M.Sc Thesis, for His Patience, Motivation, Enthusiasm, and Immense Knowledge

ASSESSMENT OF GROUNDWATER RESOURCES IN BOGRA DISTRICT USING GROUNDWATER MODEL

MUHAMMAD ABDULLAH

DEPARTMENT OF CIVIL ENGINEERING BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY

OCTOBER, 2014

Assessment of Groundwater Resources in Bogra District using Groundwater Model

by Muhammad Abdullah Student No. 0409042201 (F)

In Partial fulfillment of the requirement for the degree of MASTER OF SCIENCE IN CIVIL ENGINEERING

Department of Civil Engineering BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY, DHAKA

October, 2014

The thesis titled Assessment of Groundwater Resources in Bogra District using Groundwater Model submitted by Muhammad Abdullah, Student No: 0409042201 (F), Session: April, 2009 has been accepted as satisfactory in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering (Geotechnical) on October 2014.

BOARD OF EXAMINERS

Dr. Syed Fakhrul Ameen Professor Department of Civil Engineering Chairman BUET, Dhaka (Supervisor)

Dr. Mahbuboor Rahman Choudhury Member Assistant Professor (Co-Supervisor) Department of Civil Engineering BUET, Dhaka

Member Dr. Tanvir Ahmed Assistant Professor Department of Civil Engineering BUET, Dhaka

Member Dr. A.M.M. Taufiqul Anwar (Ex - officio) Professor and Head Department of Civil Engineering BUET, Dhaka

Member Dr. A.F.M Afzal Hossain (External) Deputy Executive Director (P&D) Institute of Water Modelling (IWM) H-496, R-32, New DOHS, Mohakhali, Dhaka.

CANDIDATE’S DECLARATION

It is hereby declared that this thesis or any part of it has not been submitted elsewhere for the award of any degree or diploma.

Muhammad Abdullah

iii

ACKNOWLEDGEMENT

Foremost, I would like to express my sincere gratitude to my supervisor Dr. Syed Fakhrul Ameen, Professor, Department of Civil Engineering, BUET for his special support of my M.Sc thesis, for his patience, motivation, enthusiasm, and immense knowledge.

I also express profound gratitude to my co-supervisor Dr. Mahbuboor Rahman Choudhury, Assistant Professor, Department of Civil Engineering, BUET for his continuous guidance and encouragement for the research. His nice and careful guidance, constructive suggestions immensely contributed to the improvement of this thesis.

Furthermore, I would like to thank Institute of Water Modelling and their experts, specially to Md. Atiqur Rahman, Junior Engineer, Irrigation Management Division, Institute of Water Modelling for his kind help and fruitful discussion about the groundwater model.

Last but not the least; I would like to thank my wife for supporting me spiritually throughout the research.

ABSTRACT

Groundwater is the largest source of usable fresh water in the world. Domestic & irrigation water needs are being met in many parts of the world using groundwater, especially where surface water supplies are not available. The use of groundwater compared to surface water is much higher in north-western districts in Bangladesh. Due to over abstraction in the recent years of north-western districts, groundwater is lowering at an alarming rate. The objective of the study is to analyze the trend & extent of groundwater table due to expanding status of water demand. To assess groundwater resources of Bogra district due to future water demands, change in crop pattern & increase of water demand is considered.

MIKE SHE hydrologic model has been used to simulate fluctuating water table to assess groundwater resources. MIKE SHE is a deterministic, physics-based, distributed & integrated hydrologic model, deals with entire hydrologic cycle. Precipitation, evapotranspiration & groundwater abstraction are fundamental hydro-meteorological & hydro-geological inputs. The rainfall data was obtained from daily records of eight BWDB stations; evapotranspiration data was obtained from only one BWDB station in the study area. Groundwater abstraction is assumed as the integration of irrigation & domestic water needs. The model inputs regarding land use classification, topography & lithologic layers underneath the upper soil were incorporated directly from IWM.

Few input data has been projected using suitable projection models. Model has been simulated within the period from 2006 to 2030. The simulated phreatic surface & their trends have been compared with the observed levels & their trends. Finally, the model has been adjusted using calibration parameters.

Lower phreatic surface & higher depletion rate is found in south-western upazilas of Bogra district. The depletion rates vary from 0.00 to 2.92 cm/year for mean depth of phreatic surface. Maximum depth of phreatic surface varies from 1.20 cm/year to 14.45 cm/year. After a drought rainfall year, lower phreatic surface is observed. However this is regained in subsequent years with an average rainfall. Contribution to groundwater recharge in Bogra district is mainly due to rainfall.

TABLE OF CONTENTS

ACKNOWLEDGEMENT …………………………………………………….. iv ABSTRACT ...………………………………………………….………………. v TABLE OF CONTENTS ……………………………………………………… vi LIST OF TABLES …………………………………………………………….. viii LIST OF FIGURES ...….……………………… …………………………….. ix ACRONYMS AND ABBREVIATIONS …………………………………….. xiii

CHAPTER 1 INTRODUCTION...... 2 1.1 General ...... 2 1.2 Objectives of the Study ...... 3 1.3 Structure of the Thesis ...... 4

CHAPTER 2 LITERATURE REVIEW ...... 2

2.1 General ...... 2 2.2 Developments of Hydrologic Model ...... 2 2.3 Basic Theory of Modelling ...... 7 2.4 MIKE SHE Hydrologic Model ...... 10 2.5 Researches Related to Groundwater Resources of the Study Area ...... 13

CHAPTER 3 HYDROLOGICAL DATA COLLECTION ...... 2 3.1 General ...... 2 3.2 Data Collection for MIKESHE Hydrologic Model ...... 18 3.2.1 Hydrometeorology of the study area ...... 19 3.2.2 Hydrogeology of the study area ...... 24 3.2.3 Topography of the study area ...... 32 3.2.4 Lithology of the study area ...... 33

CHAPTER 4 DATA PROCESSING AND MODEL SETUP ...... 38

4.1 General ...... 38

4.2 Prediction of Data up to Year 2030 ...... 38 4.2.1 Precipitation ...... 38 4.2.2 Evapotranspiration ...... 41 4.2.3 River water level...... 41 4.2.4 Groundwater level at model boundary ...... 41 4.2.5 Irrigation water demand ...... 42 4.2.6 Domestic water demand ...... 43 4.3 Model Set Up ...... 44 4.3.1 Model build-up ...... 45 4.3.2 Model calibration...... 53 4.3.3 Model validation ...... 56

CHAPTER 5 RESULTS AND DISCUSSIONS ...... 58

5.1 General ...... 58 5.2 Groundwater Level at Study Area Boundary Wells (Period: 2005-2012) 5.3 Predicted Phreatic Surface using MIKE SHE Hydrologic Model for Present Crop Pattern ...... 62 5.3.1 Predicted trend and depletion rate of phreatic surface (period: 2006 to 2030) 63 5.3.2 Annual fluctuation of phreatic surface with rainfall & abstraction ...... 66 5.4 Predicted Phreatic Surface using MIKE SHE Hydrologic Model for Change in Crop Pattern ...... 72 5.4.1 Predicted depth of phreatic surface ...... 72 5.4.2 Predicted depletion of groundwater level after consecutive drought years ..... 74 5.4.3 Depth of phreatic surface above suction limit of hand tube well ...... 79 5.5 Assessment of Groundwater Resources in Bogra District ...... 82 5.5.1 Water balance components ...... 82 5.5.2 Groundwater recharge ...... 84

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS . 88

6.1 Conclusions ...... 88 6.2 Recommendations for Future Research ...... 89

REFERENCES ...... 89

vii

LIST OF TABLES

CHAPTER 3

Table 3.1: Details of rainfall stations within/around the Bogra district (source: BWDB) ..... 19 Table 3.2: Evaporation station in Bogra district (source: BWDB)...... 23 Table 3.3: Occupied area in rabi (dry) season for boro and non boro crops ...... 27 Table 3.4: Crop calendar for Bogra district (source: IWM) ...... 29 Table 3.5: Population and domestic water demand in 2012 ...... 31 Table 3.6: Water requirement in year 2012 for Bogra district ...... 31

CHAPTER 4

Table 4.1: Factors for rainfall data distribution within 8 rainfall stations ...... 40 Table 4.2: United Nations (2011) considered population growth rate for Bangladesh Table 4.3: Check projected population density with United Nations (2011) ...... 44 Table 4.4: Grid cells used for model setup ...... 47 Table 4.5: Geographical limits of the study area ...... 47

CHAPTER 5

Table 5.1: Depletion rates of groundwater level at model boundary wells (period: 2005- 2012) ...... 60 Table 5.2: Depletion rate of phreatic surface for all upazilas under Bogra district (period: 2006-2030) ...... 64 Table 5.3: Predicted depth of phreatic surface for change in crop pattern & water demands (mean depth of phreatic surface) ...... 72 Table 5.4: Predicted depth of phreatic surface for change in crop pattern & water demands (maximum depth of phreatic surface) ...... 73 Table 5.5: Water balance components of the study area from 1st January, 2011 to 2nd January, 2012 ...... 83

viii

LIST OF FIGURES

CHAPTER 2

Figure 2.1: Classification of hydrologic models according to Chow, Maidment & Mays, 1988 ...... 8 Figure 2.2: Hydrologic processes simulation by MIKE SHE hydrologic model ...... 10 Figure 2.3: Schematic representation of the conceptual components in MIKE SHE hydrologic model ...... 11

CHAPTER 3

Figure 3.1: Map of the study area (Bogra district) ...... 2 Figure 3.2: Schematic diagram of MIKE SHE hydrologic model ...... 18 Figure 3.3: Rainfall, Evaporation, Groundwater and River water level monitoring stations within/around the Bogra district (source: BWDB) ...... 20 Figure 3.4: Double mass curve for rainfall data under rainfall station, R033 in Sherpur upazila (period: hydrologic year 2006 to 2011) ...... 20 Figure 3.5: Distribution of rainfall into Dry and Wet seasons (source: BBS) ...... 21 Figure 3.6: Distribution of monthly rainfall for Bogra district (source: BBS, period: 1985- 2011) ...... 21 Figure 3.7: Fluctuation of annual rainfall in Bogra district (source: BBS) ...... 22 Figure 3.8: Rainfall deviation from annual normal rainfall in Bogra district (source: BBS) . 22 Figure 3.9: River water level hydrograph for river Jamuna with maximum and minimum water level (source: IWM, at Mathurpara station; period: January to December, 2012) ...... 23 Figure 3.10: River water level hydrograph for river Jamuna at Mathurpara station (source: IWM, period: 2005 to 2012) ...... 24 Figure 3.11: Groundwater observation wells under BWDB within/around the Bogra district (source: IWM) ...... 25 Figure 3.12: Contours for maximum groundwater table for year 1985 (source: IWM) ...... 25 Figure 3.13: Contours for maximum groundwater table for year 2010 (source: IWM) ...... 26 Figure 3.14: Land use pattern throughout Bogra district (source: IWM; referenced year: 2012) ...... 28 Figure 3.15: Land use pattern according to boro crop throughout Bogra district (source: IWM; referenced year: 2012) ...... 28

Figure 3.16: Land use pattern for the MIKE SHE hydrologic model (source: IWM, 2013).. 30 Figure 3.17: Topography within/around the Bogra district (source: IWM, 2013) ...... 32 Figure 3.18: Plan of sections for lithologic layers ...... 33 Figure 3.19: Lithological layer selection from bore log data ...... 34 Figure 3.20: Lithological cross section A-A along easting 440000 ...... 35 Figure 3.21: Lithological cross section B-B along northing 720000 ...... 35 Figure 3.22: Distribution of hydraulic conductivity of the aquifer ...... 36 Figure 3.23: Top soil condition of the study area ...... 37

CHAPTER 4

Figure 4.1: Flowchart of the general methodology for present study ...... 38 Figure 4.2: Projected annual rainfall (source: Rajib, Rahman, Islam and McBean, 2010) .... 39 Figure 4.3: Rainfall deviation from annual projected rainfall (source: Rajib, Rahman, Islam and McBean, 2010) ...... 39 Figure 4.4: Projected rainfall considered in present study (period: 2011-2030) ...... 40 Figure 4.5: Projection of river water level of Jamuna river (at Mathurpara; period: 2012- 2030) ...... 41 Figure 4.6: Projection of groundwater levels for boundary condition 01 (period: 2012-2030)42 Figure 4.7: Projection of groundwater levels for boundary condition 02 (period: 2012-2030)42 Figure 4.8: Irrigation demands considered in the models (period: 2012-2030) ...... 43 Figure 4.9: Projection of domestic water demand (period: 2012-2030)...... 44 Figure 4.10: Model area for MIKE SHE hydrologic model ...... 45 Figure 4.11: Model domain and grid ...... 46 Figure 4.12: Thiessen polygons for individual rainfall station in the study area ...... 48 Figure 4.13: Location of river water and groundwater monitoring stations in Bogra district 50 Figure 4.14: Typical surface water-groundwater interaction plot ...... 50 Figure 4.15: Initial groundwater level for the model on 3rd May, 2005 ...... 52 Figure 4.16: Location of wells used for calibration and validation ...... 54 Figure 4.17: Calibration of model for groundwater level at the well: GT1020005 at Bogra Sadar upazila ...... 54 Figure 4.18: Calibration of model for groundwater level at the well: GT1094024 at Shibganj upazila ...... 55

Figure 4.19: Validation of model for groundwater level at the well: GT1020005 at Bogra Sadar upazila ...... 56 Figure 4.20 Model validation against GWL of well id: GT1094024 at Shibganj upazila ..... 56

CHAPTER 5

Figure 5.1: Location of groundwater wells at model boundary ...... 58 Figure 5.2: Depletion trend of maximum groundwater level at Gobindaganj ...... 59 Figure 5.3: Depletion trend of average groundwater level at Gobindaganj ...... 59 Figure 5.4: Depletion trend of minimum groundwater level at Gobindaganj ...... 60 Figure 5.5: Depletion rate of groundwater level at the wells in the study area (period: 2005- 2012; unit: cm/year; data: average groundwater level) ...... 61 Figure 5.6: Rainfall stations in the study area and Sherpur upazila ...... 62 Figure 5.7: Hydrograph of phreatic surface under Sherpur upazila ...... 63 Figure 5.8: Depletion rate of phreatic surface for Sherpur upazila (period: 2006-2030) ...... 63 Figure 5.9: Depletion rate using mean depth of phreatic surface (spatial distribution map) .. 64 Figure 5.10: Depletion rate using maximum depth of phreatic surface (spatial distribution map) ...... 65 Figure 5.11: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (mean depth of phreatic surface*) ...... 67 Figure 5.12: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (maximum depth of phreatic surface**) ...... 67 Figure 5.13: Fluctuation of phreatic surface due to groundwater abstraction under Sherpur upazila (mean depth of phreatic surface) ...... 68 Figure 5.14: Fluctuation of phreatic surface due to groundwater abstraction under Sherpur upazila (maximum depth of phreatic surface) ...... 68 Figure 5.15: Fluctuation of phreatic surface in dry months due to rainfall under Sherpur upazila (mean depth of phreatic surface) ...... 69 Figure 5.16: Fluctuation of phreatic surface in dry months due to rainfall under Sherpur upazila (maximum depth of phreatic surface) ...... 69 Figure 5.17: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (mean depth of phreatic surface; period: 1st March in each year***) ...... 70 Figure 5.18: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (maximum depth of phreatic surface; period: 1st March in each year***) ...... 70

Figure 5.19: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (mean depth of phreatic surface; period: 1st May in each year****) ...... 71 Figure 5.20: Fluctuation of l phreatic surface due to rainfall under Sherpur upazila (maximum depth of phreatic surface; period: 1st May in each year****) ...... 71 Figure 5.21: Predicted depletion of phreatic surface for present crops (period: 2012-2023*)75 Figure 5.22: Predicted depletion of phreatic surface for present crops (period: 2012-2023; annual demand increment: 1.25%**) ...... 76 Figure 5.23: Predicted depletion of phreatic surface for present crops (period: 2012-2023; annual demand increment: 2.25%***) ...... 77 Figure 5.24: Predicted depletion of phreatic surface for boro crop in all crop areas (period: 2012-2023; annual demand increment: 1.25%)...... 78 Figure 5.25: Percent of cells having depth of phreatic surface is above 6m due to different crop pattern ...... 79 Figure 5.26: Number of cells having depth of phreatic surface above 6m for present crop pattern (same crop demand) ...... 80 Figure 5.27: Number of cells having depth of phreatic surface above 6m for present crop pattern (change in crop demand ...... 81 Figure 5.28: Water balance components of the study area for year 2011 ...... 82 Figure 5.29: Comparison of annual recharge between boundary condition 01 and boundary condition 02 for present crops (period: 2012 to 2030) ...... 84 Figure 5.30: Comparison of annual recharge using boundary condition 01 and 02 in drought year for present crops (upazila wise; year: 2022) ...... 85 Figure 5.31: Comparison of annual recharge due to increment of crop demand for present crops ...... 86 Figure 5.32: Effects of crop pattern on groundwater recharge for same fluctuation of annual rainfall from 2012 to 2030 ...... 86 Figure 5.33: Effects of crop pattern on percent of groundwater recharge to applied water ... 87

xii

ACRONYMS AND ABBREVIATIONS

BADC Bangladesh Agricultural Development Corporation BBS Bangladesh Bureau of Statistics BC Boundary Condition BMDA Bangladesh Multipurpose Development Authority BTM Bangladesh Transverse Mercator BUET Bangladesh University of Engineering and Technology BWDB Bangladesh Water Development Board CP Crop Pattern CWR Crop Water Requirement DEM Digital Elevation Model DPHE Department of Public Health Engineering DTW Deep Tubewell FAO Food and Agricultural Organization FIWR Field Irrigation Water Requirement GW Groundwater GWL Groundwater Level GWT Groundwater Table (same as Phreatic Surface) GIS Geographical Information System GoB Government of Bangladesh HTW Hand Tubewell HYV High Yielding Variety IWM Institute of Water Modelling MDG Millennium Development Goals MIKE SHE Modelling Software of DHI for Groundwater Flow Simulation MoWR Ministry of Water Resources MPO Master Plan Organization MT Metric Ton NWMP National Water Management Plan NWPo National Water Policy SIWR Scheme Irrigation Water Requirement SRDI Soil Resources Development Institute STW Shallow Tubewell TW Tubewell WARPO Water Resources Planning Organization UN United Nations

CHAPTER 1 INTRODUCTION

1.1 General

Water is a renewable resource and the availability of water is complicated because of its uneven distribution over the localities (FAO, 2012). Evaporation and precipitation are work together to replenish our fresh water supply constantly and quickly (Altaner, 2012). Groundwater is the largest source of usable, fresh water in the world (Subramanya, 1994; Chow, Maidment and Mays, 1988). In many parts of the world, domestic, agricultural and industrial water needs are being met using groundwater; where surface water supplies are not available (Siebert and D”oll, 2010). In Bangladesh from 1985 to 2008, contribution of groundwater has increased from 38% to 79% and surface water has declined from 62% to 21% (Shaw et. al., 2011). The ratio of groundwater to surface water use is much higher in north-western districts of Bangladesh compared to other parts of the country (Shahid and Behrawan, 2008). More than 90% of the population in Bangladesh relies on groundwater; about 97 percent rural people are using over 10 million hand tube wells to fulfill their drinking water needs (Amin, 2009).

Ground-water depletion, a term often defined as long-term water-level declines caused by sustained ground-water pumping, is a key issue associated with ground-water use (USGS, 2004). In recent years, due to over abstraction, the groundwater in north-western region of Bangladesh is lowering at an alarming rate. Lowering of groundwater table during dry months is a serious issue to operate shallow tubewell, hand tubewell and dug wells. In addition, many ponds and tanks become derelict due to lowering of groundwater table which creates water shortage for domestic use as well as for the livestock population (NWMP, 2001). The ground water level declined substantially during the last decade causing threat to the sustainability of water use for irrigation in the region and also affecting other sectors (Jahan et. al., 2010). In greater Rajshahi area, extraction exceeds recharge and groundwater table declined 3 meters between 2004 and 2010 (Luby, 2013). Declination of water table affects water quality; specially arsenic is a function of water depth (Harvey et.al., 2006).

In the north-west region of Bangladesh, tubewell intensity is increased from 6.9 to 36 per square kilometer, deep tubewell almost doubled, shallow tubewell reached more than five times higher and irrigated land increased 1.6 times between 1984-85 and 2010-2011 (Dey et.al., 2013). According to Bangladesh Bureau of Statistics, population of Bangladesh increased from 28.93 million in 1901 to 149.77 million in 2011. More use of irrigation water especially groundwater has contributed to increase in crop productivity in Bangladesh. Rice yield for example, increased from 1.0 MT/ha in 1971/72 to 2.8 MT/ha in 2008/09. Much of this increase in yield was due to an increase in the share of rice area especially during the boro season which increased from 10% in 1971/72 to 44% in 2006/07. Rajshahi Division reported the highest percentage (27.22%) of households with Hybrid boro cultivation. In the next 25 years, food demand of the country is expected to increase by 29% (NWMP, 2001). Uses of land and water resources particularly ground water that have evolved overtime to sustain agricultural production and meet the growing demand of increased population are likely to intensify in the future. In addition of major surface water diversion, added pressure on groundwater will lead to further depletion of sources. The National Water Management Plan considered an expected increase in irrigation demands by at least a quarter over the next 25 years (NWMP, 2001).

Piped households use on an average almost three times more water than that of un-piped households (Thompson et al., 2001). The proportion of the population without access to safe drinking water in Bangladesh was 29 percent in 1990. The MDG target is to bring it down to 14.5 percent by 2015. In other words, the target is to raise the coverage of safe drinking water from 71 percent in 1990 to 85.5 percent in 2015. The Government of Bangladesh plans to increase the coverage of safe water to 100 percent in urban areas and to 96.5 percent in rural areas by 2015. As per target, improved pipe water supply in secondary towns, upazila levels, pourashavas as well as rural water supply projects have been taken under special consideration by GoB funds as well as by foreign aided fund under DPHE (DPHE, 2014).

A number of works have been carried out to study the impact of climate change on irrigation water demand; all of them predicted to increase in water requirements for respective crops due to climate change at their individual study (Fischer et al., 2006; Elgaali et al., 2007; Yano et al., 2007; Rodriguez Diaz et al., 2007; de Silva et al., 2007). In north-west region of Bangladesh, climate change will increase the daily use of water for irrigation by an amount

3 of 0.8 mm/day during the end of this century; but there will be no appreciable change in total irrigation water demand due to the shortening of irrigation period by approximately 13 days and an increase of effective precipitation by 48.5 mm during irrigation period (Shahid, 2011). Basak J.K. et.al (2010) predicted from model analysis, significant reduction in yield of boro rice due to climate change; average yield reductions of over 20% and 50% have been predicted for boro rice for the years 2050 and 2070, respectively. Climate change will increase irrigation rate, hence abstraction rate of groundwater which in turn may cause negative impacts on groundwater resources in the region and the declining groundwater level will increase the irrigation cost (Shahid, 2012).

Now, it is becoming a challenging issue to fulfill irrigation and domestic water demand that will gradually increase in future. Change in crop pattern could be the probable option to sustain water level within limit. In Bangladesh, wheat ranks second position in respect to land having an annual production of 0.976 million tons and total area of 0.56 million hectares (BBS, 2005). Irrigation at optimum level is one of the most important tools for boosting up the yield of wheat (Razzaque et al., 1992). The water requirement for boro rice is much higher than that of wheat. The water requirement of wheat is only 25-33% of boro rice (BARI, 1990). The production cost of wheat is also less than that of boro rice. Dey N.C. et.al (2013) claims that, in the context of sustainability of groundwater use for irrigation, wheat production has to be emphasized in north-west region of Bangladesh.

Bogra district is selected as the study area, a north-western district which is characterized for groundwater depletion in the recent years due to increasing trend of water use (Abdullah, 2014). To assess the groundwater resources of Bogra district, the available input hydrological data are being used from the project (IWM, 2013). The current study is attempted to understand the trend and extent of groundwater table both at spatial and temporal scale, through analyzing the status of expanding water demand of Bogra district.

1.2 Objectives of the Study

The objective of the study is to assess the groundwater resources of the underlying aquifer system of the Bogra district from recorded data base & also for prediction years up to 2030 using MIKE SHE hydrologic Model. However, the specific aims of the study are as follows:

1. To analyze the trend of groundwater level at the study area boundary wells using observed data recorded by BWDB. (These data will be used as input of MIKE SHE prediction model.)

2. To predict the trend of the depth of phreatic surface using MIKE SHE hydrologic model and its spatial (upazila wise) and temporal distribution (annual fluctuation up to year 2030) within Bogra district.

3. To assess the groundwater resources from prediction models using variables such as, i) Groundwater level at study area boundary (model boundary) wells ii) Different crop demands for present crop pattern

iii) Change in crop pattern in dry season (Rabi crops)

1.3 Structure of the Thesis

The thesis represents the total achievement carried out under the study. It is comprised of six chapters including a list of mentioned references in the thesis.

CHAPTER 1 : Focuses on the thesis background, brief description of the existing problems and bottlenecks, justification of the study and study objectives.

CHAPTER 2 : Presents the brief summary of the modelling background, MIKE SHE hydrologic model simulation and previous studies related to this study.

CHAPTER 3 : Contains the data requirements for MIKE SHE groundwater model.

CHAPTER 4 : Presents the general approach and methodology that has been applied during the modelling works. Projection of data up to year 2030 for model inputs. It includes development of model, calibration and validation of the model.

CHAPTER 5 : Reflects the result and analysis of the modelling works from simulated data. It includes trend analysis of groundwater level from observed data, trend analysis of groundwater table from simulated results, water balance calculation and assessment of groundwater resource.

CHAPTER 6 : Presents the conclusions and recommendations.

Appendices of the report include the followings:

Appendix-A : Trend of groundwater level for boundary wells Appendix-B : Trend of groundwater level inside the study area Appendix-C : Analysis of Precipitation Data Appendix-D : Population and Household Data Appendix-E : Base Crop Water Requirements Appendix-F : Lithologic Layers Appendix-G : Irrigation equipments and their approximate production Appendix-H : Model Calibration and Validation Appendix-I : Model Outputs: Water Balance Chart Appendix-J : Trend Analysis of Phreatic Surface from Predicting Model Appendix-K : Abstraction Data Appendix-L : Trend Detection Test and Analyses

CHAPTER 2 LITERATURE REVIEW

2.1 General

Hydrology means the science of water; that deals with the occurrence, circulation and distribution of water of the earth and earth’s atmosphere (Subramanya, 1994). Hydrologic system is a structure or volume in space, surrounded by a boundary, that accepts water and other inputs, operates on them internally, and produces them as output (Chow, Maidment and Mays, 1988).

In the hydrological cycle, water evaporates from the oceans, lakes and rivers, from the soil and is transpired by plants. This water vapor is transported in the atmosphere and falls back to the earth as rain and snow. It infiltrates to the groundwater and discharges to streams and rivers as base flow. It also runs off directly to streams and rivers that flow back to the ocean. The hydrologic cycle is a closed loop and our interventions do not remove water; rather they affect the movement and transfer of water within the hydrologic cycle.

2.2 Developments of Hydrologic Model

The science of hydrology began with the conceptualization of the hydrologic cycle. Some of the Greek philosophers correctly described some aspects of the hydrologic cycle. For example, Anaxagoras of Clazomenae (500-428 B.C.) formed a primitive version of the hydrologic cycle (e.g. the sun lifts water from the sea into the atmosphere). Another Greek philosopher, Theophrastus (circa 372-287 B.C.) gave a sound explanation of the formation of precipitation by condensation and freezing. Meanwhile, independent thinking occurred in ancient Chinese, Indian and Persian civilizations (Essink, 2000).

During the Renaissance, a gradual change occurred from purely philosophical concepts of hydrology toward observational science, e.g. by Leonardo da Vinci (1452-1519). Hydraulic measurements and experiments flourished during the eighteenth century, when Bernoulli’s equation and Chezy’s formula were discovered. Hydrology advanced more rapidly during the nineteenth century, when Darcy developed his law of porous media flow in 1856 and Manning proposed his open-channel flow formula (1891). However, quantitative hydrology

7 was still immature at the beginning of the twentieth century. Gradually, empiricism was replaced with rational analysis of observed data. For example, Sherman devised the unit hydrograph method to transform effective rainfall to direct runoff (1932) and Gumbel proposed the extreme value law for hydrological studies (1941). Like many sciences, hydrology was recognized only recently as a separate discipline (e.g. in 1965, the US Civil Service Commission recognized a hydrologist as a job classiﬁcation).

In nineties, the main subject in hydrological study was to recognize about how much water is available, how much can be extracted, what are the effects on piezometric heads, etc. The spectacular boom in computer possibilities during recent times makes hydrologic analysis possible on a larger scale. As a result, hydrologists have analyzed problems in more detail and with shorter computation intervals than before. Complex theories describing hydrologic processes have been applied using computer simulations. Also interactions between surface water systems and groundwater systems in terms of quality and quantity became within the reach of the hydrologist. Huge quantities of observed data have easily been processed for statistical analysis. Moreover, during the past decade, developments in electronics and data transmission have made possible to retrieve instantaneous data from remote recorders (e.g. satellites), which lead to the development of real-time programs for water management.

2.3 Basic Theory of Modelling

“A model is a simplified representation of a complex system.” Modelling (also called simulation or imitation) of specific elements of the real world could help, considerably in understanding the hydrological problem. It is an excellent way to organize and synthesize ﬁeld data. Modelling should contribute to the perception of the reality, yet applied on the right way. In fact, hydrological models should only be applied to help the user with the analysis of a problem, nothing more, nothing less. It is only part of the way to understand or percept a hydrological process. In general, two main categories of models are widely used:

o A physical model or scale model, being a scaled-down duplicate of a full-scale prototype;

o A mathematical model; MIKE SHE is a mathematical model that is to be used in the current study.

Mathematical Model A mathematical model is a model in which the behavior of the system is represented by a set of equations, perhaps together with logical statements, expressing relations between variables and parameters (Clarke, 1973).

Classification (Figure 2.1) is based on the way the mathematical model is designed, e.g. how the model domain or problem area is schematized; what the characteristics of the data are (variables and/or parameters) and how they are utilized in the model.

Figure 2.1: Classification of hydrologic models according to Chow, Maidment & Mays, 1988

Deterministic and Stochastic Model A model is regarded deterministic, if all variables are regarded as free from random variation, or, if the chance of occurrence of the variables involved in such a process is ignored and the model is considered to follow a definite law of certainty and thus not any law of probability. A deterministic model is one that is defined by cause-and-effect relations. A deterministic model treats the hydrologic processes in a physical way. MIKE SHE is a deterministic model.

A model is regarded stochastic, if any of the variables are regarded as random variables, having distributions in probability.

Lumped and Distributed Model A lumped model neglects the spatial distribution in the input variables and the parameters in the model domain. A lumped model is a system with a particular quantity of matter, whereas a distributed model is a system with specified regions of space. For example, a lumped model treats variables, such as natural groundwater recharge, in the area of a catchment surface as a single (1D) unit, whereas a distributed model calculates the variables from one point in the area to another point (2D or 3D). MIKE SHE is a distributed model.

Empirical, Conceptual and Physically Based Model An empirical model is based on observation and experiment, not on physically sound theory. In the empirical approach, physical laws are not taken into account. These models are often applied in inaccessible (ungauged) areas, where only little is known about the area involved.

A model is regarded as a conceptual model, if physical processes are considered which are acting upon the input variables to produce output variables. In the conceptual approach, an attempt is made to add physical relevance to the variables and parameters used in the mathematical function which represent the interactions between all the processes that affect the system. An example of simple conceptual models is the formulation of Darcy (law of porous media ﬂow). Conceptual models are widely applied, as they are easy to use, apply limited input data, and can always be calibrated.

A physically based model is based on the understanding of the physics of the processes involved. They describe the system by incorporating equations grounded on the laws of conservation of mass, momentum and energy. The parameters of a physically based model are identical with or related to the respective prototype characteristics (e.g. storage capacities, transmissivities). Physically based models often apply deterministic and distributed input data. They can be applied in measured as well as unmeasured systems. Physically based models have the advantage that they have universal applications. The measured or estimated model parameters and hydrologic stresses (e.g. differences in natural groundwater recharge, human impacts such as groundwater extractions) can be adjusted in the input data file, so that the model is geographically and climatically transferable to any other area. MIKE SHE is a physically based model.

Common Physically Based Model Physically based hydrological model MIKE SHE and MODFLOW, are widely used. The main difference between the two models is that MIKE SHE includes unsaturated zone, so it calculates infiltration, actual Evapotranspiration and recharge from their physical laws. On the other hand MODFLOW deals with saturated zones only. So, in case of groundwater flow study where irrigation is not present, MODFLOW can be used. Otherwise MIKE SHE will be more appropriate.

2.4 MIKE SHE Hydrologic Model

MIKE SHE is an advanced, flexible framework for hydrologic Modelling. From 1977 onwards, a consortium of three European organizations: the Institute of Hydrology in the United Kingdom, SOGREAH in France, and the Danish Hydraulic Institute in Denmark have developed MIKE SHE. The integrated hydrological Modelling system of MIKE SHE is shown in Figure 2.2. MIKE SHE has proven valuable in hundreds of research and consultancy projects covering a wide range of climatological and hydrological regimes (Graham and Butts, 2005).

Figure 2.2: Hydrologic processes simulation by MIKE SHE hydrologic model

MIKE SHE, in its original formulation, could be characterized as a deterministic, physics- based, distributed model code. It was developed as a fully integrated alternative to the more traditional lumped, conceptual rainfall-runoff models. A physics-based code is one that solves the partial differential equations describing mass flow and momentum transfer. The Saint Venant equations (Chow, Maidment and Mays, 1988) for open channel flow and the Darcy equation (Chow, Maidment and Mays, 1988) for saturated flow in porous media are physics-based equations.

The process-based, modular approach implemented in the original SHE code has made it possible to implement multiple descriptions for each of the hydrologic processes. In the simplest case, MIKE SHE can use fully distributed conceptual approaches to model the watershed processes (Figure 2.3). MIKE SHE hydrologic model consider the variables as precipitation and evapotranspiration, unsaturated flow, overland flow and saturated groundwater flow.

Figure 2.3: Schematic representation of the conceptual components in MIKE SHE hydrologic model

Precipitation and Evapotranspiration Precipitation is usually a direct input in MIKE SHE, whereas radiation and water vapor transport in the atmosphere is typically bound up in Evapotranspiration (ET). Evapotranspiration refers to the sum of the processes of direct evaporation from free water surfaces and transpiration of sub-surface water either directly or via plants. Evapotranspiration is an important component of the water balance. Evapotranspiration can be 70% of rainfall in temperate climates and even exceed annual rainfall in arid areas (Bedient and Huber, 2002).

Unsaturated Flow The unsaturated zone is usually heterogeneous and characterized by cyclic fluctuations in the soil moisture as soil moisture is replenished by rainfall and removed by evapotranspiration and recharge to the groundwater table. Unsaturated flow is assumed to be primarily vertical, since gravity dominates infiltration. Therefore, to reduce the computational burden, unsaturated flow in MIKE SHE is calculated only vertically.

Overland Flow Ponded water can occur, for example, when rainfall cannot infiltrate fast enough, when groundwater flows onto the surface (e.g. in wetlands), or when streams flood over their banks. Ponded water is routed downhill as surface runoff. The flow path and quantity is determined by the topography and flow resistance, as well as losses due to evaporation and infiltration along the path it takes. Water flow on the ground surface is calculated using a semi-distributed, slope-zone approach based on the Mannings equation (Chow, Maidment and Mays, 1988).

Saturated Groundwater Flow Groundwater plays a significant role in the hydrological cycle. During drought periods groundwater discharge sustains stream flow. Irrigation and abstraction can influence natural recharge and discharge, thereby changing the flow regime in a catchment. In MIKE SHE, the spatial and temporal variations of the hydraulic head in the saturated groundwater zone are described mathematically by the 3D Darcy equation (DHI, 2007).

2.5 Researches Related to Groundwater Resources of the Study Area

In the surrounding of the study area, a significant number of studies on groundwater resources, water demand, land use for crop pattern, extension of crop intensity and their effects on groundwater level were done. The available study reports, project documents, published scientific articles have collected and reviewed to get information on the study area & corresponding groundwater resources prior to this study. Some of the important studies are briefly described below.

NWMP (2001) Preparation of National Water Management Plan to monitor activities within all water related sector, to provide information and to advice on best practice on water related issues in Bangladesh. With the estimation and prediction of the water resources in all sectors, water demand in dry period has been estimated and predicted for future 25 years. Study assessed, the main determinant in overall demand for water resources in the future is the growth of irrigation demand. As per study, water supply for urban and rural domestic & commercial use will be more than twice as before and irrigation demand are expected to increase potentially by at least a quarter (1/4) over the next 25 years.

IWM (2006) Integrated surface water and groundwater model study in the Barind area which covers 25 upazilas of Rajshahi, Chapainawabganj and Naogaon ditricts was carried out by IWM. MIKE SHE integrated with MIKE 11 Modelling system having 1000m x 1000m grid size covers about 7500 sq. km of study area. Based on available data up to 2005, study found the 11 upazilas (out of 25) have less groundwater resources create some problem to meet the present water demand of Boro crops.

Rahman and Saha (2008) Study for cropping pattern planning for a flood prone area (Bogra district), using Remote Sensing and GIS to reduce the losses of climate change impact has been done. The objective of the study was to carryout land suitability analysis for crops for the flood and post flood seasons using GIS aided Multi Criteria Evaluation (MCE) technique with suitability prioritization, existing land use patterns, flood impact and expert knowledge. It also suggests suitable cropping pattern for land use planning to combat adverse effect of flood using GIS aided integrated analysis. Study area was Bogra district and data has been collected from SPARRSO,

BWDB and SRDI. Study showed that, 18.99 percent of the total area was additionally suggested for rice, and 23.36 and 23.05 percent of the total area were suggested for mustard and potato respectively, which can increase the total crop production of the area. For dry seasonal crops wheat, potato and mustard were suggested to emphasize in the whole study area instead of boro crops.

Akram (2009) MIKE SHE and MODFLOW have been used to simulate the regional groundwater flow in the high Barind area of NW Bangladesh. The study area was about 2236 km2 which cover 9 upazilas in Rajshahi, Naogaon and Nawabganj Districts. Simulation shows that recharge occurs mainly due to rainfall, while the contribution of irrigation (in the winter) is very negligible. Upazila wise groundwater resources have been assessed based on safe yield criteria, where groundwater table would be replenished every year. The main difference between the two models is that MIKE SHE includes unsaturated zone, so it calculates infiltration, actual evapotranspiration and recharge from their physical laws. On the other hand MODFLOW deals with saturated zones only. So, in case of groundwater flow study where irrigation is not present, MODFLOW can be used. Otherwise MIKE SHE will be more appropriate. Study use only MIKE SHE hydrologic model to assess groundwater resource & MIKE SHE result shows that the available groundwater resources (before irrigation starts) vary in the range of 180 mm in Tanore upazila to 913 mm in Nawabganj Sadar upazila. Usable resources have been assumed as 90% of the available resources as there are some natural losses set out the study area during irrigation season.

Islam (2009) Barind Aquifer – Ganges River interaction has been investigated over 55 km reach of the Ganges River from Godagari to Charghat, having an area of 916 km2. Study area covered three upazilas of Rajshahi District. It has been observed from the study that, the gain of groundwater from river to aquifer occurs only for a short period from July to September. On the contrary loss of groundwater from aquifer to river occurs for a longer period from October to June. The magnitude and duration of groundwater loss from aquifer to river is higher in upper part than in lower part of the study area. During the study period the yearly average lateral groundwater outflow from aquifer to river was estimated as 0.29 Mm3 per kilometer varies from 0.20 Mm3 to 0.45 Mm3. The trend of lateral outflow from groundwater (aquifer to river) has been increasing over the years.

Dey et. al. (2013) Study conducted on Sustainability of Groundwater Use for Irrigation in North-West Bangladesh under National Food Policy Capacity Strengthening Programme implemented by FAO in collaboration with FPMU/Ministry of Food and Disaster Management with financial support of EU and USAID. Objective of the study was to quantitatively assess the trends in water table depths and crop areas in the designated study area for the past 30 years. Financial & economic profitability of different crops along with likely changes over time due to decline of water tables. Recommend policies for sustainable use of irrigation water in northwestern Bangladesh. The study area was five north-western districts of Bangladesh as Rajshahi, Pabna, Bogra, Rangpur and Dinajpur. Sample survey conducted through structured questionnaire, focus group discussion, consultation meeting and workshops have been done for this study. Secondary data have been collected from BWDB, BMDA, BADC and BBS. Study shows, within 10 major crops area, boro alone increased more than 9 times during 1980/81 to 2009/10. Study suggested according to crop pattern and benefit-cost ratio (BCR), wheat, potato, maize, mustard and these types of less irrigation demand crops should be emphasized in future.

IWM (2013) Deep Tubewell Installation Project Phase II, of Barind Multi Purpose Development Authority (BMDA) covers 65 Upazilas of Pabna, Sirajganj, Bogra, Gaibandha, Rangpur, Kurigram, Nilphamari and Lalmonirhat districts having gross area of 17, 455 km2 and cultivable area of 12, 765 km2. The objectives of this project was to assess upazilawise groundwater resources and recharge potential; surface water resource assessment; additional number of required DTWs. To fulfill the above objectives an extensive field data collection program was taken which includes test drilling, aquifer test, topographic and cross section survey, water quality test land water level measurement. Accordingly hydrogeological investigation upto 150m depth was conducted at 8 locations and 10 numbers of aquifer test were completed up to interim report. A model up to the depth of 80m was developed and a number of options were simulated to see the impact of irrigation expansion as well as impact due to climate change. It was found that within the study area, groundwater table (GWT) was from 1 to 13m from ground surface in dry period. In some areas of Bogra, Sirajganj & Pabna, groundwater level went below suction limit of Hand Tubewell (HTW) & Deep Tara Set (DTS) and Shallow Tubewell (STW) became inoperable in that period, but in monsoon it was recharged fully. Transmissivity and Hydraulic conductivity of the study area was good and potential for groundwater development. Upazilawise groundwater resources were estimated through water balance analysis. In order to

16 meet the future demand, it would be needed to install additional 14, 184 DTWs. It has been seen that due to climate change, the groundwater level may drop about 0.5 to 1.0m in some study areas. It was also identified that there is no separate aquifer in deeper strata up to 150m depth.

CHAPTER HYDROLOGICAL DATA COLLECTION

3.1 General

The study area is Bogra district consisting of 12 upazilas is shown in Figure 3.1. The study area is bounded on the north by Gaibandha and Joypurhat zila, on the east by Jamalpur and Sirajganj zila, on the south by Sirajganj and Natore zila, and on the west by Naogaon and Joypurhat zila. It lies between 24032' and 25007' north latitudes and between 88058' and 88095' east longitudes. According to BBS (2011), the total area of the study area is about 2900 sq km.

According to agricultural statistics (BBS, 2011), hottest month is October having monthly average temperature is 33.80C and coldest month is January having monthly average temperature is 10.10C. August is the month of maximum relative humidity is about 85% (monthly average) and March is the month of minimum relative humidity is about 66% (monthly average).

Figure 3.1: Map of the study area (Bogra district)

3.2 Data Collection for MIKESHE Hydrologic Model

According to the model requirements, significant amount of data have been collected from Institute of Water Modelling (IWM), Bangladesh Bureau of Statistics (BBS), Bangladesh Water Development Board (BWDB), Bangladesh Agricultural Development Corporation (BADC) and Bangladesh University of Engineering and Technology (BUET). Significant numbers of input data have been taken directly from the project titled as “Groundwater Resources Study and IIS Development of Pabna, Sirajgonj, Bogra, Gaibandha, Rangpur, Kurigram, Nilphamari and Lalmonirhat Districts through Mathematical Model Study” (IWM, 2013).

Only BWDB stations data has taken to prepare MIKE SHE model in current study; there were available data that were collected from BWDB stations and used in the mentioned project (IWM, 2013). The data has to be used in this study after checking quality & consistency, and then processed as per required format for the model running. In addition to the data quality checking, data analysis has to be carried out for estimation of different model parameters. For the model development of MIKE SHE hydrologic model (Figure 3.2) the following data is required.

Figure 3.2: Schematic diagram of MIKE SHE hydrologic model

Data Requirements for MIKE SHE Hydrologic Model are as follows:

o Hydrometeorology of the study area i.e. precipitation, Evapotranspiration, stream water level data

o Hydrogeology of the study area i.e. groundwater level & abstraction data o Land Use of the study area i.e. land use map & crop calendar throughout the year o Topography of the study area o Lithology of the study area including hydraulic properties of the aquifer

3.2.1 Hydrometeorology of the study area

Precipitation, Evapotranspiration, stream water level data are the main hydro-meteorological inputs for the groundwater model that is described below.

Precipitation There are eight BWDB rainfall stations (Table 3.1) that have influence in the study area as well as model area shown in Figure 3.3. Station wise data is recorded by BWDB on daily basis from 1985 to 2011; these data are collected from IWM. The mean rainfall in the study area has come out around 1775mm during the period 1985 – 2011. Missing data are filled up by taking average of the data of stations surrounding the station in question. It is assumed that the normal rainfalls of surrounding stations are within 10 to 12% of that concerned station (Subramanya, 1994). Quality checking of rainfall data includes visual inspection of plots, preparation of double mass curves, estimation of yearly mean values, and comparison of monthly values. An example plot of a double mass curve is given in Figure 3.4. More plots on rainfall data consistency checking are presented in Appendix-C. The analysis reveals that rainfall data are consistent for all the stations.

Table 3.1: Details of rainfall stations within/around the Bogra district (source: BWDB)

SL No. Station ID Station Name Data Availability 1 R006 Gabtali 1985 - 2011 2 R011 Dhunat 1985 - 2011 3 R022 Nandigram 1985 - 2011 4 R024 Nawkhila 1985 - 2011 5 R033 Sherpur 1985 - 2011 6 R169 Dubchanchia 1985 - 2011 7 R181 Khetlal 1985 - 2011 8 R216 Shibganj 1985 - 2011

Figure 3.3: Rainfall, Evaporation, Groundwater and River water level monitoring stations within/around the Bogra district (source: BWDB)

10000

8000

6000

4000

2000 Station R033 in R033 in Station mm

0 Accumulated Monthly Rainfall at 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure 3.4: Double mass curve for rainfall data under rainfall station, R033 in Sherpur upazila (period: hydrologic year 2006 to 2011)

According to rainfall data under Bangladesh Bureau of Statistics, The yearly average rainfall in the study area is about 1775 mm according data archive from 1985 to 2011. Only 7.5% precipitation falls in the dry months from November to April and rest 92.5% of precipitation falls from May to October in the wet season (Figure 3.5). Monthly distribution of rainfall and annual rainfall trend are shown in Figure 3.6 and Figure 3.7 as per BBS data. Deviation of yearly rainfall from the annual average rainfall (1775 mm) is shown in Figure 3.8; annual rainfall were fluctuated over the years from average rainfall and these deviation can extend due to climate change effect.

7.5 %

Dry Wet 92.5 %

Figure 3.5: Distribution of rainfall into Dry and Wet seasons (source: BBS)

400 352

319 292 295 300

193 200 152

Monthly Rainfall in mm 100 75

19 7 12 11 7 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Figure 3.6: Distribution of monthly rainfall for Bogra district (source: BBS, period: 1985-2011)

2500 2343 2290 2302

2198 2175

2086

2026

1943 1915

1866 1856 2000 1774 1771

1721

1657

1598 1560

1476 1464 1463 1456

1454 1432

1500 1335 1239 1204 1167

1000

Annual Rainfall in mm 500

0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year

Figure 3.7: Fluctuation of annual rainfall in Bogra district (source: BBS)

Figure 3.8: Rainfall deviation from annual normal rainfall in Bogra district (source: BBS)

Evapotranspiration

Evapotranspiration data have collected directly from the referenced IWM study (IWM, 2013). According to the collected data, BWDB maintains only one evaporation station in the study area shown in Figure 3.3 and Table 3.2. It has been observed that there is relatively little variation of Evapotranspiration between the study area and outside the study area. It is due to the fact that important parameters such as temperature and sunshine hours are largely similar across the area (IWM, 2013). From 1985 to 2011 data, annual evaporation value in the study area is around 1462mm (about 4 mm/day). As such, data from one station has been

23 used for the whole study area. The daily evaporation values outside the range of 2.0-7.0 mm have considered as rejected.

Table 3.2: Evaporation station in Bogra district (source: BWDB) Sl. Station ID Station Name Data Availability 1 CL6 Bogra 1985 - 2011

River Water Level Some major rivers and a list of beels are the main surface water sources in the study area. Major Rivers passing through the study area are Jamuna, Karatoya, Nagar, Bangali and Ichamati. Among all rivers, Jamuna is contributing major role and dominating surface water resources. Water level is recorded five times daily at these locations. These water level data has collected and processed for the period of 2005 to 2012. Collected data has checked by plotting hydrograph. The river water level data for other locations has generated by linear interpolation or extrapolation. For model build-up, calibrated river water level data from calibrated model under referenced IWM project is used (IWM, 2013). All calibrated data has checked with the nearest stations observed data. River water level hydrograph of River Jamuna at Mathurpara BWDB station is shown in Figure 3.9 that is showing least amount of water in the dry season compared to wet season. River water level hydrograph of Jamuna at Mathurpara station is shown in Figure 3.10.

Figure 3.9: River water level hydrograph for river Jamuna with maximum and minimum water level (source: IWM, at Mathurpara station; period: January to December, 2012)

Figure 3.10: River water level hydrograph for river Jamuna at Mathurpara station (source: IWM, period: 2005 to 2012)

3.2.2 Hydrogeology of the study area

Groundwater wells at study area boundary wells and abstraction data due to irrigation & domestic water requirement are discussed in this article.

Groundwater Level

Groundwater observation level data is an important parameter for the groundwater model as it is used for calibration, boundary condition and initial condition of the model. There are 30 groundwater observation wells of BWDB is selected in/around the study area is shown in Figure 3.11. Among them 6 observation wells are on the study area boundary, which have used as boundary condition and 24 observation wells are inside the study area which can be used for calibration purpose.

The frequency of measurement in the observation wells is generally conducted once in a week. The measured groundwater levels are expressed in terms of national datum, mPWD. Data has checked by visual inspection of those time series plots of groundwater levels and missing data is filled up by interpolation of nearby stations. However, topology, groundwater level fluctuation and rainfall pattern of those nearby stations are taken into consideration during filling the missing data.

Comparative contours of maximum groundwater table through study area for year 1985 and 2010 are shown in Figure 3.12 and Figure 3.13. Detail groundwater level hydrograph including average, wet and dry periodic trend is discussed in Article 5.2, Appendix–A and Appendix–B.

Figure 3.11: Groundwater observation wells under BWDB within/around the Bogra district (source: IWM)

Figure 3.12: Contours for maximum groundwater table for year 1985 (source: IWM)

Figure 3.13: Contours for maximum groundwater table for year 2010 (source: IWM)

Abstraction due to Water Use In the study area abstraction data is not available. To overcome this limitation, water abstraction data for 2005 to 2012 have been estimated. Main assumption behind this estimation that the irrigation and domestic water requirement is directly proportional to the rate of abstraction. Information on cropping pattern and crop coverage throughout the study area for different crops are the based data including domestic population data, abstraction obtained. Total abstraction by the DTWs and STWs for different cropping seasons (Rabi, Kharif-I and Kharif-II) have been estimated based on the seasonal irrigation water requirement.

Spatial and temporal variation of water demand due to irrigation and domestic water requirement has been considered for the study. Irrigation water requirement mainly depends on land use map, cropping pattern and intensities through the study area. Domestic water demand depends on population and their consumption pattern of that area.

Agricultural Practice In the study area, main crops area rice-paddy, jute, wheat, potato and variety of vegetables and they grow in rain fed and irrigated condition. The following major cropping patterns prevail within the study area based on survey data conducted by IWM in 2012 (IWM, 2013) is shown in Figure 3.14 as follows: 1. Boro-Fallow-Fallow 2. Boro-Fallow-T.Aman 3. Boro-Jute-Fallow 4. Boro-T.Aus-T.Aman 5. Homestead 6. Mustard-Boro-Jute 7. Sugarcane 8. Vegetable-Jute-T.Aman 9. Water Body 10. Wheat-Fallow-T.Aman

Boro, Wheat, potato and winter vegetables are the main Rabi (December to May) crops, while Kharif-I (May to August) crops are Aus & Jute and Kharif-II (August to December) grow HYV Aman, Local variety Aman and rainy season vegetables. Sugarcane grows in very small scale. Drought and inadequate irrigation facilities are the major limitations to intensive land use and optimum crop production.

In the Dry season (Rabi), major contribution of land is occupying boro rice about 53.2% of total study area and about 70% of crop area within Bogra district. Occupied area specially boro and non boro area is shown in Table 3.3 and Figure 3.15.

Table 3.3: Occupied area in rabi (dry) season for boro and non boro crops Sl. No. Area Type Area in sq.km % of Crop Area 1 Boro Area 1,570.10 About 70% 2 Non Boro Area 684.12 About 30%

Figure 3.14: Land use pattern throughout Bogra district (source: IWM; referenced year: 2012)

Figure 3.15: Land use pattern according to boro crop throughout Bogra district (source: IWM; referenced year: 2012)

Crop Calendar for Present Crop Pattern Water demand of wheat, vegetables and mustard are almost same (see Appendix-E). Wheat is considered as sample crop with representative demand of all other dry crops except boro. Jute and sugarcane is considered as null demand like as fallow land. Adopted crop calendar for the different cropping pattern in the study area based on IWM survey conducted in 2012 is in shown in Table 3.4.

Table 3.4: Crop calendar for Bogra district (source: IWM)

Month Land I.D. 1 2 3 7 July Fallow Fallow Aus Fallow August Fallow Fallow Aus Fallow T-Aman Fallow T-Aman T-Aman September T-Aman Fallow T-Aman T-Aman October T-Aman Fallow T-Aman T-Aman November T-Aman Fallow T-Aman T-Aman December T-Aman Fallow T-Aman T-Aman T-Aman Fallow T-Aman T-Aman January T-Aman Fallow T-Aman T-Aman HYV-Boro HYV-Boro HYV-Boro Wheat February HYV-Boro HYV-Boro HYV-Boro Wheat March HYV-Boro HYV-Boro HYV-Boro Wheat April HYV-Boro HYV-Boro HYV-Boro Wheat May HYV-Boro HYV-Boro HYV-Boro Wheat Fallow Fallow Aus Fallow June Fallow Fallow Aus Fallow

Crop Water Demand Upazila wise crop water requirement for different cropping pattern has been assessed by IWM under the referenced project according to 2012 survey (IWM, 2013) has shown in Appendix-E and considered as input data for the study. These data has used in this study for base irrigation demand in the year 2012.

According to crop water demand pattern, land use is categorized into seven (7) individual patterns is shown in Figure 3.16.

Figure 3.16: Land use pattern for the MIKE SHE hydrologic model (source: IWM, 2013)

Domestic Water Demand For domestic water demand calculation, Bogra district has subdivided into zila town, upazila town and rural areas; where, Bogra Sadar upazila has considered as zila town, urban population in rest of 11 upazilas has considered as upazila town and all rural areas has considered as rural. Per capita water demand is assumed as 120 l/c/d for zila town, 100 l/c/d for upazila town and 50 l/c/d for rural population respectively (Ahmed, M.F. & Rahman, M.M., 2003). Another Consideration is per capita demand is assumed constant from 1991 to 2030.

Domestic water demand for year 2012 is shown in Table 3.5 and details on domestic population and water demand are shown in Appendix-D.

Table 3.5: Population and domestic water demand in 2012

Upazila Area in Population 3 Upazila Water Demand in m /d sq.km. (BBS data) Adamdighi 173 196,545 11,848 Bogra Sadar 179 572,113 54,138 Dhunat 249 294,929 15,898 Dhupchanchia 163 178,266 10,061 Gabtali 251 321,735 17,173 Kahaloo 241 224,621 11,945 Nandigram 273 183,464 10,181 Sariakandi 436 273,350 14,648 Sherpur 300 339,397 19,763 Shibganj 320 382,752 20,278 Sonatola 139 189,450 10,770 Shajahanpur 225 295,407 18,005

Total Water Requirement Total water requirement is calculated to sum of the domestic and irrigation water demand for present year (2012) is shown in Table 3.6. For year 2012, domestic demand of Bogra district is about 5% of irrigation water demand.

Table 3.6: Water requirement in year 2012 for Bogra district Gross Cultivable Annual Water Requirement for Year 2012 Upazila Area Area Irrigation Domestic Total 3 3 3 (km2) (km2) Mm mm Mm mm Mm mm Adamdighi 173 144 106 616 4 25 110 641 Bogra Sadar 179 137 105 587 20 113 125 700 Dhunat 249 195 139 560 6 23 145 583 Dhupchanchia 163 101 88 538 4 23 92 561 Gabtali 251 203 150 595 6 25 156 620 Kahaloo 241 170 126 524 4 18 130 542 Nandigram 273 246 225 826 4 14 229 840 Sariakandi 436 278 194 446 5 12 199 458 Shahjahanpur 225 164 117 518 7 49 124 567 Sherpur 300 233 176 588 7 32 183 620 Shibganj 320 265 168 525 7 25 175 550 Sonatola 139 117 81 582 4 12 85 594

3.2.3 Topography of the study area

From updated topography of the study area, level variation throughout the study area is about 13m; from 10.35 mPWD in Dhunat to 23.57 mPWD in Shibganj upazila. Northern part of the study area is higher elevation part than that of southern part. Topography of the study area is shown in Figure 3.17.

Figure 3.17: Topography within/around the Bogra district (source: IWM, 2013)

The available topographic map of the study area was prepared in the sixties. Lot of development activities have taken place since then which have resulted in changes of land level, drainage catchments and settlement areas. Survey conducted by IWM for updating of land level of 20 km2 under the referenced project (IWM, 2013). Topographic digital elevation map (DEM) has been updated and now, is available in IWM.

3.2.4 Lithology of the study area

A general purpose subsurface lithology of the study area has been prepared by IWM through analyzing sedimentary structure, its grain size, hydraulic properties, its thickness and depth (IWM, 2013). Same litho-logical layers have been taken for this study to prepare MIKE SHE hydrologic model. Considering litho-logical variation and ground water flow capacity, 3 hydro-stratigraphic units of the study area have been defined as top soil, aquitard and aquifer (Emaduddin et.al, 2008). To check litho-logical layers in the study area, one bore hole has been conducted and litho-logical layers prepared from the data consistent with IWM referenced project (IWM, 2013). From this bore log data (Figure 3.18), upper aquifer (sand dominated layer) has found from depth 18m to 82m (about 64m); top soil (clay dominated layer) has found below ground level upto 6m depth, aquitard from 6m to 18m depth. The nearest cross section (Section A-A: Figure 3.20 and Section B-B: Figure 3.21) show similar litho-logical pattern that is used in this study. The bore hole location and nearest cross sectional plan is shown in Figure 3.19. Rest of the cross sections have been shown in Appendix-F.

Figure 3.18: Plan of sections for lithologic layers

(a) Bore log data (b) Adopted lithological layer Figure 3.19: Lithological layer selection from bore log data

Litho-logical Cross Section along the Easting

Top Soil

Aquitard

Aquifer Level in mPWD

Chainage in meter (start from north)

Figure 3.20: Lithological cross section A-A along easting 440000

Litho-logical Cross Section along the Easting

Top Soil

Aquitard

Aquifer Level in mPWD

Chainage in meter (start from west)

Figure 3.21: Lithological cross section B-B along northing 720000

Aquifer Properties Aquifer tests have been performed in accordance with IWM developed data base to understand the aquifer geometry and aquifer characteristics which include vertical & horizontal hydraulic conductivity and specific yield. These properties have been used as main parameters in groundwater model for assessing groundwater resource base and

36 development potential. For this study calibrated model data under referenced project (IWM, 2013) was used. It is found that in Bogra district area horizontal hydraulic conductivity mostly varies from 33 m/day to 75 m/day. High hydraulic conductivity indicates that the aquifer is highly permeable. Spatial distribution map of hydraulic conductivity is shown in Figure 3.22.

Figure 3.22: Distribution of hydraulic conductivity of the aquifer

Top Soil Soils of the study area are classified into five (5) groups: 1) Barind Track Soils, 2) Grey Terrace Soils, 3) Shallowly Flooded Silty Clays, 4) Tista Flood Plain Soils, and 5) Unstable Alluvium Charlands (IWM, 2013). All of soil types are suitable for growing rice paddy and vegetables throughout the years. Top soil in south-western part of Bogra district is clay dominated than north-eastern part (Shamsudduha et.al. 2010). Top soil condition map of the study area is shown in Figure 3.23.

Figure 3.23: Top soil condition of the study area

CHAPTER 4 DATA PROCESSING AND MODEL SETUP

4.1 General

Modelling of any physical phenomenon is an iterative development of a process. Model refinements are based on the availability and quality of data, hydrological understanding and scopes of the project. The general approach that has been followed in the current study can be summarized in the flowchart given in Figure 4.1.

Data Collection & Data Processing

Projection of data up to year 2030

Model Build-up

Calibration and Validation

Model Output

Figure 4.1: Flowchart of the general methodology for present study

4.2 Prediction of Data up to Year 2030

For prediction model of MIKE SHE, required data that has been projected are described in this article from year 2012 to 2030.

4.2.1 Precipitation

Predicted rainfall data (annual and monthly rainfall) of Bogra district from the year 2012 to 2030 has collected from Bangladesh University of Engineering and Technology under the study titled “Multi-Model Application for Climate Change Projections for Bangladesh and Assessment of Thesis Impacts on Small Drinking Water Systems” and relative works according to the study (Rajib, Rahman, Islam and McBean, 2010). The predicted rainfall under this study is shown in Figure 4.2. This data show high precipitation value compared with recorded previous data. Similar deviation of annual rainfall from annual average

39 rainfall is found between recorded rainfall (refer Figure 3.8) and predicted rainfall (Figure 4.3); so, same annual fluctuation trend of rainfall is considered in the present study. From projected annual rainfall of Bogra district (Figure 4.2 and Figure 4.3, year 2021 and 2022 can be said as consecutive drought years for minimum rainfall events having about 28% and 23% less rainfall from average annual rainfall. On 1st May, 2023 groundwater level is assumed to be critical due to consecutive droughts and irrigation water abstraction; the effects of phreatic surface after consecutive drought years will be discussed in year 2023, defined as critical year in the study.

5000

3954 3673

4000 3552 3376 3376 3304 2913 2890 2795 2751 2743 2743

3000 2516 2419 2348 2302 2227 2138 2136 2009 2000

1000 Annual Rainfall in mm

0 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year Figure 4.2: Projected annual rainfall (Source: Rajib, Rahman, Islam and McBean, 2010)

-15

-30 Average Rainfall (%)

RainfallDeviation from Annual -45 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year

Figure 4.3: Rainfall deviation from annual projected rainfall (Source: Rajib, Rahman, Islam and McBean, 2010)

The rainfall data from year 2012 to 2030 is used with a dividing factor on these projected data for realistic projection and the future run of the model, the key assumption of this study. For predicting years (2012 to 2030) predicted rainfall is considered the same as the average annual rainfall of the data from 1995 to 2011 is 1672mm. Projected rainfall considered in this study is shown in Figure 4.4. Also, projected rainfall data in 2011 has considered the same as recorded 2011 data is 1721mm. Distribution of monthly projected rainfall data throughout 8 rainfall stations using a multiplying factor for each station shown in Table 4.1.

2500 2354 2302 2198 2187 2115 2086 2026 2010 2010 1967 1943

2000 1856 1735 1721 1664 1657 1638 1634 1634 1598 1560 1498 1476 1464 1456 1454 1440 1398 1371 1335 1500 1326 1273 1272 1204 1196 1167

1000

Annual Rainfall in mm 500

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year Figure 4.4: Projected rainfall considered in present study (period: 2011-2030)

Table 4.1: Factors for rainfall data distribution within 8 rainfall stations SL Station ID Total Rainfall in mm from % of Total Multiplying Factor No. (BWDB) year 1985 to 2011 Rainfall (Source: IWM) 1 R006 53,456 13.84 1.11 2 R011 48,812 12.64 1.01 3 R022 46,986 12.16 0.97 4 R024 45,820 11.86 0.95 5 R033 47,988 12.42 0.99 6 R169 40,682 10.53 0.84 7 R181 52,733 13.65 1.09 8 R216 49,769 12.89 1.03 Average 48,281 12.50 1.0

4.2.2 Evapotranspiration

Evapotranspiration data found as almost constant value over the time from 1985 to 2011 as 4 mm/day (Chapter 3). Therefore, Evapotranspiration value is predicted (assumed) as steady state value 4 mm/day from 2012 to 2030.

4.2.3 River water level

For projection of river water level data, 5.11 cm/year depletion rate (refer Appendix-N) has been found for Jamuna River only; however in dry season, water level data for other rivers approaches to zero. Therefore for projecting model, water level follows the same time series plotted for year 2012 that has to be extended up to 2030. Projected water level plot of Jamuna River is shown in Figure 4.5.

Figure 4.5: Projection of river water level of Jamuna river (at Mathurpara; period: 2012-2030)

4.2.4 Groundwater level at model boundary

Projected groundwater level data for six (6) boundary wells are very important variables of the MIKE SHE hydrologic model. For projected groundwater level of the model boundary wells, two (2) boundary conditions named as “Boundary Condition 01” and “Boundary Condition 02” have been considered.

Boundary Condition 01: Assumption is, there is minor effect or no effect on groundwater table due to outer aquifer; so depletion rate has assumed as zero. A time series data plot including present and projected data for “Well Id GT3230006” has been shown in Figure 4.6.

Figure 4.6: Projection of groundwater levels for boundary condition 01 (period: 2012-2030)

Boundary Condition 02: Assumption is, same groundwater level depletion rate of individual model boundary wells will be continued over the years up to 2030. A time series data plot including present and projected data for “Well Id GT3230006” has been shown in Figure 4.7.

Figure 4.7: Projection of groundwater levels for boundary condition 02 (period: 2012-2030)

4.2.5 Irrigation water demand

According to National Water Management Plan (NWMP), irrigation demands are expected to increase potentially by at least a quarter over the next 25 years (WARPO, 2001). From 2012 to 2030, 3 sets of crop demand increment were taken that exponentially increased from 1.0 in 2012 to values of (i) no increment; (ii) 1.25 times of present demand and (iii) 1.50 times of present demand respectively in 2030 is shown in Figure 4.8. Year 2012 is

43 considered as base year (present demand year). To attain 1.25 times and 1.50 times of present demand, annual increment rate has been considered as 1.25% and 2.25% respectively. Land use (crop coverage area) expansion within the gross area is considered as key reason for the increment of water demand.

1.5 Annual increment rate: 1.25% 1.4 Annual increment rate: 2.25% 1.3 Same demand from 2012 to 2030

1.2

1.1

0.9

Crop waterdemannd in times 2012 2017 2022 2027 Projection Year

Figure 4.8: Irrigation demands considered in the models (period: 2012-2030)

4.2.6 Domestic water demand

Based on BBS (2011) population data, population projection up to year 2030 has been done. As per United Nations (2011), the population growth projections are shown in Table 4.2.

Table 4.2: United Nations (2011) considered population growth rate for Bangladesh

Rate of natural increase (per 1,000 Growth Rate as % population) Projection Type 2010- 2015- 2020- 2025- 2010- 2015- 2020- 2025- 2015 2020 2025 2030 2015 2020 2025 2030

Constant 15.4 15.0 14.0 12.6 1.54 1.50 1.40 1.26

High 15.6 15.0 13.7 11.6 1.56 1.50 1.37 1.16 Medium 13.5 11.9 10.1 8.2 1.35 1.19 1.01 0.82 Low 11.4 8.6 6.2 4.4 1.14 0.86 0.62 0.44

For urban and rural areas of Bogra district, growth rate for the projected year from 2015 to 2030 has been considered high and low rate respectively. Projected population has been shown in Appendix-D and density comparison between United Nations estimation and

44 studied data in Table 4.3. Density for the year 2011 was 1173 people per sq. km, according to BBS, 2011 data.

Table 4.3: Check projected population density with United Nations (2011) Population density (persons per square km) Comparison of Density 2011 2015 2020 2025 2030 1046 1111 1192 1272 1344 UN High Projection Medium 1045 1099 1162 1217 1263 for Low 1044 1088 1131 1161 1183 Bangladesh Constant 1046 1110 1191 1272 1351 Projection for Bogra district 1,173 1,244 1,307 1,359 1,401

Water demand using 5 years interval population data is shown in Figure 4.9. Adamdighi upazila as a typical example.

16,000

/d 3 12,000

8,000

4,000 Water Demand in m

0 1990 1995 2000 2005 2010 2015 2020 2025 2030 Projection Year

Figure 4.9: Projection of domestic water demand (period: 2012-2030)

4.3 Model Set Up

The groundwater model up to a depth of 80m for the study area was developed using MIKE SHE hydrologic modelling tools to understand the groundwater flow dynamics and to assess the groundwater resources under the present and various development scenarios. Model was developed covering entire study area with grids size of 1000m×1000m. After satisfactory calibration and verification of the model, the model has been applied for various development options to identify suitable cropping options for groundwater resources.

The model has been calibrated using data for the period 2005 to 2009. In order to get further reliability, calibrated model has also been validated using the recent data of 2010 to 2012. Finally taking the calibrated and validated parameters the model has been applied for various development scenarios by assigning future abstractions and corresponding land use patterns and thus to achieve the objectives set forth in the study particularly comparison of groundwater resources for different cropping pattern.

The model area spreads over 12 Upazilas of Bogra district having an area about 3479 sq km. The model area is higher than the study area. It is shown in Figure 4.10. This has been done in order to have less boundary impact.

Figure 4.10: Model area for MIKE SHE hydrologic model

4.3.1 Model build-up

Groundwater model setup involves a geometrical description and specification of physical characteristics of the hydrological system of the study area. The major components of the model setup include evapotranspiration, unsaturated zone, saturated zone, overland flow and river systems. Brief descriptions of the groundwater model setup are given below.

Simulation Specification The default time step control and computational control parameters for overland flow (OL), unsaturated zone (UZ) and Saturated Zone (SZ) have been used for entire simulation period (3rd May, 2005 to 31st December, 2030). However, simulation periods of the calibration, validation and prediction models were different and user specified.

Model Domain and Grid Size The study area has been discretized into 1000m x 1000m square grids as shown in Figure 4.11. The number of cells in x and y directions is 90 and 70 respectively. The model has 3721 grid cells, where 802 grids are within the boundary cells and the rest 2919 grids are computational cells are shown in Table 4.4. The grid cells are the basic units to provide all the spatial and temporal data as input and to obtain corresponding data as output. A geographical limit of the study area is shown in Table 4.5.

Figure 4.11: Model domain and grid

Table 4.4: Grid cells used for model setup 2 Upazilas of the Bogra District Numbers of Grid Cells (area: 1 km ) Upazila wise Study area Model area Adamdighi 166 Bogra Sadar 173 Dhunat 248 Dhupchanchia 164 Gabtali 246 Kahaloo 244 2919 3721 Nandigram 267 Sariakandi 434 Shahjahanpur 223 Sherpur 299 Shibganj 321 Sonatola 134

Table 4.5: Geographical limits of the study area Model Range Maximum Minimum Easting 480000 390000 Northing 710000 780000

Topography

A well-prepared digital elevation model (DEM) is essential for visualizing the floodplain topography and for accurate Modelling. A DEM of 300 m resolution has been developed to define the topography of the study area and used in the model. Topographic data for the study area has been extracted from the topographic database developed by FAP-19 based on irrigation planning maps available at IWM. The DEM has been updated using the surveyed data and updated topographic map was shown in Article 3.2.3.

Precipitation

Rainfall data is very essential input for the model. Precipitation data has already mentioned in previous articles (Article 3.2.1 and Article 4.2.1) that eight (8) rainfall stations are available in and around the model area. To account for the spatial variation in rainfall, the time series data for each station has been associated with an area. This area has been estimated by Thiessen Polygon Method. The rainfall data for the relevant stations have been collected from BWDB office. After checking the consistency of these data, the time series input files for precipitation have been computed, projected for future scenarios and

48 incorporated in the model. To account for the spatial variation in rainfall, the time series data for each station has been assigned to thiessen polygon is shown in Figure 4.12.

Figure 4.12: Thiessen polygons for individual rainfall station in the study area

Evapotranspiration

Time series data for the potential Evapotranspiration are given as input to the model. The evaporation data of Bogra station, used in the model is discussed in Article 3.2.1 and Article 4.2.2.

Land Use and Vegetation

Land use and vegetation are used in the model to calculate actual Evapotranspiration depending on the actual crops grown in the study area. The major part of the study area is agricultural land. It has homestead and water bodies also. Under the IWM mentioned project, spatial distribution of crops have been determined from a comprehensive field campaign. However, for the model input, these cropping types and cropping pattern have further been simplified considering the major crops that require irrigation water (refer Article 3.2.2).

River Systems

The river systems are included in the model described in Article 3.2.1 and Article 4.2.3. The river model is coupled with the groundwater model.

Surface Water- Groundwater Interaction

The aquifers are often fed by seepage from rivers, ponds and other water bodies or may discharge through seepage to feed rivers, ponds and water bodies. Two conditions may exist that determine how groundwater use has an effect on the surface water resources. These conditions are:

• An interconnected river and aquifer, where the river is losing water to the aquifer. • An interconnected river in which the river is gaining water from the groundwater.

In the first condition river losses will increase in response to groundwater pumping. In the second condition, river gains will decrease in response to groundwater pumping. In either case, groundwater pumping will result in a depletion of surface water. At high river stage, there is a direct lateral flow from the river to the aquifer induced by head difference. When the river stage is below the groundwater level the flow reverses and groundwater discharge from the aquifer to the rivers base flow.

Due to natural discharge and withdrawal for irrigation and domestic purposes the groundwater level in April attains the lowest level. As soon as the irrigation stopped, there is a rise of groundwater level due to recovery of groundwater and there is a sharp rise after June with the beginning of rainfall because about 30% of total rainfall goes to aquifer as recharge (Karim, 1972).

Jamuna contributes considerably to groundwater recharge in the Bogra district; contributions to groundwater recharge from other rivers are insignificant compared to Jamuna (IWM, 2013). Location of monitoring wells and surface water-groundwater interaction hydrograph for river Jamuna and surrounding groundwater conditions are shown in Figure 4.13 and Figure 4.14.

Figure 4.13: Location of river water and groundwater monitoring stations in Bogra district

Figure 4.14: Typical surface water-groundwater interaction plot

Overland Flow

When the net rainfall rate exceeds the infiltration capacity of the soil, water gets ponded over the ground surface. This water is then called surface runoff, to be routed down-gradient towards the river system. Overland water starts flowing when it exceeds the specified detention storage. Detention storage can be specified either as spatially distributed or as constant. Initial water depth on the ground surface is also required as input data that can also be distributed or constant. The study area is dominated by agricultural land and the main crops are different varieties of paddy. Overland flows are governed by the roughness of topography.

A lower value of roughness has been considered in the model since the area is mainly of agricultural land. A Manning number (M) 10 has been specified describing the surface roughness. Since the area is dominantly agricultural, a constant value has been considered for the entire area. Exchange of overland flow and groundwater flow occurs when a soil becomes completely saturated and at the same time there is pond water on the ground surface. Like river-aquifer exchange, leakage coefficient along with hydraulic conductivity is taken for overland-groundwater exchange.

Litho-logic Layers and Corresponding Properties

Same lithologic layers with aquifer properties that is discussed in Article 3.2.4 have used to prepare MIKE SHE hydrologic model. Horizontal hydraulic conductivity has taken for top soils & aquitard as per developed data base of IWM incorporated into calibrated model under referenced project (IWM, 2013).

Initial Condition of Groundwater Level

Initial conditions in terms of potential heads of groundwater have been specified in the model is shown in Figure 4.15. Potential heads of the monitoring wells are used to generate initial condition contour map and it is taken applicable for all the layers alike.

Figure 4.15: Initial groundwater level for the model on 3rd May, 2005

Boundary Condition

Boundary condition must be specified for all the layers along the boundary of the model area. In total 6 monitoring wells are available along boundary line of the study area. Two (2) boundary conditions for boundary groundwater level discussed in Article 4.2.4, are used in the model. The layers are leaky in nature and thus interconnected. Therefore the same boundary condition is applied in all the layers.

Pumping Wells and Abstractions

For model calibration and verification, water requirement and abstraction data for the period of 2005 to 2012 were needed, which required information of cropping pattern and crop coverage that has been discussed in Article 3.2.2.

Spatial and Vertical Discretization

The study area is discretized into 3721 cells having 1000m grid squares in its horizontal plan. The three computational layers define the vertical discretization of the 3-D groundwater model. Special consideration is given to the unsaturated zone, where the vertical resolution is as fine as 0.1m, 0.5m, 1m and 5m towards the increasing depths.

Model Calibration Parameters

Model calibration parameters for groundwater model are listed below:

o Horizontal Hydraulic Conductivity o Vertical Hydraulic Conductivity o Specific Yield o Storage Co-efficient

These parameters are applied on the 3 defined layers as top soil, aquitard and aquifer. Horizontal hydraulic conductivity is considered to be the same value that is shown in Figure 3.22. Specific yield and storage co-efficient is taken directly from the referenced project (IWM, 2013); that is not discussed in the present study. Mainly vertical hydraulic conductivity is calibrated with the range of one-fourth (1/4) to one-twentieth (1/20) of the horizontal hydraulic conductivity.

4.3.2 Model calibration

The purpose of model calibration is to achieve an acceptable agreement with measured data by adjusting the input parameters within acceptable range. The model has been calibrated for the period from 2005 to 2009. The calibration process is an iterative process, where the focus in mainly on the groundwater observations, and on when they have reached a reasonable fit with the observed data. The first step model calibration is the identification of the calibration targets. The second step consists of determining the acceptable range of errors between simulated and measured calibrated targets. At the third step, trial and error and inverse simulations have been performed until simulated parameters are within the acceptable range of errors.

The model consists of three layers, each one represented by 3721 active cells. This equates to a possible 3x3721 input variables that can be altered to achieve the calibration target. The

54 calibration has been based on the comparison between the calculated and observed head on original observation well data rather than interpolated values because of the uncertainty involved in the interpolation process. A set of 9 observation wells has been selected for calibration matching is shown in Figure 4.16. Calibration results for the wells of Bogra Sadar and Shibganj upazila are shown in Figure 4.17 and Figure 4.18 respectively. Rest of wells calibrated figures has been shown in Appendix-H.

Figure 4.16: Location of wells used for calibration and validation

Figure 4.17: Calibration of model for groundwater level at the well: GT1020005 at Bogra Sadar upazila

Figure 4.18: Calibration of model for groundwater level at the well: GT1094024 at Shibganj upazila

Reasons for Deviation between Simulated and Observed Groundwater Level In general, the overall calibration of the model is acceptable; but further improvement is possible. Reasons for deviation of calibration data between observed and simulated groundwater levels has been marked as:

i. There is no field observation data for crop water requirement and irrigation water demand. Irrigation water demand data collected from IWM under the referenced project (IWM, 2013). The project estimated crop water requirement for adopted land use pattern according to field survey conducted in 2012. Similarly, domestic consumption per capita has been taken as a general value that was assessed for whole Bangladesh from Ahmed, M.F. & Rahman, M.M. (2003) and industrial water demand has been taken as a percent of domestic demand. So, exact consumption has not been incorporated in the model as abstraction data. So, the abstraction due to irrigation as well as domestic & industrial use might not be the accurate one.

ii. Almost even distribution of irrigation water extraction has given overestimation of drawdown in low densely irrigation area and vise versa. In areas with a high density of DTW the simulated drawdown will be underestimated and in areas with a low density of DTW the drawdown might be overestimated. The hydrological response to a DTW and a STW is very different. As a DTW has higher capacity than a STW, the localized impact from a DTW is more likely to be higher than the impact from even a number of STW’s.

iii. Due to non-availability of position (X and Y coordinate) and screen depth of DTW, DTW could not be added into the model with their actual location and screen depth. In the model, all the irrigation abstraction by DTW and STW is distributed over the irrigated areas. Consequently the overall water balance is correct, as the correct

amount of water is abstracted. Though for some areas the local drawdown would not be simulated correctly.

iv. Simulated grid size is large that could not exactly behave as original geological formation and other local factors.

The calibration will be improved if the DTW abstraction is handled as an actual groundwater abstraction.

4.3.3 Model validation

To check whether the calibrated model is an adequate representation of the physical system or not, validation has to be carried out on the calibrated model. The common test for validation is to run the calibrated model to check whether the prediction reasonably matches the observation data set. In the present study the MIKE SHE hydrologic model has been validated by groundwater level of 9 wells for the period from 2010 to 2012. Validation results for the wells of Bogra Sadar and Shibganj upazila are shown in Figure 4.19 and Figure 4.20 respectively. Rest of wells validated figures are shown in Appendix-H.

Figure 4.19: Validation of model for groundwater level at the well: GT1020005 at Bogra Sadar upazila

Figure 4.20 Model validation against GWL of well id: GT1094024 at Shibganj upazila

Overall, validation results show similar trend of groundwater fluctuation and good matching of groundwater levels between observed and simulated values for the validation periods. From the results of the model validation, it could be concluded that the parameters used in the calibrated model are acceptable, thus the model can be used for prediction purposes.

CHAPTER 5 RESULTS AND DISCUSSIONS

5.1 General

To effectively manage water sectors, irrigation, domestic as well as environment; reliable assessment of groundwater resource is a vital part. Groundwater model attempts to simulate the behavior of groundwater system, to enhance the contemplating option in present state of problem & also to project for future. Water budget of the aquifer and its effect due to pumping, change in land use pattern, climate change and other variables on groundwater storage, stream flow and environmental variables are discussed in this chapter. Assessment of groundwater for the study area has been done based on water budget simulation and aquifer recharge for the period from 2005 up to 2012. From 2012 to 2030, projection model of groundwater & assessment of groundwater resource have been analyzed.

5.2 Groundwater Level at Study Area Boundary Wells (Period: 2005-2012)

Past records of groundwater level at boundary wells of the study area is a necessary input in MIKE SHE hydrologic model. These groundwater levels are recorded from BWDB wells located in study area boundary. Locations of boundary wells are shown in Figure 5.1.

Figure 5.1: Location of groundwater wells at model boundary

Groundwater levels at boundary wells (Source: BWDB) is analyzed and trend of groundwater level is determined for different period of the years using linear regression method. Groundwater table categorized as wet, average and dry period for maximum, average and minimum groundwater level respectively. From the analysis of the past records, it is observed that the trend of ground level is depleting over the years.

The depletion rate from the year 2005 to 2012 at Gobindaganj upazila for maximum, average & minimum groundwater level data are shown in Figure 5.2, Figure 5.3 and Figure 5.4 respectively. Details plot having depletion rate for the remaining 5 wells has been shown in Appendix-A.

Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 11.97 cm/year from maximum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure 5.2: Depletion trend of maximum groundwater level at Gobindaganj

5 Depletion Rate ≈ 7.41 cm/year from average data Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year

Figure 5.3: Depletion trend of average groundwater level at Gobindaganj

5 Depletion Rate ≈ 8.54 cm/year from minimum groundwater level Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure 5.4: Depletion trend of minimum groundwater level at Gobindaganj

Wet seasonal groundwater level is considered the average groundwater level in the month October & November when groundwater level reaches highest elevation with respect to other months. Similarly dry seasonal groundwater level is considered as the average groundwater level of the month April & May when groundwater level reaches lowest elevation with respect to other months. Average groundwater level is the yearly average of all recorded data. Depletion rate of groundwater level in selected model boundary wells for wet, average and dry period are shown in Table 5.1. Detail trend analyses are shown in Appendix-L.

Table 5.1: Depletion rates of groundwater level at model boundary wells (period: 2005-2012)

Depletion Rate in cm/year Groundwater well Corresponding From wet From From dry ID at model upazila at model period average period boundary boundary data data data 3230006 Gobindaganj 11.97 7.41 8.54 3288016 Saghatta 15.18 11.61 23.1 3958015 Madarganj 10.62 5.48 0 8850006 Kazipur 17.48 2.01 0 6991013 Singra 87.78 24.35 19.49 3861005 Khetlal 41.1 27.67 26.46

Depletion Rate in Wet Season It is observed for six (6) BWDB monitoring well data from Table 5.1 that, in wet season when water level stayed at maximum elevation, groundwater depletion rate varies widely for different boundary wells. The variation is about 11 cm/year for Madarganj upazila and about 88 cm/year for Singra upazila.

Depletion Rate in Dry Season Similarly, from six (6) BWDB monitoring well data (Table 5.1) that, in dry season when water level stayed at minimum elevation, groundwater depletion rate varies from about zero (groundwater level remain same) for Madarganj & Kazipur upazila to about 27 cm/year for Khetlal upazila.

Depletion Rate in Average Data It is found from six (6) BWDB monitoring wells for all recorded data (Table 5.1) that, groundwater depletion rates is about 2 cm/year for Kazipur upazila and about 28 cm/year for Khetlal upazila; almost similar rates for dry seasonal trend is used for the projection model up to 2030. Depletion trend for average groundwater level data of inside monitoring wells under Bogra district is supported by these trends which is shown in Figure 5.5 and details are shown in Appendix-B: Trend of groundwater level inside the study area.

Analyzing wet season, dry season and average data of groundwater wells it can be concluded that in recent years, depletion rate in western & south-western upazilas around/inside the study area is higher than eastern & north-eastern upazilas.

Figure 5.5: Depletion rate of groundwater level at the wells in the study area (period: 2005-2012; unit: cm/year; data: average groundwater level)

5.3 Predicted Phreatic Surface using MIKE SHE Hydrologic Model for Present Crop Pattern

Present crop pattern with present water demand according to base year 2012 has been extended up to year 2030. The phreatic surface from the MIKE SHE model is analyzed for present crop pattern in this article. The phreatic surface indicates the level of the water table; portion below this level is considered as saturated. Depth of phreatic surface is the vertical distance of water table from the surface.

Hydrographs for simulated phreatic surface at pre-selected location (Sherpur upazila: Figure 5.6) show that the maximum depth of groundwater table occurs within the period of April to May (end of the dry period) and minimum depth of groundwater table occurs within the period of October to November (end of the wet period) (Hydrograph: Figure 5.7). Hydrographs of observed groundwater table also support the above findings (IWM, 2013). To analyze the trend, phreatic surface on 1st May (maximum depth of phreatic surface) of each consecutive year is considered.

Figure 5.6: Rainfall stations in the study area and Sherpur upazila

Mean Depth of Pheratic Surface of the Sherpur upazila 0

-2

-4

-6 Jul Jul Jan Jan Jun Jun Oct Oct Feb Sep Feb Sep Apr Apr Surface (meter) Surface Dec Dec Mar Mar Aug Aug Nov Nov May May

Mean Depth of Pheratic 2022 2023 Year Figure 5.7: Hydrograph of phreatic surface under Sherpur upazila

Mean depth of phreatic surface is the average value of all cells within the domain having individual area of 1 km2. For Sherpur upazila (say), it is the average phreatic surface of 299 cells within the upazila. Maximum depth of phreatic surface is the maximum value of all cells within the domain having individual area of 1 km2. For Sherpur upazila (say), it is the maximum phreatic surface of 299 cells within the upazila.

5.3.1 Predicted trend and depletion rate of phreatic surface (period: 2006 to 2030)

The study area consists of 2919 individual grid cells, which having 1 km by 1 km grid area (Article 4.3.1). Sherpur upazila is divided into 299 cells that indicates MIKE SHE model have 299 depth of phreatic surface. Mean & maximum depth of phreatic surface under Sherpur upazila is the average & maximum value of 299 individual cells within the upazila respectively. Depletion rate using the model in Sherpur upazila is shown in Figure 5.8.

Mean Depth of Pheratic Surface Year May-06May -08May -10May -12May -14May -16May -18May -20May -22May -24May -26May -28May -30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 2.92 cm/year Depletion Rate for Maximum Pheratic Surface ≈ 14.45 cm/year Depth Depth of Pheratic Surface in meter -15 Figure 5.8: Depletion rate of phreatic surface for Sherpur upazila (period: 2006-2030)

Depletion rates of 12 upazilas in the study area are shown in Table 5.2; details analyses are shown in Appendix-L and details plot for remaining 11 upazilas are shown in Appendix-J. Spatial distribution of depletion rate for mean and maximum phreatic surface are shown in Figure 5.9 and Figure 5.10 respectively.

Table 5.2: Depletion rate of phreatic surface for all upazilas under Bogra district (period: 2006-2030)

Depletion Rate in cm/year from MIKE SHE Hydrologic Model Upazila under Mean depth of Phreatic Maximum depth of Phreatic Bogra district Surface of the upazila Surface of the upazila Adamdighi 2.26 2.59 Bogra Sadar 0.47 5.07 Dhunat 1.17 5.77 Dhupchanchia 0.32 4.89 Gabtali 0.14 3.21 Kahaloo 1.57 9.75 Nandigram 0.33 7.99 Sariakandi 0 1.20 Shahjahanpur 0.22 11.57 Sherpur 2.92 14.45 Shibganj 0.91 6.28 Sonatola 1.50 4.12

Figure 5.9: Depletion rate using mean depth of phreatic surface (spatial distribution map)

Figure 5.10: Depletion rate using maximum depth of phreatic surface (spatial distribution map)

This study found from the output of MIKE SHE hydrologic model, the average groundwater table is not intending to deplete so much up to year 2030. In the context of upazila wise depletion, Sherpur upazila is showing the highest probability of depletion at the rate of about 2.92 cm/year while Sariakandi upazila is showing depletion rate approaches to zero (Table 5.2).

Depletion rate in south-western part is comparatively higher for both mean and maximum phreatic surface than in north-eastern part (Figure 5.9 and Figure 5.10). Eastern and north- eastern part, especially river (Jamuna) surrounded region of the study area shows less depletion than western or south-western part. Another thing is that, higher hydraulic conductivity of the underlying aquifer under eastern and north-eastern part of Bogra district will accelerate higher recharge than other parts (Figure 3.22: Hydraulic Conductivity Map).

5.3.2 Annual fluctuation of phreatic surface with rainfall & abstraction

In this article fluctuation of groundwater table with fluctuation of rainfall & groundwater abstraction is discussed. Year 2022 and 2023 (input drought years) is selected to analyze monthly variation (first day in any month; say 1st January for month of January) of phreatic surface due to monthly fluctuation of rainfall & groundwater abstraction (Figure 5.11 to Figure 5.14).

Fluctuation of dry months phreatic surface due to dry months rainfall using mean and maximum depths are shown in Figure 5.15 and Figure 5.16 respectively. To analyze annual fluctuation of phreatic surface with rainfall, two (2) types of simulated data is analyzed. Maximum abstration period (March in each year) and maximum depth of phreatic surface (May in each year) are shown in Figure 5.17 to Figure 5.20. Rainfall for Sherpur upazila is taken from the station R033 (refer Figure 5.6).

Rainfall either in dry months or wet periods is the key factor for groundwater replenishment. Similary annual fluction of phreatic surface either mean or maximum depths depends mainly on annual fluctuation of rainfall. In dry months, abstraction exceeds recharge; that accelerates lowering trend of phreatic surface. In wet months, rainfall ensures enough recharge to replenish groundwater position. From January to May due to abstraction of groundwater for rabi crops with very less amount of rainfall, groundwater goes down rapidly. In this period, comparatively high rainfall can raise the water table up or vise versa (Figure 5.12).

Mean Depth of Pheratic Surface Monthly Rainfall 12 750

8 500

4 250

0 0

-4 -250 Monthly Rainfall in mm -8 -500 upazila (meter) upazila

-12 -750 Jul Jul Jan Jan Jun Jun Oct Oct Feb Sep Feb Sep Apr Apr Dec Dec Mar Mar Aug Aug Nov Nov May May 2022 2023 Mean depthMean ofpheratic surface in Sherpur Year

Figure 5.11: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (mean depth of phreatic surface*)

Maximum Depth of Pheratic Surface Monthly Rainfall 12 750

8 500

4 250

0 0

-4 -250 Monthly Rainfall in mm

-8 -500 Sherpur upazila (meter) upazila Sherpur

-12 -750 Maximum Maximum depth ofpheratic surface in Jul Jul Jan Jan Jun Jun Oct Oct Feb Sep Feb Sep Apr Apr Dec Dec Mar Mar Aug Aug Nov Nov May May 2022 2023 Year

Figure 5.12: Fluctuation of phreatic surface due to rainfall under Sherpur upazila (maximum depth of phreatic surface**)

* Mean depth of phreatic surface under Sherpur upazila is the mean value of 299 model cells within Sherpur upazila.

** Maximum depth of phreatic surface under Sherpur upazila is the single cell having maximum value within 299 model cells inside the Sherpur upazila.

Mean Depth of Pheratic Surface Monthly Abstraction 12 150

8 100

4 50

0 0

-4 -50 (meter) Monthly Abstraction in mm -8 -100

-12 -150 Jul Jul Jan Jan Jun Jun Oct Oct Feb Sep Feb Sep Apr Apr Dec Dec Mar Mar Aug Aug Nov Nov May May 2022 2023

Mean depthpheratic ofMeansurface in Sherpur upazila Year

Figure 5.13: Fluctuation of phreatic surface due to groundwater abstraction under Sherpur upazila (mean depth of phreatic surface)

Maximum Depth of Pheratic Surface Monthly Abstraction 12 150

8 100

4 50

0 0

-4 -50 upazila (meter) upazila Monthly Abstraction in mm -8 -100

-12 -150 Jul Jul Jan Jan Jun Jun Oct Oct Feb Sep Feb Sep Apr Apr Dec Dec Mar Mar Aug Aug Nov Nov May May Maximum Maximum depth ofpheratic surface in Sherpur 2022 2023 Year

Figure 5.14: Fluctuation of phreatic surface due to groundwater abstraction under Sherpur upazila (maximum depth of phreatic surface)

Depth of Mean Pheratic Surface Monthly Rainfall 12 600

8 400

4 200

0 0

-4 -200 Monthly Rainfall in mm -8 -400 upazila (meter) upazila

-12 -600 Jan Jan Jan Jan Jan Feb Feb Feb Feb Feb Apr Apr Apr Apr Apr Mar Mar Mar Mar Mar May May May May May 2020 2021 2022 2023 2024 Mean depthpheratic ofMeansurfacein Sherpur Year

Figure 5.15: Fluctuation of phreatic surface in dry months due to rainfall under Sherpur upazila (mean depth of phreatic surface)

Depth of Maximum Pheratic Surface Monthly Rainfall

12 600

8 400

4 200

0 0 Monthly Rainfall in mm -4 -200 upazila (meter) upazila -8 -400

-12 -600 Jan Jan Jan Jan Jan Feb Feb Feb Feb Feb Apr Apr Apr Apr Apr Mar Mar Mar Mar Mar May May May May May Maximum Maximum depthpheratic ofsurfacein Sherpur 2020 2021 2022 2023 2024 Year

Figure 5.16: Fluctuation of phreatic surface in dry months due to rainfall under Sherpur upazila (maximum depth of phreatic surface)

Depth of Mean Pheratic Surface Annual Rainfall 12 2400

8 1600

4 800

0 0 Annual Rainfall in mm -4 -800 upazila (meter) upazila

-8 -1600

-12 -2400 Meandepth of pheratic surface in Sherpur

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year

Figure 5.17: Fluctuation of phreatic surface due to rainfall under Sherpur st upazila (mean depth of phreatic surface; period: 1 March in each year***)

Depth of Maximum Pheratic Surface Annual Rainfall 12 2400

8 1600

4 800

0 0 Annual Rainfall in mm -4 -800

Sherpur upazila (meter) upazila Sherpur -8 -1600 Maximum Maximum depth ofpheratic surface in

-12 -2400 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year

Figure 5.18: Fluctuation of phreatic surface due to rainfall under Sherpur st upazila (maximum depth of phreatic surface; period: 1 March in each year***)

*** 1st March in each year is considered for the maximum abstraction period due to rabi crops.

Depth of Mean Pheratic Surface Annual Rainfall 12 2400

8 1600

4 800

0 0 Annual Rainfall in mm

upazila (meter) upazila -4 -800

-8 -1600 Mean depthMean ofpheratic surface in Sherpur

-12 -2400 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year

Figure 5.19: Fluctuation of phreatic surface due to rainfall under Sherpur st upazila (mean depth of phreatic surface; period: 1 May in each year****)

Depth of Maximum Pheratic Surface Annual Rainfall 12 2400

8 1600

4 800

0 0 Annual Rainfall in mm

-4 -800 upazila (meter) upazila

-8 -1600

-12 -2400 Maximum Maximum depth ofpheratic surface in Sherpur 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Year

Figure 5.20: Fluctuation of l phreatic surface due to rainfall under Sherpur st upazila (maximum depth of phreatic surface; period: 1 May in each year****)

**** 1st May in each year is considered for the maximum depth of phreatic surface in 1st May.

5.4 Predicted Phreatic Surface using MIKE SHE Hydrologic Model for Change in Crop Pattern

In this article, analysis for different crop pattern has discussed. Year 2022 & year 2023 are selected for maximum depth of predicted phreatic surface (at the end of dry period) due to consecutive drought years of 2021 and 2022 (Figure 5.19 and Figure 5.20). Irrigation water demand is predicted from year 2012 to year 2030 are discussed in Article 4.2.5. Three (3) types of crop pattern in dry season have considered as (i) present crop pattern (about 70% boro & 30% wheat), (ii) only boro in the whole crop area, and (iii) only wheat (a sample of less water demand crop) in the whole crop area. Also, three (3) types of crop water demand has considered as (i) no increment (same crop demand is considered from year 2012 to 2030), (ii) annual demand increment rate is 1.25% to attain 1.25 times of present demand, and (iii) annual increment rate 2.25% to attain 1.50 times of present demand.

5.4.1 Predicted depth of phreatic surface

For present crop pattern with present crop demand, predicted phreatic surface are discussed in Article 5.3. Predicted phreatic surface for variable demand crops are discussed in this article. Table 5.3 and Table 5.4 show predicted depths of mean and maximum phreatic surface for year 2023 respectively due to variable crops. To analyze the trend, phreatic surface on 1st May (maximum depth of phreatic surface) of each consecutive year is considered.

Table 5.3: Predicted depth of phreatic surface for change in crop pattern & water demands (mean depth of phreatic surface)

Depth of Mean Phreatic Surface (meter) in 2023* Depth of Present crop pattern; demand is Boro for all Wheat for all Mean Upazilas of considered: 2012-2030 crop areas; crop areas; Phreatic Bogra demand demand Surface Same Annual Annual district increment increment (meter) demand increment increment rate rate in 2012 up to rate: rate: (annually): (annually): 2030 1.25% 2.25% 1.25% 1.25% Adamdighi 4.8 4.96 5.63 6.29 5.83 4.26 Bogra Sadar 4.1 4.17 3.91 4.05 3.92 3.10 Dhunat 3.64 3.70 3.82 3.93 4.13 3.23 Dhupchanchi 4.46 4.53 5.04 5.54 5.37 3.91 Gabtali 3.49 3.65 3.39 3.12 3.18 2.84 Kahaloo 3.65 3.76 4.65 5.54 5.31 3.68 Nandigram 4.17 4.29 4.64 4.99 4.76 3.31 Sariakandi 3.96 4.02 3.79 3.55 3.74 3.18

Depth of Mean Phreatic Surface (meter) in 2023* Depth of Present crop pattern; demand is Boro for all Wheat for all Mean Upazilas of considered: 2012-2030 crop areas; crop areas; Phreatic Bogra demand demand Surface Same Annual Annual district increment increment (meter) demand increment increment rate rate in 2012 up to rate: rate: (annually): (annually): 2030 1.25% 2.25% 1.25% 1.25% Shahjahanpur 4.54 4.69 4.45 4.39 4.80 3.14 Sherpur 4.21 4.23 4.42 4.61 4.52 3.23 Shibganj 4.53 4.64 4.47 4.29 4.49 3.70 Sonatola 4.21 4.33 3.99 3.65 3.95 3.31

Table 5.4: Predicted depth of phreatic surface for change in crop pattern & water demands (maximum depth of phreatic surface)

Depth of Maximum Phreatic Surface (meter) in 2023* Depth of Present crop pattern; demand is Boro is Wheat is Maximum considered: 2012-2030 occupied all occupied all Phreatic crop area; crop area; Upazila Surface Same Annual Annual demand demand (meter) in demand increment increment increment increment 2012 up to rate: rate: rate rate 2030 1.25% 2.25% (annually): (annually): 1.25% 1.25% Adamdighi 7.53 8.59 9.44 10.53 9.99 6.78 Bogra Sadar 5.61 6.21 6.66 7.25 6.31 4.88 Dhunat 5.93 6.92 7.43 7.98 7.63 4.70 Dhupchanch 6.37 7.26 7.51 7.80 7.49 6.34 Gabtali 4.42 4.79 5.16 5.40 5.14 3.77 Kahaloo 4.82 6.42 10.15 13.97 11.02 5.68 Nandigram 5.41 7.28 9.17 11.99 9.85 4.81 Sariakandi 4.77 6.46 6.56 6.68 6.55 5.24 Shahjahanpu 8.11 9.40 10.16 11.04 10.67 5.99 Sherpur 6.22 9.73 10.74 11.87 10.89 5.40 Shibganj 6.04 6.68 7.17 7.14 8.02 6.46 Sonatola 5.83 6.11 6.30 5.85 6.57 5.00

* 2023 is selected, a most affected year up to 2030 after two consecutive drought years of 2021 and 2022. About 28% and 23% less rainfall is predicted from the average rainfall of Bogra district for year 2021 and 2022 respectively.

5.4.2 Predicted depletion of groundwater level after consecutive drought years

Year 2023 is considered as least rainfall affected year (after consecutive droughts) from about 20 projection years due to two consecutive droughts in year 2021 and year 2022. Depletion of phreatic surface from present condition (year 2012) to selected drought year (year 2023) is discussed in this article. Predicted depletion of phreatic surface (from year 2012 to 2023) due to change in crop pattern is shown in Figure 5.21 to Figure 5.24.

It is found from Figure 5.21 to Figure 5.24 that, for present crop pattern it is not so much alarming up to year 2030; that will not deplete phreatic surface so much due to selected droughts. But phreatic surface in some areas in south-western region can go far below from present level. But due to higher demand crops, a higher probability for depleting phreatic surface in a significant portion of the south-western zone of Bogra district. Specially for higher demand crops, phreatic surface of underlying aquifer under Adamdighi, Sherpur, Shahjahanpur, Nandigram & Kahaloo upazila is being predicted to go comparatively higher depths compare to other upazilas (Figure 5.22 to Figure 5.24) and this will require more energy cost to lift water for irrigation.

(a) mean depth of phreatic surface (b) maximum depth of phreatic surface Figure 5.21: Predicted depletion of phreatic surface for present crops (period: 2012-2023*)

* 2023 is selected, a most affected year up to 2030 after two consecutive drought years of 2021 and 2022. About 28% and 23% less water is predicted from the average rainfall of Bogra district for year 2021 and 2022 respectively (refer Figure 4.3)

(b) mean depth of phreatic surface (b) maximum depth of phreatic surface Figure 5.22: Predicted depletion of phreatic surface for present crops (period: 2012-2023; annual demand increment: 1.25%**)

** Annual demand increment rate: 1.25% is considered to attain 1.25 times of present demand in 2030.

(a) mean depth of phreatic surface (b) maximum depth of phreatic surface Figure 5.23: Predicted depletion of phreatic surface for present crops (period: 2012-2023; annual demand increment: 2.25%***)

*** Annual demand increment rate: 2.25% is considered to attain 1.50 times of present demand in 2030

(a) mean depth of phreatic surface (b) maximum depth of phreatic surface Figure 5.24: Predicted depletion of phreatic surface for boro crop in all crop areas (period: 2012-2023; annual demand increment: 1.25%)

5.4.3 Depth of phreatic surface above suction limit of hand tube well

Traditional hand tube well is used in major portion of the study area for drinking purpose. This analysis is attempted to show how many areas could be disturbed for higher demand crops in the dry period. Figure 5.25 to Figure 5.27 show the affected cells having depth of phreatic surface is above 6m (suction limit of traditional hand tube well).

Year 2012 Year 2023 for same crop demand Year 2023 for Demand Increment 1.25% Year 2023 for Demand Increment 2.25% 30

15 6 meter 10

0 % of Cells having depth of phreatic surface above above surface phreatic of depth having Cells of %

Upazila

Figure 5.25: Percent of cells having depth of phreatic surface is above 6m due to different crop pattern

Figure 5.25 shows for higher demand crops, number of cells having depth of phreatic surface above 6m are showing an increasing trend. But, in some areas this effect is significant than in other areas. Hand tubewells that are used in south-western upazilas of the study area are more vulnerable than north-eastern area (Figure 5.26 and Figure 5.27). More lifting mechanism is suspected to use in south-western upazilas for higher irrigation crops in the dry season.

(a) Year 2012 (b) Year 2023: Present crop water demand Figure 5.26: Number of cells having depth of phreatic surface above 6m for present crop pattern (same crop demand)

(a) Year 2023: Annual demand increment rate: 1.25% (b) Year 2023: Annual demand increment rate: 2.25% Figure 5.27: Number of cells having depth of phreatic surface above 6m for present crop pattern (change in crop demand

5.5 Assessment of Groundwater Resources in Bogra District

Groundwater resource of the study area has been assessed based on the recharge characteristics. According to the NWMP guideline, available resource is considered maximum potential recharge and usable recharge which is 75% of potential recharge (IWM, 2013). Recharge means the replenishment of groundwater storage that is depleted by withdrawal through artificial and natural processes. Recharge to groundwater begins with the rainfall from late May and continues up to October while recharge from irrigated crop field occurs from December to end of March. Recharge to groundwater depends on water balance components such as physical, climatic and hydraulic properties related to soil and aquifers.

5.5.1 Water balance components

Water Balance includes the hydrological components these are related with the inflow and outflow of any system. The differences between inflow and outflow are the net storage change within the system.

Water balance components of Bogra district for the period of 1st January, 2011 to 2nd January, 2012 is shown in Figure 5.28 and for this data, water balance in tabular form is given in Table 5.5.

Figure 5.28: Water balance components of the study area for year 2011

Table 5.5: Water balance components of the study area from 1st January, 2011 to 2nd January, 2012

Unsaturated Saturated Zone River System GW Sl. Rechar Discharg No Components Inflow Outflow Inflow Outflow Inflow Outflow ge e . (mm) (mm) (mm) (mm) (mm) (mm) (mm) (mm) 1 Rainfall 1345 — — — — — — —

2 Evapotranspiration — 1377 — — — — — — (ET) 3 Abstraction — — — 975 — — — — 4 Irrigation 975 — — — — — — —

5 Capilary rise & ET 0 — — 0 — — — — from SZ to UZ

6 Deep Percolation — 943 943 — — — 943 0 from UZ to SZ 7 Boundary Flow 7 6 19 104 — — 0 85 8 Base Flow — — 0 0 0 0 0 0 9 Overland flow to — 6 — — 6 — — — 10 Drain flow to river — — 0 4 4 0 0 4 11 OL storage Change 0 4 — — — — — — 12 Drain — — 0 4 — — 0 4 Total 2327 2336 962 1087 10 0 943 93

Net Balance = -9 -125 10 850

Explanation Inflow/Outflow of Change in Storage River/Aquifer Net Recharge

Percentage of ET to applied water 59% (rainfall + irrigation) = Percentage other components 63% to applied water (rainfall +

Percentage net recharge to applied water 37% (rainfall + irrigation) =

`From the Table 5.5 it is appeared that, in unsaturated zone (UZ), 2327mm water enters into the system mainly from rainfall and irrigation while 2336mm water goes out from UZ mainly as Evapotranspiration (ET), Deep percolation to saturated zone, overland flow to river and outflow through boundary. The amount of ET is 59% of applied water (rainfall and irrigation application). The net flow, 943mm from the UZ enters into the SZ. In addition, another 19mm enters into SZ making total inflow of 962mm while 1087mm goes out from SZ resulting in a negative change of storage of 125mm. From the table mentioned above, net recharge has been found is 850mm for the study area.

5.5.2 Groundwater recharge

Two boundary conditions based on groundwater level projection are considered for projection model (Article 4.2.4); 3 crop patterns & 3 crop demand increment pattern (Article 4.2.5) are the main predicting scenarios of the study.

Effect of Boundary Condition for Groundwater Level To analyze the effects of boundary condition on the annual recharge in the groundwater model, two model scenarios for boundary conditio 01 and boundary condition 02 (refer Article 4.2.4) have been considered for present crop pattern that will be continued from 2012 to 2030. Water demand in 2030 has considered as 1.25 times of present (2012) crop demand (annual demand increment rate is 1.25%). Annual recharges for both boundary condition 01 & 02 are shown in Figure 5.29 and upazila wise comparison for drought year is shown in Figure 5.30.

Annual applied water is defined as the sum of the annual rainfall water and annual abstraction water from groundwater table.

Recharge for Boundary Condition 02 Recharge for Boundary Condition 01 Applied Water Annual Rainfall in mm 4000

3500

3000

2500

2000

1500 applied water in mm 1000

500 Annual rechage,annual rainfall and annual 0 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 Year Figure 5.29: Comparison of annual recharge between boundary condition 01 and boundary condition 02 for present crops (period: 2012 to 2030)

Recharge for Boundary Condition 01 Recharge for Boundary Condition 02 2000

1500

1000

500 Annual Rechage in mm

0 Dhunat Gabtali Sherpur Kahaloo Sonatola Shibganj Sariakandi Nandigram Adamdighi Bogra Sadar Shahjahanpur Dhupchanchia

Upazila

Figure 5.30: Comparison of annual recharge using boundary condition 01 and 02 in drought year for present crops (upazila wise; year: 2022)

Figure 5.29 and Figure 5.30 show, there is a minor effect of boundary condition on annual recharge that can be neglected for other prediction models. So, model scenarios for boundary condition 01 is considered to analyze phreatic surface that is discussed in Article 5.3 and Article 5.4. The fluctuation of annual recharge is dependent on the yearly fluctuation of annual applied water.

Effect of the Increase of Crop Water Demand To analyze the effects of crop water demand for the projecting years, 3 model scenarios are considered for present crop pattern from the year 2012 to 2030. 3 crop water demand up to 2030 has been considered. These are no increment of crop water demand, increment rate 1.25% per year and increment rate 2.25% per year (Article 4.2.5). Comparison of annual recharge for different demand crops is shown in Figure 5.31. Details on annual recharge assessment for different crops are shown in Appendix-I.

Same Demand (No increment) Annual increment rate is 1.25% Annual increment rate is 2.25% 2000

1500

1000

500 Annual Rechage in mm

0 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 Year Figure 5.31: Comparison of annual recharge due to increment of crop demand for present crops

From the Figure 5.31 it is observed that, groundwater recharge is increasing with the increment of crop water demand. The fluctuation of annual recharge is dependent mainly on the fluctuation of annual applied water.

Effect of Change in Crop Pattern To analyze the effects of change in crop pattern on annual recharge, crop water demand increment is considered for all 3 model scenaios as 1.25% per year. The effect of change in crop pattern on annual recharge is shown in Figure 5.32.

Present Crops Boro in all crop area Wheat in all crop area 2500

2000

1500

1000

500 Annual Rechage in mm

0 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 Year Figure 5.32: Effects of crop pattern on groundwater recharge for same fluctuation of annual rainfall from 2012 to 2030

Figure 5.32 shows the fluctuation of annual recharge for different crop pattern that depends mainly on annual applied water. High water demand crop (e.g. Boro) is the reason for more groundwater abstraction, that increases the applied water eventually which increases groundwater recharge. The effect of change in crop pattern on groundwater recharge corelate with applied water is shown in Figure 5.33

Present Crops Boro in all crop area Wheat in all crop area 55% 50% 45% 40% 35% 30% 25% 20%

% Recharge% to Applied Water 15% 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year Figure 5.33: Effects of crop pattern on percent of groundwater recharge to applied water

For present crops, predicted groundwater recharge is assessed up to year 2030, which is about 30% to 45% of applied water. This percentage of ground wate recharge is assessed higher for higher annual rainfall. For higher water demand crops in future the groundwate recharge will be higher up to 53% of applied water. Applied water is the sum of the rainfall and irrigation water.

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusions

The objective of the study is to assess the groundwater resources of the underlying aquifer system of the Bogra district using MIKE SHE hydrologic model. The recorded groundwater level and other relevant data are used to predict the phreatic surface up to year 2030. Assessment of groundwater resources are also done for average rainfall & drought rainfall events. The other factors consider in the model are change in crop pattern and increase of crop water demand.

The main conclusions drawn from this study are given below:

 Trend of phreatic surface

o Uneven lowering trend of phreatic surface has found in different upazilas of Bogra district for present crop pattern from year 2006 to 2030. The depletion rates vary from 0.00 to 2.92 cm/year for mean depth of phreatic surface. Maximum depth of phreatic surface varies from 1.20 cm/year to 14.45 cm/year.

o South-western upazilas of Bogra district (Kahaloo, Nandigram, Shajahanpur & Sherpur) shows higher rate of groundwater depletion. In rest of the upazilas of Bogra district are comparative lower rate of groundwater depletion. Sariakandi upazila shows no change in phreatic suface.

 Lowering of phreatic surface after drought (about 70% of average rainfall) years

o Yearly fluctuation of phreatic surface of Bogra district depends on annual rainfall pattern. Lower phreatic surface is found after drought rainfall years.

o After drought rainfall year, predicted depletion of mean phreatic surface in north- eastern upazila is less (depletion is about 1meter).

o In south-western upazilas of Bogra district, mean phreatic surface may deplete up to 3m. However in few cells, it may extend up to 9m.

 Groundwater recharge

o For average rainfall year, depletion of mean phreatic surface is not significant throughout the Bogra district. Maximum depletion of mean phreatic surface is only 0.53 meter from 2012 to 2030. Lowering of phreatic surface in drought rainfall year is recovered by subsequent higher rainfall. Contribution to groundwater recharge in Bogra district is mainly due to rainfall.

6.2 Recommendations for Future Research

The recommendations for the future research are as follows:

 The model setup was prepared using average rainfall event (from 1995 to 2011) and rainfall projection data is taken from the study (Rajib, Rahman, Islam and McBean, 2010). It is recommended to incorporate rainfall events considering recent climate change and other climate-logical effects for the future research.

 Spatial variation of the depth of phearatic surface considering an upazila as a unit is presented. Future research with micro-zonation at union level is recommended.

 It is recommended to use other groundwater/hydrologic software models similar to MIKE SHE model.

REFERENCES

Abdullah, M. (2014). Depleting Trend of Fresh Water Resources with Increase in Water Use is a Threat: A Case Study of Bogra District”. Technical Journal; River Research Institute, Faridpur, Bangladesh.

Ahmed, M.F. and Rahman, M.M. (Eds.) (2003). Water Supply & Sanitation: Rural and Low Income Urban Communities. ITN-Bangladesh, BUET, Dhaka. ISBN 984-31-0936-8.

Akram, F. (2008). A Comparative Assessment of Groundwater Resource and Recharge Potential in Barind Area using MODFLOW and MIKE SHE models. Master of Science in Water Resources. Bangladesh University of Engineering and Technology.

Altaner, S. (2012). Physical Resources: Water, Pollution, and Minerals - Chapter Introduction. Connexions module: m41523. Revision 3.

Amin, M.N. (2009). Ground Water Utilization for Agricultural Development: Some Policy Suggestions. Thoughts on Economics, Vol. 19, No. 01. ISSN: 02568586.

BADC [Bangladesh Agricultural Development Corporation] (2005). Minor Irrigation Report 2004-05. Government of the People’s Republic of Bangladesh. Ministry of Agriculture.

BADC [Bangladesh Agricultural Development Corporation] (2008). Minor Irrigation Report 2007-08. Government of the People’s Republic of Bangladesh. Ministry of Agriculture.

BADC [Bangladesh Agricultural Development Corporation] (2011). Minor Irrigation Report 2010-11. Government of the People’s Republic of Bangladesh. Ministry of Agriculture.

BARI [Bangladesh Agricultural Research Institute] (1990). Means of profitable wheat cultivation. Wheat Research Center, Bangladesh Agril. Res. Inst. Nashipur, Dinajpur. Page 1-11.

Basak, J.K., Ali, M.A., Islam, M. N. and Rashid, M.A. (2010). Assessment of the effect of climate change on boro rice production in Bangladesh using DSSAT model. Journal of Civil Engineering (IEB), 38 (2) (2010) 95-108.

BBS [Bangladesh Bureau of Statistics] (1991). Bangladesh Population & Housing Census. Government of the People’s Republic of Bangladesh. Ministry of Planning.

BBS [Bangladesh Bureau of Statistics] (2001). Bangladesh Population & Housing Census. Government of the People’s Republic of Bangladesh. Ministry of Planning.

BBS [Bangladesh Bureau of Statistics] (2005). Statistical pocket book of Bangladesh. Government of the People’s Republic of Bangladesh. Ministry of Planning. P.194.

BBS [Bangladesh Bureau of Statistics] (2008). Bangladesh Agriculture Census. Government of the People’s Republic of Bangladesh. Ministry of Planning Retrieved from http://www.bbs.gov.bd/.

BBS [Bangladesh Bureau of Statistics] (2011). Bangladesh Population & Housing Census. Retrieved from http://www.bbs.gov.bd/.

Bedient, P.B. and Huber, W.C. (2002). Hydrology and Floodplain Analysis, 3rd Edition, Prentice Hall, NJ.

Bhuyan, A.R. (n.d.). Millennium Development Goals (MDGs): A Review of Bangladesh’s Achievements. Retrieved from Online database.

Chow, V.T., Maidment, D.R. and Mays, L.W. (1988). Applied Hydrology. McGraw-Hill Series in Water Resources and Environmental Engineering. McGraw-Hill Book Company. New York. ISBN 0-07-100174-3. de Silva, C.S., Weatherhead, E.K., Knox, J.W. et. al. (2007) Predicting the impacts of climate change - a case study on paddy irrigation water requirements in Sri Lanka. Agric Water Manage 93(1-2): 19-29.

Dey, N.C., Bala, S.K., Islam, A.K.M.S. Rashid, M.A. and Hossain M. (2013). Sustainability of Groundwater Use for Irrigation in Northwest Bangladesh, brac, Bangladesh.

DHI [Danish Hydraulic Institute] (2011), MIKE SHE User Manual Volume 2: Reference Guide, edited.

DPHE [Department of Public Health Engineering] (2014). http://www.dphe.gov.bd.org. 24 July, 2014.

Elgaali, E., Garcia, L.A. and Ojima, D.S. (2007). High resolution modeling of the regional impacts of climate change on irrigation water demand. Climatic Change 84:441–461.

Emaduddin, A., Afzal, H., and Hassan, M. (2008). Water scarcity, global changes and groundwater management responses in the context of northwest region of Bangladesh.

Essink, G.O. (2000). Groundwater Modelling. Utrecht University, Interfaculty Centre of Hydrology Utrecht, Institute of Earth Sciences, Department of Geophysics, L4018/GWMI (2000).

FAO [Food and Agriculture Organization] (2012). Coping with water scarcity. An action framework for agriculture and food security. Retrieved from http://www.fao.org/docrep/016/i3015e/i3015e.pdf

Fischer, G., Tubiello, F.N., van Velthuizen, H. et. al. (2006). Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080. Technological Forecasting & Social Change 74: 1083–1107.

Graham, D.N. and Butts, M. B. (2005) Flexible, integrated watershed modelling with MIKE SHE. In Watershed Models, Eds. V.P. Singh & D.K. Frevert Pages 245-272, CRC Press. ISBN: 0849336090.

Harvey, C.F., Ashfaque, K.N., Yu, W., Badruzzaman, A.B.M., Ali, M.A., Oates, P.M., Michael, H.A., Neumann, R.B., Beckie, R., Islam, S. and Ahmed, M.F. (2006). Groundwater Dynamics and Arsenic Contamination in Bangladesh, Chemical Geology, 228, 112-136.

Islam, M.T. (2009). A Study on Barind Aquifer-Ganges River Interaction. Master of Engineering in Water Resources. Department of Water Resources Engineering. Bangladesh University of Engineering and Technology.

IWM [Institute of Water Modelling] (2006). Groundwater Model Study for Deep Tube Well Installation Project in Barind Area. Final Report. Dhaka, Bangladesh.

IWM [Institute of Water Modelling] (2013). Groundwater Resources Study and IIS Development of Pabna, Sirajgonj, Bogra, Gaibandha, Rangpur, Kurigram, Nilphamari and Lalmonirhat Districts through Mathematical Model Study. Interim Report.

Jahan, C.S., Mazumder, Q.H., Islam, A.T.M.M. and Adham, M.I. (2010). Impact of irrigation in barind area, NW Bangladesh-an evaluation based on the meteorological parameters and fluctuation trend in groundwater table. Journal Geological Society of India.

Karim, M. A. (1972). Ground Water Potential in Northern Areas of Dinajpur and Rangpur Districts, Bangladesh Water Development Board.

LGD [Local Government Division] (2011). Sector Development Plan (FY 2011-25), Water Supply and Sanitation Sector in Bangladesh. Dhaka: Ministry of Local Government, Rural Development and Cooperatives, Government of the People's Republic of Bangladesh.

Luby, S. (2013). Water management for a healthy Bangladesh: The need for multi-sector collaboration. Paper presented at National Public Health Conference, Dhaka, Bangladesh. 15 January.

NWMP [National Water Management Plan] (2001). Main Report, Volume 2, Ministry of Water Resources. Government of Bangladesh.

Rahman, M.M., Kamal, A.H.M., Mamun, A.A. and Miah, M.S.U. (2011). Study on the Irrigation Water Distribution System developed by Barind Multipurpose Development Authority. Journal of the Bangladesh Association of Young Researchers (JBAYR): Volume 1, Number 2, June 2011, Page: 63-71, ISSN 1991-0746 (Print), ISSN 2220-119X (Online). DOI: 10.3329/jbayr.v1i2.10032.

Rahman, M.R. and Saha, S.K. (2008). Remote Sensing, Spatial Multi Criteria Evaluation (SMCE) and Analytical Hierarchy Process (AHP) in Optimal Cropping Pattern Planning for a Flood Prone Area. International Journal of Spatial Science. Vol. 53, No. 2, December 2008. Page 161-177.

Rajib, M.A. (2010). Multi-Model Application for Climate Change Projections for Bangladesh and Assessment of Thesis Impacts on Small Drinking Water Systems. M.Sc thesis in Civil ( Environment) Engineering. Bangladesh University of Engineering and Technology. October, 2010. BUET Central Library, 551.69095492/AND/2010.

Rajib, M.A., Rahman, M.M., Islam, A.K.M.S. and McBean, E.A. (2010). Analyzing the Future State of Monthly Precipitation Pattern in Bangladesh from Multi-Model Projections using both GCM and RCM. World Environment & Water Resources Congress 2011, American Society of Civil Engineers (ASCE), Palm Spring, California, USA.

Razzaque, M.A., Suffium, M.A. and Badruddin, M. (1992). Wheat in national economy of Bangladesh. Crop Sci. Proc. First Biennial Conf. CSSB, 18-20 January, 1992: 13-26.

Rodriguez, Diaz, J.A., Weatherhead, E.K., Knox, J.W. et. al. (2007). Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Reg Environ Change (2007) 7:149–159.

Sattar, M.A. and Haq, K.A. (n.d). Hydrogeological Potentiality of Intensive Farmer Managed Tube Wells Irrigation System in Bangladesh: A Case Study. HO15149.

Shahid, S. (2008). Spatial and temporal characteristics of droughts in the western part of Bangladesh. Hydrol Process 22: 2235–2247. doi:10.1002/hyp.6820

Shahid, S. (2011). Impacts of Climate Change on Irrigation Water Demand in Northwestern Bangladesh. Climatic Change 105(3-4): 433-453.

Shahid, S. (2012). Climate Change Impacts on Groundwater-Dependent Irrigation System in Northwest Bangladesh. International Conference on Climate, Water and Policy (ICCWP) 2012, Busan, Republic of Korea 11-13 Sep 2012. http://www.apcc21.org/

Shahid, S. and Behrawan, H. (2008). Drought risk assessment in the western part of Bangladesh, Journal of Natural disaster. 46 (3): 391-413.

Shamsudduha, M., Taylor, R.G., Ahmed, K.M. and Zahid, A. (2010). The impact of intensive groundwater abstraction on recharge to a shallow regional aquifer system: evidence from Bangladesh. Electronic Supplementary Material (ESM). HJ-2010-1619. Page 2, 13.

Shaw R., Nguyen H., Habiba U. and Takeuchi Y. (2011): Overview and characters of Asian monsoon drought, in Drought in Asian Monsoon Region, page 35, Emerald Publisher, UK.

Siebert, S. and D¨oll, P. (2010). Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation, J. Hydrol., 384(3– 4), 198–217, doi:10.1016/j.jhydrol.2009.07.031, 2010.

Subramanya, K. (1994). Engineering Hydrology. Second Edition. Tata McGraw Hill Publishing Company Limited. ISBN 0-07-462449-8.

Thompson, J., Porras, I.T., Tumwine, J.K., Mujwahuzi, M.R., Katui-Katua, M., Johnstone, N. and Wood, L. (2001). 30 years of Change in Domestic Water Use & Environmental

Health in East Africa. International Institute for Environment and Development. Russell Press, Nottingham.

UN [United Nations] (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition. Department of Economic and Social Affairs, Population Division.

UNICEF [United Nations International Children's Emergency Fund] (2011). National Policy on Drinking Water. UNICEF, Bangladesh.

USGS [United States Geological Survey] (2004). Ground-Water Depletion Across the Nation. USGS- science for a challenging world. http://water.usgs.gov/ogw.

Yano, T., Aydin, M. and Haraguchi, T. (2007). Impact of Climate Change on Irrigation Demand and Crop Growth in a Mediterranean Environment of Turkey. Sensors 7: 2297- 2315.

APPENDIX-A

Trend of Groundwater Level for Boundary Wells

APPENDIX-A: Trend of Groundwater Level for Boundary Wells

5 Depletion Rate ≈ 10.62 cm/year from maximum groundwater level Ground Level mPWD in Water Ground 0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A1: Trend of wet seasonal groundwater level in Madarganj (Well ID: 3958015)

5 Depletion Rate ≈ 5.48 cm/year from average data Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A2: Trend of groundwater level for average data in Madarganj (Well ID: 3958015)

10 Ground Level mPWD in Water Ground 5 Depletion Rate ≈ almost zero from minimum groundwater level 0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A3: Trend of dry seasonal groundwater level in Madarganj (Well ID: 3958015)

A-1

Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 11.97 cm/year from maximum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A4: Trend of wet seasonal groundwater level in Gobindaganj (Well ID: 3230006)

5 Depletion Rate ≈ 7.41 cm/year from average data Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A5: Trend of groundwater level for average data in Gobindaganj (Well ID: 3230006)

5 Depletion Rate ≈ 8.54 cm/year from minimum groundwater level Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A6: Trend of dry seasonal groundwater level in Gobindaganj (Well ID: 3230006)

A-2

10 Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 15.18 cm/year from maximum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A7: Trend of wet seasonal groundwater level in Saghatta (Well ID: 3288016)

5 Depletion Rate ≈ 11.61 cm/year from average data Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A8: Trend of groundwater level for average data in Saghatta (Well ID: 3288016)

Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 23.10 cm/year from minimum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A9: Trend of dry seasonal groundwater level in Saghatta (Well ID: 3288016)

A-3

Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 41.10cm/year from maximum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A10: Trend of wet seasonal groundwater level in Khetlal (Well ID: 3861005)

5 Depletion Rate ≈ 27.67 cm/year from average data Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A11: Trend of groundwater level for average data in Khetlal (Well ID: 3861005)

5 Depletion Rate ≈ 26.46 cm/year from minimum groundwater level Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A12: Trend of dry seasonal groundwater level in Khetlal (Well ID: 3861005)

A-4

Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 87.78 cm/year from maximum groundwater level 0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A13: Trend of wet seasonal groundwater level in Singra (Well ID: 6991013)

5 Ground Level mPWD in Water Ground Depletion Rate ≈ 24.35 cm/year from average data 0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A14: Trend of groundwater level for average data in Singra (Well ID: 6991013)

10 Ground Level mPWD in Water Ground 5 Depletion Rate ≈ 19.49 cm/year from minimum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A15: Trend of dry seasonal groundwater level in Singra (Well ID: 6991013)

A-5

5 Depletion Rate ≈ 17.48 cm/year from maximum groundwater level Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A16: Trend of wet seasonal groundwater level in Kazipur (Well ID: 8850006)

5 Depletion Rate ≈ 2.01 cm/year from average data Ground Level mPWD in Water Ground

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A17: Trend of groundwater level for average data in Kazipur (Well ID: 8850006)

Ground Level mPWD in Water Ground 5 Depletion Rate ≈ Almost zero from minimum groundwater level

0 3-Jan-05 2-Jan-06 2-Jan-07 2-Jan-08 1-Jan-09 1-Jan-10 31-Dec-10 31-Dec-11 30-Dec-12 Year Figure A18: Trend of dry seasonal groundwater level in Kazipur (Well ID: 8850006)

A-6

APPENDIX-B Trend of Groundwater Level Inside the Study Area

Appendix-B: Trend of Groundwater Level Inside the Study Area

GT1006001 25.0

20.0 Groundwater Depletion Rate ≈ 43.29 cm/year 15.0

10.0

5.0

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B1: Trend of groundwater level inside the study area (Well ID: GT1006001)

GT1006003 25.0

20.0 Groundwater Depletion Rate ≈ 42.85 cm/year 15.0

10.0

5.0

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B2: Trend of groundwater level inside the study area (Well ID: GT1006003)

GT1020004 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 10.55 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B3: Trend of groundwater level inside the study area (Well ID: GT1020004)

B-1

GT1020005 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 21.21 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B4: Trend of groundwater level inside the study area (Well ID: GT1020005)

GT1027008 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 11.46 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B5: Trend of groundwater level inside the study area (Well ID: GT1027008)

GT1040010 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 3.39 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B6: Trend of groundwater level inside the study area (Well ID: GT1040010)

B-2

GT1040011 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 4.78 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B7: Trend of groundwater level inside the study area (Well ID: GT1040011)

GT1040012 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 10.69 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B8: Trend of groundwater level inside the study area (Well ID: GT1040012)

GT1054014 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 46.65 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B9: Trend of groundwater level inside the study area (Well ID: GT1054014)

B-3

GT1067015 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 25.81 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B10: Trend of groundwater level inside the study area (Well ID: GT1067015)

GT1081017 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ No Depletion

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B11: Trend of groundwater level inside the study area (Well ID: GT1081017)

GT1081019 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 1.64 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B12: Trend of groundwater level inside the study area (Well ID: GT1081019)

B-4

GT1081020 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 0.99 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B13: Trend of groundwater level inside the study area (Well ID: GT1081020)

GT1088021 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 7.67 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B14: Trend of groundwater level inside the study area (Well ID: GT1088021)

GT1088022 25.0

20.0

15.0

10.0

5.0 Groundwater Depletion Rate ≈ 19.89 cm/year

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B15: Trend of groundwater level inside the study area (Well ID: GT1088022)

B-5

GT1088023 25.0

20.0 Groundwater Depletion Rate ≈ 12.56 cm/year

15.0

10.0

5.0

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B16: Trend of groundwater level inside the study area (Well ID: GT1088023)

GT1094024 25.0

20.0

15.0

10.0 Groundwater Depletion Rate ≈ 29.60 cm/year 5.0

Groundwater Level in mPWD in Level Groundwater 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure B17: Trend of groundwater level inside the study area (Well ID: GT1094024)

B-6

APPENDIX-C Analysis of Precipitation Data

APPENDIX-C: Analysis of Precipitation Data

10000

8000

6000

mm 4000

2000

Accumulated Monthly 0 Rainfall at Station R006 in in R006 Station at Rainfall 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C1: Double Mass curve for Hydrologic of year 2006-2011 at Station R006

10000

8000

6000

mm 4000

2000

Accumulated Monthly 0 Rainfall at Station R011 in in R011 Station at Rainfall 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C2: Double Mass curve for Hydrologic of year 2006-2011 at Station R011

10000

8000

6000

4000 mm 2000

0 Accumulated Monthly 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Rainfall at Station R022 in in R022 Station at Rainfall Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C3: Double Mass curve for Hydrologic of year 2006-2011 at Station R022

C-1

10000

8000

6000 mm 4000

2000 Accumulated Monthly

Rainfall at Station R024 in in R024 Station at Rainfall 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C4: Double Mass curve for Hydrologic of year 2006-2011 at Station R024

10000

8000

6000

mm 4000

2000 Accumulated Monthly

Rainfall at Station R033 in in R033 Station at Rainfall 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C5: Double Mass curve for Hydrologic of year 2006-2011 at Station R033

10000

8000

6000 mm 4000

2000 Accumulated Monthly

Rainfall at Station R169 in in R169 Station at Rainfall 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C6: Double Mass curve for Hydrologic of year 2006-2011 at Station R169

C-2

10000

8000

6000

4000

2000 Station R181 in R181 in Station mm 0

Accumulated Monthly Rainfall at 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C7: Double Mass curve for Hydrologic of year 2006-2011 at Station R181

10000

8000

6000

4000

2000

at Station in R0216 Station at mm 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Accumulated Monthly Rainfall Accumulated Monthly Rainfall of 8 stations mean in mm

Figure C8: Double Mass curve for Hydrologic of year 2006-2011 at Station R216

C-3

APPENDIX-D Population and Household data

APPENDIX-D: Population and Household data

Table D1: Statistical Population and Household in the study area

Population Household Upazila Name 2011 2001 1991 2011 2001 1991 Adamdanga 195,186 187,012 170,326 49,600 41,495 31,785 Total Urban 39,946 38,390 31,968 10,118 8,518 5,973 Rural 155,240 148,622 138,358 39,482 32,977 25,812 Bogra Sadar 555,014 694,077 588,783 131,862 150,300 107,224 Total Urban 350,397 210,038 164,114 81,251 40,205 28,803 Rural 204,617 484,039 424,669 50,611 110,095 78,421 Dhunat 292,404 270,810 246,984 74,897 63,557 48,901 Total Urban 22,673 18,058 16,653 5,606 4,042 3,331 Rural 269,731 252,752 230,331 69,291 59,515 45,570 Dhupchanchia 176,678 160,894 149,112 45,390 37,436 29,153 Total Urban 22,406 21,761 14,037 5,401 4,795 2,696 Rural 154,272 139,133 135,075 39,989 32,641 26,457 Gabtali 319,588 290,190 265,926 83,411 67,685 52,782 Total Urban 21,455 3,143 2,789 5,493 703 535 Rural 298,133 287,047 263,137 77,918 66,982 52,247 Kahaloo 222,376 195,565 183,230 58,261 45,303 36,537 Total Urban 13,887 9,477 8,104 3,636 2,147 1,550 Rural 208,489 186,088 175,126 54,625 43,156 34,987 Nandigram 180,802 168,155 147,557 45,853 37,042 27,470 Total Urban 18,496 5,423 4,014 4,528 1,146 731 Rural 162,306 162,732 143,543 41,325 35,896 26,739 Sariakandi 270,719 240,083 229,563 75,614 55,719 46,406 Total Urban 18,543 17,320 6,640 5,069 4,049 1,392 Rural 252,176 222,763 222,923 70,545 51,670 45,014 Sherpur 332,825 286,308 229,005 81,753 68,346 45,258 Total Urban 54,082 45,445 29,195 12,473 10,431 5,680 Rural 278,743 240,863 199,810 69,280 57,915 39,578 Shibganj 378,700 352,415 312,773 99,242 83,120 63,721 Total Urban 21,643 8,609 8,111 5,576 1,978 1,456 Rural 357,057 343,806 304,662 93,666 81,142 62,265 Sonatola 186,778 167,547 146,028 48,569 38,364 27,170 Total Urban 24,720 11,405 9,982 6,514 2,450 0 Rural 162,058 156,142 136,046 42,055 35,914 0 Shajahanpur 289,804 72,685 Total Urban 62,140 N/A 13,454 N/A Rural 227,664 59,231

D-1

8 1991

2001 6 2011 )

5 4 (x 10

2 Upazilawise Total Population

Upazila

Figure D1: Upazila wise Population in the study area according to Population Census

) 1991 4

2001 12 2011

4 Upazilawise Total Household (x10

Upazila

Figure D2: Upazila wise Household in the study area according to Population Census

D-2

Table D2: Projected Population in the study area

Baseline Population Projected Population Upazila Name in 2011 2015 2020 2025 2030 Adamdanga 195,186 200,622 210,808 219,155 225,820 Total Urban 39,946 41,766 44,995 48,171 51,035 Rural 155,240 158,856 165,813 170,984 174,785 Bogra Sadar 555,014 623,409 664,376 702,403 735,528 Total Urban 350,397 407,796 439,321 470,329 498,296 Rural 204,617 215,612 225,055 232,073 237,232 Dhunat 292,404 302,502 316,557 327,453 335,768 Total Urban 22,673 24,116 25,981 27,814 29,468 Rural 269,731 278,385 290,577 299,639 306,300 Dhupchanchia 176,678 183,030 191,870 198,898 204,376 Total Urban 22,406 24,603 26,505 28,375 30,063 Rural 154,272 158,427 165,365 170,522 174,313 Gabtali 319,588 328,177 343,303 354,965 363,822 Total Urban 21,455 22,505 24,245 25,956 27,500 Rural 298,133 305,672 319,058 329,009 336,323 Kahaloo 222,376 231,355 242,005 250,209 256,435 Total Urban 13,887 15,466 16,662 17,838 18,899 Rural 208,489 215,889 225,343 232,371 237,536 Nandigram 180,802 191,449 200,675 207,999 213,700 Total Urban 18,496 25,106 27,047 28,956 30,678 Rural 162,306 166,343 173,628 179,043 183,023 Sariakandi 270,719 281,243 294,322 304,468 312,214 Total Urban 18,543 22,771 24,531 26,263 27,824 Rural 252,176 258,472 269,791 278,205 284,390 Sherpur 332,825 359,114 376,891 391,241 402,565 Total Urban 54,082 61,179 65,908 70,560 74,756 Rural 278,743 297,935 310,982 320,681 327,810 Shibganj 378,700 394,908 413,085 427,085 437,711 Total Urban 21,643 26,337 28,373 30,376 32,182 Rural 357,057 368,571 384,712 396,710 405,529 Sonatola 186,778 197,465 207,105 214,822 220,870 Total Urban 24,720 29,636 31,927 34,180 36,212 Rural 162,058 167,829 175,179 180,642 184,658 Shajahanpur 289,804 312,217 328,314 341,622 352,322 Total Urban 62,140 72,319 77,910 83,409 88,369 Rural 227,664 239,898 250,404 258,213 263,953

D-3

Table D3: Water Demand Calculation up to year 2030 in the study area

Water Demand in m3/d Upazila Name 2011 2015 2020 2025 2030 Adamdighi 11,757 12,119 12,790 13,366 13,843 Total Urban 3,995 4,177 4,500 4,817 5,104 Rural 7,762 7,943 8,291 8,549 8,739 Bogra Sadar 52,278 59,716 63,971 68,043 71,657 Total Urban 42,048 48,936 52,719 56,439 59,796 Rural 10,231 10,781 11,253 11,604 11,862 Dhunat 15,754 16,331 17,127 17,763 18,262 Total Urban 2,267 2,412 2,598 2,781 2,947 Rural 13,487 13,919 14,529 14,982 15,315 Dhupchanchia 9,954 10,382 10,919 11,364 11,722 Total Urban 2,241 2,460 2,651 2,838 3,006 Rural 7,714 7,921 8,268 8,526 8,716 Gabtali 17,052 17,534 18,377 19,046 19,566 Total Urban 2,146 2,251 2,425 2,596 2,750 Rural 14,907 15,284 15,953 16,450 16,816 Kahaloo 11,813 12,341 12,933 13,402 13,767 Total Urban 1,389 1,547 1,666 1,784 1,890 Rural 10,424 10,794 11,267 11,619 11,877 Nandigram 9,965 10,828 11,386 11,848 12,219 Total Urban 1,850 2,511 2,705 2,896 3,068 Rural 8,115 8,317 8,681 8,952 9,151 Sariakandi 14,463 15,201 15,943 16,537 17,002 Total Urban 1,854 2,277 2,453 2,626 2,782 Rural 12,609 12,924 13,490 13,910 14,220 Sherpur 19,345 21,015 22,140 23,090 23,866 Total Urban 5,408 6,118 6,591 7,056 7,476 Rural 13,937 14,897 15,549 16,034 16,391 Shibganj 20,017 21,062 22,073 22,873 23,495 Total Urban 2,164 2,634 2,837 3,038 3,218 Rural 17,853 18,429 19,236 19,836 20,276 Sonatola 10,575 11,355 11,952 12,450 12,854 Total Urban 2,472 2,964 3,193 3,418 3,621 Rural 8,103 8,391 8,759 9,032 9,233 Shajahanpur 17,597 19,227 20,311 21,252 22,035 Total Urban 6,214 7,232 7,791 8,341 8,837 Rural 11,383 11,995 12,520 12,911 13,198

D-4

APPENDIX-E Base Crop Water Requirements

APPENDIX-E: Base Crop Water Requirements

Table E1: Field Irrigation Water Requirement for Wheat Field Irrigation Water Requirement Upazila Unit Nov Dec Jan Feb Mar mm/d 0.40 1.10 2.00 3.50 3.70 Adamdighi mm/m 11.00 33.00 60.00 104.00 112.00 L-s/ha 0.04 0.13 0.23 0.40 0.43 mm/d 0.40 1.00 2.00 3.50 3.70 Bogra Sadar mm/m 11.00 31.00 60.00 104.00 111.00 L-s/ha 0.04 0.12 0.23 0.40 0.43 mm/d 0.40 1.10 1.90 3.40 3.40 Dhunat mm/m 11.00 33.00 57.00 102.00 103.00 L-s/ha 0.04 0.13 0.22 0.39 0.40 mm/d 0.40 1.10 2.00 3.50 3.80 Dhupchanchia mm/m 11.00 33.00 61.00 105.00 114.00 L-s/ha 0.04 0.13 0.23 0.41 0.44 mm/d 0.40 1.00 1.90 3.50 3.60 Gabtali mm/m 11.00 31.00 57.00 104.00 109.00 L-s/ha 0.04 0.12 0.22 0.40 0.42 mm/d 0.40 1.10 2.00 3.50 3.80 Kahaloo mm/m 11.00 33.00 61.00 105.00 114.00 L-s/ha 0.04 0.13 0.23 0.41 0.44 mm/d 0.40 1.10 2.00 3.60 3.80 Nandigram mm/m 11.00 34.00 61.00 107.00 114.00 L-s/ha 0.04 0.13 0.24 0.41 0.44 mm/d 0.40 1.00 1.90 3.50 3.60 Sariakandi mm/m 11.00 31.00 57.00 104.00 109.00 L-s/ha 0.04 0.12 0.22 0.40 0.42 mm/d 0.40 1.10 1.90 3.40 3.60 Shahjahanpur mm/m 11.00 32.00 58.00 103.00 109.00 L-s/ha 0.04 0.13 0.23 0.40 0.42 mm/d 0.40 1.10 2.00 3.40 3.60 Sherpur mm/m 11.00 33.00 60.00 102.00 109.00 L-s/ha 0.04 0.13 0.23 0.39 0.42 mm/d 0.40 1.00 2.00 3.50 3.70 Shibganj mm/m 11.00 31.00 60.00 104.00 111.00 L-s/ha 0.04 0.12 0.23 0.40 0.43 mm/d 0.40 1.20 2.00 3.60 3.70 Sonatola mm/m 11.00 35.00 61.00 107.00 112.00 L-s/ha 0.04 0.13 0.24 0.41 0.43

E-1

Table E2: Field Irrigation Water Requirement for HYV Boro Field Irrigation Water Requirement Upazila Unit Dec Jan Feb Mar Apr May mm/d 3.10 7.00 5.40 6.80 5.50 1.30 Adamdighi mm/m 93.00 210.00 162.00 205.00 164.00 39.00 L-s/ha 0.36 0.81 0.63 0.79 0.63 0.15 mm/d 3.20 7.40 5.90 7.30 5.70 0.90 Bogra Sadar mm/m 95.00 221.00 176.00 218.00 172.00 26.00 L-s/ha 0.36 0.85 0.68 0.84 0.66 0.10 mm/d 3.10 6.90 5.30 6.50 4.60 0.70 Dhunat mm/m 93.00 207.00 159.00 195.00 137.00 20.00 L-s/ha 0.36 0.80 0.62 0.75 0.53 0.08 mm/d 3.20 7.40 5.90 7.40 5.90 1.30 Dhupchanchia mm/m 95.00 222.00 178.00 221.00 177.00 39.00 L-s/ha 0.37 0.86 0.69 0.85 0.68 0.15 mm/d 3.10 6.90 5.40 6.70 4.90 0.80 Gabtali mm/m 93.00 208.00 161.00 201.00 148.00 23.00 L-s/ha 0.36 0.80 0.62 0.78 0.57 0.09 mm/d 3.10 7.00 5.40 6.90 5.50 1.20 Kahaloo mm/m 93.00 210.00 163.00 206.00 164.00 36.00 L-s/ha 0.36 0.81 0.63 0.79 0.63 0.14 mm/d 3.20 7.40 6.00 7.40 6.00 1.40 Nandigram mm/m 95.00 222.00 180.00 222.00 180.00 41.00 L-s/ha 0.37 0.86 0.69 0.86 0.70 0.16 mm/d 3.20 7.30 5.90 7.20 5.30 0.80 Sariakandi mm/m 95.00 219.00 176.00 216.00 160.00 25.00 L-s/ha 0.36 0.85 0.68 0.83 0.62 0.10 mm/d 3.20 7.30 5.80 7.20 5.40 0.80 Shahjahanpur mm/m 95.00 220.00 175.00 216.00 161.00 23.00 L-s/ha 0.37 0.85 0.68 0.83 0.62 0.09 mm/d 3.20 7.40 5.80 7.20 5.50 0.70 Sherpur mm/m 95.00 222.00 175.00 216.00 164.00 20.00 L-s/ha 0.37 0.85 0.67 0.83 0.63 0.08 mm/d 3.10 7.00 5.40 6.80 5.30 0.80 Shibganj mm/m 93.00 209.00 161.00 203.00 159.00 23.00 L-s/ha 0.36 0.81 0.62 0.78 0.62 0.09 mm/d 3.20 7.80 6.50 7.80 6.20 1.00 Sonatola mm/m 97.00 234.00 195.00 235.00 185.00 31.00 L-s/ha 0.37 0.90 0.75 0.90 0.71 0.12

E-2

Table E3: Field Irrigation Water Requirement for HYV Aman Field Irrigation Water Requirement Upazila Unit Jun Jul Aug Sep Oct Nov mm/d 0.20 2.00 0.10 0.10 2.90 3.00 Adamdighi mm/m 7.00 61.00 3.00 2.00 86.00 91.00 L-s/ha 0.03 0.24 0.01 0.01 0.33 0.35 mm/d 0.20 1.60 2.80 3.20

Bogra Sadar mm/m 7.00 48.00 84.00 96.00

L-s/ha 0.03 0.19 0.32 0.37

mm/d 0.20 1.20 2.70 3.00

Dhunat mm/m 6.00 37.00 82.00 90.00

L-s/ha 0.02 0.14 0.31 0.35

mm/d 0.20 2.30 0.40 0.20 3.30 3.30 Dhupchanchia mm/m 7.00 68.00 12.00 7.00 100.00 98.00 L-s/ha 0.03 0.26 0.05 0.03 0.39 0.38 mm/d 0.20 1.10 2.20 3.00

Gabtali mm/m 6.00 32.00 65.00 89.00

L-s/ha 0.02 0.13 0.25 0.34

mm/d 0.20 2.10 0.20 0.10 2.90 3.10 Kahaloo mm/m 7.00 62.00 6.00 3.00 88.00 92.00 L-s/ha 0.03 0.24 0.02 0.01 0.34 0.35 mm/d 0.20 1.70 0.10 0.10 2.50 3.20 Nandigram mm/m 7.00 50.00 4.00 2.00 74.00 96.00 L-s/ha 0.03 0.19 0.01 0.01 0.29 0.37 mm/d 0.20 1.30 2.60 3.20

Sariakandi mm/m 7.00 38.00 77.00 95.00

L-s/ha 0.03 0.15 0.30 0.37

mm/d 0.20 1.30 2.70 3.20

Shahjahanpur mm/m 7.00 40.00 81.00 95.00

L-s/ha 0.03 0.15 0.31 0.37

mm/d 0.20 1.40 0.10 2.80 3.20

Sherpur mm/m 7.00 42.00 2.00 84.00 95.00

L-s/ha 0.03 0.16 0.01 0.32 0.37

mm/d 0.20 1.40 2.40 3.00

Shibganj mm/m 7.00 43.00 73.00 90.00

L-s/ha 0.03 0.17 0.28 0.35

mm/d 0.20 2.20 0.10 0.30 3.80 3.50 Sonatola mm/m 7.00 65.00 3.00 10.00 115.00 104.00 L-s/ha 0.03 0.25 0.01 0.04 0.44 0.40

E-3

Table E4: Field Irrigation Water Requirement for HYV Aus Field Irrigation Water Requirement Upazila UNIT Mar Apr May Jun mm/d 0.60 8.40 2.60 Adamdighi mm/m 18.00 251.00 77.00 L-s/ha 0.07 0.97 0.30 mm/d 0.60 8.50 1.80 Bogra Sadar mm/m 18.00 256.00 53.00 L-s/ha 0.07 0.99 0.20 mm/d 0.60 7.80 1.10 Dhunat mm/m 18.00 234.00 33.00 L-s/ha 0.07 0.90 0.13 mm/d 0.60 8.70 2.70 Dhupchanchia mm/m 18.00 260.00 82.00 L-s/ha 0.07 1.00 0.32 mm/d 0.60 8.00 1.30 Gabtali mm/m 18.00 241.00 39.00 L-s/ha 0.07 0.93 0.15 mm/d 0.60 8.40 2.20 Kahaloo mm/m 18.00 251.00 67.00 L-s/ha 0.07 0.97 0.26 mm/d 0.60 8.70 2.90 0.10 Nandigram mm/m 19.00 262.00 88.00 2.00 L-s/ha 0.07 1.01 0.34 0.01 mm/d 0.60 8.30 1.70 Sariakandi mm/m 18.00 249.00 51.00 L-s/ha 0.07 0.96 0.20 mm/d 0.60 8.30 1.50 Shahjahanpur mm/m 18.00 250.00 44.00 L-s/ha 0.07 0.96 0.17 mm/d 0.60 8.40 1.20 Sherpur mm/m 18.00 251.00 36.00 L-s/ha 0.07 0.97 0.14 mm/d 0.60 8.20 1.30 Shibganj mm/m 18.00 247.00 39.00 L-s/ha 0.07 0.95 0.15 mm/d 0.60 8.80 2.40 Sonatola mm/m 19.00 265.00 71.00 L-s/ha 0.07 1.02 0.28

E-4

Table E5: Field Irrigation Water Requirement for Potato Field Irrigation Water Requirement Upazila Unit Nov Dec Jan Feb Mar Apr mm/d 0.70 1.40 2.20 3.10 3.10 0.50 Adamdighi mm/m 22.00 42.00 67.00 94.00 94.00 15.00 L-s/ha 0.08 0.16 0.26 0.36 0.36 0.06 mm/d 0.70 1.30 2.20 3.10 3.10 0.50 Bogra Sadar mm/m 22.00 40.00 66.00 94.00 93.00 15.00 L-s/ha 0.08 0.15 0.26 0.36 0.36 0.06 mm/d 0.70 1.40 2.10 3.10 2.90 0.30 Dhunat mm/m 21.00 42.00 64.00 92.00 86.00 9.00 L-s/ha 0.08 0.16 0.25 0.35 0.33 0.04 mm/d 0.70 1.40 2.30 3.20 3.20 0.50 Dhupchanchia mm/m 22.00 42.00 68.00 95.00 96.00 15.00 L-s/ha 0.08 0.16 0.26 0.37 0.37 0.06 mm/d 0.70 1.30 2.10 3.10 3.00 0.40 Gabtali mm/m 0.08 0.15 64.00 94.00 91.00 12.00 L-s/ha 0.08 0.15 0.25 0.36 0.35 0.05 mm/d 0.70 1.40 2.30 3.20 3.20 0.50 Kahaloo mm/m 22.00 42.00 68.00 95.00 96.00 15.00 L-s/ha 0.08 0.16 0.26 0.37 0.37 0.06 mm/d 0.70 1.40 2.30 3.20 3.20 0.50 Nandigram mm/m 22.00 43.00 68.00 97.00 96.00 16.00 L-s/ha 0.08 0.17 0.26 0.37 0.37 0.06 mm/d 0.70 1.30 2.10 3.10 3.00 0.40 Sariakandi mm/m 22.00 40.00 64.00 94.00 91.00 12.00 L-s/ha 0.08 0.15 0.25 0.36 0.35 0.05 mm/d 0.70 1.40 2.20 3.10 3.00 0.40 Shahjahanpur mm/m 22.00 41.00 65.00 93.00 91.00 12.00 L-s/ha 0.08 0.16 0.25 0.36 0.35 0.05 mm/d 0.70 1.40 2.20 3.10 3.00 0.40 Sherpur mm/m 21.00 42.00 67.00 92.00 91.00 13.00 L-s/ha 0.08 0.16 0.26 0.36 0.35 0.05 mm/d 0.70 1.30 2.20 3.10 3.10 0.50 Shibganj mm/m 22.00 40.00 66.00 94.00 93.00 15.00 L-s/ha 0.08 0.15 0.26 0.36 0.36 0.06 mm/d 0.70 1.50 2.30 3.20 3.10 0.50 Sonatola mm/m 22.00 44.00 68.00 97.00 94.00 15.00 L-s/ha 0.09 0.17 0.26 0.37 0.36 0.06

E-5

APPENDIX-F Lithologic Layers

APPENDIX-F: Lithologic Layers

Figure F1: Litho-logical Cross section along easting 440000 (start from north)

Figure F2: Litho-logical Cross section along easting 450000 (start from north)

F-1

Figure F3: Litho-logical Cross section along northing 730000 (start from west)

F-2

APPENDIX-G Irrigation Equipments and Their Coverage Area

APPENDIX-G: Irrigation Equipments and Their Coverage Area

Table G1: Irrigation equipment and corresponding coverage area according to Minor Irrigation Report 2010-11

Total Upazila Area of DTW Area of STW Upazila Name Area in Nos. of DTW Nos. of STW in Hectare in Hectare Hectare Adamdighi 16883 216 4977 1429 2909 Bogra sadar 17658 140 2870 3222 6541 Dhupchanchia 16244 281 6235 1083 1832 Dhunat 24773 9 135 14092 33098 Gabtali 23961 22 377 10370 23795 Kahaloo 24042 563 12662 735 1714 Nandigram 26522 330 7461 4659 10940 Sariakandi 40850 6 93 8085 19453 Shajahanpur 22169 17 2336 4600 10799 Sherpur 29593 146 2878 9414 22121 Shibganj 31492 285 6335 9606 22428 Sonatola 15675 105 333 7541 17704

800 2004-05 600 2007-08

2010-11 400 200 0 Nos. of Nos. of DTW

Figure G1: Upazila wise Deep tube wells according to Minor Irrigation Report

16 2004-05 2007-08

) ) 12 3 2010-11 8 4 0 Nos. of of Nos. STW (10

Figure G2: Upazila wise Shallow tube wells according to Minor Irrigation Report

G-1

Table G2: Irrigation equipments in the study area from Minor Irrigation Report

Numbers of DTW Numbers of STW Upazila 2004 - 05 2007 - 08 2010 - 11 2004 - 05 2007 - 08 2010 - 11 Adamdighi 212 217 216 1,384 1,240 1,429 Dhupchanchia 231 235 281 1,064 1,076 1,083 Dhunat 1 2 9 10,028 10,101 14,092 Gabtali 16 22 22 8,082 8,472 10,370 Kahaloo 533 596 563 684 615 735 Bogra Sadar 122 150 140 2,740 2,599 3,222 Nandigram 236 326 330 4,774 4,102 4,659 Sariakandi 3 5 6 5,077 6,152 8,085 Sherpur 44 93 146 8,526 9,070 9,414 Shibganj 236 259 285 7,177 8,515 9,606 Sonatola 6 3 105 5,546 4,335 7,541 Shajahanpur 67 92 17 4,637 3,853 4,600 Total 1,707 2,000 2,120 59,719 64,022 74,836

Table G3: Yearly increase rate of Irrigation equipments from Minor Irrigation Report

DTW Nos. Increasing Rate in % STW Nos. Increasing Rate in % 2004-05 2007-08 2004-05 Upazila 2004-05 to 2007-08 to 2004-05 to to 2007- to 2010- to 2010- 2007-08 2010-11 2010-11 08 11 11 Adamdighi 0.78 -0.15 0.31 -3.60 4.84 0.53 Dhubchancia 0.57 6.14 3.32 0.37 0.22 0.30 Dhunat 25.99 65.10 44.22 0.24 11.74 5.83 Gabtali 11.20 0.00 5.45 1.58 6.97 4.24 Kahaloo 3.79 -1.88 0.92 -3.48 6.12 1.21 Bogra Sadar 7.13 -2.27 2.32 -1.75 7.43 2.74 Nandigram 11.37 0.41 5.75 -4.93 4.34 -0.41 Sariakandi 18.56 6.27 12.25 6.61 9.54 8.06 Sherpur 28.33 16.22 22.13 2.08 1.25 1.67 Shibganj 3.15 3.24 3.19 5.86 4.10 4.98 Sonatola -20.63 227.11 61.13 -7.88 20.27 5.25 Shajahanpur 11.15 -43.04 -20.43 -5.99 6.08 -0.13 Total 5.42 1.96 3.68 2.35 5.34 3.83

G-2

Table G4: Coverage area under Irrigation equipments from Minor Irrigation Report DTW Coverage Area in Hectare STW Coverage Area in Hectare Upazila 2004 - 05 2007 - 08 2010 - 11 2004 - 05 2007 - 08 2010 - 11 Adamdighi 5,398 7,556 4,977 7,152 3,406 2,909 Dhupchanchia 6,753 7,151 6,235 5,755 3,334 1,832 Dhunat 12 56 135 20,978 25,356 33,098 Gabtali 142 542 377 21,487 21,160 23,795 Kahaloo 12,607 16,069 12,662 5578 1574 1714 Bogra Sadar 1,750 3,837 2,870 12,117 6,745 6,541 Nandigram 3,243 8,680 7,461 13,515 10,863 10,940 Sariakandi 62 111 93 15,116 17,202 19,453 Sherpur 863 1,805 2,878 23,498 23,610 22,121 Shibganj 6,083 7,268 6,335 22,141 21,429 22,428 Sonatola 122 69 333 13,445 11,118 17,704 Shajahanpur 1,272 2,615 2,336 14,494 10,223 10,799 Total 38,367 55,759 46,692 175,276 156,020 173,334

Table G5: Yearly increase rate of Irrigation Coverage Area from Minor Irrigation Report DTW Coverage Area Increasing STW Coverage Area Increasing Rate in % Rate in % Upazila 2004-05 2007-08 2004-05 2004-05 to 2007-08 to 2004-05 to to 2007- to 2010- to 2010- 2007-08 2010-11 2010-11 08 11 11 Adamdighi 11.86 -12.99 -1.34 -21.91 -5.12 -13.92 Dhupchanchia 1.93 -4.47 -1.32 -16.64 -18.09 -17.37 Dhunat 67.11 34.09 49.69 6.52 9.29 7.90 Gabtali 56.28 -11.40 17.67 -0.51 3.99 1.71 Kahaloo 8.42 -7.64 0.07 -34.41 2.88 -17.85 Bogra Sadar 29.91 -9.23 8.59 -17.74 -1.02 -9.77 Nandigram 38.84 -4.92 14.90 -7.02 0.24 -3.46 Sariakandi 21.43 -5.73 6.99 4.40 4.18 4.29 Sherpur 27.89 16.83 22.23 0.16 -2.15 -1.00 Shibganj 6.11 -4.48 0.68 -1.08 1.53 0.21 Sonatola -17.30 68.99 18.22 -6.14 16.77 4.69 Shajahanpur 27.15 -3.69 10.66 -10.98 1.84 -4.79 Total 13.27 -5.74 3.33 -3.80 3.57 -0.19

G-3

APPENDIX-H Model Calibration and Validation

APPENDIX-H: Model Calibration and Validation

Figure H1: Calibration of the groundwater level in Shibganj (Well ID: GT1094024)

Figure H2: Validation of the groundwater level in Shibganj (Well ID: GT1094024)

Figure H3: Calibration of the groundwater level in Sherpur (Well ID: GT1088021)

Figure H4: Validation of the groundwater level in Sherpur (Well ID: GT1088021)

H-1

Figure H5: Calibration of the groundwater level in Bogra Sadar (Well ID: GT1020005)

Figure H6: Validation of the groundwater level in Bogra Sadar (Well ID: GT1020005)

Figure H7: Calibration of the groundwater level in Dhunat (Well ID: GT1027008)

Figure H8: Validation of the groundwater level in Dhunat (Well ID: GT1027008)

H-2

Figure H9: Calibration of the groundwater level in Gabtali (Well ID: GT1040010)

Figure H10: Validation of the groundwater level in Gabtali (Well ID: GT1040010)

Figure H11: Calibration of the groundwater level in Sariakandi (Well ID: GT1081019)

Figure H12: Validation of the groundwater level in Sariakandi (Well ID: GT1081019)

H-3

APPENDIX-I

Model Outputs: Water Balance Chart

I-1

APPENDIX-I 01: Water Balance Chart for Present Crops 01

(Due to BC 01, Same demand up to 2030)

Year 2011 Year 2012

Year 2013 Year 2014

I-1

Year 2015 Year 2016

Year 2017 Year 2018

Year 2019 Year 2020

I-2

Year 2021 Year 2022

Year 2023 Year 2024

Year 2025 Year 2026

I-3

Year 2027 Year 2028

Year 2029 Year 2030

I-4

APPENDIX-I02: Water Balance Chart for Present Crops

(Due to BC 02, Demand Increment 1.25% per year)

Year 2030 Year 2029

Year 2028 Year 2027

I-5

Year 2026 Year 2025

Year 2024 Year 2023

Year 2022 Year 2021

I-6

Year 2020 Year 2019

Year 2018 Year 2017

Year 2016 Year 2015

I-7

Year 2014 Year 2013

I-8

APPENDIX-I03: Water Balance Chart for Present Crops

(Due to BC 01, Demand Increment 2.25% per year)

Year 2030 Year 2029

Year 2028 Year 2027

I-9

Year 2026 Year 2025

Year 2024 Year 2023

Year 2022 Year 2021

I-10

Year 2020 Year 2019

Year 2018 Year 2017

Year 2016 Year 2015

I-11

Year 2014 Year 2013

I-12

APPENDIX-I04: Water Balance Chart for Only Boro in All Crop Area

(Due to BC 01, Demand Increment 1.25% per year)

Year 2030 Year 2029

Year 2028 Year 2027

I-13

Year 2026 Year 2025

Year 2024 Year 2023

Year 2022 Year 2021

I-14

Year 2020 Year 2019

Year 2017 Year 2016

Year 2015 Year 2014

I-15

Year 2013 Year 2012

I-16

APPENDIX-I05: Water Balance Chart for Only Wheat in All Crop Area

(Due to BC 02, Demand Increment 1.25% per year)

Year 2030 Year 2029

Year 2028 Year 2027

I-17

Year 2026 Year 2025

Year 2024 Year 2023

Year 2022 Year 2021

I-18

Year 2020 Year 2019

Year 2018 Year 2017

Year 2016 Year 2015

I-19

Year 2014 Year 2013

I-20

APPENDIX- J Trend Analysis of Phreatic Surface from Predicting Model

APPENDIX- J: Trend Analysis of Phreatic Surface from Predicting Model

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 2.26 cm/year -15 Ground Level mPWD in Water Ground Depletion Rate for Maximum Pheratic Surface ≈ 2.59 cm/year -18 Figure J1: Trend Analysis of Phreatic Surface from predicting model for Adamdighi Upazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 0.47 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 5.07 cm/year -18 Figure J2: Trend Analysis of Phreatic Surface from predicting model for Bogra Sadar Upazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 1.17 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 5.77 cm/year -18 Figure J3: Trend Analysis of Phreatic Surface from predicting model for Dhunat Upazila

J-1

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 0.32 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 4.89 cm/year -18 Figure K4: Trend Analysis of Phreatic Surface from predicting model for DhupchanchiaUpazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 0.14 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 3.21 cm/year -18 Figure J5: Trend Analysis of Phreatic Surface from predicting model for Gabtali Upazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12

-15 Depletion Rate for Mean Pheratic Surface ≈ 1.57 cm/year Ground Level mPWD in Water Ground Depletion Rate for Maximum Pheratic Surface ≈ 9.75 cm/year -18 Figure J6: Trend Analysis of Phreatic Surface from predicting model for Kahaloo Upazila

J-2

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12

Depletion Rate for Mean Pheratic Surface ≈ 0.33 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 7.99 cm/year -18 Figure J7: Trend Analysis of Phreatic Surface from predicting model for Nandigram Upazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12

Depletion Rate for Mean Pheratic Surface ≈ zero

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 1.20 cm/year -18 Figure J8: Trend Analysis of Phreatic Surface from predicting model for Sariakandi Upazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 0.22 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 11.57 cm/year -18 Figure J9: Trend Analysis of Phreatic Surface from predicting model for Shahjahanpur Upazila

J-3

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 0.91 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 6.28 cm/year -18 Figure J10: Trend Analysis of Phreatic Surface from predicting model for Shibganj Upazila

Mean Pheratic Surface Maximum Pheratic Surface

Year May-06 May-10 May-14 May-18 May-22 May-26 May-30 0

-3

-6

-9

-12 Depletion Rate for Mean Pheratic Surface ≈ 1.50 cm/year

Ground Level mPWD in Water Ground -15 Depletion Rate for Maximum Pheratic Surface ≈ 4.12 cm/year -18 Figure J11: Trend Analysis of Phreatic Surface from predicting model for Sonatola Upazila

J-4

APPENDIX-K Abstraction Data

APPENDIX-K: Abstraction Data

Figure K1: Adopted land use pattern for the MIKE SHE hydrologic model

Table K1: Crop Calendar for the study area Land ID All crops in Month 1 2 3 7 Crops Land ID July Fallow Fallow Aus Fallow Aus 3 Fallow Fallow Aus Fallow Aus 3 August T-Aman Fallow T-Aman T- T-Aman 1+3+7 Septembe T-Aman Fallow T-Aman T- T-Aman 1+3+7 October T-Aman Fallow T-Aman T- T-Aman 1+3+7 November T-Aman Fallow T-Aman T- T-Aman 1+3+7 T-Aman Fallow T-Aman T- T-Aman 1+3+7 December T-Aman Fallow T-Aman Wheat T- 1+3+7 T-Aman Fallow T-Aman Wheat T- 1+3+7 January HYV- HYV- HYV- Wheat HYV - 1+2+3+ February HYV- HYV- HYV- Wheat HYV- 1+2+3+ March HYV- HYV- HYV- Wheat HYV- 1+2+3+ April HYV- HYV- HYV- Fallow HYV- 1+2+3+ HYV- HYV- HYV- Fallow HYV- 1+2+3+ May Fallow Fallow Aus Fallow Aus 3 June Fallow Fallow Aus Fallow Aus 3

K-1

Table K3: Present coverage area under Rabi crops in Bogra district

Upazilas of Bogra Crop area (sq. km) under Crop area (sq. km) district Boro crop under Wheat Adamdighi 106.00 38.17 Bogra Sadar 104.40 32.22 Dhunat 163.31 31.47 Dhupchanchia 90.47 10.91 Gabtali 180.61 22.57 Kahaloo 121.36 49.04 Nandigram 240.20 6.21 Sariakandi 179.58 97.98 Shahjahanpur 105.12 58.64 Sherpur 169.85 62.89 Shibganj 136.66 128.72 Sonatola 49.47 67.48

Table K4: Monthly Irrigation and Domestic Water Requirement

Monthly crop water requirement in mm Monthly Upazilas of Domestic Ja Fe Ma Ap Ma Ju Au No De Bogra district Jul Sep Oct demand n b r r y n g v c in mm Adamdighi 71 115 155 104 16 0 0 1 2 73 74 3 2 Bogra Sadar 70 114 152 102 11 0 0 0 0 64 71 2 9 Dhunat 72 110 146 92 9 0 0 0 0 62 67 2 2 Dhupchanchia 63 98 135 99 12 0 0 4 4 62 60 2 2 Gabtali 77 117 159 107 10 0 0 0 0 53 70 3 2 Kahaloo 59 96 132 86 13 0 0 2 2 64 66 2 2 Nandigram 98 150 205 159 51 2 0 1 3 70 87 3 1 Sariakandi 52 90 117 69 9 0 0 0 0 49 58 2 1 Shahjahanpur 59 101 133 79 12 0 0 0 0 61 70 2 2 Sherpur 69 112 150 96 13 0 0 0 2 67 74 3 2 Shibganj 57 104 136 73 12 0 0 0 0 62 74 3 2 Sonatola 56 114 142 73 15 0 0 1 7 87 78 2 2

N.B. for domestic water requirement, every month is considered as 30 days

K-2

APPENDIX-L Trend Detection Test and Analyses

APPENDIX-L: Trend Detection Test and Analyses

Table L1: Hydrologic data list in details used for trend analysis

Collect Main Represen Data Type ed Data Range Details Source tation from Data Collection and Projection Study area boundary wells: GT323006, Groundwat GT328806, GT3958015, GT8850006, BWDB IWM 2005-2012 Table N2 er level GT6991013 and GT3861005. Study area inside wells: calibrated wells. Rainfall stations: R006, R011, R022, BWDB IWM 1985-2011 Rainfall R024, R033, R169, R181 and R216. Table N3 BBS BUET 1985-2011 Declared rainfall for Bogra district. Evapotrans BWDB IWM 1985-2012 Evaporation station: CL6. piration Table N4 Surface BWDB IWM 2005-2012 River Jamuna at Mathurpara Station. water level Depth of Upazila wise predicted depth of Phreatic MIKE Phreatic MIKE SHE 2006-2030 Surface under Bogra district for present Table N5 SHE Surface crop pattern

Table L2: Trend of groundwater level using Mann-Kendall trend test, Sen’s slope method and linear regression analysis

Mann-Kendall trend test Sen’s Trend from Station ID/ Data Data Type Kendall’s slope linear regression Area Range Statistics, S tau (cm/year) (cm/year) GT3230006 -10,971 -0.126 -6.94 -7.41 GT3288016 -9,441 -0.108 -13.70 -11.61 Weekly GT3958015 groundwater 2005- -4,587 -0.053 -5.05 -5.48 GT8850006 level at study 2012 -153 -0.002 0 -2.01 area boundary GT6991013 -23,938 -0.275 -27.10 -24.35 GT3861005 -29,083 -0.334 -24.51 -27.67

GT1006001 -55397 -0.640 -36.82 -43.29 GT1006003 -60565 -0.700 -39.66 -42.85 GT1020004 -2298 -0.027 -2.71 -10.55 GT1020005 -21838 -0.252 -16.73 -21.21 GT1027008 -7843 -0.091 -6.38 -11.46 GT1040010 400 0.005 0.33 -3.39 GT1040011 Weekly -1555 -0.018 -1.44 -4.78 groundwater 2005- -8235 -0.095 -7.55 -10.69 GT1040012 level inside the 2012 GT1054014 study area -41755 -0.482 -32.69 -46.65 GT1067015 -45745 -0.528 -23.58 -25.81 GT1081019 2661 0.031 2.19 -1.64 GT1081020 5156 0.060 6.23 -0.99 GT1088021 -4209 -0.049 -4.21 -7.67 GT1088023 -3907 -0.045 -6.87 -12.56 GT1094024 -28856 -0.333 -24.56 -29.6

L-1

Table L3: Trend of rainfall data using Mann-Kendall trend test, Sen’s slope method and linear regression analysis

Mann-Kendall trend test Sen’s Trend from Station Data Data Type Kendall’s slope linear regression ID/ Area Range Statistics, S tau (mm/year) (mm/year) R006 -47 -0.13 -10.90 -13.93 R011 -37 -0.11 -6.60 -11.04 R022 -79 -0.23 -18.93 -17.03 R024 Annual rainfall 1985- -115 -0.33 -23.69 -24.38 R033 data 2011 -67 -0.19 -15.21 -15.58 R169 -71 -0.20 -14.10 -12.23 R181 -41 -0.12 -7.19 -8.66 R216 -121 -0.35 -27.21 -26.14 Annual -79 -0.23 -16.09 -15.62 Bogra 1985- Nov. ito f Apr. ll -23 -0.07 -1.16 -0.80 district 2011 May to Oct. -67 -0.21 -15.89 -14.02

Table L4: Trend of Evapotranspiration and surface water level data

Mann-Kendall trend test Sen’s Trend from Station ID/ Data Data Type slope linear regression Area Range Statistics, S Kendall’s tau (cm/year) (cm/year) Evapotranspir 1985- CL06 - - 0 0 ation 2011 Daily water 2005- Mathurpara -3,04,781 -0.073 -9.12 -5.11 level of river 2012

Table L5: Trend of the depth of phreatic surface using Mann-Kendall trend test, Sen’s slope method and linear regression analysis

Trend from Mann-Kendall trend test Sen’s Upazila of Bogra Data linear Data Type slope district Range Kendall’s regression Statistics, S (cm/year) tau (cm/year) Adamdighi -67 -0.224 -2.65 -2.26 Bogra Sadar Mean depth -7 -0.023 -0.33 -0.47 Dhunat of phreatic -33 -0.111 -1.33 -1.17 Dhupchanchia surface from -70 -0.234 -4.17 -0.32 Gabtali MIKE SHE 22 0.074 0.90 -0.14 hydrologic Kahaloo 2006 to -53 -0.177 -2.00 -1.57 model for Nandigram 2030 -56 -0.187 -2.80 -0.33 present crop Sariakandi pattern 48 0.16 2.39 0 Shahjahanpur (data on 1st -38 -0.127 -2.60 -0.22 Sherpur May in each -36 -0.120 -1.42 -2.92 Shibganj year) 14 0.047 0.82 -0.91 Sonatola 18 0.060 1.00 -1.50

Adamdighi Maximum -70 -0.234 -3.79 -2.59 Bogra Sadar depth of -108 -0.361 -6.11 -5.07 Dhunat phreatic -123 -0.411 -6.19 -5.77 Dhupchanchia surface from -79 -0.265 -5.35 -4.89 Gabtali MIKE SHE -87 -0.290 -4.19 -3.21 Kahaloo hydrologic 2006 to -146 -0.488 -9.97 -9.75 Nandigram model for 2030 -92 -0.307 -7.11 -7.99 Sariakandi present crop -26 -0.087 -0.55 -1.20 Shahjahanpur pattern -114 -0.388 -11.75 -11.57 st Sherpur (data on 1 -122 -0.407 -12.24 -14.45 Shibganj May in each -88 -0.293 -6.30 -6.28 Sonatola year) -97 -0.324 -4.88 -4.12

L-2

Figure L1: Groundwater level trend using linear regression analysis

Figure L2: Groundwater level trend using Sen’s slope method

L-3