodies, 159 semi perennial/seasonal waterbodies exist in the arsenic affected unions. Semi perennial waterbodies are those where water are being available up to mid of dry season (end of January to first week of February). A further 339 waterbodies (those are shrimp gher) were also found in the highly arsenic affected unions in the south western districts.

Table 4.2:District wise number and areas of waterbodies for arsenic affected unions

Perennial Seasonal* Gher District Number Area (ha) Number Area (ha) Number Area (ha)

Bagerhat 9 125.79 2 122.62 24 2994.52 Barisal 3 3.03 4 16.99 Bogra 12 44.61 Brahamanbaria 27 371.97 7 109.53 Chandpur 28 75.10 2 3.51 Chittagong 3 6.04 Chuadanga 20 279.53 1 4.22 Comilla 27 103.48 7 70.48 Dhaka 18 132.82 Faridpur 48 429.80 6 14.74 Feni 1 9.68 Gaibandha 7 77.59 3 52.48 Gopalganj 11 434.33 5 147.35 Jessore 77 1346.09 5 71.03 28 1295.36 Jhalokati 3 6.53 1 5.50 Jhenaidah 10 18.95 Khulna 6 250.78 2 165.77 145 22016.39 Kishoreganj 24 455.82 13 86.29 Kurigram 3 2.58 Kushtia 9 44.09 5 37.26 Lakshmipur 11 46.19 Madaripur 9 339.81 1 79.38 Magura 10 108.36 Manikganj 42 235.77 3 15.63 Maulvibazar 8 261.83 3 223.44 Munshiganj 1 3.50 Mymensingh 5 18.46 Narail 31 213.56 28 416.39 Narsingdi 13 181.98 Natore 1 21.49 Nawabganj 38 385.43 2 66.27 Netrakona 33 421.73 79 672.44 Noakhali 2 15.20 Pabna 45 207.28 16 97.89 Pirojpur 1 7.93 Rajshahi 63 635.45 4 11.34 Rangpur 2 3.72

Data Analysis

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Perennial Seasonal* Gher District Number Area (ha) Number Area (ha) Number Area (ha)

Satkhira 11 126.69 2 52.77 339 25559.60 Sirajganj 6 6.95 3 2.15 Sunamganj 164 3037.87 17 375.73 Sylhet 353 2399.66 34 369.28 Tangail 14 41.71 2 5.31 Total 732 7289.62 159 1653.18 339 25559.60

Seasonal*: water is being available up to mid of dry season (end of January to beginning of February)

4.2.3 Summary of river data

Attribute data of waterbodies was analyzed using MS Access to generate summary information. From the analyzed data a summary table of waterbodies containing the numbers and area were produced and is presented in Table 4.3. The table shows the information of the rivers in different districts according to the classified width and total length. Total length of the river stretch is observed to be around 11,340 km. Around 2500 numbers of river has width more than 100 meter, which could be a potential source of surface water.

Table 4.3: District wise river information for affected unions River width class District

Below 15m 15m - 25m 25m - 50m 50m - 100m Above 100m Total length (km)

Bagerhat 631.4 206.2 20.3 414.0 1271.9 Barisal 87.8 794.7 48.6 0.3 359.4 1290.8 Bogra 4.2 3.1 0.0 7.3 Brahamanbaria 33.2 31.4 0.0 64.6 Chandpur 72.5 201.3 4.2 1.6 142.0 421.5 Chittagong 11.7 15.2 29.0 55.9 Chuadanga 69.1 3.1 3.8 75.9 Comilla 194.1 275.8 9.3 57.2 536.5 Dhaka 10.8 46.3 0.0 57.2 Faridpur 45.5 258.8 3.8 308.0 Feni 6.6 146.4 1.1 100.1 254.1 Gaibandha 12.8 1.3 0.0 14.1 Gopalganj 38.9 332.1 38.1 69.7 478.8 Jessore 20.2 76.5 69.5 9.9 114.7 290.8 Jhalokati 37.8 88.7 0.2 16.2 142.9 Khulna 4.7 379.6 139.4 37.1 385.3 946.1 Kishoreganj 37.5 23.0 1.0 61.5 Kurigram 15.5 0.0 15.5 Kushtia 58.1 13.7 4.8 76.6 Lakshmipur 5.5 216.5 18.1 2.9 210.0 453.0 Madaripur 67.2 343.6 1.0 0.5 30.8 443.0 Magura 75.6 4.5 0.1 80.1 Manikganj 53.2 65.8 0.0 119.0 Maulvibazar 36.0 31.2 0.0 67.2 Munshiganj 70.1 85.1 12.2 167.3 Mymensingh 9.7 0.0 9.7 Narail 7.3 75.8 24.0 12.8 64.7 184.6 Narayanganj 24.4 8.6 0.1 33.1 Narsingdi 18.1 0.0 18.1

Data Analysis

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River width class District Below 15m 15m - 25m 25m - 50m 50m - 100m Above 100m

Total length (km)

Natore 10.2 0.0 10.2 Nawabganj 28.5 9.2 0.0 37.7 Netrakona 79.7 92.9 0.7 173.3 Noakhali 9.9 315.4 38.9 56.1 420.3 Pabna 89.8 44.5 0.3 134.6 Pirojpur 204.8 31.9 4.5 56.2 297.3 Rajbari 2.4 6.4 0.0 8.8 Rajshahi 9.8 5.2 6.3 21.3 Rangpur 16.6 0.0 16.6 Satkhira 30.8 493.7 208.9 40.5 310.7 1084.6 Shariatpur 64.7 136.0 0.4 8.0 209.1 Sherpur 29.2 29.2 Sirajganj 11.6 10.3 0.0 21.8 Sunamganj 169.7 120.3 0.4 290.4 Sylhet 425.0 207.7 9.6 642.3

4.3 Analysis of pond data

After processing and preparing of field data, pond data was analyzed. Analysis of pond data included summary of pond survey, average pond size, water availability, usage of ponds, vegetation types and coverage, physical water quality (color and odor) and screening pond for identification of potential ponds for safe water option technology adaptation.

4.3.1 Summary of pond data

More than 6000 ponds were surveyed under 39 unions and union wise number of pond is presented in table 4.4. It has been observed that more than 50% union have a large number of ponds and the number ranges from 200 – 445. Highest number of pond (445) has been observed at Kadla union under Kachua upazila of Chandpur district and the minimum number of ponds (3) has been found at Tambulpur union at Pirgachha upazila of Rangpur district.

Table 4.4: Union wise summary of pond surveyed

District Upazila Union Number of ponds Bagerhat Mollahat Gaola 311 Bagerhat Morrelganj Daibagnyahati 81 Brahamanbaria Banchharampur Salimabad 41 Brahamanbaria Nabinagar Shibpur 163 Chandpur Hajiganj Dakshin Rajargaon 346 Chandpur Kachua Kadla 445 Chandpur Kachua Pathair Paschim 194 Chandpur Matlab Durgapur 248 Chandpur Shahrasti Uttar Meher 367 Comilla Chandina Maijkhara 421 Comilla Daudkandi Majidpur 3 Comilla Homna Chanderchar Paschim 37 Comilla Homna Chanderchar Purba 52 Comilla Laksam Bagmara 8

Data Analysis

33

District Upazila Union Number of ponds Comilla Laksam Paschimgaon 274 Comilla Laksam PSA (Ward No.2) 7 Comilla Muradnagar Sreekail 257 Gopalganj Gopalganj Sadar Suktail 105 Gopalganj Kashiani Parulia 104 Gopalganj Kotali Para Radhaganj 423 Gopalganj Tungi Para Patgati 202 Jessore Abhaynagar Sreedharpur 45 Khulna Paikgachha Kapilmuni 237 Khulna Terokhada Sachiadah 115 Madaripur Rajoir Badar Pasha 287 Narail Kalia Kalabaria 178 Pabna Bera Masundia 13 Pabna Santhia Karanja 27 Pirojpur Mathbaria Betmore Rajpara 16 Pirojpur Pirojpur Sadar Sikdar Mallik 16 Rangpur Pirgachha Tambulpur 3 Satkhira Assasuni Durgapur 277 Satkhira Kalaroa Keralkata 294 Satkhira Kaliganj Champaphul 38 Satkhira Tala Khalishkhali 280 Sirajganj Royganj Chandaikona 9 Sylhet Balaganj Paschim Pailanpur 50 Sylhet Kanaighat Purba Dighirpar 54 Total 6382

4.3.2 Pond size

Pond size i.e. area has been calculated using the length and width parameter from surveyed information. The area has then classified in different categories and graphically presented in Figure 4.1. It has been found that 32% of the total surveyed ponds have an area under 500 – 1000 m2 area and 2% of total surveyed pond has under 3000 – 4000 m2 area. Another major class is 300 – 500 m2, which is around 19%. Further from Table 4.5 it has been found that more than 2000 pond has fallen under 500 – 1000 m2 area class.

Figure 4.1: Statistics of pond area of surveyed pond.

Summery of pond area for surveyed unions

15%

19%

32%

23%

6% 2% 3%

0-300300-500500-10001000-20002000-30003000-4000> 4000

Area class (m2)

Data Analysis

34

Table 4.5: Union wise pond area class

Pond Area Class (m2) District Upazila Union No. of Pond

C1 C2 C3 C4 C5 C6 C7

Bagerhat Bagerhat Sadar Kara Para 354 195 40 72 32 10 3 2

Bagerhat Mollahat Gaola 311 159 67 67 12 3 3

Bagerhat Morrelganj Daibagnyahati 81 49 18 9 4 1

Brahamanbaria Banchharampur Salimabad 41 1 11 23 6

Brahamanbaria Nabinagar Shibpur 163 1 14 43 57 24 7 17

Chandpur Hajiganj Dakshin Rajargaon 346 7 57 132 104 23 13 10

Chandpur Kachua Kadla 445 9 63 222 121 22 2 6

Chandpur Kachua Pathair Paschim 194 8 68 87 18 4 9

Chandpur Matlab Durgapur 248 14 59 94 74 6 1

Chandpur Shahrasti Uttar Meher 367 59 128 136 26 7 11

Comilla Chandina Maijkhara 421 2 52 136 161 51 4 15

Comilla Daudkandi Majidpur 3 1 2

Comilla Homna Chanderchar Paschim 37 3 7 15 11 1

Comilla Homna Chanderchar Purba 52 1 11 14 21 4 1

Comilla Laksam Bagmara 8 1 1 1 3 1 1

Comilla Laksam Paschimgaon 274 4 25 60 80 52 25 28

Comilla Laksam WARD NO-02 7 4 3

Comilla Muradnagar Sreekail 257 1 13 76 120 28 8 11

Gopalganj Gopalganj Sadar Suktail 105 33 33 16 12 6 1 4

Gopalganj Kashiani Parulia 104 11 38 34 16 4 1

Gopalganj Kotali Para Radhaganj 423 25 51 142 121 45 10 29

Gopalganj Tungi Para Patgati 202 83 54 42 14 5 1 3

Jessore Abhaynagar Sreedharpur 45 11 9 13 7 2 3

Khulna Paikgachha Kapilmuni 237 11 74 107 41 2 2

Khulna Terokhada Sachiadah 115 54 31 22 7 1

Madaripur Rajoir Badar Pasha 287 36 74 114 47 10 2 4

Narail Kalia Kalabaria 178 42 55 55 19 5 1 1

Pabna Bera Masundia 13 7 2 2 1 1

Pabna Santhia Karanja 27 8 4 13 2

Pirojpur Mathbaria Betmore Rajpara 16 2 7 3 2 1 1

Pirojpur Pirojpur Sadar Sikdar Mallik 16 10 5 1

Rangpur Pirgachha Tambulpur 3 3

Satkhira Assasuni Durgapur 277 38 92 112 31 2 2

Data Analysis

35

Pond Area Class (m2) District Upazila Union No. of Pond

C1 C2 C3 C4 C5 C6 C7

Satkhira Kalaroa Keralkata 294 15 54 122 77 17 8 1

Satkhira Kaliganj Champaphul 38 9 11 13 5

Satkhira Tala Khalishkhali 280 84 66 89 29 10 2

Sirajganj Royganj Chandaikona 9 1 1 4 1 2

Sylhet Balaganj Paschim Pailanpur 50 17 16 13 2 1 1

Sylhet Kanaighat Purba Dighirpar 54 18 25 11

Total 6382 965 1211 2092 1465 379 110 160

% of Total 100 15 19 33 23 6 2 3

C1= 0- 300, C2 = 300 – 500, C3 = 500 – 1000, C4 = 1000 – 2000, C5 = 2000 – 3000, C6 = 3000 – 4000, C7 > 4000

4.3.3 Water availability from pond data

Water availability has been estimated from the available data collected from the field survey in terms of water volume. The availability is calculated using two parameters, which are water depth and the pond area. Two types of water depth collected during the field survey: (i) present or current water depth which means the depth of water in the pond at surveyed date and (ii) dry season water depth which means anticipated minimum water depth may exist during peak dry season (March – April). The present water depth has not been considered to calculate the water availability because of the pond survey is executed at different date starting from first week of December 2005 to second week of February 2006. Therefore, the dry season water depth of the pond has been used to calculate the water availability. The dry season water volume was calculated using the following formula: Wav = Pa * Dwd Calculated dry season water volume is further classified in different categories and presented in Figure 4.2. It has been found that, 33% of the ponds falls under 1000 – 3000 m3 volume class and which is the major class and the number of pond is approximately 2134. The minimum number of pond is observed within the class of ‘> 5000 m3‘ which is less 5% of the total surveyed pond. The number of pond under this class is around 240.

Figure 4.2: Dry season water volume of ponds for surveyed unions.

Further from Table 4.6, it has been found that 33% of the total surveyed pond has dry season water volume within 1000 – 3000 m3 and 26% of total surveyed pond water volume within 500 – 1000 m3

which indicates that about 60% of the total surveyed pond water volume is within 500 – 3000 m3. The rest 40% of the pond has the dry season water volume within a range of 70 – 500 m3 which has

Dry season water volume of pond for surveyed unions

4% 14%

13%

26%

33%

6% 4%

0-100100-300300-500500-10001000-30003000-5000> 5000

Volume class (m3)

Data Analysis

36

substantiall potential for adoption of alternate safe water technology. Only 4% of the total surveyed pond has the dry season water volume within 0 – 1000 m3. From data analysis results (Table 4.6) it has also been found that that several unions namely Dakshin Rajargaon and Uttar Meher under Chandpur district, Maijkhara under Comilla district and Radhaganj under Gopalganj district have more than 200 ponds and water volume ranges from 500 – 3000 m3 water availability.

4.3.4 Dry season water depth

Ponds under different water depth class has also been analyzed and presented in Table 4.7and figure 4.3. It has been observed that (table 4.7) 57% of the total surveyed pond has the dry season water depth within 1-2 meter, 38% of the total surveyed pond have the water depth within 0-1 meter, 5% of the total surveyed pond has the water depth within 2-3 meter and only 1% of the total surveyed pond has the water depth greater than 3 meter.

4.3.5 Usages of the ponds

Data on usage of the pond has been analyzed from the field data and presented in Table 4.8. It may be noted that complex matrices (with 14 combination) has been developed through mathematical combination of analysis because each large number of ponds have multiple usages. From Table 4.8 it has been observed that more than 50 % of total ponds have been used for fish + bath wash. And it has been observed that around 40% of the total surveyed ponds have been used for drink + fish + bath wash and 3% of total surveyed ponds have been used for only bath wash. Less than 1% of the surveyed pond has been used for only drinking, 3% of the surveyed pond has been used for only fish culture (commercially) and 3% of the surveyed pond has been used for only bath/ wash. A very few percent (<1%) of surveyed ponds have been used for both drink/cooking and fish culture. A few percent (2%) of the surveyed pond has been used for both drink and bath/wash.

Table 4.6: Union wise number of pond under different water volume class

Dry season water volume class (m3)

C1 C2 C3 C4 C5 C6 C7

District Upazila Union Total Pond

(No of pond)

Bagerhat Bagerhat Sadar Kara Para 354 66 159 37 46 40 4 2

Bagerhat Mollahat Gaola 311 45 135 57 52 18 3 1

Bagerhat Morrelganj Daibagnyahati 81 13 44 12 8 2 1 1

Brahamanbaria Banchharampur Salimabad 41 5 16 9 11

Brahamanbaria Nabinagar Shibpur 163 1 3 25 79 29 26

Chandpur Hajiganj Dakshin Rajargaon 346 4 19 104 175 25 19

Chandpur Kachua Kadla 445 2 26 135 248 21 13

Chandpur Kachua Pathair Paschim 194 6 40 115 21 12

Chandpur Matlab Durgapur 248 7 30 104 99 7 1

Chandpur Shahrasti Uttar Meher 367 1 20 104 185 41 16

Comilla Chandina Maijkhara 421 2 26 118 214 45 16

Comilla Daudkandi Majidpur 3 1 2

Comilla Homna Chanderchar Paschim 37 4 4 15 14

Comilla Homna Chanderchar Purba 52 1 5 19 23 3 1

Data Analysis

37

Dry season water volume class (m3)

C1 C2 C3 C4 C5 C6 C7

District Upazila Union Total Pond

(No of pond)

Comilla Laksam Bagmara 8 1 3 2 1 1

Comilla Laksam Paschimgaon 274 6 9 42 122 54 41

Comilla Laksam WARD NO-02 7 4 3

Comilla Muradnagar Sreekail 257 7 44 160 31 15

Gopalganj Gopalganj Sadar Suktail 105 9 33 21 20 14 4 4

Gopalganj Kashiani Parulia 104 12 34 35 17 3 3

Gopalganj Kotali Para Radhaganj 423 4 24 37 117 163 40 38

Gopalganj Tungi Para Patgati 202 4 60 46 57 25 4 6

Jessore Abhaynagar Sreedharpur 45 4 8 6 12 12 1 2

Khulna Paikgachha Kapilmuni 237 1 33 67 80 47 6 3

Khulna Terokhada Sachiadah 115 26 48 13 15 12 1

Madaripur Rajoir Badar Pasha 287 7 57 57 86 63 10 7

Narail Kalia Kalabaria 178 6 68 35 43 18 7 1

Pabna Bera Masundia 13 10 1 2

Pabna Santhia Karanja 27 12 3 4 5 3

Pirojpur Mathbaria Betmore Rajpara 16 4 4 4 3 1

Pirojpur Pirojpur Sadar Sikdar Mallik 16 1 8 5 2

Rangpur Pirgachha Tambulpur 3 1 2

Satkhira Assasuni Durgapur 277 2 35 56 91 84 8 1

Satkhira Kalaroa Keralkata 294 34 77 76 95 9 3

Satkhira Kaliganj Champaphul 38 1 6 7 14 8 2

Satkhira Tala Khalishkhali 280 39 76 48 66 41 7 3

Sirajganj Royganj Chandaikona 9 1 1 1 2 2 1 1

Sylhet Balaganj Paschim Pailanpur 50 11 12 15 9 1 2

Sylhet Kanaighat Purba Dighirpar 54 10 18 20 6

Total 6382 252 905 828 1633 2134 390 240

% of Total 100 4 14 13 26 33 6 4

C1 = 0-100, C2=100-300, C3=300-500, C4=500-1000, C5=1000-3000, C6=3000-5000, C7=>5000

Data Analysis

38

Dry season water depth

38%

56%

1%5%

0-1

1-2

2-3

>3

Figure 4.3: Dry season water depth (meter) at surveyed ponds

Table 4.7: Union wise number of pond under different dry season water depth class District Upazila Union Pond

surveyed Ponds under different water depth

( 0-1) m (1-2) m (2-3) m >3 m Bagerhat Bagerhat Sadar Kara Para 354 299 54 1 Bagerhat Mollahat Gaola 311 222 86 3 Bagerhat Morrelganj Daibagnyahati 81 68 11 2 Brahamanbaria Banchharampur Salimabad 41 25 14 2 Brahamanbaria Nabinagar Shibpur 163 5 125 30 3 Chandpur Hajiganj Dakshin Rajargaon 346 30 287 27 2 Chandpur Kachua Kadla 445 40 370 31 4 Chandpur Kachua Pathair Paschim 194 28 154 12 Chandpur Matlab Durgapur 248 63 172 13 Chandpur Shahrasti Uttar Meher 367 32 284 45 6 Comilla Chandina Maijkhara 421 97 321 3 Comilla Daudkandi Majidpur 3 2 1 Comilla Homna Chanderchar Paschim 37 17 19 1 Comilla Homna Chanderchar Purba 52 16 32 4 Comilla Laksam Bagmara 8 3 5 Comilla Laksam Paschimgaon 274 55 186 21 12 Comilla Laksam WARD NO-02 7 7 Comilla Muradnagar Sreekail 257 29 221 7 Gopalganj Gopalganj Sadar Suktail 105 63 42 Gopalganj Kashiani Parulia 104 48 52 3 1 Gopalganj Kotali Para Radhaganj 423 126 273 24 Gopalganj Tungi Para Patgati 202 71 104 25 2 Jessore Abhaynagar Sreedharpur 45 25 18 2 Khulna Paikgachha Kapilmuni 237 134 93 10 Khulna Terokhada Sachiadah 115 89 25 1 Madaripur Rajoir Badar Pasha 287 162 121 4 Narail Kalia Kalabaria 178 135 38 5 Pabna Bera Masundia 13 13 Pabna Santhia Karanja 27 23 4 Pirojpur Mathbaria Betmore Rajpara 16 9 5 2 Pirojpur Pirojpur Sadar Sikdar Mallik 16 6 10 Rangpur Pirgachha Tambulpur 3 3 Satkhira Assasuni Durgapur 277 53 202 18 4 Satkhira Kalaroa Keralkata 294 217 68 8 1 Satkhira Kaliganj Champaphul 38 5 27 4 2 Satkhira Tala Khalishkhali 280 177 99 3 1 Sirajganj Royganj Chandaikona 9 4 4 1 Sylhet Balaganj Paschim Pailanpur 50 6 37 7 Sylhet Kanaighat Purba Dighirpar 54 9 42 3 Total 6382 2409 3613 319 41 % of Total 38 56 5 1

Water depth (m)

Data Analysis

39

Table 4.8: Summary result of uses of the pond

District Upazila Union Surveyed pond X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15

Bagerhat Bagerhat Sadar Kara Para 354 2 332 16 4

Bagerhat Mollahat Gaola 311 1 61 17 203 24 5

Bagerhat Morrelganj Daibagnyahati 81 1 69 11 0

Brahamanbaria Banchharampur Salimabad 41 1 16 23 1

Brahamanbaria Nabinagar Shibpur 163 1 1 1 3 7 2 148 0

Chandpur Hajiganj Dakshin Rajargaon 346 1 1 1 2 1 31 5 303 1

Chandpur Kachua Kadla 445 1 1 1 2 1 62 7 1 366 3

Chandpur Kachua Pathair Paschim 194 3 2 2 36 3 143 3 2

Chandpur Matlab Durgapur 248 1 4 1 2 32 3 204 1 0

Chandpur Shahrasti Uttar Meher 367 6 1 2 4 57 11 1 279 3 3

Comilla Chandina Maijkhara 421 2 1 81 15 320 2

Comilla Daudkandi Majidpur 3 3 0

Comilla Homna Chanderchar Paschim 37 1 1 35 0

Comilla Homna Chanderchar Purba 52 1 11 39 1

Comilla Laksam Bagmara 8 8 0

Comilla Laksam Paschimgaon 274 3 1 2 76 14 173 5 0

Comilla Laksam WARD NO-02 7 7 0

Comilla Muradnagar Sreekail 257 3 3 22 1 227 1

Gopalganj Gopalganj Sadar Suktail 105 3 99 1 1 1 0

Gopalganj Kashiani Parulia 104 2 32 3 65 2

Gopalganj Kotali Para Radhaganj 423 51 1 1 362 8

Gopalganj Tungi Para Patgati 202 6 14 11 7 150 7 7

Jessore Abhaynagar Sreedharpur 45 1 3 5 11 21 3 1

Khulna Paikgachha Kapilmuni 237 11 3 9 1 3 204 2 1 3

Khulna Terokhada Sachiadah 115 15 1 1 92 6

Madaripur Rajoir Badar Pasha 287 10 37 223 2 1 14

Narail Kalia Kalabaria 178 11 1 1 1 7 1 150 1 4 1

Pabna Bera Masundia 13 1 12 0

Pabna Santhia Karanja 27 1 1 14 11 0

Pirojpur Mathbaria Betmore Rajpara 16 1 1 1 8 4 1

Pirojpur Pirojpur Sadar Sikdar Mallik 16 1 3 2 10 0

Rangpur Pirgachha Tambulpur 3 2 1

Satkhira Assasuni Durgapur 277 1 19 6 9 1 219 1 1 18 2

Satkhira Kalaroa Keralkata 294 3 4 10 276 1

Satkhira Kaliganj Champaphul 38 38 0

Satkhira Tala Khalishkhali 280 2 4 1 269 3 1

Sirajganj Royganj Chandaikona 9 2 7 0

Sylhet Balaganj Paschim Pailanpur 50 5 4 1 6 4 29 1

Data Analysis

40

District Upazila Union Surveyed pond X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15

Sylhet Kanaighat Purba Dighirpar 54 1 11 4 38 0

Total 6382 25 174 3 172 31 2 102 3 3273 5 62 2 2439 17 72

% of Total 100 0 3 0 3 0 0 2 0 51 0 1 0 38 0 1

Note: X1= Only Drink, X2= Only Fish, X3= Only Irrigation, X4= Only Bath/Wash, X5= Only Drink+Fish, X6= Only Drink+Irrigation, X7= Only Drink+Bath/Wash, X8= Only Fish+Irrigation, X9= Only Fish+Bath/Wash, X10= Only Irrigation+Bath/Wash, X11= Only Drink+Fish+Irrigation+Bath/Wash, X12= Only Drink+Fish+Irrigation, X13= Only Drink+Fish+Bath/Wash, X14= Only Fish+Irrigation+Bath/Wash, X15= Not Use

4.3.6 Vegetation coverage of pond

In rural areas most of the ponds are covered with vegetation such as water hyacinth, algae, water grass and other bushes. The coverage of vegetation inside the pond was collected from the field. Further these data were analyzed and presented in Figure 4.4 and Table 4.9. It is found that 72% of the total surveyed pond falls under (0 – 25) % vegetation coverage, 16% under (25 –50) % vegetation coverage and 12% of the total surveyed ponds have more than 50% vegetation coverage. From table 4.7 it is observed that 4617 ponds out of 6382 have vegetation coverage within 25% limit range.

Further, data has also analyzed on vegetation type and presented in Table 4.10. It is observed (Table 4.10) 2% of the total surveyed pond has the vegetation type ‘water hyacinth + algae +other’, 9% of the total surveyed pond has the vegetation type only ‘water hyacinth’, 7% of the total surveyed pond has the vegetation type only ‘algae’ and substantial percentage which 47% of the total surveyed pond has the vegetation type ‘other’ category. The ‘other’ category of vegetation includes water grass, bushes and other tress etc.

Percentage of total surveyed pond uder different vegetation coverage category

72%

16%

12%

0-2525-50

> 50

Vegetataion Coverage (%)

Figure 4.4: Percentage of total surveyed pond under different vegetation category

Data Analysis

41

Table 4.9: Percentage % of vegetation coverage of pond

District Upazila Union Total. Pond 0-25 25-50 50-Above Bagerhat Bagerhat Sadar Kara Para 354 232 43 79 Bagerhat Mollahat Gaola 311 228 31 52 Bagerhat Morrelganj Daibagnyahati 81 48 14 19 Brahamanbaria Banchharampur Salimabad 41 29 9 3 Brahamanbaria Nabinagar Shibpur 163 142 19 2 Chandpur Hajiganj Dakshin Rajargaon 346 234 98 14 Chandpur Kachua Kadla 445 292 129 24 Chandpur Kachua Pathair Paschim 194 142 38 14 Chandpur Matlab Durgapur 248 191 41 16 Chandpur Shahrasti Uttar Meher 367 291 69 7 Comilla Chandina Maijkhara 421 324 83 14 Comilla Daudkandi Majidpur 3 3 Comilla Homna Chanderchar Paschim 37 29 5 3 Comilla Homna Chanderchar Purba 52 31 19 2 Comilla Laksam Bagmara 8 6 1 1 Comilla Laksam Paschimgaon 274 193 78 3 Comilla Laksam Ward no - 2 7 6 1 Comilla Muradnagar Sreekail 257 213 39 5 Gopalganj Gopalganj Sadar Suktail 105 65 9 31 Gopalganj Kashiani Parulia 104 32 18 54 Gopalganj Kotali Para Radhaganj 423 285 38 100 Gopalganj Tungi Para Patgati 202 122 28 52 Jessore Abhaynagar Sreedharpur 45 41 3 1 Khulna Paikgachha Kapilmuni 237 206 17 14 Khulna Terokhada Sachiadah 115 80 18 17 Madaripur Rajoir Badar Pasha 287 156 37 94 Narail Kalia Kalabaria 178 145 10 23 Pabna Bera Masundia 13 13 Pabna Santhia Karanja 27 25 1 1 Pirojpur Mathbaria Betmore Rajpara 16 10 5 1 Pirojpur Pirojpur Sadar Sikdar Mallik 16 14 2 Rangpur Pirgachha Tambulpur 3 2 1 Satkhira Assasuni Durgapur 277 224 27 26 Satkhira Kalaroa Keralkata 294 246 25 23 Satkhira Kaliganj Champaphul 38 32 2 4 Satkhira Tala Khalishkhali 280 193 27 60 Sirajganj Royganj Chandaikona 9 8 1 Sylhet Balaganj Paschim Pailanpur 50 36 9 5 Sylhet Kanaighat Purba Dighirpar 54 48 4 2 Total 6382 4617 997 768 % Total 72 16 12

Data Analysis

42

Table 4.10: Status of different type vegetation found in the pond

Pond surveyed Hyacinth + Algae +Other

Hyacinth Algae OtherDistrict Upazila Union

(number of ponds) Bagerhat Bagerhat Sadar Kara Para 354 2 17 29 128 Bagerhat Mollahat Gaola 311 50 50 55 Bagerhat Morrelganj Daibagnyahati 81 3 32 Brahamanbaria Banchharampur Salimabad 41 1 1 25 Brahamanbaria Nabinagar Shibpur 163 2 101 Chandpur Hajiganj Dakshin Rajargaon 346 16 1 2 225 Chandpur Kachua Kadla 445 16 1 1 312 Chandpur Kachua Pathair Paschim 194 8 2 3 118 Chandpur Matlab Durgapur 248 11 1 172 Chandpur Shahrasti Uttar Meher 367 10 10 4 221 Comilla Chandina Maijkhara 421 3 1 350 Comilla Daudkandi Majidpur 3 3 Comilla Homna Chanderchar Paschim 37 4 25 Comilla Homna Chanderchar Purba 52 1 1 41 Comilla Laksam Bagmara 8 7 Comilla Laksam Paschimgaon 274 2 8 7 175 Comilla Laksam WARD NO-02 7 2 2 Comilla Muradnagar Sreekail 257 8 2 1 171 Gopalganj Gopalganj Sadar Suktail 105 34 9 22 Gopalganj Kashiani Parulia 104 82 6 7 Gopalganj Kotali Para Radhaganj 423 1 37 135 113 Gopalganj Tungi Para Patgati 202 48 66 42 Jessore Abhaynagar Sreedharpur 45 3 6 25 Khulna Paikgachha Kapilmuni 237 3 31 10 85 Khulna Terokhada Sachiadah 115 1 8 5 53 Madaripur Rajoir Badar Pasha 287 3 135 16 66 Narail Kalia Kalabaria 178 3 26 10 54 Pabna Bera Masundia 13 2 Pabna Santhia Karanja 27 11 1 Pirojpur Mathbaria Betmore Rajpara 16 4 5 Pirojpur Pirojpur Sadar Sikdar Mallik 16 1 10 Rangpur Pirgachha Tambulpur 3 1 2 Satkhira Assasuni Durgapur 277 7 22 156 Satkhira Kalaroa Keralkata 294 2 20 59 27 Satkhira Kaliganj Champaphul 38 1 2 14 Satkhira Tala Khalishkhali 280 1 14 6 148 Sirajganj Royganj Chandaikona 9 1 5 1 Sylhet Balaganj Paschim Pailanpur 50 11 2 10 Sylhet Kanaighat Purba Dighirpar 54 1 1 24 Total 6382 112 560 460 3029 % of Total 2 9 7 47

Data Analysis

43

4.3.7 Physical water quality (color)

The preliminary assessment of the water quality is investigated mainly observing the color. The color of water is identified as good, bad and medium by eye estimation. The color may deteriorate further during the driest part of the season when the water volume reduces further in the month of April-May. The pond water colors were presented in Table 4.11.

Good quality of pond water has the color of ash, clean water and very clear. The Normal quality indicates the gray, brownish and some sort of green color. The bad quality indicates the black, muddy and deep reddish color.

Table 4.11: union wise pond water color information.

Water color District Upazila Union Surveyed Pond Bad Good Medium

Bagerhat Bagerhat Sadar Kara Para 354 7 199 148 Bagerhat Mollahat Gaola 311 20 117 174 Bagerhat Morrelganj Daibagnyahati 81 32 49 Brahamanbaria Banchharampur Salimabad 41 2 17 22 Brahamanbaria Nabinagar Shibpur 163 13 92 58 Chandpur Hajiganj Dakshin Rajargaon 346 43 118 185 Chandpur Kachua Kadla 445 19 137 289 Chandpur Kachua Pathair Paschim 194 1 80 113 Chandpur Matlab Durgapur 248 14 96 138 Chandpur Shahrasti Uttar Meher 367 26 127 214 Comilla Chandina Maijkhara 421 25 120 276 Comilla Daudkandi Majidpur 3 1 2 Comilla Homna Chanderchar Paschim 37 5 26 6 Comilla Homna Chanderchar Purba 52 8 17 27 Comilla Laksam Bagmara 8 1 7 Comilla Laksam Paschimgaon 274 28 94 152 Comilla Laksam WARD NO-02 7 2 5 Comilla Muradnagar Sreekail 257 12 169 76 Gopalganj Gopalganj Sadar Suktail 105 9 14 82 Gopalganj Kashiani Parulia 104 42 3 59 Gopalganj Kotali Para Radhaganj 423 50 118 255 Gopalganj Tungi Para Patgati 202 15 39 148 Jessore Abhaynagar Sreedharpur 45 2 13 30 Khulna Paikgachha Kapilmuni 237 15 77 145 Khulna Terokhada Sachiadah 115 5 31 79 Madaripur Rajoir Badar Pasha 287 31 15 241 Narail Kalia Kalabaria 178 6 30 142 Pabna Bera Masundia 13 6 7 Pabna Santhia Karanja 27 8 17 2 Pirojpur Mathbaria Betmore Rajpara 16 2 2 12 Pirojpur Pirojpur Sadar Sikdar Mallik 16 2 4 10 Rangpur Pirgachha Tambulpur 3 1 1 1 Satkhira Assasuni Durgapur 277 13 64 200 Satkhira Kalaroa Keralkata 294 11 96 187

Data Analysis

44

Water color District Upazila Union Surveyed Pond Bad Good Medium

Satkhira Kaliganj Champaphul 38 2 8 28 Satkhira Tala Khalishkhali 280 4 116 160 Sirajganj Royganj Chandaikona 9 9 Sylhet Balaganj Paschim Pailanpur 50 10 16 24 Sylhet Kanaighat Purba Dighirpar 54 1 24 29 Total 6382 461 2151 3770 % of Total 100 7 34 59

From color data analysis result (Table 4.11) it is observed that 59% of the total surveyed pond has the medium water quality in terms of color, 34% of the total surveyed pond has the good water quality and 7% of the total surveyed pond has the bad water quality. Sample photographs of ponds with good, medium and bad water quality has been given in Figure 4.5, 4.6 and 4.7 respectively.

Figure 4.5: Pond with good quality of water

Figure 4.6: Pond with normal quality of water

Data Analysis

45

Figure 4.7: Pond with bad quality of water

4.4 Identification of potential ponds for safe water options

The physical parameters of the ponds were collected from the field and analyzed. The present study on pond survey only examines the physical parameters which will help in identifying the potential ponds for further investigation and selection with potential for drinking purposes. A water quality monitoring program may be executed to provide the alternative safe water options from the initially identified ponds for further assessment. It is not practically possible or it would be very cost effective to monitor the water quality for all the ponds for safe water options.

Therefore, it is necessary to screen the ponds to identify potential ponds for alternate safe water technology through further water quality test.

Potential grading of the pond will be useful for adaptation of safe water technology especially for PSF (CEGIS, 2005). The criteria were used for calculation of potentiality considering the design or recommended criteria through literature review.

Generally in the areas where PSF systems have been developed, tubewells are not successful as suitable fresh water aquifers are not available at reasonable depths (WHO, 2005). The recommended criteria for PSF are as below:

The pond must be large enough to ensure that it will not dry out in the dry season.

It is also important to ensure that the salinity and iron content of the pond water not exceed 600 ppm and 5 ppm, respectively at any time of the year.

Surface area should be 1/4 acre (11,000 square feet) or more. This ensures an adequate water supply.

Depth should be at least 8 feet in the deepest part and side slopes should be 3:1 or flatter.

Aquatic growth at the edge of the pond should be kept to a minimum. One of the better ways to reduce aquatic growth is to limit the amount of nitrogen and phosphorus that enters the pond.

Based on above criteria and available field data, several parameters were identified as a indicator for calculation of potentiality score and the parameters: (i) percentage of vegetation coverage, (ii) dry season water depth, (iii) usage of the pond, (iv) physical water quality of pond water (color of the

Data Analysis

46

pond water) and (v) pond area. Different lookup tables (1 to 7) were generated containing the indicator parameter and their score values. The relative score value has been used for the calculation of potential ponds with the upper limit value of 1 and lower limit value of 0 at different scale of interval. Further individual weighting factors for each indicator were assigned and the weighting factor for each indicator are given in Lookup Table 6. It may be noted that the score in the lookup tables and weights for different indicators have been used for calculation are possible to best judgement from the available data and may be updated with more precise data.

Further the final potential score has been calculated using the following formula:

VF = V1 * 0.1 + V2 * 0.3 + V3 * 0.3 + V4 * 0.1 + V5 * 0.2

Where,

VF = Final potential score

V1 = Individual score for vegetation coverage of the pond,

0.1 = weights for V1

V2 = Individual score for dry season water depth of the pond,

0.3 = weights for V2

V3 = Individual score for usage of the pond,

0.3 = weights for V3

V4 = Individual score for color of the pond water

0.1 = weights for V4

V5 = Individual score for the pond area

0.2 = weights for V5

From calculated scores, the potentiality class were generated are (i) High, (ii) Medium and (iii) Low. The potentially class with the score has been presented in Lookup Table 4.7.

Lookup Table 1: Criteria for % of vegetation coverage of the pond (V1) % of vegetation coverage Score

< 25% 1 25% - 40% 0.6 40% – 60% 0.3

>60% 0

Lookup Table 2: Criteria for dry season water depth (V2) water depth (m) Score

>2 1 1 – 2 0.6 0.5 - 1 0.4 <0.5 0

Lookup Table 3: Criteria for usage of pond water, (V3) Usage of Pond Water Score

Only drinking 1 Drink + Bath/ Wash 0.6 Only Bath / Wash 0.4

Drink + Fishing + Bath/Wash 0.2 Other 0

Data Analysis

47

Lookup Table 4: Criteria for physical color of pond water, (V4) Water color Score

Good 1 Normal 0.5

Bad 0

Lookup Table 5: Criteria for pond Area, (V5) Pond area Score

> 2000 1 > 1000 – 2000 0.6

> 500 –100 0.4 > 200 – 500 0.2

< 200 0

Lookup Table 6: Weights of different indicators Indicator Parameter Weights

V1 0.1 V2 0.3 V3 0.3 V4 0.1 V5 0.2

Lookup Table 7: Potentiality class of the pond Potential score Potential class

VF > 0.6 High VF = 0.6 – 0.4 Medium

VF < 0.4 Low/ Less

Using the setting criteria and proposed methodology union wise number of potential pond were calculated and analysis results were presented in Table 4.12. From the computation result (Table 4.12 and Figure 4.8) of potential ponds it is observed that 6% of the total ponds have high potential, 53% of the total ponds have the medium potential and 41% of the total ponds have very less or low potentiality.

Table 4.12: Union wise apparently potential ponds

Number of potential ponds District Upazila Union Pond surveyedHigh Medium Low

Bagerhat Bagerhat Sadar Kara Para 354 1 52 301 Bagerhat Mollahat Gaola 311 8 96 207 Bagerhat Morrelganj Daibagnyahati 81 12 69 Brahamanbaria Banchharampur Salimabad 41 21 20 Brahamanbaria Nabinagar Shibpur 163 29 119 15 Chandpur Hajiganj Dakshin Rajargaon 346 31 242 73 Chandpur Kachua Kadla 445 34 323 88 Chandpur Kachua Pathair Paschim 194 20 139 35 Chandpur Matlab Durgapur 248 9 169 70 Chandpur Shahrasti Uttar Meher 367 51 252 64 Comilla Chandina Maijkhara 421 33 309 79 Comilla Daudkandi Majidpur 3 2 1 Comilla Homna Chanderchar Paschim 37 18 19

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Number of potential ponds District Upazila Union Pond surveyedHigh Medium Low

Comilla Homna Chanderchar Purba 52 3 33 16 Comilla Laksam Bagmara 8 7 1 Comilla Laksam Paschimgaon 274 51 180 43 Comilla Laksam WARD NO-02 7 3 4 Comilla Muradnagar Sreekail 257 16 203 38 Gopalganj Gopalganj Sadar Suktail 105 33 72 Gopalganj Kashiani Parulia 104 8 39 57 Gopalganj Kotali Para Radhaganj 423 11 244 168 Gopalganj Tungi Para Patgati 202 20 79 103 Jessore Abhaynagar Sreedharpur 45 7 22 16 Khulna Paikgachha Kapilmuni 237 12 105 120 Khulna Terokhada Sachiadah 115 21 94 Madaripur Rajoir Badar Pasha 287 6 133 148 Narail Kalia Kalabaria 178 5 64 109 Pabna Bera Masundia 13 13 Pabna Santhia Karanja 27 4 23 Pirojpur Mathbaria Betmore Rajpara 16 2 6 8 Pirojpur Pirojpur Sadar Sikdar Mallik 16 7 9 Rangpur Pirgachha Tambulpur 3 3 Satkhira Assasuni Durgapur 277 8 150 119 Satkhira Kalaroa Keralkata 294 5 164 125 Satkhira Kaliganj Champaphul 38 2 22 14 Satkhira Tala Khalishkhali 280 4 67 209 Sirajganj Royganj Chandaikona 9 1 3 5 Sylhet Balaganj Paschim Pailanpur 50 6 24 20 Sylhet Kanaighat Purba Dighirpar 54 6 32 16 Total 6382 389 3399 2594 % of total 6 53 41

Figure 4.8: Status of potential ponds

Status of potential ponds for surveyed unions

6%

53%

41%High

Medium

Low

Potential class

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4.5 Result and discussion

The dry season surface water availability has becoming low in the recent times, as the upstream withdrawl increases along with low rainfall intensity (only 15-30% of total annual rainfall occurs during October to April). Increasing trend of sedimentation and low flow persistence in the rivers causes most of the waterbodies and riverbed drying up. In addition, extraction of water for fish catching and high demand of irrigation water for HYV crops casuses the process of dessication rapidly. From the field observations it was found that water available up to mid of the dry season (end of January to first week of February) and after that most of the water areas are dried up. The following articles explain the results and findings from field investigation and observations from feature extraction process using remote sensing data.

4.5.1 Waterbodies

Primary level of quality control to digitizing waterbodies was established from initial stage. And it was done with the help of resolution merged images with resolution of 6m and SoB topo sheets. During digitization uncertain classes were marked for field validation. Feature types declared undefined due to i) cover water area by water hyacinth, ii) irrigation practices around bank line iii) dissimilar shape or area in IRS panchromatic images with respect to merged image and iv) some image dates were early in the dry season (early December to early January). For field validation some uncertain category waterbodies and defined perennial water bodies were selected so that the distribution is representative of the whole area. Field area selection was done considering the large area zoning north-east (Sylhet) zone (haor area), south-west (Khulna) zone (shrimp culture area), north-west (Rajshahi) zone. With the idea of field observation remaining uncertained catagory features of that zone were corrected. Perennial waterbodies hold water throught the year but it is difficult to quantify the number of useable perennial waterbodies without detailed field investigation. It depends on the depth and volume of water available including the physical quality.

A substantial number of waterbodies at the south west region of Bangladesh were digitized and those are mainly shrimp gher (Figure 4.9). The decision to separate Gher from regular surface water bodies was made after field verification. About 45 different sample areas were selected and visited for validation in the south-west zone. All over the south-west region there are a huge number of shrimp culture across a large area and hence there are very few waterbodies are left out of commercial use. Most of the cases small ghers were ignored due to no other features during gher digitization.

Figure 4.9: An integration view of Gher areas.

In the north-east zone, it is seen that major part of Sylhet district and part of Maulvi Bazar district has scattered perennial water bodies. And in some cases boro rice is cultivated adjacent to the water bodies (Figure 4.10 and 4.11). Topographies of this zone is flat. Turbidity is high in most of the cases where water depth is low. In most of the cases water bodies are linked with narrow stream or canals. Peoples use the surface water for irrigation and the water bodies pumped out for fishing

Some large low laying areas in Faridpur, Madaripur and Gopalganj district shown in Figure 4.12 which were initially confirmed, but ultimately rejected with the aid of field verification. These are mainly a single cropped area and generally practice for boro rice. During rainy season these area are flooded within a variable depth of 8 ft to 12 ft flood level.

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Figure 4.10: Waterbodies and its use at haor areas in north-east (Sylhet) zone.

Figure 4.11: Water body adjacent to the large irrigation area in Hobiganj district.

Figure 4.12: A sample of a IRS 1D panchromatic image over low laying areas of Gopalganj

Sadar, Gopalganj district.

4.5.2 River and khals

During digitization of river, some problems arises like abrupt changes of river width, confusing with river bank lines and number of chars in river, which is inundated during rainy season. All char areas were separated using different code. Most of rivers and khals are found to be with minimum depth of water during the field work due to low rainfall, minimum transboundary inflows which is around 10-20% less than the annual mean value. Delineated river and khals were checked with reference to the NWRD details river database, information of Bangladesh Water Development Board, topographic maps associated with coastal area maps (Finn Map) and Integrated Coastal Resources Database for southern part to assure the quality output. Karatoya river which was found (Figure 4.13) seasonal in image as well as in BWDB database i.e., these types of river dried up in dry season. It is difficult to quantify the number of useable perennial river without detailed field investigation. It depends on the

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depth and volume of water available including the physical quality. During the limited sample field verification several river were found to appareantly potential for pipe water supply after further quality test and detailed field investigation.

Figure 4.13: Field observation for river analysis.

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4.5.3 Pond survey

Based on the analysis and procedure described in selecting the potential ponds for further investigation, the weightage factors are decided considering the best possible combination of the parameters. Depth of water in the pond along with the area and present usages has given higher weightage value for selecting the potential ponds. From the analyses, it has been found that around 3788 number of ponds, which is about 60% of the total surveyed ponds, are apperantly medium to high potentiality for safe water option technology. It will further help in identifying the pond where further investigation could be taken regarding water availability assessment throughout the years, quality and community to be served by the pond.

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Chapter 5

Map preparation and Software Development

5.1 Map preparations

Inventory of perennial surface water sources and detailed pond survey for 35 unions are the major activities including the preparation of union wise perennial surface water source maps. Thus after data processing and preparation union wise perennial surface water source maps were produced in ArcView GIS environment. More than 1100 maps were produced and converted into JPEG format to store the digital maps for further usages by DPHE. Afterwards all the database and developed digital maps were incorporated into the database. Software has been developed for automated map printing. The brief description of the developed software has been given in section 5.2. Sample maps of perennial surface water sources were given in Annex –B

5.2 Software development

Simple and user friendly software has been developed for exploring the database and automated map preparations and interface of the software is given in Figure 5.1. The software has the five modules, these area : (i) Data entry module, (ii) Data Explorer, (iii) Export (iv) Query and (v) Analysis.

Figure 5.1: Interface of automated map printing software

Data Entry module:

Field survey data is stored in MS Access database format. A user-friendly data entry form is incorporated in the software for the entry of the surveyed data. The data entry form is designed in harmony with the physical layout of the data collection questionnaire. The interface of the data entry module were given figure 5.2.

Data Explorer Module:

The data explorer module help user for retrieving of different types GIS data from the database. Different types of options including data layer adding, deleting, zooming and printing facilites are incorporated.

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Figure 5.2: Data entry interface of automated map printing software

Export module:

This module is designed to export the data in dbase (*.dbf) format. There are different types of options for exporting data. Data can be exported according to Districts, Upazillas and Villages. The sample output of ponds survey from the software has given in Annex-C.

Query Module:

The Query module of the software is designed to solve different types of queries of the data. The module also provides statistical analysis of the data. The query can be saved and loaded in this module.

Analysis Module:

The analysis module of the software is designed to compute the potentiality score of ponds based on the criteria and weights described earlier. The module provides a brief prototype framework to compute the potential score for identification of ponds deploying the safe water technology.

5.3 Training on developed software

After developing the software, it was given over to DPHE and training was organized to develop capacity of their professionals/officials concerned so that the system can be used for arsenic mitigation option planning. A user manual has also been developed to operate the software smoothly and efficiently.

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Chapter 6

Conclusion and Recommendation

6.1 Conclusion

The study has been conducted for preparing the inventory of perennial surface water sources where more than 80% of the tubewells are arsenic contaminated using satellite images and fields survey. The project is divided into two distinct phases: feasibility and implementation phase. The feasibility phase was conducted to develop the methodology for data extraction from the satellite based images and the implementation phase has been executed following the methodology developed and recommendations accepted by APSU/DPHE.

The study uses the 6 meter panchromatic IRS images and as well as the resolution merged product of medium resolution LANDSAT TM (30 m) / IRS LISS III (24 m) for capturing the waterbodies and river data. Field verification has also been done for assessing the accuracy. It may be noted that only 25% of total union has been surveyed for field checking. From the field survey of waterbodies and river database, around 90% of accuracy has been achieved.

A seperated field survey was conducted to collected detailed pond data for 35 unions using GPS and field questionnaire. The pond location maps has been prepared from field data were given in Annex-D. The physical parameters of ponds were collected from the field are the size, water depth, usages, vegetation coverage and color of pond water. This parameters were used to compute the potentiality scores of ponds, which will help the planners and decision makers in selection of pond for adopting the possible safe water technology. It will also help to identify/ scrutinize the pond data from the detailed survey and recommendation can be made for further quality assessment. It has been observed that around 2500 numbers of ponds, which is about 40% of the total surveyed ponds has medium to high potentiality for adopting the alternate safe water technology (e.g. PSF) through further water quality test.

In Bangladesh, the surface water availability especially during the dry season is very low in most of the rivers and Beels. From the field observations (Annex-E), it is found that major waterbodies are dried up within mid January. Though water is available in several places but those are not usable in maximum cases because of: (i) water has been pumped out for catching fish, (ii) water is being used for irrigation purposes and (iii) river beds and beels are used for agriculture by constructing temporary closure in the river. However a number of waterbodies have been identified in different places, those may be useful for rural pipe water supply if the availability of water is ensured during the dry months with acceptable quantity and quality.

The detailed ponds survey shows that the database will be very useful and helpful in arsenic mitigation planning activities of DPHE and as wells other relevant government and non-government organization giving indicative information on prioritize the areas for further investigation. Further the prototype software may able to play an inductive role for further enhancement by interlinking it with the existing data available at DPHE.

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6.2 Recommendation

From the experiences and observations of the study following recommendations can be made:

(i) The detailed pond database developed is very useful which could be used for identification of potential ponds for safe water options technology.

(ii) The methodology developed for identification of potential ponds in the present study could be applied for rest of the highly arsenic affected unions of the country. It will minimize the cost in selecting the potential sources of alternative water bodies, thus enhancing the arsenic mitigation planning activities.

(iii) The river, khal and water bodies identified during the project study may be further investigated for quantification of water availability and quality.

(iv) Further 300 unions can be identified from the highly arsenic affected areas for detailed pond survey and database development.

(v) The developed software can be further enhanced to make it as a decision support system (DSS) for adoption of union wise safe water technology.

Detail study may be executed through intensive data collection procedures covering the water quality testing using handy tools and GPS survey for locating the possible sources from the initial filtered surface water sources.

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References

BGS/DFID, 2001, Arsenic contamination of groundwater in Bangladesh (Vol1-Vol 4), British Geological Survey (BGS) and Department of International Development (DFID) February, 2001

BWDB, 2005. Rivers of Bangladesh, Bangladesh Water Development Board, Water Science, June-2005.

BBS, 1991. Bangladesh Bureau of Statistics, Population Census 91, 1991.

BMRC, 2002. Research Studies on Health Impact of Arsenic Exposure, Bangladesh Medical

Research Council (BMRC), May 2002.

CEGIS, 2005. Report on Development of Arsenic Decision Support System (ADSS), October 2005.

World Bank, 1997, Arsenic Contamination in Groundwater in Bangladesh: Status Review and Proposal for Rapid Investigation Phase, World Bank Fact Finding Mission Report, April 1997.

WHO, 1996. Guidelines for Drinking Water Quality, Second Edition, Vol2, Health Criteria and other Supporting Information, WHO, Geneva.

WHO, 2005. Water Safety Plans: Managing drinking-water quality from catchment to consumer,

Protection of the Human Environment Water, Sanitation and Health, World Health Organization, Geneva 2005