DEVELOPMENT OF A GIS BASED ALLUVIAL PLAIN CONJUNCTIVE USE CONTAMINANT TRANSPORT MODEL OF PARTS OF D. I. KHAN USING 3D MODELING APPROACH
ANWAR QADIR
Department of Earth Sciences Quaid i Azam University, Islamabad 2013
1
DEVELOPMENT OF A GIS BASED ALLUVIAL PLAIN CONJUNCTIVE USE CONTAMINANT TRANSPORT MODEL OF PARTS OF D. I. KHAN USING 3D MODELING APPROACH
ANWAR QADIR
Department of Earth Sciences Quaid i Azam University, Islamabad 2013
2
DEVELOPMENT OF A GIS BASED ALLUVIAL PLAIN CONJUNCTIVE USE CONTAMINANT TRANSPORT MODEL OF PARTS OF D. I. KHAN USING 3D MODELING APPROACH
ANWAR QADIR
Submitted for the degree of Doctor of Philosophy at the Quaid I Azam University, Islamabad
2011
3
Certificate Certified that Mr Anwar Qadir carried out the work contained in this dissertation under my supervision
Dr. Zulfiqar Ahmad Department of Earth Sciences Quaid I Azam University Islamabad Pakistan
Dr. M. Gulraiz Akhter Chairman Department of Earth Sciences Quaid I Azam University Islamabad Pakistan
4
Abstract The goal of this study was to develop a groundwater flow model and a contaminant transport model to understand the fate of the Arsenic in the groundwater. Visual MODFLOW 4.0, the Waterloo Hydrogeologic Inc. software was utilized for this study. A three dimensional, finite difference, groundwater flow model was used to develop a regional conceptualization of the flow system in the D. I. Khan area. The D. I. Khan sand aquifer system has been divided into three gently sloping geohydrologic units. The 2900 Km 2 study area was divided into 33852 cells with dimensions of 500 m by 500 m comprising of 186 columns and 182 rows and containing three layers. The finite difference block centered grid was used with an average depth of model simulation was set to 100 meters that almost equals to the average depth of existing tube wells (water wells). The model region was bounded by Indus River, Takwarrah Nala and the Sheikh Haider Zaman Nala in the east, northeast and southwest respectively. The area also consists of Gomal Nala, Chashma Right Bank Canal (CRBC) and Paharpur canal. The model was also adorned with the various hydraulic parameters spatial distributions including hydraulic conductivity (K), initial heads, recharge, porosity, specific storage (Ss), specific yield (Sy), top and bottom elevation of aquifers. The model was initially run for 10 years in steady state for the year 1985. In steady state a single time step was used. The model was calibrated with several runs by modifying the hydraulic conductivities and recharge values. The Parameter Estimation and Testing (PEST) has been used to do the calibration with the minimum and maximum ranges of 30 hydraulic conductivities and recharge values. The model was also calibrated in transient state in 1985 using steady state heads as initial conditions and assessing the draw downs. The area has been studied with respect to the various anthropogenic activities and found to be contaminated with high salinity, sulphates and Arsenic. The contaminant transport model MT3D was used with the observed Arsenic (As) concentrations and calibrated in 2010 for steady state and non steady state conditions. Model realizations were further projected up to 2025 to monitor the spreading of Arsenic concentration in the groundwater. The preliminary management scenarios were also discussed to address the issue at hand for safety of the inhabitants in the study area. The study results include estimates of hydraulic and transport properties, direction of regional flow, contaminant transport, its fate, prevention, remediation and a discussion of the
i results to gain a more complete understanding of the subsurface flow and contaminant transport system. Perhaps this work will be the first step in learning more about the subsurface flow system of the D. I. Khan aquifer, and provide a useful tool to manage and properly plan future management of the groundwater resources.
ii
Acknowledgement I want to express my infinite sense of gratitude to almighty Allah who is the master of all universes and who blessed me with the strength, patience and consistency to complete this work. It is a great opportunity to express sincere gratitude and sense of devotion to my supervisor Professor Dr. Zulfiqar Ahmad , Department of Earth Sciences, Quaid i Azam University, Islamabad for providing me an opportunity to enhance my skills not only in hydrogeology but various other facets of the earth sciences. This thesis was not possible to finish without his consistent encouragement, sympathetic attitude, critical remarks on the technical aspects and support. Higher Education Commission (HEC), Pakistan is acknowledged for granting indigenous fellowship for the finances incurred on this study. I would also like to record my sincerest thanks to faculty members and staff of Earth Sciences Department for their cooperation during my stay at the department. I would also thank Engr. Tahir Nawaz Qureshi (Chief Engineer, WAPDA) and Dr. A. D. Khan (PCRWR) for providing the data regarding this research. Special acknowledgements are extended to the Water Management Department and Public Health Engineering Department, D. I. Khan for the provision of data. Cordial and humble thanks are extended to my friends and Ph.D fellows whose help is undeniable including Dr. M. Sadiq (NESCOM), Dr. Arshad Ashraf (NARC), Dr. Gulraiz Akhter, Associate Professor, Quaid I Azam University, Dr. Azam Tasneem, PINSTECH, Islamabad, Dr. M. Zafar, Associate Professor, Bahria University, Dr. Shaheena Tariq, Associate Professor, COMSATS, Islamabad and Dr. Tahseenullah Khan, Professor, Bahria University, Mr. M. Hanif (SUPARCO), Alwina Farooq, Usman Mustafa, Masood Ali Khan and Hamid Hussain for supporting me during this research work at various stages. I express my sincere gratitude to my wife, parents and family members for their understanding, endless love and moral support during the entire study period. I also express my deepest gratitude for the local and international examiners for highlighting the relevant problems of research.
iii
TABLE OF CONTENTS
1 INTRODUCTION ...... 1
1.1 AIMS AND OBJECTIVES ...... 3
1.2 PREVIOUS WORK ...... 4
2 STUDY AREA DETAILS ...... 5
2.1 LOCATION ...... 5
2.2 TOPOGRAPHY AND RELIEF ...... 6 2.2.1 Mountain Highlands ...... 7 2.2.2 Piedmont Plain ...... 7 2.2.3 Flood Plain ...... 7 2.2.4 Aeolian Deposits ...... 7 2.2.5 Gravel Fans ...... 8
2.3 CLIMATE ...... 8 2.3.1 Precipitation ...... 9 2.3.2 Temperature ...... 9 2.3.3 Potential Evapotranspiration ...... 10
2.4 TECTONIC FRAME WORK OF THE REGION ...... 11 2.4.1 Geology of the study area...... 12 2.4.2 Consolidated rocks ...... 13 2.4.3 Unconsolidated rocks ...... 13 2.4.3.1 Piedmont Deposits...... 14 2.4.3.2 Punjab Type Deposits...... 14 2.4.3.3 Soils ...... 14
2.5 AGRICULTURE ...... 16
2.6 IRRIGATION ...... 16
2.7 HYDROLOGY ...... 17
2.8 HYDROGEOLOGY ...... 18
3 CONCEPTUAL MODEL ...... 20
3.1 DATA ...... 20
iv
3.1.1 Well data ...... 21 3.1.2 Pumping wells ...... 24 3.1.3 Model grid ...... 24 3.1.4 Model Input parameters ...... 25 3.1.4.1 Initial conditions ...... 25 3.1.4.2 Model Boundaries ...... 25 3.1.4.3 Hydraulic conductivity, specific yield and Porosity ...... 27 3.1.5 Recharge components ...... 28 3.1.5.1 Rivers ...... 29 3.1.6 Discharge Components ...... 30
3.2 ASSUMPTIONS ...... 30 3.2.1 Assumption of the flow model...... 30
4 GROUNDWATER FLOW MODEL ...... 32
4.1 MODELING APPROACH ...... 32
4.2 NUMERICAL MODELING OF GROUNDWATER ...... 32
4.3 VISUAL MODFLOW ...... 34 4.3.1 Mathematical Model ...... 34 4.3.2 Discretization ...... 35
4.4 D. I. KHAN GROUNDWATER FLOW MODEL ...... 37 4.4.1 Steady state Model ...... 37 4.4.2 Transient Flow ...... 38
4.5 CALIBRATION ...... 38 4.5.1 Steady State Calibration...... 38 4.5.2 Maps ...... 40 4.5.3 Transient Calibration ...... 44 4.5.4 Sensitivity analysis (Steady and Transient state) ...... 48 4.5.4.1 Hydraulic Conductivity ...... 48 4.5.4.2 Recharge ...... 49 4.5.4.3 Specific Yield ...... 50 4.5.5 Equipotential Maps and their analysis...... 50 4.5.6 Drawdowns...... 62 v
4.5.7 Groundwater budget ...... 67
5 CONTAMINANT TRANSPORT MODEL ...... 69
5.1 DERA ISMAIL KHAN AQUIFER MONITORING ...... 69
5.2 HYDROCHEMISTRY ...... 69
5.3 PREVIOUS WORK ...... 70
5.4 CONCEPTUALIZATION ...... 70
5.5 TRANSPORT PACKAGE ...... 72
5.6 THE BASIC PACKAGE ...... 72 5.6.1 Advection Package ...... 72
5.7 TRANSPORT PARAMETERS ...... 73
5.8 ASSUMPTIONS AND FACTORS FOR TRANSPORT MODEL ...... 74
5.9 TRANSIENT TRANSPORT CALIBRATION ...... 74
5.10 FATE OF ARSENIC PLUME AND TRANSPORT ...... 76
5.11 SOURCES OF ARSENIC MOBILIZATION IN D. I. KHAN GROUNDWATER ...... 82
5.12 ARSENIC MASS BALANCE ...... 83
5.13 REMEDIATION OF ARSENIC IN GROUNDWATER ...... 84
5.14 IMPLICATIONS OF PUMPING IN ARSENIC CONTAMINATED GROUNDWATERS ...... 84
5.15 PARTICLE TRACKING FOR THE ARSENIC SOURCE DETERMINATION ...... 88
6 CONCLUSIONS ...... 89
6.1 GROUNDWATER NUMERICAL MODELING ...... 89
6.2 CONTAMINANT TRANSPORT MODELING ...... 90 References Reprint of Research Paper “Source evaluation of physicochemically contaminated groundwater of Dera Ismail Khan area, Pakistan Appendices
vi
LIST OF FIGURES
Figure 1.1 Location map of Dera Ismail Khan (www.magazine.com.pk) ...... 1 Figure 1.2 Modeled area detailed map showing various streams and canals in blue color. The Tehsil and District boundaries in dark grey color...... 3 Figure 2.1 Geology of Dera Ismail Khan and the location of the modeling area marked by dashed lined rectangle...... 6 Figure 2.2 Physiographic map of Dera Ismail Khan (study area) (Fraser, 1958)...... 8 Figure 2.3 Meteorological data of D. I. Khan showing temperatures maximum ( red ) and minimum ( blue ; 30 years mean monthly). Source www.allmetsat.com ...... 9 Figure 3.1 Fence diagrams showing the subsurface lithologic divisions of the subsurface ...... 22 Figure 3.2 Three dimensional Conceptual model of the study area ...... 23 Figure 3.3 Model grid discretization in MODFLOW showing the variable grid consisting of 182 rows and 186 columns i.e. 500×500m. The green zone marks the inactive zone and white zone the active zone. The coordinates are in meters. The blue colored stream boundaries are visible...... 26 Figure 3.4 Initial model setting with constant head boundaries...... 27 Figure 3.5 The spatial discretization of hydraulic conductivity zones (Table 3 2) within polygons...... 29 Figure 3.6 The distribution of canal network in Pakistan ...... 30 Figure 4.1 The groundwater modeling process ...... 33 Figure 4.2 Discretized hypothetical aquifer system in MODFLOW...... 37 Figure 4.7 Calibration scattergram results for steady state...... 40 Figure 4.8 The steady state equipotential map of layer 1, year 1985...... 41 Figure 4.9 The steady state equipotential map of layer 2, year 1985...... 41 Figure 4.10 The steady state equipotential map of layer 3, year 1985...... 42 Figure 4.11 Steady state equipotential surface and velocity vectors simulated in layer 1 ...... 42 Figure 4.12 Steady state equipotential surface and velocity vectors simulated in layer 2 ...... 43 Figure 4.13 Steady state equipotential surface and velocity vectors simulated in layer 3 ...... 43 Figure 4.14 Location of ten pumping wells used for the water levels measurements ...... 45
vii
Figure 4.15 Non steady state calibration ...... 46 Figure 4.16 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 1&2 ...... 46 Figure 4.17 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 3&4 ...... 46 Figure 4.18 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 5&6 ...... 47 Figure 4.19 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 7&8 ...... 47 Figure 4.20 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 9&10 ...... 47 Figure 4.21 Sensitivity Analysis of hydraulic conductivity for steady state simulations ...... 49 Figure 4.22 Sensitivity Analysis of Recharge for steady state simulations ...... 49 Figure 4.23 Sensitivity Analysis of specific yield for non steady state simulations ...... 50 Figure 4.24 The non steady state equipotential surface simulation map of layer 1, year 1995 .... 52 Figure 4.25 The non steady state equipotential surface simulation map of layer 2, year 1995 .... 52 Figure 4.26 The non steady state equipotential surface simulation map of layer 3, year 1995 .... 53 Figure 4.27 The non steady state equipotential surface simulation map of layer 1, year 2005 .... 53 Figure 4.28 The non steady state equipotential surface simulation map of layer 2, year 2005 .... 54 Figure 4.29 The non steady state equipotential surface simulation map of layer 3, year 2005 .... 54 Figure 4.30 The non steady state equipotential surface simulation map of layer 1, year 2010 .... 55 Figure 4.31 The non steady state equipotential surface simulation map of layer 2, year 2010 .... 55 Figure 4.32 The non steady state equipotential surface simulation map of layer 3, year 2010 .... 56 Figure 4.33 Non steady state equipotential surface and velocity vectors simulated in layer 1, 1995 ...... 56 Figure 4.34 Non steady state equipotential surface and velocity vectors simulated in layer 2, 1995 ...... 57 Figure 4.35 Non steady state equipotential surface and velocity vectors simulated in layer 3, 1995 ...... 57 Figure 4.36 Non steady state equipotential surface and velocity vectors simulated in layer 1 in year 2005 ...... 58
viii
Figure 4.37 Non steady state equipotential surface and velocity vectors simulated in layer 2 in year 2005 ...... 59 Figure 4.38 Non steady state equipotential surface and velocity vectors simulated in layer 3 in year 2005 ...... 59 Figure 4.39 Non steady state equipotential surface and velocity vectors simulated in layer 1, year 2010 ...... 60 Figure 4.40 Non steady state equipotential surface and velocity vectors simulated in layer 2, year 2010 ...... 61 Figure 4.41 Non steady state equipotential surface and velocity vectors simulated in layer 3, year 2010 ...... 61 Figure 4.42 Decline in water levels in wells with respect to long term pumping ...... 62 Figure 4.43 Water table depth map of year 1985 ...... 63 Figure 4.44 Water table depth map of year 1995 in response to long term pumping ...... 63 Figure 4.45 Water table depth map of year 2005 in response to long term pumping ...... 64 Figure 4.46 Water table depth map of year 2010 and development of cone of depressions in response to long term pumping ...... 65 Figure 4.47 Non steady state drawdown (m) map of layer 1 year 2010 ...... 66 Figure 4.48 Non steady state drawdown (m) map of layer 2 year 2010 ...... 66 Figure 4.49 Non steady state drawdown (m) map of layer 3 year 2010 ...... 67 Figure 4.50 Total groundwater volume balances in the model simulations ...... 68 Figure 5.1 The model discretization into grid of 100×100 m. The blue and white colour mark the inactive and active areas...... 74 Figure 5.2 The arsenic concentration calibration in 2010 in Transient state conditions ...... 75 Figure 5.3 A 3 D model of the contaminant plumes of Arsenic in groundwater ...... 76 Figure 5.4 The contaminant concentration with respect to time in layer 1 ...... 77 Figure 5.5 The contaminant concentration with respect to time in layer 2 ...... 78 Figure 5.6 The contaminant concentration with respect to time in layer 3 ...... 79 Figure 5.7 Arsenic plumes development in 2010 marking the initial conditions in layer 1 ...... 80 Figure 5.8 Expansion of Arsenic plume in the cross section along A A’, year 2010 ...... 80 Figure 5.9 Arsenic plume showing a change in the concentration in 2013 in layer 1 ...... 81 Figure 5.10 Expansion Arsenic plume in the cross section along A A’, year 2013 ...... 81
ix
Figure 5.11 Arsenic plumes with increased concentration marked by the high intensity colors in 2015 in layer 1 ...... 82 Figure 5.12 Expansion of arsenic plume in the cross section along A A’, year 2015 ...... 83 Figure 5.13 Development of isolated plumes along hydraulic gradients of 34 pumping wells, layer1, 2015 ...... 85 Figure 5.14 Contaminant Plumes along the cross section A A’ ...... 86 Figure 5.15 Showing vertical migration along B B’ plume I in eastwest direction...... 87 Figure 5.16 Showing vertical migration along C C’ plume II in eastwest direction ...... 87 Figure 5.17 Showing vertical migration along D D’ plume III in eastwest direction ...... 87 Figure 5.18 The Arsenic advection flow path along the concentration gradients to the wells. .... 88
x
LIST OF TABLES
Table 2 1 Month wise mean temperature and precipitation recorded at D.I. Khan Station during year 2010 ...... 10 Table 2 2 Evapotranspiration (mm) measured in Dera Ismail Khan (Thornthwaite method) and the adjoining areas (Malik, 1985)...... 11 Table 2 3 Approximate correlation of middle to late Tertiary and Quaternary rocks in the vicinity of D. I. Khan (Hemphil & Kidwai, 1973) ...... 15 Table 2 4 The Rodkohi system streams (Nalas) discharges ...... 17 Table 3 1 The aquifer lithological description and layering on the basis of well data records and the zones correspond to the Fig 3.4...... 24 Table 4 1The PEST parameters...... 38
xi
CHAPTER 1
1 Introduction
The changing climates and their impacts are responsible for the manifestation of depletion of fresh water resources and reduction in well yields. The water quantity and the quality are the two issues which are dealt on priority basis. Apart from the climatic changes there are numerous other factors such as the population increase, water usage, water distribution, anthropogenic activities which also have strong impact on the water resources. The future economic growth will be relying on properly managed water resources. For a proper management strategy the understanding of the hydrologic system i.e. its quantification and quality assessment is extremely important. The current study deals with the development of numerical flow and contaminant transport model of an alluvial plain in Dera Ismail Khan (D. I. Khan), Khyber Pakhtoon Khwa formerly known as North West Frontier Province (NWFP), Pakistan (Figure 1.1).
Dera Ismail Khan
Figure 1.1 Location map of Dera Ismail Khan (www.magazine.com.pk)
The Model is constructed in visual MODFLOW 4.0 which uses the finite difference technique to simulate heads at a block centered nodal grid. The software has proven capabilities in simulating the condition in various regions and aspects (Bordeleau, 2007). A three layered finite difference gird of 186 columns and 182 rows was overlaid on the study area with a constant node spacing of 500 × 500 m both in x and y axes. The model was run initially with constant head and stream boundaries but finally the model settled down with the
1 stream boundaries for the Indus River, Takwarrah Nala and Sheikh Haider Zaman Nala (Figure 1.2). Various hydraulic parameters including hydraulic conductivity (K), initial heads, recharge, porosity, specific storage (Ss), specific yield (Sy), top and bottom elevation of aquifers are incorporated in the model and the spatial distributions of these are interpolated using the Visual MODFLOW. The initial data were obtained from previous work of Naqvi (1977) and Malik (1985). Ten K zones have been defined using reliable pumping test data of 10 test wells. The model comprised of three layers based on the lithological correlations in the study area. The model was initially run for 10 years in steady state for the year 1985. In steady state a single time step was used. The model was calibrated with several runs by modifying the hydraulic conductivities and recharge values. The Parameter Estimation and Testing (PEST) has been used to do the calibration with the minimum and maximum ranges of 36 hydraulic conductivities and recharge values (Table 3 2). The steady state model was further used for transient conditions and run for 25 years upto 2010. The model is run into different time steps depending on the seasonal sowing time periods of the crops i.e. Rabi and Kharif that signify two stress periods in one year. The model was also calibrated in transient state in 1985 using steady state heads as initial conditions and assessing the draw downs. The area has been studied with respect to the various anthropogenic activities and found to be contaminated with high salinity, sulphates and Arsenic (As). Nearly sixteen water samples were collected and analyzed for the arsenic concentration. Several of the samples indicated high concentration of 5 and 10 ppb that fall in the above permissible limits set by World Health Organization (WHO) and Pakistan Standard Quality Control Association (PSQCA) respectively. The model was further refined in the transport modeling area with a grid spacing of 100×100 m in the Chashma Right Bank Canal (CRBC) command area to model arsenic fate in three dimensions. The contaminant transport model MT3D was used with the observed arsenic concentrations and calibrated in 2010 for steady state and non steady state conditions. Model realizations were further projected up to 2015 to monitor the spreading of Arsenic concentration
2 in the groundwater. The preliminary management scenarios were also discussed to address the issue at hand for safety of the inhabitants in the study area.
Figure 1.2 Modeled area detailed map showing various streams and canals in blue color. The Tehsil and District boundaries in dark grey color.
1.1 Aims and objectives
a) The development of the conceptual model of the area (utilizing the lithologic and pumping test existing data) b) Development of a GIS data base for study area. c) Utilizing the GIS database to develop the numerical flow model using three dimensional finite difference approach. d) To analyze the spatial distribution of hydrochemical constituents concentration and the demarcation of sources for contamination.
3
e) Simulation of the contaminants coupled with the flow model to assess the vulnerability of the study area. f) Management of contaminated D. I. Khan aquifer.
1.2 Previous work
The hydrogeological work in this area date back to 60’s. Different organizations (public and private) worked in this area. The earliest groundwater investigations were done by Water Logging And Salinity Investigation Department (WASID) (Hood, 1970) and the most comprehensive work by Malik (1985). The hydrogeology section of Water And Power Development Authority (WAPDA) Peshawar worked in detail about the Paharpur Tehsil of the D. I. Khan area (Naqvi, 1977). The inception of the Chashma Right Bank Canal led to a never ending hydrogeological work in the study area in which not only government but the private consultants also got there recent work published. There are numerous reports available in the library of WAPDA and CRBC project. All of these reports were reviewed thoroughly and the data was acquired to be used as input in the model. The research carried out by the International Water Management Institute (IWMI) also provided details of the water and the salinity balances, which was compared with other canal irrigated areas (Kijne, 1996). Most of the details about recharge of the Paharpur canal has been taken from the groundwater resources of Pakistan (Ahmed, 1972) and the soil units from Fraser (1958). The status of water logging and salinity was discussed in some of the literature by Qureshi (2008) and Qureshi et al. (2003).
4
CHAPTER 2
2 Study area details
2.1 Location
Dera Ismail Khan is the southern most district of Khyber Pakhtoon Khwa, lying between 31 ° 15 ′ and 32 ° 32 ′ North latitudes and 70 ° 11 ′ and 71 ° 20 ′ East longitudes. The total area of the district is 7326 square kilometers. The district is bounded on the north by Tank and Lakki Marwat districts, on the east by Mianwali and Bhakkar districts of the Punjab province, on the south by Dera Ghazi Khan District of the Punjab province and on the west by tribal area adjoining Dera Ismail Khan district (Shirani), South Waziristan Agency and Tank district (Figure 2.1). The town is situated on the west bank of the Indus River. The study area i.e. the CRBC stage II command area lies between the Indus River and the Chashma Right Bank Canal. Dera Ismail Khan District’s western boundary is surrounded by the mountains and hills of south Waziristan Agency and tribal area adjoining Dera Ismail Khan District. The western part of the plain is, therefore, drained by a large number of streams and hill torrents. After the rains, the hill torrents spread the alluvial soil, which is predominantly clay. After continuous rain, it becomes soft and tenacious mud. The plain extends down into the Dera Ismail Khan district of the Khyber Pakhtoon Khwa Province and is known as “Dera Jat”. The area close to the Indus River is riverine. It is known as “Kacha” area. The level is below the old bank of the Indus River. The Indus River flows along the eastern Boundary of the district. The old Paharpur Canal gets regular water for irrigation after completion of Chashma Barrage at the Indus River. Additional water supply has been provided by Chashma Right Bank Canal. The population of the District has been reported to be 0.634 million by the census report of 1981.
5
Figure 2.1 Geology of Dera Ismail Khan and the location of the modeling area marked by dashed lined rectangle.
2.2 Topography and Relief
The slope of the study area depends upon the physiography of the region. The highest region lies towards north in the Sulaiman range about 350 meters above mean sea level (a.m.s.l). The lowest point lies in the south near the Indus River and is about 152 meters. The gradients are steepest on gravel fans near mountains and are about 0.3 percent whereas in the mid slope of the plain and in the Indus flood plain, the gradient ranges from 0.09
6 to 0.2. The general elevation data has been acquired from the field measurements with GPS and the Digital Elevation Model (DEM) i.e. SRTM 30 NESPAK (1998). The D. I. Khan basin is a part of the Lower Indus basin and is composed of alluvial sediments derived from the Indus and its tributaries. Physiographically the area can be divided into five major units based largely on the data from Fraser (1958) i.e., Mountain highlands, Piedmont plain, Flood Plain, Aeolian deposits and Gravel fans (Figure 2.2).
2.2.1 Mountain Highlands
The mountain highlands largely consist of consolidated sedimentary rocks which formed as a result of the tectonic forces operated in the area. They include north westward extending outcrops of Khisor and Marwat Ranges, joining in the west to irregular masses of Sur Ghar mountains and Shaikh Budin Hills respectively. The other two folded ranges include the Bhittani Ranges and the Shirani Hills. At most places in the area the mountain highlands are separated from plain by coalescing gravel and sand fan. The general slope of these deposits are southward and eastward with grain size grading in the same direction and merge into piedmont plains.
2.2.2 Piedmont Plain
The area between the mountain highlands and the lowlands of the Indus river is the piedmont plain. It is smooth surfaced consisting of large number of drainage channels crossing the plain for joining the Indus River.
2.2.3 Flood Plain
The areas lying adjacent to the river Indus consist of the flood plains. It may be divided into abandoned and active flood plains. These flood plains are mostly covered by the alluvium carried by the major drainage channels that cross them.
2.2.4 Aeolian Deposits
In the north and the northeastern portion sand dunes form a thick cover on the underlying piedmont deposits. Their lithology varies from silty clay to fine sand.
7
2.2.5 Gravel Fans
The gravel fans occur in the western and northwestern parts along the mountain ranges. They form thick gravel and boulder deposits intercalated with clay.
Figure 2.2 Physiographic map of Dera Ismail Khan (study area) (Fraser, 1958).
2.3 Climate
The area lies in the domain of hot semi arid climate with seasonal considerable fluctuations in temperature and rainfall. The average monthly temperature in D. I. Khan shows a
8 hot period from May until September with mean exceeding 30 ° C. In winter the average monthly temperature drops below 12 °C in December and January. Rainfall is concentrated in two wet seasons, the first with maximum in March/April and the second with maximum in July/August. The average yearly precipitation at Tank and D. I. Khan is 258 mm and 261mm.
2.3.1 Precipitation
The only long term data is available from D. I. Khan and Tank meteorological stations which are located in the study area. The average yearly precipitation in D. I. Khan measured over a period from 1947 to 1981 is about 249 mm. The amount of rainfall received is not very high. The maximum is recorded in July. The monthly 2010 mean maximum and minimum temperature and precipitation recorded at Dera Ismail Khan and Tank is given in (Table 2 1 and Figure 2.3).
70 a 70 b 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Jul Jan Jun Oct Jul Apr Feb Sep Dec Aug Nov Jan Mar Jun Oct May Apr Feb Sep Dec Aug Nov Mar May
Figure 2.3 Meteorological data of D. I. Khan showing temperatures maximum ( red ) and minimum ( blue ; 30 years mean monthly). Source www.allmetsat.com
2.3.2 Temperature
The summer season is dry and hot. The temperature begins to rise from April and the months of May, June, July and August are extremely hot. June is the hottest month during which the mean maximum and minimum temperature is recorded around 42 °C and 27 °C respectively. In May and June the humidity is very low. The area is under periodic dust storms. The hot wind blows across the district. The cool wave starts somewhat in October. December, January and February are the cold months. In winter, the daytime temperature is not very low; however, there is sharp decrease at night. The weather is cold and the frost is severe. In January the mean maximum and the minimum temperature is around 20 °C and 4 °C respectively.
9
Table 2 1 Month wise mean temperature and precipitation recorded at D.I. Khan Station during year 2010
Mean Temperature (ºC) Precipitation Month Maximum Minimum (Millimeters) January 20.29 4.18 10.02 February 22.09 7.29 17.48 March 26.90 12.86 34.76 April 33.45 18.53 21.66 May 38.75 23.14 17.23 June 41.51 26.76 14.40 July 38.54 26.92 60.76 August 37.35 26.40 57.52 September 36.67 23.80 17.62 October 33.35 17.35 4.77 November 27.71 10.47 2.11 December 21.93 5.27 10.38 Annual 31.55 16.90 268.74
2.3.3 Potential Evapotranspiration
The monthly potential evapotranspiration in the project area has been estimated with the help of two different methods. In the first method Thornthwaite formula has been applied using available data of temperatures recorded at D. I. Khan and Tank meteorological stations. The second method for computation of potential evapotranspiration (Ep) is based on the available measured pan evaporation (Ew) at Tank and two other locations by multiplying Ew with a factor of 0.7. The results of the methods are shown below in Table 2 2 (Malik, 1985).
10
Table 2 2 Evapotranspiration (mm) measured in Dera Ismail Khan (Thornthwaite method) and the adjoining areas (Malik, 1985).
Method Ep=Ew×0.7 Ep=Ew×0.7 Ep=Ew×0.7 Ep=Ew×0.7 Month Tank D. I. Khan Leiah Mianwali January 8 9 53 55 February 13 18 74 60 March 57 78 106 104 April 159 143 152 162 May 205 198 188 192 June 220 214 234 278 July 220 213 205 265 August 106 198 190 221 September 176 168 194 182 October 134 121 153 154 November 43 41 69 68 December 14 13 52 52 Total 1455 1414 1670 1793
2.4 Tectonic frame work of the region
The Khyber Pakhtoon Khwa is located in the extreme northwest of the Indo Pakistan subcontinent where it merges with the Eurasian continent due to the consequence of a collision between the Indian and the Eurasian plates. Around 130 million year ago, the Indian plate existed with a supercontinent named as Gondwanaland which was actually a piece of another supercontinent named as Pangea existed around Permian in the geological past. Around 55 million year ago the Indian plate which broke apart from Gondwanaland and finally collided with the Eurasia in the north. During its journey towards north it collided with an Island arc now known as the Kohistan Island arc. Between 55 and 40 Myr ago the movement of the plates was restricted to a small counter clockwise rotation that turned the Indian plate by about 90o. In this 15 Myr period it converged not only northward with Eurasia but also westward with Arabian shield and the various tectonic elements of the Iran Afghanistan orogenic belt. The subcontinent’s twist during the Eocene was responsible for the first major bend in the Zagros Chitral convergence zone. From the late Eocene onwards the northward movement relative to Africa took place at a more leisurely pace. The anticlockwise rotation also continued. By 20 Myr ago, the subcontinent had moved 500 to 600 km northward from its 40 Myr position. This
11 northward advance was made possible partly by the development of a new convergence zone within the Indo Pakistan subcontinent. This deep crustal dislocation now expressed as the central crystalline Axis of the High Himalaya, developed 100 to 200 km south of the Indus suture zone. To the northwest, the Pakistani edge of the subcontinent converged further with the central Iran and Afghanistan micro continents. By 20 Myr ago it had moved much closer to the present location of the Turan continental block, so that a pincer like squeeze was being applied on all sides of the central Iran and Afghanistan micro continents (Hemphil & Kidwai, 1973). From 20 Myr ago to the present, the leading edge of the Indo Pakistani shield, under thrusting along the central crystalline axis, moved 300 500 km northward and the subcontinent rotated further counter clockwise. The main effects of this rotation were the continued convergence of the microcontinental blocks of central Iran and Afghanistan, the formation of the Pamir Knot . the rise of the Tibetan plateau and the growth of the Himalayas, with the accompanying formation of the Indo gangetic basin (foredeep). This Indo gangetic basin covers the north of the Indo Pakistan from the Sulaiman Range in the west to the mountains of Nagaland and Manipur in the east. The Dera Ismail Khan basin is the western extremity of the Indo gangetic plain and the Peshawar valley was the part of it during an early stage of its formation. Evidence for this is provided by deformed early molasse deposits (Murree Formation), which have been found in the Peshawar basin (Hemphil & Kidwai, 1973).
2.4.1 Geology of the study area
The unconsolidated and the semi consolidated sediments filling the groundwater basin of the investigated area have mainly originated from two different sources. In the central, western and northern parts of the area, the fill consists of sediments brought by the streams and rivers from the consolidated rocks surrounding the basin to the West. These deposits have extended to the east where they interfinger with the Punjab type sand deposits brought by the large rivers that emplace the alluvium of the Indus plain. The process of erosion of the uplifted areas and deposition in the topographically low lying and probably subsiding areas began with the crustal movements in the middle Tertiary time and has continued into the Holocene (Malik, 1985).
12
2.4.2 Consolidated rocks
The mountain ranges of the drainage basin are a part of southwestern extension of the Himalayas known as Balochistan Arc. They form both past and present source of sediments that make up the Western and the Central part of the alluvial fill of the plain and also have a great influence on the chemical quality of both surface and groundwater. The approximate distributions of the principal rock units in the area are shown in (Table 2 3). Various rock formations of different ages have been grouped in Tertiary, Mesozoic and Paleozoic sedimentary rocks. The extension of tertiary rocks below the unconsolidated fill could not be determined. It is however obvious that the basement consist of sandstone and shale that crop out in the adjacent mountain ranges north and west of alluvial fills. The top of these consolidated deposits could neither be established in the borehole nor from the resistivity soundings owing to the similarity of the alluvial sediments with the source material. However in the area west and northwest of Kot Azam, the bedrock appears to be at the depth of 120 170 meters near the mountains with steep eastwards slope as determined from the resistivity sounding data. Shale claystone and siltstone may all be considered impermeable rocks. Only fractured sandstone has some importance for groundwater storage but it is expected that water bearing capacity of these rocks is far less than that of the alluvial fill (Malik, 1985).
2.4.3 Unconsolidated rocks
Surficial deposits of Quaternary age include alluvium and sand dunes of the Indus and Bannu plains, alluvial fans along the hill fronts, and unconsolidated detritus along the slopes and in the valleys of the mountain ranges and foothills. These deposits are of Holocene age. Older terrace deposits cover extensive areas underlain by rocks of early Tertiary age in the foothills of the Sulaiman Range and early and late Tertiary age in the hills east of Waziristan. These terrace deposits are strongly cemented and in places attain a thickness of more than 100 meters. They may be as young as late Pleistocene. The unconsolidated deposits in the area range from middle to late Pleistocene to Holocene age (Table 2 3). They consist of clay, silt, and sand and gravel sediments and constitute the principal groundwater reservoirs. Most of the unconsolidated deposits were laid down as a filling in low areas that were created by the crustal downwraping and that might have
13 subsided further as the filling continued. Within the main part of the investigated area, the fill can be divided into two broad types: 1. The piedmont deposits locally derived from the adjacent northern and western mountains. 2. The Punjab type deposits laid down by the rivers that emplaced the alluvium of the Indus Basin. Locally these deposits are overlain by deposits of Holocene age like dune sand, recent alluvial fans, alluvial deposits along the modern drainage ways and in the active flood plain of the Indus River.
2.4.3.1 Piedmont Deposits
These deposits consist of coarse sands, gravel sand boulder sediments immediately adjacent to the mountains. Downslope they decrease in grain size and in central part of the fill they consist mainly of silty clay. Further towards east the deposits interfinger with the Punjab type of sediments in a transition zone parallel to the Indus River. On the whole there is a trend of decreasing grain size from south to north; probably this phenomenon can be attributed to the decreasing grain size to the source material in the Siwalik group.
2.4.3.2 Punjab Type Deposits
These deposits consist of very thick sand layers with minor intercalations of thin clay layers. The encountered thickness of the sand unit is from 250 meters to more than 420 meters. To the west in the transitional zone the sand section interfingers with the piedmont deposits. Both the river low land and the piedmont plain appear to be underlain by layers of fine textured formations. A clay section was encountered in most of the test holes drilled by WASID along the Indus River. In the piedmont plain the clay section was more difficult to be defined especially near the mountains.
2.4.3.3 Soils
The study area consists of majorly two types that is stratified soils and the homogenized soils. The western areas lying in the mountainous region the soils are sandy and their age varies from Sub Recent to Recent. They are easily eroded by the wind and most are excessively drained. Their material grades from coarse sand near the foot of the hills to sandy loams along the major torrents. All the soils of this region are moderately to strongly calcareous and have pH
14
values between 8.2 and 8.6. The general texture of the soil in the Rod Kohi areas range from silty clay
Table 2 3 Approximate correlation of middle to late Tertiary and Quaternary rocks in the vicinity of D. I. Khan (Hemphil & Kidwai, 1973)
STRATIGRAPHIC HYDROGEOLOGICAL PERIOD EPOCH TYPES OF ROCKS UNITS CHARACTERISTICS Unconsolidated deposits of clay, sand and gravel Aquifer except clay and Holocene consisting of stream and clayey deposits. River deposits piedmont and related deposits.
Potwar Loess Loess like silt and gravel.
Coarse boulder and Boulder gravel conglomerate with Conglomerate thick clay, sand and Permeable when loose and pebbly grits. fractured. Pleistocene
Coarse grift, sandstone Pinjor Zone and conglomerate. QUATERNARY Sandstone soft with clay Tatrot Zone and shale beds.
Sandstone, gray, white Dhok Pathan Zone and brown with clay and shale beds. Secondary permeability Sandstone, massive and may exist. Nagri Zone thick bedded, gray with subordinate shale beds. Shale, bright red, nodular, and clay with siltstone, Chinji Stage Impermeable. sandstone and pseudo conglomerates. Sandstone, Dark red, hard Secondary permeability Miocene Miocene Plieocene Kamlial Stage with red to purple shale may exist. and pseudo conglomerate. GROUP SIWALIK
Secondary permeability Nari Formation Limestone and shale. may be considered. TERTIARY Oligocene
15 to silty loam. The soils irrigated by the canal region are flood plain soils and show a coarse character as compared to the soils of the piedmont region. The soils vary from coarse to fine character lying on the sandy substratum (SSOP, 1969).
2.5 Agriculture
Wide variety of crops are sown in the Dera Ismail Khan mainly in the Rabi and Kharif seasons. The major crops include the Wheat, Maize, Sugarcane, Gram, Millets/Sorghum, Berseem, Rice and Oil seeds (SSOP, 1969). Nearly 75% of the total area is under cultivation. Out of the total cropped area, 65% includes food crops. The crops are wheat, millet, gram cereals, sugarcane, and fodder. The economy of the area is mainly based on agriculture. Farm output provides not only the food requirements of the area but also a sizable portion of it is exported to other parts of the country (Ahmad & Qadir, 2011).
2.6 Irrigation
The main reliance of the inhabitants for the irrigation is dependent upon number of sources of water for Irrigation as mentioned below: • The Indus River • Western tributaries and streams • Open wells (dug wells) • Tube wells (Water wells) • Precipitation The irrigation in the western regions mostly depends on hill torrents, which is controlled by the Deputy Commissioner/Collector. The system known as Rodkohi Irrigation System is governed over by a century old traditions, meticulously worked out. It consists of a number of streams of the west enlisted in
Table 2 4 where water is diverted to be utilized for irrigation. However, a lot of water is wasted because of a poorly developed distribution system.
16
Table 2 4 The Rodkohi system streams (Nalas) discharges
Perennial High Flood Stream/Zam/Nala (Cusecs) (Cusecs) Gomal Zam 100 160,000 Tank Zam 40 120,000 Daraban Zam 35 50,000 Chowdwan Zam 20 31,000 Sheikh Haider Zam 10 40,000 Takwarah Nala N/A 120,000 Ramak Nala N/A 20,000 Total 205 541,000
On the eastern side near the banks of the river Indus the irrigation is mostly done either by directly diverting the water of Indus in the form of the canals or by pumping the water out of the river and putting that in the channel. Another means of irrigation in the study area is augmented the CRBC and Paharpur canals diverted from Indus at Chashma Barrage. Apart from this a number of tubewells have been installed by the Irrigation department and by some private owners in the lower part of piedmont plain and in the adjacent part of flood plain above Paharpur canal in the north and west of D. I. Khan town. In the areas south of D. I. Khan besides tubewells a large number of open wells have been constructed by the local farmers on which they have fitted centrifugal pumps to lift water both for irrigation and drinking purposes. In the area between Gomal and Kot Azam on the left bank of Gomal River, nine tube wells have been installed under Khanwand Scheme in 1972 74. The major part is rain fed (barani) and agricultural activities in the rainfed area totally depend upon rain water. The bulk of the water supply for domestic purposes is obtained from tube wells, small water pumps, hand pumps, ponds to store runoff, the Indus River and from the western tributaries and streams.
17
A large number of tube wells installed by Public Health Engineering Department (PHED) and other government and private agencies are also supplying water in the main towns and villages of the district.
2.7 Hydrology
D. I. Khan is very important from the point of view of the developing agriculture and industry. The area utilizes two sources of water the surface and the groundwater. The surface water resources consist of the Indus River, western tributaries and streams and the ponds storing the rainfall water. The Indus River is the major stream in D. I. Khan area running along the eastern boundary of the District. Tributaries from the western mountains of the drainage basin carry runoff to the Indus River. The magnitude of the flow of the Indus River near D.I. Khan has been reported to be 11×10¹º m³/year of which about 9×10¹º m³ is discharged during the period from April to September due to glacial melt. A large number of intermittent and perennial streams enter the study area from the western mountains. Some of them arise from within the alluvial fill and are being recharged by groundwater whereas the large streams have originated from mountainous parts of the catchment area and some come from the adjacent areas of Afghanistan. The intermittent streams contain water during most of the year, but cease flowing during the hottest or driest period. The major streams which sustain perennial flow are Tank, Zam, Gumal River and Khora River (Daraban Zam). They are the major source of irrigation water in the upper part of piedmont plain where almost the entire flow is diverted to the Indus lands through irrigation channels. The only water that reaches the Indus River is the flood water that occurs in response to heavy rains in the mountainous part of the drainage basin. These streams also form an important source of recharge to the groundwater body during the course of their normal flow as well as high flow when they leave the mountainous areas and enter the adjacent alluvial deposits. The prominent streams, flowing within the district are Gumal/Luni, Gejastan, Sawan in the west and Gudh and Paniala in the north. They have little perennial flow, so none of these reaches the Indus River except when there is flood. The details for the flood discharges from the streams were taken from the reports published by the Flood commission of Pakistan (NESPAK, 1998).
18
2.8 Hydrogeology
Dera Ismail Khan is an alluvial plain and consists of various hydrogeological units. Deposition and down cutting by the major streams were extensive at the end of the Tertiary Period and during the Quaternary Period. Repeated deposition and erosion left remnants of alluvial deposits at higher elevations as the streams progressively lowered their beds. As a result series of alluvial terraces were formed. Alluvium, as distinguished from alluvial terraces, is the most recent material deposited within the confines of the present flood plain. The alluvial terraces and alluvium usually form a single aquifer, although some outlying alluvial terraces are hydraulically independent. Highly permeable windblown sand derived from the alluvium and alluvial terraces overlies the alluvial deposits in many places and readily store recharge from precipitation and conducts the recharge downward. It has got different recharge sources and discharge sources. The data from wells is sorted out and checked in the field during various tubewell installations to determine the general texture of lithologies encountered in the bore holes. On the basis of texture of soil it has been found that generally area comprises of alluvium and this alluvium can be divided into three zones vertically on the basis of grain size. The study area consists of three broad hydrogeological zones. • The gravels and boulders of the alluvial fan deposits • The silty clay series of the piedmont plain • The Punjab type sand deposits of the active and abandoned flood plains along the Indus.
The alluvial fan deposits are a recharge zone along the western boundary. The zone is a few kilometers wide and 200m thick (Hood, 1970).
19
CHAPTER 3
3 Conceptual model
The conceptual model is based upon procured data, field reconnaissance and its understanding. It is the first step towards the development of a numerical model. It needs an in depth understanding of the hydrological, meteorological, hydrogeological, geological and geomorphological processes by taking into account all the available data. The hydrogeological system of the study area is modeled as multi layered aquifers with variable thicknesses. Aquifers are composed of alluvial deposits containing medium to fine sands of sub recent to recent age. In general, the thickness of the alluvial sediments exceeds more than 300 meters (Malik, 1985). The alluvial deposits are also comprised of intercalation of sand and clay (Figure 3.1). Inspite of local heterogeneity, the alluvial complex behaves as a large and contiguous groundwater reservoir. The conceptual model is divided into three layered aquifer (Figure 3.2) on the basis of subsurface lithology encountered in water wells and pumpage from various tubewells. The model layers contain the following specifications:
Sediments occurring at shallow depths are composed of finer materials i.e. silt and fine sand, therefore, the first layer has been assumed to extend 20 meters below the average watertable. The second layer is assumed to extend from 20m to 40m (thickness 20m) to represent groundwater withdrawals from comparatively shallow tubewells. The third layer extends from 40m to 100m depth (thickness 60m) to count for abstractions from the deep tubewells. The groundwater for domestic and local use is mainly obtained from shallow depth of 20m through hand pumps while for agriculture purposes it is obtained through tubewells from deeper depths of 32 40m and in some cases may go up to 60m depth (Qureshi et. al., 2003).
3.1 Data
The data was based upon the well log correlations, monitoring of water levels, precipitation and recharge data, evapotranspiration data and discharge data and paleo
20 hydrochemical data (Malik, 1985). Moreover, recent data also acquired during the field work to bridge the gap and or shortcoming in the previous available data. The main emphasis was to study the response of aquifer within the Chashma Right Bank Canal (CRBC) command area by the recent changes due to irrigation and other anthropogenic activities. The CRBC command area located in the upper Indus basin (Figure 1.2) drains its water from the Indus River at Chashma Barrage and irrigates an area of 0.470 million acres of Dera Ismail Khan (DIK) Division, Khyber Pakhtoon Khwa (KPK) (Ahmed, 1972). This canal was completed in three stages i.e. in 1987, 1992 and 2002.
3.1.1 Well data
The groundwater levels of 1985 were procured from various sources including WAPDA, WASID, PHED reports and various private tubewell owners. The data was interpolated with Kriging using Surfer 9 (Virdee et al. 1984, Kumar 1996, Kumar et al. 2006 and Volpi et al. 1978).
The data from wells is sorted out and checked in the field during various tube well installations to determine the general texture of lithologies encountered in the bore holes. On the basis of texture of soil, area in general comprised of alluvium which can be divided into three layers due to variability in thicknesses of subsurface lithologies. All of the well data acquired is incorporated in the software Rockware 2002 for the geological correlation which leads to the establishment of continuity of aquifers layers. As such, the whole lithologies encountered in the boreholes have been divided into three hydrostratigraphic units with their data interpolated generating the surfaces for each unit and the spatial distribution of different lithologies (Table 3 1).
21
Figure 3.1 Fence diagrams showing the subsurface lithologic divisions of the subsurface
22
Evapotranspiration Precipitation
CRBC Paharpur Canal River Inflow Gradient
Slice 1 Slice 2 (20m)
Unconfined Aquifer Water Table Slice 3 (20 m)
Layer 1 River outflow Layer 2 Slice 4 (60 m)
Layer 3 Groundwater outflow
Figure 3.2 Three dimensional Conceptual model of the study area
23
Table 3 1 The aquifer lithological description and layering on the basis of well data records and the zones correspond to the Fig 3.4.
Layers 1 2 3 Thickness (m) 20 20 60 Zone 1 Sand+Clay+Gravels Sand+Gravels Gravels Zone 2 Sand+Clay+Gravel Sand+Clay+Gravel Sand+Clay+Gravel Zone 3 Clay+Sand Clay+Sand Clay+Sand Zone 4 Clay+Sand Clay+Sand Clay+Sand Zone 5 Fine Sand Fine to medium Sand Medium Sand Zone 6 Sand+Clay+Gravels Sand+Gravels Gravels Zone 7 Sand+Silt+Gravel Sand+Silt+Gravel Sand+Gravel Zone 8 Fine Sand Fine to Medium Sand Medium Sand Zone 9 Silt+Sand Silt+Sand Silt+Sand Zone 10 Fine Sand Fine to Medium Sand Fine to Medium Sand Z O N A T N I O
3.1.2 Pumping wells
The study area consists of number of pumping wells that range from small pumps to bigger tubewells (water wells). The pumps used are the submersibles as well as the diesel operated pumps to extract the water from the ground for various purposes. The data from existing wells has been taken from the previous reports and also collected during the extensive field work for its reliability and authenticity. These wells and their respective discharges in the model are also important for assessment of the future of groundwater development potential in the D. I. Khan area. Appendix A summarizes the general features of the pumping well design specifically the screen setting depths.
3.1.3 Model grid
The model grid comprises 186 columns and 182 rows for a 500m×500m model grid. Thus, the total model area was discretized into 33852 cells. From the layout of the conceptual model, the actual model was devised into three aquifer layers with varying thickness both laterally and vertically. The layer 1 represents an unconfined aquifer, while Layers 2 and 3 are considered to be confined/unconfined (variable transmissivity) aquifers. The finite difference block centered grid was used with an average depth of model simulation was set to 100 meters
24 that almost equals to the average depth of existing tubewells (water wells) in the study area (Figure 3.2).
3.1.4 Model Input parameters
The model input parameters are described in the following sections of this chapter 3.
3.1.4.1 Initial conditions
Two wells types were used during this modeling for dictating the initial conditions to the model the shallow and the deep wells (Tubewells).
3.1.4.1.1 Water wells
Forty water wells (shallow and deep) were used to measure the water levels in the field. Data for the year 1985 was procured from agencies. Year 1985 was considered to be steady state flow regime, and the hydraulic heads of this year were used in calibration. Some of the pumping wells data of year 1985 were also utilized for the transient calibration and verification.
3.1.4.2 Model Boundaries
Three types of boundaries are used as discussed below:
3.1.4.2.1 Constant Head boundaries
The model was surrounded by three major rivers, which were treated initially as the constant head boundaries but later converted into stream boundaries as the model was not giving the desired results for the steady state model (Figure 3.3).
25
Figure 3.3 Model grid discretization in MODFLOW showing the variable grid consisting of 182 rows and 186 columns i.e. 500×500m . The green zone marks the inactive zone and white zone the active zone. The coordinates are in meters. The blue colored stream boundaries are visible.
26
Observation Well Takwarrah Nala
Indus Sheikh River Haider Zaman Nala
Figure 3.4 Initial model setting with constant head boundaries.
3.1.4.2.2 Streams boundaries
The CRBC was used as a stream boundary but kept off in the steady state condition, because it was not existed in year 1985 for which the steady state model has been calibrated in a pre CRBC regime. The nalas originating from the western mountain ranges were also taken as the stream boundaries.
3.1.4.2.3 No flow boundaries
The western ranges were considered as noflow boundaries, which remained inactive during this simulation.
3.1.4.3 Hydraulic conductivity, specific yield and Porosity
The hydraulic characteristics are the key factors that control the groundwater flow. The hydraulic charactersistics were taken from the ten tub ewells located in the study area (Malik,
1985). Apart from the hydraulic conductivity (K pump ) values, the laboratory permeability (K lab ) values of 10 wells in the study area h ave also been procured (Naqvi, 1977). The record of the hydraulic conductivities (K pump ) was not present throughout the depth of tubewell . So the values of respective lithologies (Table 3 1) were assigned using the laborator y permeabilities (K lab ). The 27
laboratory permeability values were converted to the Hydraulic conductivity values using the relationship as mentioned in (Ahmad, 1992 and 2000). The final values of the conductivities
(K model ) were the geometric mean of all values lying in individual layer in one well. Theissen method has been utilized (Theissen, 1911) for the distribution of hydraulic conductivity values in the layers. Ten Theissen polygons have been developed around the respective tubewells that consist of the hydraulic conductivity determined for that aquifer (Figure 3.4).The values of hydraulic conductivities vary from 2.5×10 02 to 5×10 07. The higher values of hydraulic conductivies are observed in the flood plain as well as in piedmont deposits. The lower values of hydraulic conductivities are found in the transition zone between piedmont and the flood plain deposits. The specific yield and porosity values were not available in the records. Therefore the values for the respective lithologies (Anderson and Woesner, 1992) were assigned and averaged out (Table 3 2).
Table 3 2 Hydraulic conductivity zonation in the three layered aquifer model. The K, Sy and POR describes the hydraulic conductivity, specific yield and porosity.
Layers 1 2 3 Properties K (m/s) Sy POR K (m/s) Sy POR K (m/s) Ss POR Zone 1 1.30×10 05 0.15 0.3 2.02×10 05 0.15 0.32 3.20×10 05 1.20×10 04 0.35
Zone 2 1.00×10 05 0.13 0.33 1.20×10 05 0.13 0.34 1.50×10 05 1.00×10 04 0.35 Zone 3 1.03×10 06 0.07 0.37 1.50×10 06 0.05 0.36 2.00×10 06 2.50×10 03 0.38 Zone 4 5.00×10 07 0.01 0.4 3.00×10 07 0.02 0.403 1.00×10 07 2.50×10 02 0.412 Zone 5 1.51×10 05 0.16 0.31 2.76×10 05 0.17 0.32 3.90×10 05 1.00×10 03 0.31 Zone 6 3.20×10 05 0.19 0.3 2.40×10 05 0.17 0.33 3.00×10 05 2.00×10 04 3.60×10 01 Zone 7 1.30E 05 0.15 0.34 1.50×10 05 0.15 0.36 2.00×10 05 1.20×10 04 3.90×10 01 Zone 8 1.50×10 06 0.09 0.39 1.70×10 06 0.09 0.4 1.00×10 05 1.70×10 03 3.70×10 01 Zone 9 2.00×10 06 0.03 0.41 3.70×10 06 0.03 0.405 3.50×10 06 2.70×10 02 4.32×10 01 Zone 10 1.21×10 05 0.15 0.31 3.00×10 05 0.16 0.3 3.31×10 05 1.10×10 03 2.90×10 01 Z O N A T O I N
3.1.5 Recharge components
Recharge from precipitation, rivers and canals have been included. The study area is divided into two zones on the basis of the CRBC and the Rodkohi command area. The water table in these canal command areas is largely dependent on the canals, streams and the Indus
28
River. Precipitation for the last thirty years has been taken from various literatures (Naqvi, 1977 and Malik, 1985).
3.1.5.1 Rivers
The study area consists of four major rivers. The Indus River forms the eastern boundary of the model. The mighty Indus is the 18th largest river in the world. The water from the Indus River is diverted in the form of canals to be utilized for the irrigation purposes. The two main diversions that drain the modeled area are the CRBC and the Paharpur canals.
Figure 3.5 The spatial discretization of hydraulic conductivity zones (Table 3 2) within polygons.
Takwarrah Nala is a branch of Tank Zam having perennial flows. Another stream Gomal Zam is a major command having perennial flows. Sheikh Haider Zaman Nala is the south eastern boundary of the model and has a negligible perrenial discharge. These three perennial streams maintain valuable resources of torrential water from the western mountain ranges.
29
3.1.6 Discharge Components
Discharge components include the evapotranspiration and the pumping wells data that is incorporated in this model. The evapotranspiration and pumping wells data have been procured from the previous literature (Naqvi, 1977 and Malik, 1985).
The schematic flow chart for the distribution of canals network operating in Pakistan is given:
River
Link Canal
Main Canal
Branch Canal
Distributaries
Minors
Figure 3.6 The distribution of canal network in Pakistan
3.2 Assumptions
Furthermore all of this data was used to prepare a numerical groundwater flow model. The Visual MODFLOW 4.0 was employed for this task. All the data discussed above was incorporated and a steady state model was developed with the following assumptions:
3.2.1 Assumption of the flow model
a) Groundwater appears to be in steady state condition until 1985 as no significant stresses were applied in that time period. b) The precipitation, river flows, canals and irrigation practices were considered as the recharge source in the study area.
30 c) The canal diversions from the Indus in the coming years will have a significant recharging impact on the underlying aquifer capacity as well as on the regional groundwater flow regimes. d) To see the impact of CRBC on the model the western boundary was selected to be far from the CRBC for diminishing the boundary effect.
31
CHAPTER 4 4 Groundwater Flow Model
4.1 Modeling approach
Three dimensional simulation of groundwater flow using of mathematical models is significant to hydrologic research. Understanding of flow, transport of solutes, water budgets in the groundwater systems are some of the facets defined by the models for effecetive management. Development of a groundwater model doesnot only aid in the elaboration of a flow system in a region, but it leads to the identification and the indepth understanding of the system (Figure 4.1).
In past, limitations of the computer systems led to the analysis of only two dimensional cases. Mostly horizontal or the vertical flows could be simulated with the help of old applications. Quasi three dimensional models were used to anlyze strict horizonytal flows in two dimensions vertical connected by an aquitard. Today, advanced technology make it possible to simulate the groundwater flow systems in three dimensions.
4.2 Numerical Modeling of Groundwater
Numerical modeling of groundwater is much more versatile and with the advancement of computers now easier to use them, the following five numerical methods are used in groundwater modeling; • Finite difference method (FDM) • Finite elements method (FEM) • Integrated finite difference method (IFDM) • Boundary Integral equation method (BIEM) • Analytic elements. The boundary Integral equation method and Analytic elements method are relatively new techniques and are not widely used. The analytic element method (AEM) requires the determination of one or more ‘‘strength’’ parameters for each analytic element in the model domain. These strength parameters are solved for by defining appropriate conditions at collocation points, usually at the elements themselves. For instance, to determine the sink density
32 of a constant strength line sink, a known potential may be specified at its center. Since all elements contribute to the potential field in an infinite domain, the resulting coefficient matrix for the set of equations is fully populated. The AEM shares this characteristic with the closely related boundary integral equation method (BIEM). In view of this full matrix, most AEM and BIEM models have employed a direct solution method, such as Gauss elimination. A popular implementation of Gauss elimination is LU decomposition followed by forward elimination and back substitution applied to the ‘‘known vector’’. The later procedure is used by the public domain AEM solver GFLOW1.
Data collection and sorting
Conceptual model Conceptual Model
development 1. Hydraulic Conductivity 2. Specific Yield
Model Definition 3. Recharge 4. Discharge 5. RS image
6. Drainage digitization Steady State Model 7. Precipitation Simulation 8. Evapotranspiration
9. Water Table 10. Boundaries data Steady State Model Sensitivity Calibration No Analysis
Yes Transient Model Simulation
Transient Model Chemical analyses in the Calibration concerned area
Contaminant Transport
Figure 4.1 The groundwater modeling process
33
The integrated finite difference method is to discretize the total flow domain into conveniently small sub domains or elements and evaluate the mass balance in each element. The final result is an algebraic equation for each node in the grid system. As with other finite difference methods, it is assumed that all recharge or withdrawal to and from the nodal area occurs at the node point and that water level in the entire nodal area is the same as at the node point. The MODFLOW, a USGS finite difference model is popular in the industry for a standard modeling practices. Its numerical stability in providing the solutions led to its selection for the current study.
4.3 VISUAL MODFLOW
MODFLOW is a modular finite difference model that simulates groundwater in a three dimensional environment. It was developed by the USGS and comprehensively models subsurface flow. specific features of the hydrologic system are dealt in separate sections of the model known as modules. Each module deals with a specific hydrologic situation of the model and can be assessed separately. It can also be integrated with different packages that furnishes its modeling flexibility. Table 4 1 lists the names of the packages included in the standard MODFLOW code, along with a brief description of each. A block centered finite difference approach is used to simulate ground water flow within the aquifer. Confined, unconfined or hybrid systems of layers can be easily handled by the system. Evapotranspiration, areal recharge, wells, drains, and streams can be simulated using the specific modules, as well as appropriate boundary conditions. The concepts and assumptions used in MODFLOW are elaborated in the following section.
4.3.1 Mathematical Model
The three dimensional transient state equation is:
34
The derivation of Equation 1 can be found in Fetter (1999). Equation 1, constitute a mathematical representation of the aquifer system. A variety of numerical solutions have been developed to obtain approximate solutions for this equation. The finite difference approach is a numerical approximation of the analytical solution to Equation 1. In this approach, the continuous system is replaced by a finite set of discrete points in space and time. The partial derivatives are replaced by terms calculated from the differences in head values at these points. The solution of the resulting multiple linear algebraic difference equations yields values of head at specific points in time, representing the time varying head distribution that would be given by an analytical solution of the partial differential equation of flow (Macdonald and Harbaugh, 1988).
4.3.2 Discretization
The ground water system is divided into cells i.e. a mesh of blocks. The notations used for the model comprised of rows (i), columns (j), and layers (k). The rows (i), columns (j), and layers (k) are the elements of the Cartesian coordinate system signifying the x for columns and y for rows and z for the layers in the model. This discretization is illustrated in the figure 4.2.
35
Table 4 1 List of packages in MODFLOW
As stated above, MODFLOW uses a block centered formulation of the finite difference equation. Thus, the discrete nodes at which heads are calculated are located at the center of each cell. The spacing of the nodes should be chosen so that the hydraulic properties of the system are uniform over the extent of the cell, because in assigning such properties, it is assumed that they are assigned to the whole cell (Macdonald and Harbaugh, 1988).
36
Figure 4.2 Discretized hypothetical aquifer system in MODFLOW.
4.4 D. I. Khan groundwater flow model
The model was run in steady state condition using Visual MODFLOW 4.0 .The model was run for transient state up to 30 years then it was again run for 10 years more to assess the future implications. The model was calibrated on the basis of the observed data and pumping tests for the steady state and transient states respectively in1985.
4.4.1 Steady state Model
The condition in which the groundwater regime where hydraulic heads are no longer changing and the magnitude and direction of flow velocity becomes constant with time is said to be the steady state (Freeze and Cherry, 1979). The steady state calibration based on the pre development potentiometric surfaces of year 1985, which was considered to be the period when the groundwater was apparently in steady state condition . The calibration o f the model was achieved by the PEST (Doherty, 1995) .
37
The analysis of the steady state map shows a good agreement between the observed and the calculated values by MODFLOW 2000. The Root mean squre (RMS) for the correlation is 0.65. The correlation coefficient is 1.0. The mean residual is around 0.055 m (Figure 4.7). The model was run for ten years initially with the constant heads for all the boundaries but the model was not giving any suitable convergence. The model was rerun changing the boundaries using the stream package of the Visual MODFLOW for the simulation that finally succeeded when all the boundaries were considered as the stream boundaries.
4.4.2 Transient Flow
Transient flow occurs when at any point in the flow field the magnitude or direction of the flow velocity changes with time (Freeze and Cherry, 1979). In steady state simulation, heads were not time dependent and specific yield values had no influence on the whole modeling processes. Therefore, the specific yield values of 10 wells (Figure 4.14) derived from pumping test analyses and the literature (Anderson and Woesner, 1992) are used and calibrated during the transient simulation by comparing the simulated heads with the observed heads of wet seasons (Figure 4.15) (Appendix B).
4.5 Calibration 4.5.1 Steady State Calibration
The observed hydraulic heads of 36 wells were initially utilized in the calibration process by the Parameter estimation and testing package (PEST); a built in module with visual MODFLOW. Following parameter settings were utilized for the PEST (Table 4 1) Table 4 1The PEST parameters.
Table 6.1: Initial settings used in parameter estimation (PEST) for steady state calibration Initial lambda 10 Lambda adjustment factor 1 Sufficient new/old Phi (Ф) ratio per optimization iteration 0.1 Limiting relative Phi(Ф) reduction between lambdas 0.03 Maximum trial lambdas per iteration 10 Maximum number of optimization iterations 30 PEST error tolerance (using Euclidean L2 integral (RMS) norm) 0.001
38
The Lambda is denoted as the Marquardt Lambda. It is a variable that PEST adjusts to help with the optimization process. Phi (Ф) is defined as the objective function that PEST try to use and calibrate the model e.g. the head, concentration and flow data. The model was run for 10 years with a single time step to achieve the steady state condition, and calibrations were made using hydraulic heads of year 1985 in 36 wells (Figure 4.7). The model calibration is checked by having the 45 degree line where X=Y fall within the 95% confidence interval lines. A 95% confidence interval allows the user to visualize a range of calculated values for each observed value with 95 percent confidence that the simulation results will be acceptable for a given observed value. The 95% interval is the interval where 95% of the total number of data points are expected to occur. Initially the model indicated a high variance. The model was run several times to achieve a satisfactory result by changing the values of the hydraulic conductivity and the recharge. These change reduced the values of average residuals. The initial model simulation resulted in high heads. The model was simulated 20 times to reach the reasonable solution. The values during the calibration were checked and it was found out that the model is stable near the low values of conductivity. The final calibrated model shows a maximum residual between observed and calculated heads around 2.53 meters, and the minimum residual around 0.001 meters. The correlation coefficient is 1.0 with a RMS of 0.674 %.
39
Figure 4.3 Calibration scattergram results for steady state.
4.5.2 Maps
Equipotential map of year 1985 (layer 1) shows a general gradient from northwest to southeast i.e. from the mountain ranges of the west to the Indus River. Overall head drop of 105 meter is observed with a higher value of 265 meter in the extreme northwest to 160m in the extreme southeast. The main recharge tends to exist from the western rivers of Gomal Zam and Tank Zam. The steady state equipotential surface map indicate s that the groundwater flows in the south east direction and it is a true replica of the surface topography (Figure 4. 8 to 4.13).
40
Figure 4.4 The s teady state equipotential map of layer 1, year 1985.
Figure 4.5 The steady state equipotential map of layer 2, year 1985.
41
Figure 4.6 The steady state equipotential map of layer 3, year 1985.
Figure 4.7 Steady state equipotential surface and velocity vectors simulated in layer 1
42
Figure 4.8 Steady state equipotential surface and velocity vector s simulated in layer 2
Figure 4.9 Steady state equipotential surface and velocity vectors simulated in layer 3
43
4.5.3 Transient Calibration
Following the steady state calibration, the transient calibration was conducted. This stage of calibration was used to determine the storage coefficients and to test the reliability of parameters determined by the pre development steady state calibration (Guvanasen et al. 1998 and Akhter, 2002). The model was calibrated using water levels measured in 10 wells (Figure 4.14) over a six months period of wet season i.e. from April 15, 1985 to September 15, 1985 using steady state hydraulic heads as the initial condition. However, assigning initial conditions for the transient model also posed a slight difficulty because the system did not fully agree with the known heads. Therefore the pest was utilized to assess the automatic parameter estimation by calibrating it with the 30 zones of the specific yield and specific storage (Table 3 2, Chapter 3). These zones were configured by visual inspection, known site hydrologic conditions, data from pump tests and estimation of specific material characteristics (Anderson and Woesner, 1992).
The final calibration and the resulting verification showed a reasonable agreement between predicted and observed heads. The results are shown in the scatter diagram (Figure 4.15). The draw downs from all ten wells are calibrated against the specific yield and storage values of the model (Figure 4.16 to 4.20).
44
Figure 4.10 Location of ten pumping wells used for the water levels measurements
45
Figure 4.11 Non steady state calibration
Figure 4.12 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 1&2
Figure 4.13 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 3&4
46
Figure 4.14 The draw downs between the observed (dashed lines) and the calculated heads (solid lines) of wells 5&6
Figure 4.15 The draw downs between the observed (dashed lines) and the calculated heads (solid lines) of wells 7&8
Figure 4.16 The drawdowns between the observed (dashed lines) and the calculated heads (solid lines) of wells 9&10
The difference between the observed and calculated in the model shows that in some areas we have a very good match between the observed and calculated drawdowns such as well s 1 4 and 10 whereas some areas show a higher discrepancies i.e. greater than unity i.e. wells 5 9. The most probable reason for this change may be attributed to the parameters controlling the flow and in this case we have the Hydraulic conductivity, Speci fic yield and Porosity.
47
4.5.4 Sensitivity analysis (Steady and Transient state)
4.5.4.1 Hydraulic Conductivity
Sensitivity analysis is to quantify the uncertainity in the calibrated model caused by uncertainity in the estimates of aquifer parameters, stresses and boundary conditions. A sensitivity analysis is an essential application. During a sensitivity analysis calibrated values for hydraulic conductivity, recharge and specific yield are systematically changed by multiplying with the factors starting from low values to high values. The uncertainity is not only the parameter values needed for our designed calculations but also about the geometry of the system we are trying to analyze (Anderson and Woessner, 1992). Sensitivity analysis is performed by changing one parameter value at a time. However, two or more parameters might also be examined to determine the results. The sensitivity of the model was tested by uniformly multiplying recharge and hydraulic conductivity by the factors of 0.2, 0.4, 0.8, 1.2, 1.5, 2.0, 2.5 and 3.0 throughout the model’s interior nodes and then the model was rerun. The process is to be carried individually with one parameter and later with the other. First, the analysis was carried out for hydraulic conductivities by multiplying with the pre defined factors and observed the changes in the mean, root mean square and standard deviation of the simulated hydraulic heads. The increase in hydraulic conductivity shows a gradual increase in standard deviation and RMS values but the mean has increased significantly. Standard deviation and RMS plots are almost identical (Figure 4.21). At low values of hydraulic conductivity, no significant change has been observed in all these three parameters.
48
Sensitivity Analysis (Hydraulic Conductivity) 2.5 0.3
2 0.25 0.2 1.5 0.15 RMS
1 Mean 0.1 St.Dev 0.5 0.05 Mean
0 0
Standard deviation deviation RMS and Standard 0.2 0.4 0.6 0.8 1.2 1.5 2 2.5 3 Factor
Figure 4.17 Sensitivity Analysis of hydraulic conductivity for steady state simulations
4.5.4.2 Recharge
In case of recharge, standard deviation and mean values decrease gradually with receding trend. The RMS tends to increase in both the negative and positive sides in response to lower and higher recharge values (away from unity) respectively (Figure 4.22). The results of the analysis show that model is more sensitive to recharge.
Sensitivity Analysis (Recharge) 4.5 2 4 1.5 3.5 1 3 0.5 0 2.5 -0.5 2 RMS -1 Mean 1.5 -1.5 St.Dev 1 -2 Mean 0.5 -2.5 0 -3
Standard deviation deviation RMS and Standard 0.2 0.4 0.6 0.8 1.2 1.5 2 2.5 3 Factor
Figure 4.18 Sensitivity Analysis of Recharge for steady state simulations
49
4.5.4.3 Specific Yield
The standard deviation and RMS show a rising trend towards lower values of specific yield (less than unity), while the mean tends to decrease continuously with the increase in recharge therefore the model is less sensitive to specific yield as compared to other parameters (Figure 4.23).
Sensitivity Analysis (Specific Yield)
4 0.3 3.5 0.25 3 0.2 2.5 0.15 0.1 2 0.05 RMS 1.5 Mean 0 St.Dev 1 -0.05 0.5 -0.1 Mean 0 -0.15 0.2 0.4 0.6 0.8 1.2 1.5 2 2.5 3 Standard deviation deviation RMS and Standard Factor
Figure 4.19 Sensitivity Analysis of specific yield for non steady state simulations
4.5.5 Equipotential Maps and their analysis
Non steady state equipotential maps are shown in figures 4.24 to 4.41. The general trend of the overall gradient remains unchanged until 2010 (Figure 4.30). The first and second layer shows the same result but with different flow velocities (Figure 4.30 4.31). The flow directions are from the higher to the lower gradients. The third layer also indicated the same flow trend for the steady state period with the exception of changes in the flow velocities (Figure 4.32). The flow model has been adorned with the vectors to display the dominant and active flow region by visual examination of figure 4.33 through figure 4.41, it is found that the upper part of the model in the northeast to northwest contain dominant vectors with long tails there by indicating the most active region of ground water flow. In the central region lesser component of flow exists due to low permeability in layer 1(Figure 4.33 to 4.41) in the extreme south along the Indus river long tail arrows with shorter dimensions could also be seen there by indicating the
50 existence of good aquifers in the upper and lower regions of the modeling regime. Furthermore, equipotential maps (Figure 4.33 to 4.41) also indicate effluent and influent streams. The streams of the region from northwest to the southeast have more influent character in the upper area and effluent character in the lower area where the ground water drains into the Indus River. The Indus River is an effluent perennial stream in this part of the region where groundwater tends to maintain the river flow as much as by 99%. The general head configurations do not show a marked change except near CRBC and pumping wells located adjacent to the Indus River. The change can be seen when the canal irrigation started in 1995, which led to the rise in the heads and hence the change in the velocity vectors had also begun.
51
Figure 4.20 The non steady state equipotential surface simulation map of layer 1 , year 1995
Figure 4.21 The non steady state equipotential surface simulation map of layer 2, year 1995
52
Figure 4.22 The non steady state equipotential surface simulation map of layer 3, year 1995
Figure 4.23 The non steady state equipotential surface simulation map of layer 1, year 2005
53
Figure 4.24 The non steady state equipotential surface simulation map of layer 2, year 2005
Figure 4.25 The non steady state equipotential surface simulation map of layer 3, year 2005
54
Figure 4.26 The non steady state equipotential surface simulation map of layer 1, year 2010
Figure 4.27 The non steady state equipotential surface simulation map of layer 2, year 2010
55
Figure 4.28 The non steady state equipotential surface simulation map of layer 3, year 2010
Figure 4.29 Non steady state equipotential surface and velocity vectors simulated in layer 1 , 1995
56
Figure 4.30 Non steady state equipotential surface and velocity vectors simulated in layer 2 , 1995
Figure 4.31 Non steady state equipotential surface and velocity vectors simulated in layer 3 , 1995
57
CRBC was constructed in 1995 (Figure 4.24 4.26 and 4.33 4.35), which played an important role in the hydrology of the region. Infiltration rates were increased, salt contents in the soil reduced and groundwaters replenished. Hence t he velocity vectors in the flow field s how a change during the model simulations in 1995 as shown in Figure 4.24 through 4.26, which represents the impact of pumping from the aquifer and commencement of the CRBC. In layer 1 during the commencement perio d of the CRBC in 1995, response of velocity vectors (Figure 4.33) appeared to recharge the groundwater s. However, some of the groundwater happened to recharge the CRBC at places for example, in layer 2 occurence of recharge of CRBC can be seen (Figure 4. 34 ). However in layer 3 the effluent part of the CRBC was diminished and groundwater started to flow towards the general gradient of the flow pattern (Figure 4.35). From the simultaneous study of these layers, it has been observed that the layer 1 apparently represents high velocities than the layer 2 and layer 3 in the northwest of Tank and Kulachi.
Figure 4.32 Non steady state equipotential surface and velocity vectors simulated in layer 1 in year 2005
58
Figure 4.33 Non steady state equipotential surface and velocity vectors simulated in layer 2 in year 2005
Figure 4.34 Non steady state equipotential surface and velocity vectors simulated in layer 3 in year 2005
59
In simulation year 2005, a change in the velocity vector map has been observed due to pumping from the water wells (Figure 4.36 4.38). From the simultaneous study of three layers it has been observed that the layer 1 (Figure 4.36) represents the highest velo city vectors in the northwest with good groundwater flow regime and layer 2 (Figure 4.37) has the lowest velocities and it represents the sluggish movement of the groundwater in layer 2. Layer 3 represents moderate flow velocities than layer 1and layer 2 (Figure 4.38). The overall flow field does not show any significant change in year 2010 (Figure 4. 39 4.41 ). The groundwater flows remain the same in the northwest in all three layers. But the flow fie ld changes were quite obvious in the south east adjacent to the Indus River where the groundwater has dominant velocity vectors with long tails from the Indus River and the CRBC.
Figure 4.35 Non steady state equipotential surface and velocity vectors simulated in layer 1, year 2010
60
Figure 4.36 Non steady state equipotential surface and velocity vectors simulated in layer 2, year 2010
Figure 4.37 Non steady state equipotential surface and velocity vectors simulated in layer 3, year 2010
61
4.5.6 Drawdowns
In 3650 days i.e. upto year 1985 the pumping wells were not operational because of keeping it as a steady state model. Afterwards beyond 3650 days the model started to show the drawdowns of the wells during the simulation. However after one year the model shows a gradual decline in levels in response to the excessive pumping. Two wells indicated steep gradient of drawdowns, that has shown these areas are more vulnerable to the aquifer depletion and a proper management practice, is needed for these areas such as in the central region of D. I. Khan near kulachi. The aquifer response to long term excessive pumping has also been simulated. A gradual decline in the levels appeared in the water wells (Figure 4.42). The maps of the respective drawdowns and water table depths are shown in (Figure 4.43 4.49).
Figure 4.38 Decline in water levels in wells with respect to long term pumping
62
Figure 4.39 Water table depth map of year 1985
Figure 4.40 Water table depth map of year 1995 in response to long term pumping
63
Figure 4.41 Water table depth map of year 2005 in response to long term pumping
64
Figure 4.42 Water table depth map of year 2010 and development of cone of depressions in response to long term pumping
65
Figure 4.43 Non steady state drawdown (m) map of layer 1 year 2010
Figure 4.44 Non steady state drawdown (m) map of layer 2 year 2010
66
Figure 4.45 Non steady state drawdown (m) map of layer 3 year 2010
4.5.7 Groundwater budget
The groundwater budget evaluation is the important study for the modeling that provides detailed description in quantitative terms (Appendix C). An effort has been made to evaluate the groundwater budgets of the different time periods i.e. from 1985 (steady state) to 2010 (Transient state). The model calculates the budget in response to the values assigned for the different data sets. During steady state the groundwater moving in is nearly equal to the groundwater moving out of the system with a percent discrepancy of less than 1. However, in the simulations ahead of 1985 showed a drastic change in response to the pumping wells, canal irrigation and variable amount of recharges. The important point to be noted that the groundwater moving into the system is more than the groundwater moving out of the system (Figure 4.50). The prospective zones for the groundwater exploitation in the study area exist near the Indus and near the western mountain ranges. The transition zone between the piedmont and the
67 floodplain deposits show lower water table depths. Therefore, exploitation may lead to the detrimental effects over the groundwater regime.
Groundwater Budget
2.5 STREAM LEAKAGE OUT
STREAM LEAKAGE IN 2 ) 3 RECHARGE IN
1.5 ET OUT
WELLS OUT 1 STORAGE OUT Volume ( m ( Billion Volume 0.5 STORAGE IN
TOTAL OUT 0 1985 1990 1995 2000 2005 2010 TOTAL IN Time (years)
Figure 4.46 Total groundwater volume balances in the model simulations
68
CHAPTER 5
5 Contaminant Transport Model
Globally, attention has been focused recently on the groundwater systems contaminated by various natural and anthropogenic arsenic (As). Arsenic is a toxic metalloid and high concentrations of it in groundwater are a risk to human health (Smith et al., 2000). Currently, there is considerable interest in understanding the processes that control arsenic transport in contaminated groundwater systems (Zhang and Selim, 2006). Such an understanding can be used to design efficient methods to treat contaminated drinking water sources. Also, a fundamental understanding of the transport, adsorption, reduction and oxidation of arsenic in groundwater systems will help better manage and mitigate the overall risks posed by arsenic contamination.
5.1 Dera Ismail Khan Aquifer Monitoring
Three detailed field surveys were carried out to collect water samples from the water wells in the study area. In the absence of multi level sampler only shallow groundwater samples were collected. In order to gather the information the samples from rivers, canals and other natural streams were also taken. The locations of the respective sampling points were delineated with Global Positioning System (GPS). The groundwater of Dera Ismail Khan Aquifer has been monitored by various agencies that include Pakistan Council for Research in Water Resources (PCRWR), Public Health Engineering Department (PHED) and WATSAN (NGO). The main task was to understand and interpret the aquifer’s hydrochemistry, which was started in 2006. Further on, a detailed investigation was carried out in 2008 adjacent to a source of contamination (Ahmad & Qadir, 2011). Then in 2010 a groundwater contamination model was constructed to determine the dispersion of arsenic within the study area.
5.2 Hydrochemistry
The details of hydrochemistry were reported in the previous studies of the D. I. Khan by Hood, 1970 and the work done by the WAPDA and PHED from 1980 to 2010 in unpublished forms. The recent data consisted of hydrochemical and heavy metal analysis of groundwater samples collected in 2006, 2008 and 2010, which was published by (Ahmad & Qadir, 2011).
69
5.3 Previous work
Previous work carried out in this regard to analyze the contaminant responses in groundwater. The prominent researchers include Ahmad et al. 2010, Ahmad & Khawaja, 1999, Andrew & Neville, 2003, Majumdar et al. 2002, Hering et al, 2008, Concha et al. 2006, Feenstra et al. 2007, ADEQ, 2002, APHA, 1995, Dasgupta & Purohit, 2001, Benke, 1998, Azeez & Nadarajan, 2000, Brannon & Pennington, 2002, Benvenuti et al. 1997, Bhosle et al. 2001, Goel, 2000, Shamrukh et al. 2001, Harrar et al. 2003, Prommer et al. 2003, Wang and Bright, 2004, Liu et al. 2004, and Konikow et al. 1996.
5.4 Conceptualization
Investigations particularly over the last few years have shown that health risk based on the presence of arsenic in the groundwater do not exclusively occur in Pakistan, but in other countries such as Bangladesh, Mongolia, Taiwan, Ghana, Argentina, Chile, Mexico and Great Britain (Cornwall) (Smedley and Kinniburgh, 2002). Values of the lethal dose of arsenic for human beings range from 0.1 0.3 g per 70 kg body weight (Holleman and Wiberg, 1990). Epidemiological studies demonstrated doubtlessly the carcinogenicity of arsenic (Lederer, 1998). Typical symptoms of arsenic poisoning are: diaphoresis, muscle spasms, nausea, vomiting, abdominal pain, garlic odour to the breath, diarrhoea, anuria, dehydration, hypertension, cardiovascular collapse, aplastic anaemia and death. Dose and time of exposure will determine both the symptoms as well as their impact on human beings. The element arsenic is already known for a long time. Products containing arsenic as part of an alloy dating into the bronze time have been found (Riederer, 1987). Mining of arsenic has been reported from the Egyptians as well as the Chinese (Azcue et al. 1994). Arsenic was also mentioned by Aristotle. During that time arsenic sulfide was used to coat silver in order to give it a golden colour. In these ancient times this yellow color was used to paint or to depilate. The origin of the word arsenic is dubious. It could either be from the Greek word arsenikon, which was used for the mineral auripigment (As 2S3), or from the Greek word arsenikos—male. Arsenic is a metalloid and belongs to the fifth main group of the periodic table of elements, the nitrogen group. It has the chemical symbol As, the atomic number 33, an atomic mass of 74.9216 [g mol 1], a density of 5.727 g/cm 3. Arsenic can have the oxidation states V, III or –III.
70
75As is the only stable isotope in nature. Radioactive isotopes range from 67As to 86As and show half live times between 1 second and 80 days. Arsenic is a rare element in the earth crust accounting to approximately 5.5×10 5 percent by volume. It is a major component of more than 200 minerals, including elemental arsenic, sulfides, oxides, arsenates and arsenites and found e.g. in volcanic rocks, coal, the sea and mineral waters. Chemical analysis of water samples revealed, arsenic to be exceeding the upper limit of Pakistan Environmental Protection Agency (Pak EPA) standard i.e 10 ppb and it is available in three spatial zones of the aquifer that will be discussed later in this chapter. Source of arsenic can be related to the following man made activities in the study area. • The industries • The agricultural lands • The urban settlement • Small scale industries • The poor sewage drainage network • Poor pumping well designs in operation Reducing conditions favourable for arsenic mobilisation have been reported most frequently from young (Quaternary) alluvial, deltaic sediments. The interplay of tectonic, isostatic and eustatic factors have resulted in complex patterns of sedimentation and rapid burial of large amounts of sediment together with fresh organic matter during delta progradation. Thick sequences of young sediments often contain groundwater with a high arsenic concentration (Plant et al, 2004). Recent groundwater extraction, either for public supply or for irrigation, has induced increased groundwater flow. This could induce further transport of arsenic (Harvey et al, 2002). High concentrations of naturally occurring arsenic are also found in oxidising conditions where groundwater pH values are high (>8) (Smedley and Kinniburgh, 2002). In such environments, inorganic As(V) predominates and arsenic concentrations are positively correlated with those of other anion forming species such as HCO3‾, F‾, H3BO3, and H2VO4‾. The high arsenic groundwater provinces are usually in arid or semi arid regions where groundwater salinity is high. Evaporation has been suggested to be an important additional cause of arsenic accumulation in some arid areas (Welch and Lico, 1998).
71
High concentrations of arsenic have also been found in groundwater from areas of bedrock and placer mineralization which are often the sites of mining activities. Arsenic concentrations of up to 5000 g/l have been found in groundwater associated with the former tin mining activity in the Ron Phibun area of Peninsular Thailand, the source most likely being oxidised arsenopyrite (FeAsS) (Plant et al., 2004). Out of these six sources responsible for contamination, three are the possibly linked with creating arsenic contamination in groundwater that is urban settlement, industries and advection effluents draining into the natural streams. The previously calibrated steady state numerical groundwater flow model was used to add sources of arsenic contamination over the well defined grid nodes. The following figure shows the conceptual model of the D. I. Khan aquifer in three dimensions in Chapter 3 figure 3.1.
5.5 Transport Package
MT3D a modular three dimensional transport model developed by Zehang 1990 for the simulation of Advection, Dispersion and Chemical reactions combining both Eulerian and Lagrangian approaches. The Eulerian approach is defining the advection terms in the model where as the Lagrangian is solving the dispersion and reaction terms. For the solution of transport the implicit GCG solver was utilized coupled with the upstream finite difference method.
5.6 The Basic Package
The basic package contains basic model information, such as grid, time discretization, which is required for all model runs.
5.6.1 Advection Package
The advection package calculates the spread of solute mass along with groundwater flow using various solution schemes. MT3D contains four solution schemes. a) Upstream Finite Difference Method b) Method of Characteristics (MOC) c) Modified Method of Characteristics (MMOC) d) Hybrid Method of Characteristics (HMOC)
72
The solution technique chosen for this study consist, of the HMOC. It is a combination of MOC and MMOC and is more flexible in dealing with various concentration field situations. MT3D provides three particle tracking options as given below: I. First order Euler Algorithm II. Fourth order Runge Kutta Algorithm III. Combination of the two above In first order Euler Algorithm, numerical errors tend to be large unless small transport steps are used. The fourth order Runge Kutta method permits the use of larger tracking steps. The computational effort required by the fourth order Runge Kutta method is considerably larger than that required by the first order Euler method.
5.7 Transport Parameters
The parameters during the transport include the Longitudinal dispersivity, transverse dispersivity, Molecular diffusion coefficient and bulk density. The parameters for the dispersivity are assigned on the basis of the theoretical values taken from the previous published data on arsenic (Majumdar et al. 2002). The values utilized for the arsenic transport in the MT3D are as follows: • Logitudinal dispersivity: 100 m; • Transverse horizontal dispersivity: 10 % of longitudinal dispersivity; • Transverse vertical dispersitivity: 1% of longitudinal dispersivity; and • Molecular diffusion: 1.0 x10 6 m2 /sec The main area chosen for transport was the CRBC command area and the rest of the modeled area was inactivated during transport modeling. The grid of the model consisted of a 100×100 m in the x and y axis (Figure 5.1). Three plumes have been developed pertaining to three main recharge sources (Figure 5.3). The behavior of the contamination is observed in the model for the three layered aquifer but only the upper aquifer is having appears to contain maximum loading. The lower layers indicate a less concentration solute that is mainly controlled by the dispersion and advection. The plume tends to expand towards the Indus.
73
Figure 5.1 The model discretization into grid of 100×100 m . The blue and white colour mark the inactive and active areas. 5.8 Assumption s and factors for Transport model
For running the transport model following assumptions and factors were considered a) The aquifer is considered to be homogeneous and isotropic with uniform density. b) There are three point sources of arsenic located at the centers of the respective plumes (Figure 5.3). c) The area between the CRBC of the Indus is considered as an active transport regions. d) The hydrologic boundaries and the pumping wells lying in the active region are the main sinks in the model. e) Some of the boundaries (streams) are acting as source of c ontamination. f) The D.I.K city has been considered as an anthropogenic source of contamination . g) Five hypothetical wells (HOW) are utilized to see the long term effect of contamination i.e. HOW 1 for Plume I, HOW 2 and 3 for Plume II and HOW 4 and 5 for Plume III.
5.9 Transient Transport calibration
The calibration between the observed arsenic concentrations in 12 monitoring wells is made in year 2010 with the calculated arsenic concentration by the MT3D model. The data was
74 collected around a traverse along the main trunk road to the Dera Ghazi Khan and the nearby areas. The initial model runs do not agree with the field observed data , therefore minor changes in the transport properties were made and model was rerun several times. Finally, t he model simulated (calculated concentrations ) matched with observed concentrations figure 5.2 with the absolute mean residual of 0.52 micrograms/l. During transient transport calibration the simulated arsenic concentration plume is shown in figure 5.3.
Figure 5.2 The arsenic concentration calibration in 2010 in Transient state conditions
75
1 Plume I
2 3 Plume II 4 5
Plume III
Figure 5.3 A 3 D model of the contaminant plumes of Arsenic in groundwater
5.10 Fate of Arsenic plume and Transport
The fate of the arsenic concentration and its transport with respect to time has been simulated by MT3D and the concentration monitored in five hypothetical observation wells (HOW). The arsenic concentrations for layer 1, layer 2 and layer 3 are shown in figures 5.4 to 5.6. The highest arsenic concentrations ranging from 30 to 60 microgram/liter are found in layer1. The breakthrough in the rise of arsenic concentration started after 9625 days and continued up to 12125 days followed by a period of stable concentration. Three major zones have been identified in figure 5.4 i.e. the rising trend, the breakthrough period and the stable concentration zone. The rising trend starts due to contaminant recharge at selective nodes and the arsenic concentrations are found to be highest in HOW 2 located at Plume II. The HOW 1 and the HOW 3 in Plume I and Plume II have shown almost identical concentrations (Figure 5.4 5.7). HOW 4 and HOW 5 in Plume III indicate lower concentrations.
76
2
3 1
4 5
Break through period
Rising Stable Trend Concentration
Figure 5.4 The contaminant concentration with respect to time in layer 1
The layer 2 shows lower arsenic concentrations than layer1 (0.02~3 micrograms/ liter) in the vicinity of dominant Plume II. However, arsenic concentration diminished in the vicinity of Plumes I and III (Figure 5.5).
77
2
3
1
4
5
Figure 5.5 The contaminant concentration with respect to time in layer 2
The layer 3 exhibits least concentration level in five hypothetical observation wells than the overlying layers 1 and 2. The arsenic concentrations are found to be negligible for all HOWs (Figure 5.6). The range of concentration values of arsenic fall in the range of 0 to 0.0017 (1.7×10 03 ) micrograms/l.
78
2
3
1
4
5
Figure 5.6 The contaminant concentration with respect to time in layer 3 The findings of this study suggest that the maximum arsenic concentrations are found to be accumulated in shallow aquifer (layer 1) in the vicinities of plumes development along the Indus River. The equiconcentration of arsenic in year 2010 (layer 1) in figures 5.7 and 5.8 categorically indicate the expansion and accumulation in three plumes areas. The plume in the extreme south along the Indus River has the following dimensions.
Table 5 1 Dimensions of the contaminant plume in year 2010 Arsenic plume number Covered area (m 2) Dimensions of plumes (m 3) Plume I 4.7 × 10 07 9.30 × 10 08 Plume II 3.7 × 10 07 7.38 × 10 08
79
Plume III 1.1 × 10 08 2.28 × 10 09 . A
Figure 5.7 Arsenic plumes development in 2010 marking the initial conditions in layer 1
The fate of arsenic plumes and their transport in year 2013 layer 1 has resulted in no significant expansion in sizes in the x and y direction but there is an increase in concentrations in z direction with time (Figure 5.9 and 5.10). In year 2015, layer 1, the expansion in the sizes of plumes in all three directions with increased concentrations can visibly be seen with the changes in color spectrum within the plumes (Figure 5.11 and 5.12). A A’
Figure 5.8 Expansion of Arsenic plume in the cross section along A A’ , year 2010
80
Figure 5.9 Arsenic plume showing a change in the concentration in 2013 in layer 1 A’ A
Figure 5.10 Expansion Arsenic plume in the cross section along A A’ , year 2013
81
A
A’
Figure 5.11 Arsenic plumes with increased concentration marked by the high intensity colors in 2015 in layer 1
5.11 Sources of Arsenic mobilization in D. I. Khan Groundwater
It is observed that occurrence of Arsenic at different localities have been originating due to different reasons. Therefore, the Arsenic may be of three origins. 1. Insitu activation of Arsenic into soil 2. Anthropogenic activities for example organic matter present in the soil may mobilize the Arsenic by the process of infiltration 3. Oxidation Reduction process by which Arsenic may be naturally mobilized from the soils (Majumdar et al., 2002)
82
A’ A
Figure 5.12 Expansion of a rsenic plume in the cross section along A A’ , year 201 5 5.12 Arsenic Mass Balance
An attemp t has been made to evaluate the total mass of arsenic in the groundwater through out the simulation time i.e. from 2010 to 2015 ( Appendix E). In response to three main identified origins of arsenic concentrations , the total mass balance of the fate of transport calculations are given in Appendix E . The terms of sinks and source define the contaminant recharge concentrations assigned in the MT3D model. The Urban settlement of D. I. Khan near the Indus River, the terminal end s of the Takwarrah Nala, Gomal Nala and the Paharpur canal are the responsible source s of contamination . The temporal evaluation of mass balance reveal s that there is a gradual increase in the budget with time. While comparing the sources and the sinks it has been noticed that the sources are adding more arsenic as compared to the release from the sinks. The main sink in this case is the Indus River. However, the addition and the subtrac tion of the mass in the aquifer is canceling each other that shows the model is in quite a n equilibrium condition with respect to the added material and the released material into the aquifer. During the mass balance analysis it has been observed that the arsenic total mass in aquifer is increasing with time till 2020. However the aquifer response is also checked when there is no pumping and no source after 2020 and it has been observed that the arsenic would remain in the aquifer in the static conditions a nd there is no change observed in the mass balance. Hence it is
83 concluded that the main transport of arsenic in this aquifer is dependent upon the pumping from the wells. Otherwise arsenic will stay still and sustain in this aquifer for longer periods.
5.13 Remediation of Arsenic in Groundwater
There are number of methods in which arsenic may be treated to reduce its concentration to National Environmental Quality Standards (NEQS) level. There is a need that all the materials related to the arsenic mobilization should be abandoned such as in fertilizers. The management should be comprising of the following main steps: 1) Cut down at the source level a) The domestic sewage disposal network should be updated and lined b) The industrial effluents and the solid waste material should be disposed off properly c) The drainage in the natural streams should be conserved to its natural ecosystem d) There should be an efficient flood water channelization 2) Cut down at the user level The findings of this study revealed that the excessive pumping may lead to the gradual flushing of arsenic out of the aquifer as shown in the (Figure 5.13 and Table 5 3) Main plumes accumulated along the Indus River are dissipating as a result of simultaneous pumping in 34 water wells (Figure 5.13 through 5.17). Each well has its own cone of depression with some moderate concentrations of arsenic.
5.14 Implications of Pumping in Arsenic contaminated groundwaters
The arsenic contaminated groundwaters are definitely sensitive to the pumping but it has got two main implications. 1. The Arsenic depletion in the aquifer 2. The Arsenic spreading in the aquifer The arsenic depletion is due to the process of advection and this will take place in the x and y directions whereas the arsenic will spread vertically if the level of the screening is either below or above the arsenic contaminated groundwaters. Figures 5.14 through 5.17 show the spreading of the plumes with depths in cross section AA’, BB’, CC’ and DD’ during the pumpage from the aquifers in third layer. This clearly shows that with time the plume would migrate to the greater depths in response to the pumping from the deeper aquifers.
84
A
B B’
C C’
D D’
A’
Figure 5.13 Development of isolated plumes along hydraulic gradi ents of 34 pumping wells, layer1, 2015
85
A’ A
Figure 5.14 Contaminant Plume s along the cross section A A’
B B’
86
Figure 5.15 Showing vertical migration along B B’ plume I in eastwest direction
C C’
Figure 5.16 Showing vertical migration along C C’ plume II in eastwest direction
D D’
Figure 5.17 Showing vertical migration along D D’ plume III in eastwest direction
87
5.15 Particle Tracking for the Arsenic source determination
Another important subroutine in the Visual Modflow is the Particle tracking in the groundwater for any contaminant. The particles are released at specific times for the determination of the advecting contaminant. Figure 5.18 and 5.19 show the pathways for the arsenic movement in the groundwater. The sources of the arsenic lie adjacent to the northern boundary i.e. Takwarrah Nala, near the D. I. Khan city and near the terminal end of the Paharpur canal.
Figure 5.18 The Arsenic advection flow path along the concentration gradients to the wells.
88
CHAPTER 6
6 Conclusions
The following conclusions have been drawn from the findings of this study:
6.1 Groundwater Numerical Modeling
1. The steady state calibration of the model shows a close agreement between simulated and observed heads with the residual mean of 0.055m, RMS of 0.65m and Correlation coefficient value of 1.0. 2. The sensitivity analysis of hydraulic conductivity shows a gradual increase in mean, standard deviation and RMS values with increase in conductivity. So the model is sensitive to the hydraulic conductivity values. 3. The transient calibration is done of wet season indicates close agreement between the observed and the simulated drawdowns in wells with the residual mean of 2.675m, RMS of 3.8m and correlation coefficient value of 0.997. 4. The groundwater in steady state condition flows from northwest to southeast. The heads fall in the range of 265 masl to 150 masl with a total drop of 115 meters. 5. Out of these three layers, aquifers velocity vectors with long tails are the most prominently observed in layers 1 and 3. 6. Groundwater flow tends to exist in the northwest of Tank area. The central part of the modeling represents a low velocity zone, and a moderate velocity zone lies in the extreme southeast. 7. In mass balance analysis the percentage of discrepancy is less than 1. The total volume of water moving in through various sources is 158068912 m 3 and water moving out through various sinks is 158076864 m 3. 8. The prospective zones for the groundwater exploitation in the study area exist near the Indus and near the western mountain ranges. 9. The transition zone between the piedmont and the floodplain deposits has low hydraulic conductivity in the range of 1.7×10 6 m/s to 1.5×10 6 with shallow water table conditions. Therefore, exploitation may lead to the detrimental effects over the groundwater regime.
89
6.2 Contaminant Transport modeling
1. Analysis of water samples has revealed that the arsenic concentration fall in the range of 10 60ug/l in groundwater, which is a potential contaminant. 2. The transient calibration of the fate of transport shows a reasonable match between the observed and the calculated arsenic concentrations with a mean residual of 0.148 ug/l, RMS of 0.743 ug/l and correlation coefficient of 0.994. 3. The spatial distribution of arsenic modeled by MT3D highlight three major plumes of arsenic accumulation along the Indus River. 4. Simulation reveals three potential plumes i.e. Plume I, Plume II and Plume III with dimensions of 9.30×10 08 m3, 7.38×10 08 m3 and 2.28×10 09 m3 in 2010. 5. Three main sources of contamination are identified. 6. The Urban settlement of D. I. Khan near the Indus River, the terminal ends of the Takwarrah Nala, Gomal Nala and the Paharpur canal are the responsible sources of contamination. 7. Five HOWs in the model furnish the calculated concentrations of arsenic to be higher in layer 1while layer 2 and layer 3 indicate lower concentrations. 8. The main sinks for the arsenic found by this study are the Indus River and the pumping wells in the modeled region. 9. The mass balance calculations reveal that the concentration of arsenic will keep on increasing in the existing circumstances and at the end of year 2025, it is estimated to be 2800×10 10 ug. 10. The remediation of groundwater may be commenced by 41 pumping water wells in the contaminated area that will flush out the arsenic concentration to a considerable extent i.e. from 55 ug/l to 35 ug/l at the rate of 511×1010 ug/year thereby reducing the mass of arsenic in the groundwater.
90
REFERENCES
(ADEQ), A. D. (2002). Integrated Water Quality Monitoring and Assessment Report. Arkansas Department of Environmental Quality Water Division Report WQ02 10 1, Arkansas Department of Environmental Quality Water Division Report WQ02 10 1. Adak Dasgupta, M., & Purohit, K. M. (2001). Status of surface and ground water quality of Mandi akudar – Part I : Physico chemical parameters. Poll. Res. , 20 ( (1)), 103– 110. Ahmad, Z., & Khawaja, A. A. (1999). Three dimension flow and solute transport modeling of the Eolian Aquifer in a uniform flow field of a chemical plant. Pakistan Journal of Hydrocarbon Research . Ahmad, Z., & Qadir, A. (2011). Source evaluation of physicochemically contaminated groundwater of Dera Ismail Khan area, Pakistan. Environmental Monitoring and Assessment , 9 21. Ahmad, Z., Kausar, R., & Ahmad, I. (2010). Highlighting the implications of selenium dispersion from disposal of Kahota industrial triangle area, Islamabad, Pakistan using three dimensional solute transport model. Environmental monitoring and Assessment , 167 , 565 579. Ahmed, N. (1972). Groundwater Resources of Pakistan. (Revised and enlarged ed.). Lahore: Shahzad Nazir. Akhter, G. M. (2002). Three dimensional numerical groundwater flow modeling of lower Bari Doab area, Punjab, Pakistan, Thesis Ph.D (Unpublished), Quaid e Azam University , Islamabad, Pakistan. Anderson, M., & and Woessner, W. (1992). Applied Groundwater Modeling. (S. D. Inc., Ed.) Academic Press. Andrew, C. B., & Neville, C. J. (2003). Groundwater flow in a desert basin: Challenges of simulating transport of dissolved chromium. Ground Water , 41(2) , 219 226. APHA. (1995). Standard Methods for Examination of Water and Waste Water. (19th ed.). Washington, D.C., U. S. A: American Public Health Association (APHA).
91
Azcue J. M., Mudroch A., Rosa F., and Hall G. E. M. (1994) Effects of abandoned gold mine tailings on the Arsenic concentrations in water and sediments of Jack of Clubs Lake, B.C. Environ. Sci. Technol. 15 , 669 678. Azeez, P. A., & Nadarajan, N. R. (2000). The impact of monsoonal wetland on ground water chemistry. Poll. Res. , 19 ((2)), 249–255. Benke, B. M. (1998). Characterization and interaction of sugarcane industry residues with soil kaolinites and Fe oxide residues. 31 47. Canada: Ph.D thesis at University of Saskatchewan, Canada. Benvenuti, M., Mascaro, I., Corsini, F., Lattazi, P., Parrini, P., & Tanilli, G. (1997). Mine water dumps and heavy metal pollution in abandoned mining district of Boccheggiano (Southern Tuscany, Italy). Environ Geol 30(3/4) , 238–243. Bhosle, A. B., Narkhede, R. K., & Balaji Rao and Patil, P. M. (2001). Studies on the fluoride content of Godavari river water at Nanded. Eco. Env. & Conserv. , 7 ((3)), 341–344. Bordeleau, G. :. (2007). E ´ tude hydroge´ologique de la Base Ae´rienne de Cold Lake, Alberta, et de´termination de l’origine des nitratesdans l’eau souterraine (Hydrogeological Study of the Cold Lake Air Weapons Range, and determination of the Origin of Nitrate in Groundwater). M.Sc thesis, INRS-ETE, Que´bec [parts inFrench and parts in English] . Brannon, J., & Pennington, J. (2002). Environmental fate and transport process descriptors for explosives. US Army Engineer Waterways Experiment Station,, Vicksburg. British Geological survey; Mott MacDonald. (1999). Arsenic in groundwater in Bangladesh, Phase1. Rapid Investigation. Report to UKDFID. Collins, R. E. (1961). Flow of fluids through porous materials. New York, Reinhold Publishing Corp. 270p. Concha, G., Nermell, B., & Vahter, M. (2006). Spatial and Temporal Variations in Arsenic Exposure via Drinking water in Northern Argentina. J. Health Popul Nutr. 24(3) , 317 326. Doherty, J., L. Brebber and P. Whyte. (1995). PEST Model –independent parameter estimation. User’s manual. Watermark Computing. Corinda, Australia.
92
Feenstra, L., Erkel, v. J., & Vasak, L. (2007). Arsenic in Groundwater: Overview and evaluation of removal methods. International groundwater resources assessment centre (igarc). Fetter, C. W. (1999) Contaminant Hydrogeology. 2 nd Edition. Prentice Hall Inc. New Jersey, 500 pp Forsythe, G.E., M.A. Malcolm, and C.B. Moler. (1977). Computer Methods for Mathematical Computations. Prentice Hall Inc. Englewood Cliffs, NJ. Fraser, I. S. (1958). Soils and landuse of the Indus plains, West Pakistan. Colombo Plan cooperative project. Canada: Government of Canada. Freeze, R. A. and Cherry, J. A. (1979). Groundwater. Englewood cliff, N. J., Prentice Hall. Goel, P. K. (2000). Water Pollution – Causes, Effects and Control. New Delhi: New age Int. (P)Ltd. Guvanasen, V., Wade, S. C. and Barcelo, M. D. (1998). Application of MODFLOW SURF to simulate regional groundwater flow in Hernando county, Florida: Ground Water, vol 38, no. 5, p. 772 783. Haitjema, H.M. (1995). Analytic Element Modeling of Groundwater Flow. Academic Press Inc. San Diego, CA. Harrar, W. G., Sonnenborg, T. O., and Henriksen, H. J. (2003). Capture zone, travel time, and solute transport predictions using inverse modeling and different geological model. Hydrogeology Journal, 11 (5), 536 548. Harvey, C.F., Swartz, C.H., Badruzzaman, A.B.M., Keonblute, N., Yu, W., Ali, M.A., Jay, J., Beckie, R., Niedam, V., Brabander, D., Oates, P.M., Ashfaque, K.N., Islam, S., Hemond, H.F. and Ahmed, M.F., 2002. Arsenic mobility and groundwater extraction in Bangladesh. Science 298, p. 1602 1606. Hemphil, W. R., & Kidwai, A. H. (1973). Stratigraphy of the Bannu and Dera Ismail Khan Areas, Pakistan. Washington: United States Government Printing Office. Prof. Paper 716 B; 36 p. Hering, J. G., Burris, D. D., Reisinger, J. H., & O'Day, P. (2008). Environmental fate and exposure assessment for Arsenic in Groundwater. Strategic Environmental Research and Development Program.
93
Hill, M. C. (1992). A computer program (MODFLOWP) for estimating parameters of a transient, three-dimensional ground-water flow model using nonlinear regression. U.S. Geol. Surv Water Resources Investigations Report 91 484, 358 p. Hill, M. C. (1998). Methods and guidelines for effective model calibration. U.S. Geol. Surv. Water Resources Investigations Report 98 4005, 90 p. Hill, M. C., R. L. Cooley, & and D. W. Pollock, :. (1998). A controlled experiment in ground-water flow model calibration using nonlinear regression, Ground Water 36 (3). 520 535. Holleman A. F. and Wiberg E. (1990) Lehrbuch der Anorganischen Chemie . Walter de Gruyter& Co, Berlin p. 130. Hood, J. W. (1970). Water resources and related geology of Dera Ismail Khan District, West Pakistan with reference to the availability of groundwater for development: U. S. Geol. Survey Water Supply Paper 1608 K, 74 p. Kijne, W. J. (1996). Water and salinity balances for irrigated Agriculture in Pakistan. Colombo, Srilanka: International Irrigation Management Institute (IIMI). Konikow, L., Goode, D., & and Hornberger, G. (1996). A three-dimensional method-of- characteristics solute-transport model (MOC3D). U.S. Geological Survey Water Resources Investigations Report 96 4267, 87 p. Leake, S. A., and Prudic, D. E. (1991). Documentation of a computer program to simulate aquifer-system compaction using the modular finite-difference ground-water flow model: book 6, chap A2. 68 p. Lederer P. (1998) Toxikologie von Arsen im Trinkwasser. In Arsen im Grund und Trinkwasser Bayerns (ed. U. Kleeberger, H. Frisch, and G. Heinrichs), pp. 73 81. Sven von Loga. Liggett, J., and P. F. Liu. (1983). The Boundary Integral Equation Method for Porous Media Flow. George Allen and Unwin Ltd. London, UK. Liu, G., Zheng, C and Gorelick, S. M. (2004). Limits of applicability of the advection dispersion model in aquifers containing connected high conductivity channels. Water Resources Research, 40, W08308.
94
Majumdar, K. P., Ghosh, N. C., & Chakravorty, B. (2002). Analysis of Arsenic contaminated groundwater domain in the Nadia district of West Bengal (India). Hydrological Sciences Journal, 47(S) , S55. Malik, Y. M. (1985). Technical Report on Kulachi-Tank Dera Ismail Khan District N. W. F. P. TNO DGV institute of Applied Geosciences. Netherlands, WAPDA. Peshawar: Hydrgeology section, planning directorate. McDonald, M., & Harbaugh, A. (1988). A Modular three dimensional finite difference Groundwater Flow Model: U. S. geological survey techniques of Water Resources Investigations, book 6, chap A1, 586 p. Naqvi, A. H. (1977). Groundwater conditions in Paharpur area, Dera Ismail Khan district, N. W. F. P. Dera Ismail Khan: Hydrogeology section planning directorate WAPDA. Narasimhan, T. N. and Witherspoon, P. A. (1976). An Integrated Finite Difference Method for Analysing Fluid Flow in Porous Media, Water Resources Res., Vol. 12, No. 1. 57 64. NESPAK. (1998). Master feasibility studies for flood management of hill torrents of Pakistan. Supporting Vol. II N. W. F. P. NESPAK. Plant, J.A., Kinniburgh, D.G., Smedley, P.L., Fordyce, F.M. and Klinck, B.A., 2004. Arsenic and Selenium. In: Holland, H.D., Turekian, K.K., 2004, Treatise on Geochemistry, Volume 9, Environmental Geochemistry. Elsevier, Amsterdam, p 17 66. Prommer , H., Barry, D. A., and Zheng, C. (2003). MODFLOW/MT3DMS based reactive multicomponent transport modeling. Ground Water, 41 (2), 247 257. Prudic, D. (1989). Documentation of a computer program to simulate stream-aquifer relations using a modular, finitedifference, ground-water flow model:. U.S. Geological Survey Open File Report 88 729, 113 p. Qureshi, A. S. (2008). Managing salinity and water logging in the Indus Basin of Pakistan. Agriculture Water Management , 95 , 5. Qureshi, A. S., Shah, T., & Akhtar, M. (2003). The groundwater economy of Pakistan. Lahore: International Water Management Institute, Pakistan.
95
Riederer J. (1987) Archäologie und Chemie - Einblicke in die Vergangenheit Staatliche Museen PreuBischer Kulturbesitz. Berlin: und Rathgen-Forschungslabor Berlin, 1987: 276 . Shamrukh, M., Corapcioglu, M. Y. and Hassona, F. A. A. (2001). Modeling the effect of chemical fertilizers on groundwater quality in the Nile Valley aquifer, Egypt. Groundwater, 39 (1), 59 67. Smedley P. L., Zhang M., Zhang G., and Luo Z. (2001) Arsenic and other edoxsensitive elements in groundwater from the Huhhot Basin, Inner Mongolia. Water-Rock Interaction , 581–584. Smedley, P.L. and Kinniburgh, D.G., 2002. A review of the source, behaviour and distribution of arsenic in natural waters. Applied Geochemistry 17, 517 568. Smith, A.H., Lingas, E.O., Rahman, M., 2000. Contamination of drinking water by arsenic in Bangladesh: a public health emergency. Bulletin of the World Health Organization 78 (9), 1093–1103. SSOP. (1969). Reconnaissance soil survey of Dera Ismail Khan. Lahore. Strack, O.D.L. (1989). Groundwater Mechanics. Prentice Hall. Englewood Cliffs, NJ. Thangarajan, M. 2004. Regional Groundwater Modeling, Capital Publishing Company, New Delhi, India. Theissen, A. H. (1911). Precipitation average for large areas. Mon. Wea. Rev., 39: 1082 1084. Wang, F., and Bright, J. (2004). Scale effect and calibration of contaminant transport models. Ground Water, 42 (5), 760 766. Welch, A.H. and Lico, M.S., 1998. Factors controling As and U in shallow ground water, southern Carson dessert, Nevada. Applied Geochemistry 13, p. 521 539. Zhang, H., Selim, H.M., 2006. Modeling the transport and retention of Arsenic (V) in soils. Soil Science Society of America Journal 70, 1677–1687. Zheng, C. (1990). MT3D, A modeular three dimensional transport model, S. S. Papadopolous and Assoc. Rockville, MD.
96
APPENDICES
97
Appendix A The hydraulic characteristics of the water wells acquired in the study area
Screen Pumping Top Screen Bottom Screen Diameter Drawdown Wells Rate (gpm) Settings (m) Settings (m) (inch) (meters) DIK 2 1330 30.48 90 10 5.5 DIK 6 1200 103 152 10 5 PP3 898 16 59 8 2.5 PP4 898 21 63 8 2.5 PP5 1347 25 65 8 4.5 WA 12 A 112 69 100 6 13.54 WA 17 265 79 153 8 12.54 WA 22 227 30 70 6 5 WA 25A 337 57 71 10 3.96 WA 31 Artesian 199 219 8 KW 4 4314 42 75 8 2.3 KW 6 1347 32 68 8 3.3 KW 2 1344 45 76 8 3.7 SPT W3 145 49 85 6 24.6
98
Appendix B The final calibration result shows a reasonable agreement between modeled and observed heads.
Well/Point X Model Y Model X World Y World Obs. Calc. Calc. Obs. Name 1 59790.88 8656.33 660190 3508283 163.7378 163.6161 0.1216959 2 52687.58 21805.33 653086.7 3521432 179.1595 179.3844 0.2248994 3 69009.98 19538.33 669409.1 3519165 165.3154 165.3688 0.05338967 4 71276.98 29210.33 671676.1 3528837 173.0742 173.2547 0.1805302 5 38632.18 61402.33 639031.3 3561029 233.6946 233.9212 0.2266036 6 74450.78 25583.33 674849.9 3525210 168.2853 168.333 0.04773833 7 37876.58 39638.33 638275.7 3539265 211.0024 210.9917 0.0107313 8 77020.08 22409.33 677419.2 3522036 164.8218 164.8082 0.0135572 9 44526.38 34198.33 644925.5 3533825 199.6422 199.6791 0.03690767 10 22158.68 57019.33 622557.8 3556646 242.8064 242.7097 0.09674424 11 26843.88 53845.33 627243 3553472 234.6503 234.7594 0.1091147 12 61755.58 37069.33 662154.7 3536696 185.7182 186.6799 0.9617469 13 60546.58 29664.33 660945.7 3529291 178.6633 179.1255 0.4622035 14 42863.98 54601.33 643263.1 3554228 222.4916 222.8944 0.4027787 16 28808.58 45986.33 629207.7 3545613 224.7141 224.7353 0.0212363 17 82611.98 33895.33 683011.1 3533522 176.6701 174.1405 2.529597 18 71811.18 21930.33 672210.3 3521557 166.218 166.2658 0.04776233 19 65070.88 15812.33 665470 3515439 164.4851 164.4483 0.03678152 20 76051.38 27740.33 676450.5 3527367 169.5907 169.5914 0.00065437 21 53896.68 28606.33 654295.8 3528233 184.3299 184.5258 0.1959484 22 54198.98 42359.33 654598.1 3541986 200.7457 201.0529 0.307248 23 47700.18 39185.33 648099.3 3538812 203.0398 203.0528 0.01304119 24 78650.68 29440.33 679049.8 3529067 170.3801 170.3291 0.0510137 25 77020.08 17875.33 677419.2 3517502 161.072 161.0519 0.02010486 26 69463.38 2611.33 669862.5 3502238 151.8023 151.3763 0.4259877 27 64627.18 4878.33 665026.3 3504505 157.039 157.0042 0.03478857 28 80193.88 21805.33 680593 3521432 163.3788 163.1505 0.2283331 29 51629.68 35256.33 652028.8 3534883 194.1594 194.2667 0.1072626 30 22914.38 40394.33 623313.5 3540021 224.4841 224.5023 0.01821934 31 69916.78 11376.33 670315.9 3511003 159.1598 159.0618 0.09797139 32 61906.78 20747.33 662305.9 3520374 167.8783 168.4286 0.5503499 33 11730.48 53392.33 612129.6 3553019 248.2267 247.8991 0.3275759 34 80798.38 25885.33 681197.5 3525512 166.376 166.4344 0.05837195 35 73846.28 37069.33 674245.4 3536696 182.075 180.8596 1.215396 36 51629.68 35256.33 652028.8 3534883 192.6 194.2667 1.666663 37 22914.38 40394.33 623313.5 3540021 223 224.5023 1.502319
99
Appendix C Flow budget calculation in m 3 for the various periods in the model simulations
Time [year] 1985 1990 1995 2000 2005 2010 Total out (m 3) 158076864 177708752 276516032 932941440 1.39×10 09 1.73×10 09 Total in (m 3) 158068912 498495744 785994624 1.46×10 09 1.93×10 09 2.29×10 09 Stream leakage out (m 3) 50260484 5002072.5 6179773.5 569363136 933756224 1.18×10 09 Stream leakage in (m 3) 41757416 5122266.5 6337922 540044928 865240384 1.07×10 09 Recharge in (m 3) 116311492 484766240 768660992 910980224 1.05×10 09 1.20×10 09 Et out (m 3) 54774200 97603768 128561040 160748400 193570240 227099200 Wells out (m 3) 0 60140400 117670768 175359184 233047600 290736032 Storage out(m 3) 0 14962509 24104462 27470700 30736440 34212024 Storage in(m 3) 0 8607231 10995720 13528388 16161363 18837358 % Change in volume 0.005 64.350 64.819 36.298 28.096 24.473
100
Appendix D The arsenic calibration data in 2010 in Transient conditions
Well/Point Obs Calc Calc. Name X Model Y Model X World Y World [ug/l] [ug/l] Obs. 1 66616.71 4945.345 667015.8 3504572 20 19.619 0.381 2 76682.75 25165.67 677081.9 3524792 19.86 19.355 0.505 3 76943.75 33453.81 677342.9 3533080 27 26.75 0.25 4 71125.7 30835.68 671524.8 3530462 20 20.269 0.269 5 80923.79 29607.53 681322.9 3529234 22.03 21.717 0.313 6 82270.53 33635.93 682669.6 3533263 28 27.785 0.215 7 79506.17 27516.55 679905.3 3527143 24.48 24.142 0.338 8 67198.51 11781.56 667597.6 3511408 20 19.394 0.606 9 74166.48 14143.69 674565.6 3513770 33.91 34.49 0.58 10 82329.59 34899.97 682728.7 3534527 36 36.569 0.569 11 79411.66 23629.91 679810.8 3523257 37.38 37.327 0.053 12 76943.75 12072.46 677342.9 3511699 27 27.334 0.334
101
Appendix E Mass transport budget after the inception of pumping wells
Total mass in Time aquifer Sinks out Source in Total out Total in Discrepancy Mass Mass Mass Mass Years Mass [ug]x10 10 [ug]x10 10 [ug]x10 10 [ug]x10 10 [ug]x10 10 % 2010 8634.76 411.548 15.7413 1224.81 1225.63 0.0669 2011 8853.61 427.48 251.861 1489.92 1490.71 0.0530 2012 9005.11 444.971 422.356 1695.37 1696.31 0.0554 2013 9237.13 463.324 674.213 1984.72 1985.79 0.0539 2014 9386.66 482.579 844.706 2195.01 2196.23 0.0555 2015 9615.75 502.812 1096.57 2487.65 2489.05 0.0562 2016 9612.62 503.939 1096.57 2539.91 2541.29 0.0543 2017 9610.89 505.02 1096.57 2581.89 2583.28 0.0538 2018 9609.04 506.086 1096.57 2618.62 2620.03 0.0538 2019 9607.71 507.073 1096.57 2652.14 2653.56 0.0535 2020 9606.68 508.11 1096.57 2683.43 2684.86 0.0533 2021 9606.38 508.11 1096.57 2715.41 2716.89 0.0545 2022 9606.26 508.11 1096.57 2738.79 2740.3 0.0551 2023 9606.09 508.11 1096.57 2760.75 2762.27 0.0550 2024 9606.01 508.11 1096.57 2786.68 2788.21 0.0549 2025 9606.16 508.11 1096.57 2806.45 2807.99 0.0548
102
Appendix F
The Arsenic and other parameters data acquired from the analyses in the Laboratory.
Labels EC TDS CO 3 HCO 3 Cl SO 4 Na K Ca Mg As As S/cm mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l ppb mg/l D1/10 1790 908 ND 829.7 41.48 17.39 179.03 11.3 159.5 71.8 20.62 0.02062 D2/10 1656 842 ND 1683.9 35.63 17.05 161.1 8.2 96.5 73.8 39.22 0.03922 D3/10 1056 529 ND 1049.4 10.64 11.00 66.1 7.5 38.4 56.2 19.27 0.01927 D4/10 1143 574 ND 1183.6 14.71 10.59 75.2 8.9 23.8 68.8 20.75 0.02075 D5/10 1675 846 ND 878.5 30.49 17.31 159 10 89 74.6 19.86 0.01986 D6/10 190 96 ND 292.8 7.27 3.34 6.6 4.3 27.4 6.1 20.65 0.02065 D7/10 3830 1868 ND 512.5 143.57 19.48 665.6 6.8 65.2 64.6 19.54 0.01954 D8/10 4000 202 ND 902.9 8.86 4.39 27.2 3.5 36.9 11.4 21.29 0.02129 D9/10 979 498 ND 1232.4 13.29 9.51 103.1 7.5 45.6 49.2 40.19 0.04019 D10/10 925 472 ND 976.2 17.73 11.87 69.6 7.1 77.4 43.6 23.42 0.02342 D11/10 1402 712 ND 1244.6 16.84 14.19 129.5 7.3 61.3 81.5 22.03 0.02203 D12/10 1830 928 ND 1366.6 29.25 16.76 246.9 8.4 26.4 69.1 24.48 0.02448 D13/10 240 120 ND 366.1 10.64 1.84 5.7 2.8 33.6 5.2 24.62 0.02462 D14/10 2100 1035 ND 1159.2 49.63 17.53 297.7 8 48.7 56.1 27.98 0.02798 D15/10 243 125 ND 366.1 8.86 1.61 6.2 2.9 37.7 5.4 30.7 0.0307 D16/10 1590 302 ND 707.7 43.43 16.43 178.8 10.7 127.3 41.7 44.63 0.04463 WHO 600 500 ND 500.0 250.00 250.00 200 12 100 150 05 0.05
103